Equipment for optimizing fluid sample volume and its usage

Abstract

Various embodiments relate to a fluid sampling apparatus comprising: a fluid composition sensor configured to receive a fluid sample and capture a plurality of particles from the fluid sample at a collection medium, wherein the fluid composition sensor is further configured to generate particle data associated with the plurality of particles using a particle imaging operation; and a controller configured to: determine an optimal sample volume associated with the sample collection operation based at least in part on particle loading conditions defined by the plurality of particles captured at the collection medium during the sample collection operation; and update one or more operating characteristics of the fluid composition sensor such that the sample collection operation is at least in part defined by the optimal sample volume.

Description

Technical Field

[0001] The exemplary implementations generally relate to an apparatus for sampling a volume of fluid from the air of the surrounding environment, including characterizing the particle content within the volume of fluid. Background Technology

[0002] Sensors and devices can be used to characterize various aspects of fluids in a variety of applications. As just one example, sensor devices can be used to monitor air conditions, such as monitoring and characterizing the particulate content of airflows. However, existing fluid sensor devices offer limited functionality in generating data indicative of certain characteristics of the fluid, such as the unique properties and concentrations of individual particles contained within the fluid flow. Fluid sensor devices can use holographic imaging methods to characterize the particle properties and concentrations of particulate matter collected via inertial impaction. Improvements in various aspects of particle sampling and analysis are desired. Generally, fluid sampling devices may be advantageous when utilizing sampling media capable of rapid and / or simplified sequential sampling of particles. For devices utilizing holographic imaging (such as lensless holography) for in-situ particle analysis, it is desirable to avoid underfilled and / or incomplete particle samples or oversaturation of the collection medium as defined by congestion within the collection medium, in order to avoid data distortion and achieve optimal particle data quality.

[0003] Therefore, there is a need for improved fluid sensor devices that can reduce optical interference from inertial impactor sampling methods by optimizing the volume of fluid sampled to illustrate the specific properties of the fluid being sampled and / or enable the analysis of multiple samples from one or more impactors. Summary of the Invention

[0004] The various embodiments described herein relate to apparatuses and methods for collecting and characterizing particles within a sample fluid. Various embodiments relate to a fluid sampling device comprising: a fluid composition sensor configured to receive a fluid sample and capture a plurality of particles from the fluid sample at a collection medium, wherein the fluid composition sensor is further configured to generate particle data associated with the plurality of particles using a particle imaging operation; and a controller configured to: determine an optimal sample volume associated with the sample collection operation based at least in part on particle loading conditions defined by the plurality of particles captured at the collection medium during the sample collection operation; and update one or more operating characteristics of the fluid composition sensor such that the sample collection operation is at least in part defined by the optimal sample volume.

[0005] In various embodiments, the controller may be further configured to determine an updated sampling duration corresponding to the optimal sample volume; wherein updating the one or more operating characteristics of the fluid composition sensor such that the sample collection operation is at least partially defined by the optimal sample volume comprises: updating the one or more operating characteristics of the fluid composition sensor such that the sample collection operation is at least partially defined by the updated sampling duration. In some embodiments, the optimal sample duration may be determined at least in part based on the elapsed sample time, as measured between an initial instance of the sample collection operation and an intermediate instance during the sample collection operation in which the fluid composition sensor generates the particle data. In various embodiments, the particle data generated by the fluid composition sensor may include a first particle image captured by an imaging device of the fluid composition sensor using lensless holography. In various embodiments, the particle loading characteristics may include a particle coverage value defined by the plurality of particles captured at the collection medium.

[0006] In various embodiments, the controller may be further configured to transmit the particle data to an external device and receive particle loading data associated with the plurality of particles from the external device, wherein the received particle loading data is at least partially defined by the particle loading conditions. In various embodiments, the controller may be further configured to cause the fluid composition sensor to stop the sample collection operation when the optimal sample volume is determined to be at least substantially equal to the total sampling volume received by the fluid composition sensor. In various embodiments, the fluid composition sensor may be configured to capture multiple particle data at each of a plurality of instances defined by a set time interval during the sample collection operation.

[0007] In various embodiments, the particle data generated by the fluid composition sensor may include first particle data generated at a first instance during the sample collection operation and second particle data generated at a second instance during the sample collection operation, wherein the second instance follows the first instance; and wherein the controller is further configured to: determine a first optimal sample volume based at least in part on first particle loading conditions defined by the plurality of particles captured in the collection medium at the first instance; and determine a second optimal sample volume based at least in part on second particle loading conditions defined by the plurality of particles captured in the collection medium at the second instance. In some embodiments, the controller may be further configured to: further update one or more operating characteristics of the fluid composition sensor when determining the second optimal sample volume, such that the sample collection operation is at least in part defined by the second optimal sample volume. In various embodiments, the optimal sample volume may be determined at least in part based on the elapsed sample time and the flow rate of the fluid composition sensor.

[0008] Various embodiments relate to a fluid sampling apparatus comprising: a fluid composition sensor configured to receive a fluid sample and capture a plurality of particles from the fluid sample at a collection medium, wherein the fluid composition sensor is further configured to generate particle data associated with the plurality of particles using a particle imaging operation; and a controller configured to: determine an optimal sample duration associated with the sample collection operation based at least in part on particle loading conditions defined by the plurality of particles captured at the collection medium during the sample collection operation; and update one or more operating characteristics of the fluid composition sensor such that the sample collection operation is at least in part defined by the optimal sample duration.

[0009] Various embodiments relate to a method for optimizing a sample collection operation, the method comprising: receiving a fluid sample containing a plurality of particles via a volume of fluid; capturing first particle data associated with the first plurality of particles received from the fluid sample at a first instance during the sample collection operation; determining optimal sample characteristics associated with the sample collection operation based at least in part on first particle loading conditions defined by the first plurality of particles; and updating one or more operational characteristics associated with the sample collection operation based at least in part on the optimal sample volume, such that the sample collection operation is at least in part defined by the optimal sample characteristics.

[0010] In various embodiments, the optimal sample characteristic may include an optimal sample volume. In various embodiments, the optimal sample characteristic may include an optimal sample duration. In various embodiments, the fluid sample may be received at a collection medium by a fluid composition sensor; and wherein the first particle data is generated by the fluid composition sensor. In some embodiments, the first particle data generated by the fluid composition sensor may include a first particle image captured by an imaging device of the fluid composition sensor using lensless holography. In various embodiments, the method may further include stopping the sample collection operation when it is determined that the optimal sample characteristic is at least substantially equal to the corresponding operating characteristic associated with the sample collection operation as measured at the first instance.

[0011] In various embodiments, the method may further include: capturing second particle data associated with the plurality of particles received from the fluid sample at a second instance during the sample collection operation, wherein the second instance follows the first instance; and determining a second optimal sample characteristic based at least in part on second particle loading conditions, the second particle loading conditions being identified at least in part based on the second particle data. In various embodiments, the first particle loading characteristic may include a particle coverage value defined at least in part by the first plurality of particles. Attached Figure Description

[0012] Now refer to the accompanying drawings, which may not be drawn to scale, and in which:

[0013] Figure 1 A schematic diagram of a fluid sensing system according to an exemplary embodiment of the present disclosure;

[0014] Figure 2 A cross-sectional view of an exemplary device according to one embodiment described herein is shown;

[0015] Figures 3A to 3B Schematic diagrams of various exemplary devices according to various embodiments are shown;

[0016] Figure 4 Various data flows between exemplary devices according to some embodiments discussed herein are illustrated schematically; and

[0017] Figure 5 This is an exemplary flowchart illustrating the steps of various embodiments of the exemplary method according to the present disclosure. Detailed Implementation

[0018] This disclosure describes various embodiments more fully with reference to the accompanying drawings. It should be understood that some, but not all, of the embodiments are shown and described herein. In fact, embodiments may take many different forms, and therefore this disclosure should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to enable this disclosure to meet applicable legal requirements. Throughout the document, similar reference numerals refer to similar elements.

[0019] First, it should be understood that although exemplary embodiments of one or more aspects are shown below, the disclosed components, systems, and methods can be implemented using any number of techniques (whether currently known or not yet available). This disclosure should in no way be limited to the exemplary embodiments, drawings, and techniques shown below, but modifications can be made within the scope of the appended claims and their equivalents. Although dimensional values ​​for various elements are disclosed, the drawings may not be drawn to scale.

[0020] The terms “example” or “exemplary” as used herein are intended to mean “served as an example, instance, or illustration.” Any implementation described herein as an “example” or “exemplary” is not necessarily more preferred or advantageous than other implementations. As used herein, “fluid” can refer to a gas, a liquid, or a combination of gas and liquid in a single flow. Thus, the term “fluid” includes a variety of easily flowing substances, such as, but not limited to, liquids and / or gases (e.g., air, oil, etc.). Consequently, various embodiments relate to fluid sensing systems, such as gas sensing systems (e.g., some embodiments are specifically configured to operate with air; others are configured to operate with other gases, such as inert gases, volatile gases, etc.), liquid sensing systems, etc.

[0021] This document describes an apparatus configured to characterize and monitor particulate matter within a volume of fluid. The apparatus discussed herein can be configured to quantify and classify particles within a volume of fluid, at least in part, based on imaging of particles received by a collection medium of a fluid composition sensor. Furthermore, the apparatus discussed herein can be configured to characterize a particulate composition within a volume of fluid by directly identifying the particle size and particle type of each particle received by the collection medium of the fluid composition sensor. By directly determining the particle size and particle type, the apparatus described herein can be configured to detect changes in the particulate composition within a volume of fluid over time and / or location.

[0022] Furthermore, the apparatus described herein can be configured to capture particle data, such as by capturing images, for example, by an imaging device of a fluid composition sensor. The apparatus may include an impactor nozzle configured to minimize reflection of a portion of a light beam emitted from an illumination source. The apparatus may also include an impactor nozzle configured to minimize imaging distortion caused by a diverging light beam emitted from an illumination source incident on its sidewalls and reflected toward the imager. For example, by minimizing the scattering of the light beam caused by the impactor nozzle, such an apparatus configuration can reduce noise that can reduce the fluid composition sensor's ability to locate, identify, and / or analyze individual particles among one or more particles disposed within a collection medium. The apparatus may similarly be configured to avoid a reduction in the fluid composition sensor's ability to reconstruct an image of one or more of the captured particles, which can result in reduced sensor performance relative to classifying one or more particles using machine learning.

[0023] Furthermore, the device described herein can be configured to increase device reliability and associated user satisfaction by incorporating a replaceable collection medium in conjunction with a fluid composition sensor. According to certain embodiments discussed herein, the collection medium used to collect particles from a given volume of fluid within the fluid composition sensor can be automatically replaced (within the fluid collection location) when a predefined sample volume or sample number of particles has passed through the device. The device described herein minimizes intermittent user interaction with the collection medium, thereby accelerating the sample collection process, reducing the physical work required by the user, promoting measurement automation, and minimizing device failures caused by misalignment during reconfiguration of one or more user-defined device components.

[0024] In various embodiments, the fluid composition sensor includes a controller (e.g., sensor sampling optimization circuitry 209) configured to determine particle loading conditions defined by a plurality of particles received from a volume of fluid by a collection medium, characterizing the spatial arrangement of the plurality of particles to identify one or more particle configurations known to negatively impact sensor accuracy and / or sensor effectiveness (e.g., lifetime) over time, such as particle aggregation, spike formation, particle contact, particles overlapping each other, and / or a collection medium “covered” by particles. This can help prevent sensor inaccuracies caused by overload of a depleted and / or disabled collection medium, where particle loading conditions cannot be accurately determined and / or identified by the sensor. Such exemplary configurations substantially minimize the amount of retesting required to obtain accurate data by defining operating parameters configured to substantially autonomously limit sensor operation when one or more of the aforementioned erroneous particle loading conditions are identified. The lifetime of the device can be increased by dynamically monitoring the loading conditions of the plurality of particles received by the collection medium and optimizing the operating parameters to selectively limit device uptime. Furthermore, the device described herein can simplify the calculation of the necessary operating time of the fluid composition sensor required for a particle sample sufficient to provide one or more statistically significant measurements.

[0025] In various embodiments, exemplary fluid sampling devices may determine an optimal sample volume associated with a sample collection operation based at least in part on particle loading conditions defined by a plurality of particles captured at the collection medium during the sample collection operation. In various embodiments, the invention may be configured to update one or more operating characteristics of a fluid composition sensor such that the sample collection operation is defined at least in part by the optimal sample volume. Furthermore, in various embodiments, exemplary fluid sampling devices may determine an optimal sample duration associated with a sample collection operation based at least in part on particle loading conditions defined by a plurality of particles captured at the collection medium during the sample collection operation. Furthermore, in various embodiments, the invention may be configured to update one or more operating characteristics of a fluid composition sensor such that the sample collection operation is defined at least in part by the optimal sample duration. In various embodiments, the invention may include a fluid sampling device configured to facilitate the dynamic determination of the optimal sample duration for a particular sample collection operation, the dynamic determination being based at least in part in real time on particle data iteratively captured by a fluid composition sensor performing at least a portion of the sample collection operation.

[0026] Now for reference Figure 1This document provides a schematic diagram of an exemplary fluid sampling device 10 configured for fluid sensing. The fluid sampling device 10 may include a controller 200 and a fluid composition sensor 100, associated with or otherwise communicating with the controller and the fluid composition sensor. The controller includes, for example, one or more processors 202 and one or more memory devices 201. Furthermore, in various embodiments, the exemplary fluid sampling device 10 may include an image database 107, associated with or otherwise communicating with the image database. In various embodiments, the fluid sampling device 10 may be embodied in or associated with a plurality of computing devices communicating with or otherwise networking with each other. For example, the fluid composition sensor 100 may have a processor communicating with the controller 200, the image database 107, and / or one or more client devices 21. In various embodiments, some or all of the mentioned components may be embodied as a fluid sampling device. For example, the exemplary fluid sampling device 10 may include a controller 200 having a processor 202 and a memory 201, a fluid composition sensor 100, and optionally an imaging database 107, one or more of which may be configured to communicate with a client device 21, as described in further detail herein. In various embodiments, at least a portion of the fluid sampling device 10, such as, for example, the exemplary fluid composition sensor 100 (e.g., the controller 200), may be configured to communicate with a client device 21 executing a mobile application. In various embodiments, the client device may include, but is not limited to, a smartphone, tablet, laptop, wearable device (e.g., a smartwatch), personal computer, etc. The client device may execute an "application" to interact with one or more components of the fluid sampling device 10.

[0027] In various embodiments, the fluid composition sensor 100 of the exemplary fluid sampling device 10 may include a sensor configured to sample a fluid sample defined by a volume of fluid, and / or monitor a particulate composition of multiple particles within the fluid sample, such as by measuring and / or characterizing product loading conditions defined by multiple particles (e.g., particle coverage value, etc.).

[0028] In various embodiments, an exemplary fluid composition sensor may be configured to perform a sample collection operation, wherein the fluid composition sensor receives a volume of fluid defining a first fluid sample. As described herein, the first fluid sample may include a plurality of particles disposed within the first fluid sample, such that the fluid composition sensor receives the plurality of particles via the first fluid sample. For example, refer to... Figure 2 An exemplary fluid sample (e.g., a first fluid sample containing a first plurality of particles) may be received by the fluid sampling device 10 at the fluid composition sensor 100. Figure 2 An exemplary fluid composition sensor 100 is illustrated, which is capable of performing one or more fluid sample collection operations, wherein the fluid composition sensor 100 receives one or more fluid samples to capture multiple particles in a collection medium over a time period extending between a first instance (e.g., a starting instance) and a second instance (e.g., an ending instance). For example, the time period between the first instance (e.g., the starting instance) and the second instance (e.g., the ending instance) of the fluid sample collection operation may define the sample duration. As described further in detail herein, in various embodiments, an exemplary fluid sampling device 10 (e.g., the fluid composition sensor 100) may be configured to determine, at least in part, the optimal sample duration for a particular fluid sample collection operation based on particle data captured by the fluid composition sensor 100, such that the fluid sampling device 10 can determine the sample duration (e.g., the amount of fluid composition sensor operation runtime) that would result in optimal particle loading conditions in the collection medium of the fluid composition sensor 100.

[0029] As shown, an exemplary fluid composition sensor 100 may include a particle imaging sensor. For example, the exemplary fluid composition sensor 100 may generate (e.g., capture) particle data associated with at least one of a plurality of particles received by a fluid sampling device 10 (e.g., fluid composition sensor 100). In this exemplary case, the exemplary fluid composition sensor 100 may capture particle data of the plurality of particles received by the fluid composition sensor 100, such as, for example, particle images as described herein. Furthermore, in various embodiments in which the fluid composition sensor 100 is configured to receive a plurality of fluid samples, each fluid sample having a corresponding plurality of particles, the particle data captured by the fluid composition sensor 100 may include first particle data specifically associated with a first plurality of particles received by the fluid composition sensor 100 via a first fluid sample, such as, for example, capturing a first particle image of at least a portion of the first plurality of particles. Furthermore, in various embodiments, the first particle data captured by the fluid composition sensor 100 may also include first particle data generated by the fluid composition sensor 100 based at least in part on the captured particle data (e.g., particle images), such as, for example, particle type data, particulate matter concentration data, particle number data, particle size data, particle density data, particle coverage value data, etc.

[0030] As shown in the figure, in various non-limiting exemplary embodiments, an exemplary fluid composition sensor 100 may include a housing 101, an impactor nozzle 104, a collection medium 106, a substrate 108 that is at least partially transparent, and an imaging device 110. In some embodiments, the fluid composition sensor 100 may also include a power supply 114 and a fan or pump 112 configured to power the fluid composition sensor 100, and the fan or pump configured to allow a volume of fluid to enter and pass through the fluid composition sensor 100. In various embodiments, the fan or pump 112 is calibrated such that the flow rate of the fluid moving through the device is at least partially based on known / determined operating characteristics (e.g., operating power) of the fan or pump 112. In various embodiments, the fluid composition sensor 100, including the collection medium 106, may be configured to guide at least a portion of a fluid sample received by the fluid composition sensor 100 along a fluid flow path within the sensor housing 101 in a direction perpendicular to the receiving surface of the collection medium 106, such that the fluid sample (e.g., a first fluid sample comprising a first plurality of particles) can interact with the collection medium 106. As a non-limiting example, in various embodiments, the collection medium 106 may include an adhesive (i.e., viscous) material, such as a gel, and may be configured to receive at least a portion of a plurality of particles via interaction with a fluid sample. For example, in various embodiments, the collection medium 106 may be disposed on at least a portion of a transparent substrate 108.

[0031] As described herein, in various embodiments, the fluid composition sensor 100 may be configured to generate (e.g., capture) particle data associated with a plurality of particles disposed within a collection medium 106 using an imaging device 110. For example, in various embodiments, the fluid composition sensor 100 may include an imaging device 110 configured to capture particle data including images of a plurality of particles received by the collection medium 106. In various embodiments, the imaging device 110 may be positioned at least substantially adjacent to (e.g., in contact with or spaced apart from) the back side 107 of a transparent substrate 108, such that the imaging device 110 can effectively capture one or more images of one or more particles captured within the collection medium 106. In various embodiments, the exemplary fluid composition sensor 100 may have a designated field of view for permanently and / or temporarily capturing particle images of at least a portion of a plurality of particles simultaneously. For example, the collection medium 106 may reside at least partially within the field of view of the imaging device 110 as described herein, such that at least a portion of the first plurality of particles captured by the collection medium 106 is visible to the imaging device 110, and first particle data including the particle image may be captured by the fluid composition sensor 100 (e.g., by the imaging device 110).

[0032] In various embodiments, imaging device 110 may be configured to capture particle data including images of one or more particles among a plurality of particles received by collection medium 106 using one or more imaging techniques such as, for example, lensless holography. In various embodiments where the imaging device is configured to utilize lensless holography, the imaging device may calculate and generate an image of one or more particles received by collection medium 106 by digitally reconstructing one or more microscopic images of the one or more particles received by collection medium 106 without using a lens. Alternatively or additionally, in various embodiments, the fluid composition sensor may include a lens-based imaging device or any other device configured to capture particle images that at least partially define particle data as described herein. In various embodiments, the lens-based imaging device may utilize one or more imaging techniques such as, for example, optical microscopy to capture particle images of a plurality of particles captured at the collection medium. In various embodiments, optical microscopy may include passing light through one or more lenses to magnify and capture a particle image of at least a portion of a plurality of particles within the collection medium, the light being transmitted through or reflected from the collection medium and / or the plurality of particles disposed in the collection medium. For example, in various embodiments, as described herein, images captured by exemplary imaging devices such as imaging device 110 may include two-dimensional images (e.g., photographs of at least a portion of the collection medium) and / or three-dimensional images (e.g., three-dimensional digital reconstructions of at least a portion of particles captured at collection medium 106).

[0033] In some embodiments, the fluid composition sensor 100 may be configured to capture, at least substantially simultaneously, multiple particle data (e.g., one or more images) associated with multiple particles in the collection medium 106. Alternatively or otherwise, in various embodiments, the imaging device 110 may be configured to capture multiple particle data at multiple sequential instances over a sample duration that includes the time period during which the fluid composition sensor 100 performs a sample collection operation. For example, the fluid composition sensor 100 may utilize the imaging device 110 to capture first particle data including a first particle image at a first time and second particle data including a second particle image at a second time, wherein the second time is after the first time. In this exemplary case, the first particle image may capture a first plurality of particles received by the fluid composition sensor 100 via a first fluid sample, and the second particle image may capture both a second plurality of particles received by the fluid composition sensor 100 via a second fluid sample and the previously received first plurality of particles. As a non-limiting example, in various embodiments, an exemplary device 10 including the fluid composition sensor 100 may be able to distinguish between particles present in the collection medium 106 at the second time and particles newly received by the collection medium 106 by comparing corresponding particle data (e.g., images) captured at a first time and a second time and identifying any particles from the second captured particle data that were not captured in the first captured particle data.

[0034] In various embodiments, as described further in detail herein, the exemplary fluid composition sensor 100 may be configured to transmit captured particle data (e.g., first particle data) and / or one or more signals (e.g., data signals, control signals) to one or more associated components of the exemplary fluid sampling device 10, such as, for example, a controller 200. It should be understood that... Figure 2 The exemplary configuration of the fluid composition sensor 100 shown is merely an example, and various embodiments, such as the fluid sampling devices described herein, may incorporate fluid composition sensors with other configurations for detecting one or more particle characteristics.

[0035] As described herein, the exemplary fluid sampling device 10 can be configured to perform both particle collection and particle analysis functions. For example, the exemplary fluid sampling device 10 can be configured to perform particle collection using a fluid composition sensor 100, and can be configured to perform particle analysis using one or both of the fluid composition sensor 100 and / or the controller 200. As described herein, the particle collection function of the fluid sampling device 10 (e.g., the fluid composition sensor 100) can correspond to: the fluid composition sensor 100 receiving a volume of fluid comprising a fluid sample containing a plurality of particles, and guiding the volume of fluid toward a receiving surface of the collection medium 106 in a flow direction at least substantially perpendicular to the collection medium 106 using an impactor nozzle 104, so as to facilitate engagement of the collection medium 106 with the volume of fluid, such that at least a portion of the plurality of particles within the volume of fluid can be disposed in the collection medium 106. Furthermore, as described herein, the particle analysis function of the fluid sampling device 10 may correspond to: the fluid composition sensor 100 capturing particle data, such as images of one or more particles received, for example, by the collection medium 106, the fluid composition sensor 100, the controller 200, and / or an external device communicating with the controller, and determining at least one particle loading condition (e.g., at least one particle characteristic) of the fluid sample received by the fluid composition sensor based at least in part on the images. Additionally, in various embodiments, the particle analysis function of the fluid sampling device 10 may include the fluid composition sensor 100 and / or the controller 200 determining the optimal sample volume and / or optimal sample duration for a sample collection operation performed by the fluid composition sensor 100, as described herein. For example, the particle analysis function of the fluid sampling device 10 may include dynamically determining, in real time, the optimal sample duration for a particular sample collection operation performed by the fluid sampling device 10, based at least in part on particle data iteratively captured by the fluid composition sensor 100 during the sample collection operation.

[0036] As described herein, in various embodiments, the particle collection and particle analysis functions of the fluid sampling device 10 may be performed at least partially sequentially. Furthermore, in various embodiments, the particle collection and particle analysis functions of the fluid sampling device 10 may at least partially overlap. For example, in various embodiments, as described herein, the fluid sampling device 10 may perform at least a portion of the particle analysis function (e.g., one or more particle analysis operations) before the particle collection function is fully completed, such that at least a portion of the particle analysis function is performed during the duration of one or more fluid sample collection operations. In various embodiments, the fluid sampling device 10 may include a controller 200, which is further described in detail herein, configured to generate and / or transmit one or more signals to facilitate the determination of optimal sample characteristics for the sample collection operation based at least partially on particle data captured by the fluid composition sensor 100. In various embodiments, the optimal sample characteristics associated with the sample collection operation may include one or more operating characteristics of the fluid composition sensor 100 performing the sample collection operation. For example, the fluid sampling device 10 may determine to perform a sample collection operation that is at least partially defined by optimal sample characteristics (such as, for example, optimal sample volume and / or optimal sample duration) that will result in the reception of multiple particles by a fluid composition sensor (e.g., by the collection medium) upon completion of the sample collection operation, and that optimal sample characteristics are defined by particle loading conditions that are at least substantially similar to predetermined optimal particle loading conditions.

[0037] As a non-limiting example, in various embodiments, the controller 200 of the exemplary fluid sampling device 10 may be configured to generate and / or transmit one or more signals configured to cause the fluid composition sensor 100 to perform a sample collection operation to update, modify, and / or otherwise adjust an initial sample duration defined as a first duration, such as, for example, the fluid composition sensor 100 being operatively configured to maintain an initial, baseline, and / or default time period for the sample collection operation. The controller 200 may cause the fluid composition sensor 100 to update, modify, and / or otherwise adjust the initial sample duration such that the fluid composition sensor 100 receives an update instruction to continue performing the sample collection operation until the sample duration associated with the sample collection operation is at least substantially equal to the updated optimal sample duration. As described herein, the updated optimal sample duration may be determined at least in part based on particle loading conditions defined by a plurality of particles within the collection medium 106 of the fluid composition sensor 100 at a given time. As a non-limiting example, in various embodiments, the fluid sampling device 10 may determine the updated optimal sample duration at least in part based on particle loading conditions, which are particle coverage values ​​defined by a plurality of captured particles. For example, the fluid sampling device 10 can determine the updated optimal sample duration based on a comparison between the particle coverage values ​​exhibited by multiple particles captured in a particle image and stored optimal particle loading conditions (e.g., stored optimal particle coverage values).

[0038] Furthermore, in various embodiments, the controller 200 of the exemplary fluid sampling device 10 may be configured to generate and / or transmit one or more signals configured to cause the fluid composition sensor 100 to terminate the sample collection operation, such as by stopping the operation of the pump 112 of the sensor 100 (e.g., by adjusting the pump 112 from an "on" operation configuration to an "off" configuration) based at least in part on determining that the updated optimal sample duration is at least substantially equal to the amount of time elapsed since the initiation of the sample collection operation (e.g., the sample duration of the sample collection operation). Alternatively or additionally, the controller 200 may be configured to generate and / or transmit one or more signals configured to cause the fluid composition sensor 100 to terminate the sample collection operation, for example by stopping the operation of the pump 112 of the sensor 100 based on determining that the sample duration of the sample collection operation has reached (e.g., at least substantially equal to and / or greater than) a predefined maximum sample collection operation duration threshold. In this exemplary case, the controller 200 may be configured to cause the fluid composition sensor 100 to stop sample collection operation even if the particle loading conditions at the collection medium of the fluid composition sensor are not at least substantially similar to the predetermined optimal particle loading conditions, so as to avoid, for example, situations where the sample collection operation continues for an indeterminate amount of time.

[0039] like Figures 1 to 3B As shown, the fluid sampling device 10 may include a controller 200 configured to determine, at least in part, optimal sample characteristics, such as, for example, optimal sample volume, associated with the sample collection operation based on particle loading conditions defined by a plurality of particles captured at the collection medium during the sample collection operation, and to update one or more operating characteristics of the fluid composition sensor such that the sample collection operation is at least in part defined by the optimal sample volume. Figure 3A As shown, controller 200 may include memory 201, processor 202, input / output circuitry 203, communication circuitry 205, imaging device data storage 107, collection medium characteristic database 204, particle imaging circuitry 206, particle type identification circuitry 207, particulate matter mass concentration calculation circuitry 208, and sensor sampling optimization circuitry 209. Controller 200 may be configured to perform the operations described herein. While the components are described with respect to functional limitations, it should be understood that a particular implementation necessarily involves the use of specific hardware. It should also be understood that some components described herein may include similar or common hardware. For example, two sets of circuits may use the same processor, network interface, storage medium, etc., to perform their associated functions, so that each set of circuits does not require duplicate hardware. Therefore, it should be understood that the use of the term "circuit" as used herein with respect to components of controller 200 includes specific hardware configured to perform functions associated with a particular circuit described herein.

[0040] The term "circuit" should be broadly understood to include hardware, and in some embodiments, to include software for configuring the hardware. For example, in some embodiments, "circuit" may include processing circuitry, storage media, network interfaces, input / output devices, etc. In some embodiments, other elements of the controller 200 may provide or supplement the functionality of a particular circuit. For example, the processor 202 may provide processing functionality, the memory 201 may provide storage functionality, and the communication circuitry 205 may provide network interface functionality, etc.

[0041] In some embodiments, processor 202 (and / or coprocessor or any other processing circuitry assisting or otherwise associated with the processor) may communicate with memory 201 via a bus for transferring information between components of the device. Memory 201 may be non-transitory and may include, for example, one or more volatile and / or non-volatile memories. For example, memory 201 may be an electronic storage device (e.g., a computer-readable storage medium). In various embodiments, memory 201 may be configured to store information, data, content, applications, instructions, etc., for enabling the device to perform various functions according to exemplary embodiments of this disclosure. It should be understood that memory 201 may be configured to store, in part or in whole, any electronic information, data, data structures, embodiments, examples, graphics, processes, operations, techniques, algorithms, instructions, systems, apparatuses, methods, lookup tables, or computer program products described herein, or any combination thereof. As a non-limiting example, memory 201 may be configured to store particle size data, particle type data, particle impact depth data, particle image data, particle shape data, particle cross-sectional area data, particle mass data, particle density data, and particulate matter mass concentration data associated with a volume of fluid. In various embodiments, the memory may be further configured to store one or more particle impact depth momentum lookup tables. Furthermore, in various embodiments, memory 201 may be configured to store one or more operational characteristics associated with a sample collection operation, such as, for example, optimal sample duration, optimal sample volume, one or more total sample volumes collected by a fluid composition sensor during the sample collection operation, as measured at one or more instances, sample duration, one or more elapsed times defined by the amount of time elapsed since the start of the sample collection operation, as measured at one or more instances. As described herein, memory 201 may be configured such that one or more operational characteristics associated with the sample collection operation can be dynamically updated at one or more instances during the sample collection operation in response to identified particle loading conditions.

[0042] Processor 202 can be embodied in a variety of different ways and may include, for example, one or more processing devices configured to execute independently. Alternatively, the processor may include one or more processors configured in series via a bus to enable independent execution of instructions, pipelines, and / or multiple threads. The term "processing circuitry" is understood to include single-core processors, multi-core processors, multiple processors within a device, and / or remote or "cloud" processors.

[0043] In an exemplary embodiment, processor 202 may be configured to execute instructions stored in memory 201 or otherwise accessible to the processor. Alternatively or otherwise, the processor may be configured to perform hard-coded functions. Thus, whether configured by hardware or software methods, or by a combination thereof, the processor may represent an entity capable of performing operations and being configured accordingly according to embodiments of this disclosure (e.g., physically embodied in circuit form). Alternatively, for example, when the processor embodies an executor of software instructions, the instructions may specifically configure the processor to perform the algorithms and / or operations described herein when executing the instructions.

[0044] In some embodiments, controller 200 may include input / output circuitry 203, which may then communicate with processor 202 to provide output to a user and, in some embodiments, receive user-provided input such as commands. Input / output circuitry 203 may include a user interface, such as a graphical user interface (GUI), and may include a display, which may include a web user interface, a GUI application, a mobile application, a client device, or any other suitable hardware or software. In some embodiments, input / output circuitry 203 may also include a display device, a display screen, a user input element (such as a touchscreen), a touch area, soft keys, a keyboard, a mouse, a microphone, a speaker (e.g., a buzzer), a light-emitting device (e.g., a red light-emitting diode (LED), a green LED, a blue LED, a white LED, an infrared (IR) LED, or a combination thereof), or other input / output mechanisms. Processor 202, input / output circuitry 203 (which may utilize processing circuitry), or both, may be configured to control one or more functions of one or more user interface elements via computer-executable program code instructions (e.g., software, firmware) stored in a non-transitory computer-readable storage medium (e.g., memory 201). Input / output circuitry 203 is optional, and in some embodiments, controller 200 may not include input / output circuitry. For example, where controller 200 does not directly interact with the user, controller 200 may generate user interface data for display on one or more other devices with which one or more users directly interact, and transmit the generated user interface data to one or more of these devices. For example, controller 200 may use user interface circuitry to generate user interface data for display on one or more display devices, and transmit the generated user interface data to those display devices.

[0045] The communication circuit 205 may be a device or circuit embodied in hardware or a combination of hardware and software, configured to receive and / or transmit data to and from a network and / or to any other device, circuit, or module communicating with the device 200. For example, the communication circuit 205 may be configured to communicate with one or more computing devices via wired (e.g., USB) or wireless (e.g., Bluetooth, Wi-Fi, cellular, etc.) communication protocols.

[0046] In various embodiments, processor 202 may be configured to communicate with particle imaging circuitry 206. Particle imaging circuitry 206 may be a device or circuit embodied in hardware or a combination of hardware and software, configured to receive, process, generate, and / or transmit data, such as images captured by imaging device 110. Furthermore, in various embodiments, particle imaging circuitry 206 may be configured to analyze one or more images captured by imaging device 110 of fluid composition sensor 100 to determine which particles among a plurality of particles present in collection medium 106 are newly received by collection medium 106 during a recent particle analysis. Particle imaging circuitry 206 may receive a first captured particle image and a second captured particle image from imaging device at a first time and a second time, respectively, where the first time indicates that device 10 begins analyzing one or more particles among a plurality of particles captured by collection medium 106, and the second time occurs after the first time. In this configuration, the device can be configured to distinguish between particles present in the collection medium 106 at the start of particle analysis and particles newly received by the collection medium 106 by comparing corresponding particle images captured at a first time and a second time and identifying any particles from the second captured particle image that were not captured in the first captured particle image.

[0047] In various embodiments, particle imaging circuit 206 may be further configured to analyze one or more images captured by imaging device 110 of fluid composition sensor 100 to determine the particle size of each of one or more of a plurality of particles within collection medium 106. In various embodiments, particle size may be defined by the cross-sectional area of ​​the particle. In various embodiments, particle imaging circuit 206 may be configured to determine the particle size of particles having any of a plurality of particle sizes. For example, particle imaging circuit 206 may be configured to determine the particle size of particles with diameters between about 0.3 micrometers and about 100 micrometers (e.g., 2.5 micrometers), and thus determine the particle size category that the particle may be associated with, such as PM10, PM4, PM2.5, or PM1. In various embodiments, controller and / or particle imaging circuit 206 may be further configured to analyze one or more images captured by imaging device 110 of fluid composition sensor 100 to determine the shape of each of one or more of a plurality of particles within collection medium 106. In various embodiments, particle shape may be defined at least in part by particle cross-sectional area. The particle imaging circuit 206 may be further configured to use one or more image focusing techniques to determine the particle impact depth 121 of each of one or more of a plurality of particles within the collection medium 106. The particle imaging circuit 206 may be configured to execute instructions stored, for example, in memory 201, for performing one or more image focusing techniques. In various embodiments, the one or more image focusing techniques may include one or more techniques such as angular spectral propagation (ASP). In other embodiments, optomechanical adjustment may be used as an image focusing technique. In various embodiments, the particle imaging circuit 206 may use one or more image focusing techniques to determine the focus depth 122 of each of one or more of a plurality of particles within the collection medium. In determining the focus depth of each of the one or more particles, the particle imaging circuit 206 may be configured to use known dimensions of the fluid composition sensor 100 to calculate, for example, the thickness of the collection medium and the distance between the transparent substrate 108 and the imaging device 110, the impact depth 121 of each of one or more of a plurality of particles within the collection medium 106. In various embodiments, for example, the impact depth 121 of a particle within the collection medium 106 can be calculated by subtracting the measured focusing depth 122 of the particle from the sum of the collection medium thickness, the transparent substrate thickness, and the distance between the transparent substrate 108 and the imaging device 110. The particle imaging circuit 206 can send and / or receive data from the imaging device data storage 107. In various embodiments, the particle imaging circuit 206 can be configured to determine the impact depth of the particle using one or more machine learning techniques.In various implementations, one or more machine learning techniques used by particle imaging circuit 206 to determine the impact depth of a particle may include depth-supervised learning using one or more labeled datasets with one or more known particle characteristics (such as particle type, particle velocity, particle size, particle shape, and / or any other data generated, transmitted, and / or received by controller 200) to estimate the impact depth of the particle.

[0048] In various embodiments, processor 202 may be configured to communicate with particle type identification circuitry 207. Particle type identification circuitry 207 may be a device or circuit embodied in hardware or a combination of hardware and software, configured to identify the particle type and / or particle species of one or more of a plurality of particles received by collection medium 106. In various embodiments, the plurality of particles within a volume of fluid may include one or more of a variety of particle types, such as bacteria, pollen, spores, mold, biological particles, soot, inorganic particles, and organic particles. In various embodiments, particle type identification circuitry 207 may use one or more machine learning techniques to determine the particle type and / or particle species of each of the plurality of particles received by collection medium 106. In various embodiments, the one or more machine learning techniques used by particle type identification circuitry 207 to determine the particle type and / or particle species of each of the plurality of particles may include analyzing images captured by imaging device 110, particle size data, particle shape data, particle loading data, and / or any other data generated, transmitted, and / or received by controller 200. In various embodiments, particle type identification circuit 207 may send and / or receive data from imaging device data storage 107. Furthermore, in various embodiments, particle type identification circuit 207 may be configured to receive determined initial particle velocity data corresponding to one or more particles from a plurality of particles 120 received by collection medium 106 from particulate matter concentration calculation circuit 208. In various embodiments, particle type identification circuit 207 may be configured to compare the determined initial particle velocity with a particle velocity approximated at least in part based on a known flow rate of fluid moving through fluid composition sensor 100, and generate velocity comparison data associated with that particle. In various embodiments, particle type identification circuit 207 may be configured to execute a feedback loop wherein one or more velocity comparison data associated with one or more particles from a plurality of particles received by collection medium 106 may limit one or more inputs to a machine learning model to improve the machine learning rate associated with one or more machine learning techniques, as described herein.

[0049] In various embodiments, device 10 may be configured to have or communicate with a collection medium characteristic database 204. The collection medium characteristic database 204 may be stored at least partially on the system's memory 201. In some embodiments, the collection medium characteristic database 204 may be remote from device 10 but connected to it. The collection medium characteristic database 204 may contain information such as one or more particle impact depth-momentum relationship lookup tables. In various embodiments, the particle impact depth-momentum relationship lookup table may include a data matrix defining the relationship between particle impact depth and initial particle momentum (i.e., the momentum of the particle at the receiving surface 105 of the collection medium 106, where the particle is received by the collection medium 106 at the receiving surface 105, as described herein) for a particular collection medium type. Various particle impact depth-momentum relationship lookup tables may include data matrices defining the relationship between particle impact depth and initial particle momentum for various collection medium types.

[0050] The particulate matter mass concentration calculation circuit 208 may be a device or circuit embodied in hardware or a combination of hardware and software, configured to determine the particulate matter mass concentration within a volume of fluid. In various embodiments, the particulate matter mass concentration calculation circuit 208 may be configured to determine the particulate matter mass concentration within a volume of fluid based on the approximate aggregate mass of a plurality of particles present within the volume of fluid. In various embodiments, the particulate matter mass concentration calculation circuit 208 may be configured to determine the approximate aggregate mass of a plurality of particles 120 present within a volume of fluid based on the aggregate mass of a plurality of particles 120 received by the collection medium 106. In various embodiments, the particulate matter mass concentration calculation circuit 208 may be configured to determine the aggregate mass of a plurality of particles 120 received by the collection medium 106 based on a corresponding estimated mass of each particle among the particles 120. In various embodiments, the particulate matter mass concentration calculation circuit 208 may be configured to estimate the corresponding mass of each particle among the particles 120 based at least in part on a correspondingly determined impact depth of each particle.

[0051] In various embodiments, the particulate matter mass concentration calculation circuit 208 can be configured to estimate the mass of a plurality of particles 120 by retrieving data corresponding to the particles, such as particle size data, particle shape data (e.g., particle cross-sectional area data, particle orientation data), and particle impact depth, and to determine the initial momentum of the particle before it is received by the collection medium 106 based on data in a particle impact depth-momentum lookup table that associates the particle impact depth with the initial momentum of the particle for a given type of collection medium 106. By using the known relationship between momentum, velocity, and mass (the momentum of a particle equals the mass of the particle multiplied by the velocity of the particle), and the known velocity of the particle (based on a control value of the airflow rate within the device 10), the particulate matter mass concentration calculation circuit 208 can be configured to determine the estimated mass of the particle.

[0052] In various embodiments, the particulate matter mass concentration calculation circuit 208 may be configured to use one or more machine learning techniques to determine the estimated mass of the particles. In various embodiments, the one or more machine learning techniques used by the particulate matter mass concentration calculation circuit 208 to determine the particle mass may include deep supervised learning using one or more labeled datasets having one or more known particle characteristics (such as particle type, particle velocity, particle impact depth, various particle weight measurements, and / or any other data generated, transmitted, and / or received by the controller 200) to estimate the particle mass. In various embodiments, the particulate matter mass concentration calculation circuit 208 may be configured to apply one or more compensation factors to the determined particle mass using one or more machine learning techniques.

[0053] Furthermore, in various embodiments, the particulate matter mass concentration calculation circuit 208 may be configured to determine the estimated density of particles based at least in part on one or more of the following: particle impact depth, estimated particle mass, particle shape, particle type, and particle size data. In various embodiments, the particulate matter mass concentration calculation circuit 208 may be configured to determine the estimated mass and / or density of each particle in the plurality of particles 120 received by the collection medium 106. In various embodiments, the particulate matter mass concentration calculation circuit 208 may be configured to apply one or more compensation factors to the estimated mass of the particles to address one or both of the particle condition associated with the particles and the environmental condition associated with the surrounding environment. In various embodiments, for example, the particulate matter mass concentration calculation circuit 208 may be configured to apply appropriate compensation factors based at least in part on the particle cross-sectional area, ambient temperature, and / or ambient humidity. In various embodiments, the particulate matter mass concentration calculation circuit 208 may be configured to determine the estimated aggregate mass of the plurality of particles 120 received by the collection medium based on the estimated mass of each particle in the plurality of particles 120 received by the collection medium 106. In various embodiments, the particulate matter mass concentration calculation circuit 208 may be configured to determine the approximate aggregate mass of a plurality of particles present in a volume of fluid based on the determined aggregate mass of the plurality of particles 120 received by the collection medium 106. In various embodiments, the particulate matter mass concentration calculation circuit 208 may be configured to determine the particulate matter mass concentration in the volume of fluid based on the approximate aggregate mass of the plurality of particles present in the volume of fluid. In various embodiments, the particulate matter mass concentration calculation circuit 208 may be configured to apply one or more scaling factors to the determined particulate matter mass concentration in the volume of fluid to address problems of experimental inefficiency, such as particle collection efficiency and detection probability factors. In various embodiments, an appropriate scaling factor may be determined based on empirical data.

[0054] Furthermore, the particulate matter mass concentration calculation circuit 208 can be configured to determine that the collection medium 106 needs to be replaced. For example, in various embodiments, the particulate matter mass concentration calculation circuit 208 can be configured to determine that a threshold amount of time has elapsed since the last replacement of the collection medium 106, the number of particles present in the collection medium 106 has exceeded a predetermined threshold number of particles, and / or the percentage of particle coverage in the field of view has exceeded a threshold particle coverage value.

[0055] The particulate matter mass concentration calculation circuit 208 may be a device or circuit embodied in hardware or a combination of hardware and software, configured to determine the particulate matter mass concentration within a volume of fluid. In various embodiments, the particulate matter mass concentration calculation circuit 208 may be configured to determine the particulate matter mass concentration within a volume of fluid based on the approximate aggregate mass of a plurality of particles present within the volume of fluid. In various embodiments, the particulate matter mass concentration calculation circuit 208 may be configured to determine the approximate aggregate mass of a plurality of particles 120 present within a volume of fluid based on the aggregate mass of a plurality of particles 120 received by the collection medium 106. In various embodiments, the particulate matter mass concentration calculation circuit 208 may be configured to determine the aggregate mass of a plurality of particles 120 received by the collection medium 106 based on a corresponding estimated mass of each particle among the particles 120. In various embodiments, the particulate matter mass concentration calculation circuit 208 may be configured to estimate the corresponding mass of each particle among the particles 120 based at least in part on a correspondingly determined impact depth of each particle.

[0056] In various embodiments, the particulate matter mass concentration calculation circuit 208 can be configured to estimate the mass of a plurality of particles 120 by retrieving data corresponding to the particles, such as particle size data, particle shape data (e.g., particle cross-sectional area data, particle orientation data), and particle impact depth, and to determine the initial momentum of the particle before it is received by the collection medium 106 based on data in a particle impact depth-momentum lookup table that associates the particle impact depth with the initial momentum of the particle for a given type of collection medium 106. By using the known relationship between momentum, velocity, and mass (the momentum of a particle equals the mass of the particle multiplied by the velocity of the particle), and the known velocity of the particle (based on a control value of the airflow rate within the device 10), the particulate matter mass concentration calculation circuit 208 can be configured to determine the estimated mass of the particle.

[0057] In various embodiments, the particulate matter mass concentration calculation circuit 208 may be configured to use one or more machine learning techniques to determine the estimated mass of the particles. In various embodiments, the one or more machine learning techniques used by the particulate matter mass concentration calculation circuit 208 to determine the particle mass may include deep supervised learning using one or more labeled datasets having one or more known particle characteristics (such as particle type, particle velocity, particle impact depth, various particle weight measurements, and / or any other data generated, transmitted, and / or received by the controller 200) to estimate the particle mass. In various embodiments, the particulate matter mass concentration calculation circuit 208 may be configured to apply one or more compensation factors to the determined particle mass using one or more machine learning techniques.

[0058] Furthermore, in various embodiments, the particulate matter mass concentration calculation circuit 208 may be configured to determine the estimated density of particles based at least in part on one or more of the following: particle impact depth, estimated particle mass, particle shape, particle type, and particle size data. In various embodiments, the particulate matter mass concentration calculation circuit 208 may be configured to determine the estimated mass and / or density of each particle in the plurality of particles 120 received by the collection medium 106. In various embodiments, the particulate matter mass concentration calculation circuit 208 may be configured to apply one or more compensation factors to the estimated mass of the particles to address one or both of the particle condition associated with the particles and the environmental condition associated with the surrounding environment. In various embodiments, for example, the particulate matter mass concentration calculation circuit 208 may be configured to apply appropriate compensation factors based at least in part on the particle cross-sectional area, ambient temperature, and / or ambient humidity. In various embodiments, the particulate matter mass concentration calculation circuit 208 may be configured to determine the estimated aggregate mass of the plurality of particles 120 received by the collection medium based on the estimated mass of each particle in the plurality of particles 120 received by the collection medium 106. In various embodiments, the particulate matter mass concentration calculation circuit 208 may be configured to determine the approximate aggregate mass of a plurality of particles present in a volume of fluid based on the determined aggregate mass of the plurality of particles 120 received by the collection medium 106. In various embodiments, the particulate matter mass concentration calculation circuit 208 may be configured to determine the particulate matter mass concentration in the volume of fluid based on the approximate aggregate mass of the plurality of particles present in the volume of fluid. In various embodiments, the particulate matter mass concentration calculation circuit 208 may be configured to apply one or more scaling factors to the determined particulate matter mass concentration in the volume of fluid to address problems of experimental inefficiency, such as particle collection efficiency and detection probability factors. In various embodiments, an appropriate scaling factor may be determined based on empirical data.

[0059] Furthermore, the particulate matter mass concentration calculation circuit 208 can be configured to determine that the collection medium 106 needs to be replaced. For example, in various embodiments, the particulate matter mass concentration calculation circuit 208 can be configured to determine that a threshold amount of time has elapsed since the last replacement of the collection medium 106, the number of particles present in the collection medium 106 has exceeded a predetermined threshold number of particles, and / or the percentage of particle coverage in the field of view has exceeded a threshold particle coverage value.

[0060] In various embodiments, device 10 may be configured to determine the amount of time that device 10 (e.g., pump 112) should remain in operation by drawing fluid through device 10, such that at least a predetermined volume of fluid is directed toward collection medium 106 (e.g., across the surface of the collection medium). The predetermined volume of fluid may be defined by a threshold volume of fluid (e.g., a minimum volume of fluid, a maximum volume of fluid) or an acceptable range of fluid volume (e.g., between the minimum and maximum volumes of fluid). In some embodiments, the volume of fluid passing through device 10 may be measured (e.g., by a fluid flow sensor); however, in other embodiments, the volume of fluid passing through device 10 may be estimated (e.g., based on a known fluid flow rate) while the pump is in an operating configuration and for a given amount of time. For example, as described herein, in various embodiments, the particulate matter mass concentration calculation circuit 208 may be configured to generate and / or transmit one or more signals to cause the fluid composition sensor 100 to initiate a particulate collection operation, wherein the sensor 100 is capable of receiving a volume of fluid containing a plurality of particles, and facilitates the engagement of the collection medium 106 with the received volume of fluid such that at least a portion of the plurality of particles within the volume of fluid may be disposed at and / or therein. For example, in various embodiments, the particulate matter mass concentration calculation circuit 208 may be configured to transmit one or more signals to cause the pump 112 to switch from an "off" configuration to an "on" operation configuration. Conversely, in various embodiments, the particulate matter mass concentration calculation circuit 208 may be configured to transmit one or more signals to cause the pump 112 to switch from an "on" operation configuration to an "off" configuration. As a non-limiting example, the particulate matter mass concentration calculation circuit 208 may be configured to emit one or more signals to cause the pump 112 to switch from an "on" operation configuration to an "off" configuration, at least in part, based on a determination that a threshold amount of time has elapsed and / or that the fluid composition sensor 100 has received a threshold volume of fluid during operation. In various embodiments, this determination may be made by the particulate matter mass concentration calculation circuit 208 based at least in part on data collected by a fluid flow sensor of the fluid composition sensor 100, which is configured to detect the flow rate of a given volume of fluid passing through at least a portion of the sensor 100. In various embodiments, the fan or pump 112 is calibrated such that the flow rate of fluid moving through the device is at least in part based on known / determined operating characteristics of the fan or pump 112 (e.g., operating power).

[0061] In some embodiments, device 10 includes a fluid composition sensor 100 configured to receive a volume of fluid. The fluid composition sensor has: a collection medium 106 housing configured to receive and hold at least a portion of the collection medium 106 to receive one or more particles from a plurality of particles within the volume of fluid; a pump 112 for moving the volume of fluid above the collection medium 106 housing; an imaging device 110 configured to capture images of at least a portion of the plurality of particles received by at least one collection medium 106; and a particulate matter mass concentration calculation circuit 208 connected to the imaging device 110 and the pump 112. The particulate matter mass concentration calculation circuit 208 is configured to calculate the total particulate matter mass of the one or more particles based on the images of the one or more particles received by at least one collection medium 106. The particulate matter mass concentration calculation circuit 208 is configured to adjust the volume of fluid above the collection medium 106 housing.

[0062] As described herein, in various embodiments, particle loading calculation circuit 208 may be configured to regulate the operation of fluid composition sensor 100, for example, by adjusting one or more operating characteristics of fluid composition sensor 100 (e.g., pump on / off configuration, pump volumetric flow rate, etc.). For example, particle loading calculation circuit 208 may be configured to regulate the operation of pump 112 of fluid composition sensor 100, for example, by adjusting (e.g., stopping) one or more operating characteristics of pump 112 of fluid composition sensor 100 before a certain amount of particles has been captured in collection medium 106, which degrades the measurement accuracy of future captured particles (e.g., because the collection medium is sufficiently filled with particles that make it impossible to identify newly captured particles and / or to accurately locate the edges of these particles). For example, as described herein, a fluid composition sensor 100 is configured to receive a volume of fluid containing a plurality of particles, such that at least a portion of the particles are disposed on and / or therein a collection medium 106, and is further configured to determine at least one of the following: particle mass, particle coverage value, and / or any other particle loading conditions defined by the plurality of particles disposed at the collection medium 106. This fluid composition sensor may experience increased inaccuracies due to measurement errors caused by the physical saturation and / or degradation of the collection medium over time due to prolonged collection of the plurality of particles. In various embodiments, the particle loading conditions as described herein may be defined at least in part by: the spatial arrangement of the plurality of particles disposed at the collection medium (e.g., particle aggregation, spike formation, particle contact, particle overlap, etc.), particle coverage value, average grayscale of all pixels in a captured image, particle mass, total light intensity, amount of collected particles, calculated particle density, etc.

[0063] For example, the increased frequency and / or extent of the aforementioned sensor inaccuracy may correspond to an increase in the number of particles collected at collection medium 106 (and the resulting physical properties of collection medium 106 change due to the increased number of particles disposed therein). Therefore, in various embodiments, one or more components of the collection medium assembly (e.g., collection medium 106) as described herein may be replaceable, such that a first collection medium can be used to receive a first plurality of particles from a first volume of fluid, and can be removed from sensor 100 and replaced with a second collection medium, which can then be used to receive a second plurality of particles from a second volume of fluid received by the sensor after the first collection medium has been removed from sensor 100. In this exemplary case, the reduction in sensor accuracy due to measurement errors caused by physical saturation and / or degradation of the collection medium over time can be addressed by replacing at least partially depleted collection medium with at least substantially new medium having fewer (e.g., zero) particles freely adhering to a given volume of fluid received by sensor 100.

[0064] As described herein, in some embodiments, particle loading calculation circuitry 208 may be configured to determine when a plurality of particles received by collection medium 106 are arranged such that at least two of the particles are non-uniformly spaced, in contact, clustered, and / or overlap each other. For example, in an exemplary case, two or more of the plurality of particles may be aligned with each other relative to an imaging device, wherein a first particle engages the collection medium at a first time and at a first position around the receiving surface, and a second particle subsequently engages the collection medium at a second time (the second time being chronologically after the first time) and at the first position around the receiving surface, such that, from the perspective of the imaging device, at least a portion of the second particle overlaps with at least a portion of the first particle. In such exemplary cases, as described above, positioning the second particle on top of the first particle prevents the entire first particle from being captured in an image taken by the exemplary imaging device, and thus prevents the controller 200 from accurately analyzing the first particle according to one or more of the operations described herein. In such exemplary cases, the controller may be configured to determine that a first portion of a plurality of particles located at a first portion of the collection medium exhibits a first aggregate particle density that is at least significantly different from a second aggregate particle density of a second portion of a plurality of particles located at a second portion of the collection medium, wherein the aggregate particle density may be defined by the number of plurality of particles within a given surface area defining a portion of the collection medium. In some embodiments, device 10 (e.g., controller 200 associated with an imaging device) may be configured to actively monitor particle spacing to maximize the operational efficiency of device 10 and / or identify particle placement on collection medium 106.

[0065] In various embodiments, controller 200 (e.g., particulate matter mass concentration calculation circuitry 208) may be configured to calculate particulate matter mass by using at least images, to calculate total particulate matter mass or to determine the amount of light extending through collection medium 106. In some embodiments, particle loading calculation circuitry 208 works in conjunction with particle imaging circuitry 206 to determine and / or characterize the spatial arrangement of one or more particles within the field of view of an imaging device, such as, for example, the spacing between particles. For example, in various embodiments, as described herein, images captured by exemplary imaging devices may include two-dimensional images (e.g., photographs of at least a portion of the collection medium) and / or three-dimensional images (e.g., three-dimensional digital reconstructions of at least a portion of particles captured at the collection medium, based at least in part on the two-dimensional position of the detected particles and the depth of focus associated with each of a plurality of particles, which may indicate the distance from the imaging device and thus may indicate the three-dimensional position of each of a plurality of particles). Therefore, in various embodiments, the particle loading calculation circuit 208 may be configured to characterize the spacing between two particles among a plurality of captured particles as the distance between the two particles by an image, wherein the distance between the two particles is defined by one or more of an x ​​component (e.g., the difference of the corresponding x coordinates), a y component (e.g., the difference of the corresponding y coordinates), and a z component (e.g., the difference of the corresponding z coordinates, which may be determined by the depth of focus relative to the imaging device).

[0066] As described herein, particle loading calculation circuitry 208 may be configured to calculate the percentage of particle coverage of the collection medium within the field of view, and may also determine that the calculated percentage of particle coverage is greater than a threshold particle coverage value. In some embodiments, particle loading calculation circuitry 208 may be configured to calculate the percentage of particle coverage of the collection medium 106 based at least in part on a determined percentage of the image covered by particles (e.g., a percentage of the field of view of an imaging device). For example, in various embodiments, in exemplary cases, a portion of the collection medium may be covered by particles, wherein the particles are disposed at the collection medium, and wherein the cross-section of the particles is positioned between the imaging device and at least a portion of the thickness of the collection medium, such that the particles at least partially interrupt the line of sight between the imaging device and at least a portion of the thickness of the collection medium. As a non-limiting example, multiple particles received by the collection medium may collectively cover at least a portion of the collection medium. As described herein, particle mass concentration calculation circuitry 208 may be configured to calculate the percentage of particle coverage of the collection medium based at least in part on a comparison of the total surface area of ​​the collection medium (e.g., the receiving surface) with the surface area of ​​the collection medium covered by multiple particles. In various embodiments, particle loading calculation circuitry 208 may be configured to determine that the particle coverage percentage of the collection medium is greater than a predetermined threshold. As a non-limiting example, in various embodiments, the predetermined threshold for particle coverage percentage may be at least approximately between 0.01% and 99.9%. Alternatively or otherwise, as described herein, in various embodiments, the particle coverage percentage may be represented by a particle coverage value generated by one or more circuits of controller 200 as a comparison value, score, ratio, coefficient, etc., defined by a predetermined optimal particle coverage condition percentage exhibited by the captured particle data (e.g., particle images). In this exemplary case, as a non-limiting example, a particle image showing a particle coverage percentage less than the predetermined optimal particle coverage condition may be assigned a particle coverage value of at least substantially less than 100%, a particle image showing a particle coverage percentage at least substantially equal to the predetermined optimal particle coverage condition may be assigned a particle coverage value of 100%, and a particle image showing a particle coverage percentage greater than the predetermined optimal particle coverage condition may be assigned a particle coverage value of at least greater than 100%. In this exemplary case, the particle loading calculation circuit 208 may be configured to identify the collection medium as "covered" and thus generate one or more signals configured to induce adjustments in the operation of the fluid composition sensor to facilitate replacement of the covered collection medium. As a non-limiting illustrative example, in an exemplary case where the detection controller 200 is configured to detect the presence of individual particles, such as in the case associated with a "cleanroom" application, a predetermined threshold for the particle coverage percentage may be less than 1%.

[0067] In some embodiments, particle loading calculation circuitry 208 may be configured to determine, at least in part, whether at least a portion of a plurality of particles received by fluid composition sensor 100 aggregates at collection medium 106 based on one or more images of collection medium 106. In various embodiments, particle loading calculation circuitry 208 may be configured to determine that a plurality of particles received by fluid composition sensor 100 aggregates such that the boundaries of the plurality of particles at least substantially overlap or are spaced less than an aggregation threshold distance to define individual clusters, and that the plurality of clusters (each cluster comprising a plurality of particles with overlapping boundaries) are spaced apart by a distance (such that the individual clusters are separated and discrete from each other), wherein a first portion of collection medium exhibits a first particle coverage value, as described above, which is disproportionate to a second particle coverage value detected at a second portion of collection medium. For example, in some embodiments, particle loading calculation circuitry 208 may be configured to determine whether particles aggregate by calculating an average distance between at least a portion of the particles. In some embodiments, particle loading calculation circuitry 208 is configured to determine, at least in part, that at least a portion of the plurality of particles received by fluid composition sensor 100 aggregates based on determining that a calculated average distance (as shown in the images) between particles at collection medium 106 is below a predetermined distance. For example, in some embodiments, particle loading calculation circuit 208 is configured to determine when the percentage of distance between particles falls below a predetermined distance. In some embodiments, particle loading calculation circuit 208 is configured to cause fluid composition sensor 100 to regulate the volume of fluid flowing through collection medium 106, such as by stopping pump 112 (e.g., by transmitting one or more signals). In some embodiments, particle loading calculation circuit 208 is configured to provide a signal when particle aggregation is determined. In some embodiments, the signal is connected to a display device. In some embodiments, the signal provided by particle loading calculation circuit 208 can provide a warning that diagnoses the presence of uneven airflow within device 10.

[0068] In various embodiments, the particulate matter mass concentration calculation circuit 208 may be configured to, at least in part, adjust the operation of the pump 112 of the fluid composition sensor 100 (e.g., adjustment between on / off configurations, adjustment of volumetric flow rate, etc.) based on determining that a predetermined total particulate matter mass threshold has been reached. For example, the particulate matter mass concentration calculation circuit 208 may be configured to transmit one or more signals that directly or indirectly cause the pump 112 to stop operating when it is determined that the predetermined total particulate matter mass threshold has been reached.

[0069] As a non-limiting example, in various embodiments, the particulate matter mass concentration calculation circuit 208 may receive a first captured particle image and a second captured particle image captured at a first time and a second time, respectively, from the imaging device of the device 10, wherein the first time indicates that the device 10 begins analyzing one or more particles among a plurality of particles 120 captured by the collection medium 106, and the second time occurs after the first time. In various embodiments, the particulate matter mass concentration calculation circuit 208 is configured to determine a first particle loading condition corresponding to the first image and to determine a second particle loading condition corresponding to the second image. In various embodiments, the particulate matter mass concentration calculation circuit 208 is configured to compare a first total particulate matter mass with a second total particulate matter mass. In various embodiments, the particulate matter mass concentration calculation circuit 208 is configured to calculate the difference between the first total particulate matter mass and the second total particulate matter mass. For example, the particulate matter mass concentration calculation circuit 208 may be configured to calculate the difference between the first total particulate matter mass and the second total particulate matter mass by identifying any particles from the second captured particle image that were not captured in the first captured particle image. In various embodiments, the particulate matter mass concentration calculation circuit 208 may be configured to adjust one or more operating characteristics (e.g., on / off configuration, volumetric flow rate, etc.) of the pump 112 of the fluid composition sensor 100, at least in part, based on determining that the difference between the calculated first total particulate matter mass and the second total particulate matter mass is greater than a predetermined difference. For example, in various embodiments, the particulate matter mass concentration calculation circuit 208 may be configured to stop the pump 112 when a predetermined difference between the first total particulate matter mass and the second total particulate matter mass is calculated. In some embodiments, the particle loading calculation circuit 208 is configured to determine the density of the particle concentration at the collection medium 106, at least in part, based on captured particle images. For example, the particle loading calculation circuit 208 may be configured to compare the calculated particle density with one or more stored particle density thresholds, and thus adjust one or more operating characteristics of the pump 112 based on determining that the calculated particle density is greater than a particle density threshold. Alternatively or additionally, the particle loading calculation circuit 208 may be configured to adjust one or more operating characteristics of the pump 112 based on determining that the calculated particle density is less than a particle density threshold.

[0070] In various embodiments, as described herein, particulate matter mass concentration calculation circuit 208 is configured to determine whether the total particulate matter mass is clustered. In various embodiments, imaging device 110 is configured to capture images at set intervals. In various embodiments, imaging device 110 is configured to capture images when a volume of fluid begins to flow over the housing of collection medium 106. In various embodiments, particulate matter mass concentration calculation circuit 208 is configured to determine whether the initiation of flow of a volume of fluid causes a spike under one or more particulate loading conditions (such as, for example, particulate matter mass). In various embodiments, a spike under particulate loading conditions may be defined as a rapid increase in particulate loading conditions, such as, for example, a rapid increase in particulate matter mass over time. As a non-limiting illustrative example, a spike may be defined as the rate of increase of one or more particulate loading conditions exceeding a defined threshold, such as, for example, the rate of increase of particulate matter mass exceeding a predetermined particulate matter mass increase rate threshold. In some embodiments, particulate matter mass concentration calculation circuit 208 is configured to calculate the percentage increase of particulate matter mass over time, such as, for example, the rate of increase of particulate matter mass calculated in a continuous measurement. In some embodiments, particulate matter mass concentration calculation circuitry 208 is configured to provide a signal indicating that one or more particle loading conditions have increased by a percentage over time above or below a predetermined threshold. As a non-limiting illustrative example, as defined herein, the controller's detection of spikes may correspond to determining that any other component of, for example, imaging equipment, collection medium, illumination source, and / or fluid composition sensor 100 may become contaminated at the start of particle collection operation and / or require recalibration of device 10.

[0071] In various embodiments, the particulate matter mass concentration calculation circuit 208 may be configured to calculate, at least in part, the total particulate matter mass of one or more of a plurality of particles received by at least one collection medium 106, based on determining the total intensity of light passing through an image captured by an imaging device, as described herein. In various embodiments, the total light intensity may correspond to a measurement at least in part based on an imaging device (such as, for example, a CMOS image sensor). As a non-limiting illustrative case, the total light intensity may be measured at least in part based on the average bit count of each pixel associated with the imaging device and / or the image generated by the imaging device. For example, an exemplary calculation of the total light intensity may be performed as a function of time during operation, whereby device 10 (e.g., controller 200) measures one or more raw signals from each pixel in an imager array corresponding to the imaging device. In various embodiments, the total light intensity (as depicted by the image of collection medium 106 and the plurality of particles received therethrough) may be at least in part based on the particle type, average refractive index, particle opacity at an optimal source wavelength (e.g., 850 nm), etc., associated with at least a portion of the plurality of particles captured at the collection medium. As a non-limiting illustrative example, the total intensity of light may be at least substantially inversely proportional to the particle concentration and / or particulate mass of a plurality of particles captured in the image (e.g., at the collection medium 106 and within the field of view of the imaging device). In various embodiments, the particulate mass concentration calculation circuit 208 may be configured to adjust the operation of the pump 112 of the fluid composition sensor 100 (e.g., on / off configuration, volumetric flow rate, etc.) at least in part based on determining that the calculated total light intensity is less than a predetermined threshold. For example, in various embodiments, the particulate mass concentration calculation circuit 208 may be configured to generate one or more signals configured to stop the pump 112 from operating (e.g., switch from an "on" operation configuration to an "off" configuration) when it is determined that the total intensity of light passing through the image is less than a predetermined intensity threshold.

[0072] In various embodiments, particulate matter mass concentration calculation circuit 208 is configured to determine the total intensity of light passing through an image captured by an imaging device, wherein the image depicts at least a portion of a collection medium in grayscale. In such exemplary cases, the image depicting the collection medium in grayscale may include one or more particles among a plurality of particles disposed at locations on the collection medium indicated by indicators, such as, for example, one or more relatively dark regions (e.g., relative to the collection medium) to distinguish the collection medium from the one or more particles disposed thereon. In various embodiments, the size, shape, color, etc., of the one or more indicators corresponding to the plurality of particles disposed at the collection medium may vary at least in part based on the aggregate mass, density, size, etc., of the one or more particles corresponding to them. As a non-limiting example, in some embodiments, particulate matter mass concentration calculation circuit 208 may be configured to determine a particle coverage value at least in part based on the number and / or coverage percentage (e.g., relative to a portion of the collection medium depicted in the grayscale image) of one or more dark spots presented in an exemplary grayscale image of the collection medium. The calculated total light intensity is less than a predetermined threshold. In some embodiments, the particulate matter mass concentration calculation circuit 208 may be configured to present an exemplary grayscale image of the collection medium, at least in part, based on determining the number and / or percentage of coverage of one or more dark spots (e.g., relative to a portion of the collection medium depicted in a grayscale image), to adjust one or more operating characteristics (e.g., on / off configuration, volumetric flow rate, etc.) of the pump 112 of the fluid composition sensor 100. The calculated total light intensity is less than a predetermined threshold.

[0073] Various embodiments relate to a method for detecting fluid particle characteristics, the method comprising: guiding a volume of fluid toward a collection medium 106, receiving one or more particles of a plurality of particles within the volume of fluid on the collection medium 106; capturing an image of one or more particles of the plurality of particles received by the collection medium 106; determining the total particulate matter mass of the one or more particles based on the image of one or more particles of the plurality of particles received by at least one collection medium 106; and adjusting the volume of the fluid.

[0074] In various embodiments, the total particulate matter mass is determined by a particulate matter mass concentration calculation circuit 208, which is configured to operate in conjunction with a controller to adjust the volume of fluid passing over the collection medium 106 and / or through the housing of device 10. In various embodiments, the particulate matter mass concentration calculation circuit 208 is configured to adjust the volume of fluid passing over the housing of collection medium 106 when a predetermined difference between a first total particulate matter mass and a second total particulate matter mass is calculated. In various embodiments, the particulate matter mass concentration calculation circuit 208 is configured to adjust the volume of fluid above the housing of collection medium 106 when the total intensity of light passing through the image decreases to a predetermined threshold. In various embodiments, pump 112 continues to operate to draw air through device 10 as long as the intensity of the image or the light intensity is above the predetermined threshold.

[0075] In various embodiments, the fluid composition sensor 100 (e.g., controller 200) may receive one or more predetermined thresholds (such as particle coverage threshold, particle separation threshold, light intensity threshold, etc.) as user input provided via a user interface. For example, in some embodiments, the user input received by controller 200 may be transmitted to particulate matter concentration calculation circuitry 208 and may include the sampled fluid and / or material. In such exemplary cases, particulate matter concentration calculation circuitry 208 may be configured to identify the corresponding predetermined threshold based at least in part on one or more lookup tables stored in memory 202 and associated with the user-selected fluid and / or material. As a non-limiting example provided by way of illustration, particulate matter concentration calculation circuitry 208 may be configured to receive a signal corresponding to a material (such as silica dust, which may be at least partially transparent to the wavelength of an exemplary light beam emitted from an illumination source within the exemplary fluid composition sensor), as described herein. In such exemplary cases, particulate matter concentration calculation circuitry 208 may use data stored in memory (e.g., lookup tables) to identify the corresponding light intensity threshold. In various embodiments, the light intensity threshold identified as corresponding to at least partially transparent silica dust material, based at least in part on the contrast between the covered and uncovered portions of the collection medium, may be different from (e.g., may be less than) the light intensity threshold corresponding to opaque materials, as described herein. As another non-limiting example, the light intensity threshold identified as corresponding to opaque materials (such as, for example, volcanic ash or soot) based at least in part on the contrast between the covered and uncovered portions of the collection medium may be different from (e.g., may be greater than) the light intensity threshold corresponding to at least partially transparent materials.

[0076] In various embodiments, the fluid composition sensor includes a controller (e.g., particulate matter mass concentration calculation circuit 208) configured to calculate the total particulate matter mass of a plurality of particles received by a collection medium from a volume of fluid, and to characterize the spatial arrangement of the plurality of particles to identify one or more particle configurations known to negatively impact sensor accuracy, and / or sensor effectiveness over time (e.g., lifetime), such as particle aggregation, spike formation, particle contact, particle overlap, and / or collection medium “covered” by particles. This can help prevent sensor inaccuracies caused by overload of a depleted and / or disabled collection medium, where particle loading conditions cannot be accurately determined and / or identified by the sensor. Such exemplary configurations substantially minimize the amount of retesting required to obtain accurate data by defining operating parameters configured to substantially autonomously limit sensor operation when one or more of the aforementioned erroneous particle loading conditions are identified. The lifetime of the device can be increased by dynamically monitoring the loading conditions of the plurality of particles received by the collection medium and optimizing the operating parameters to selectively limit device uptime. Furthermore, the device described herein can simplify the calculation of the necessary operating time of the fluid composition sensor required for a particle sample sufficient to provide one or more statistically significant measurements.

[0077] Furthermore, in various embodiments, the particulate matter mass concentration calculation circuit 208 may be configured to determine, at least in part, the initial particle velocity of one or more of the plurality of particles 120 received by the collection medium 106 based on the determined particle mass, wherein the initial particle velocity is the velocity of the particle at the receiving surface 105 of the collection medium 106. In various embodiments, the particulate matter mass concentration calculation circuit 208 may be configured to transmit the determined initial particle velocity data to a particle type identification circuit 207, the initial particle velocity data corresponding to one or more of the plurality of particles 120 received by the collection medium 106.

[0078] In various embodiments, processor 202 may be configured to communicate with sensor sampling optimization circuitry 209. In various embodiments, sensor sampling optimization circuitry 209 may be a device or circuit embodied in hardware or a combination of hardware and software, configured to determine, at least in part, an optimal sample volume associated with a sample collection operation performed by fluid sampling device 10 (e.g., by fluid composition sensor), based on identified particle loading conditions defined by a plurality of particles captured at the collection medium of the fluid composition sensor. For example, sensor sampling optimization circuitry 209 may be configured to determine the optimal sample volume associated with the sample collection operation based at least in part on first particle loading data generated at least in part based on first particle data captured at a first instance during the sample collection operation. Sensor sampling optimization circuitry 209 may be configured to receive particle loading data identifying particle loading conditions from particle imaging circuitry 206, external devices, etc., these particle loading conditions being defined by a plurality of particles present at the collection medium of the fluid composition sensor at an instance associated with particle data captured by the fluid composition sensor during the sample collection operation. For example, in various embodiments, the sensor sampling optimization circuit 209 may be configured to receive first particle loading data that identifies a first particle loading condition, the first particle loading condition including a first particle coverage value defined by a first plurality of particles associated with first particle data (e.g., a first particle image) captured by an imaging device of a fluid composition sensor at a first instance.

[0079] In various embodiments, the sensor sampling optimization circuit 209 may be configured to determine an optimal sample volume associated with a sample collection operation based at least in part on received particle loading data, such as, for example, at least in part on identified first particle loading conditions defined by a plurality of particles captured at a collection medium at a first instance during the sample collection operation. In various embodiments, the fluid sampling device 10 (e.g., the sensor sampling optimization circuit 209 of the controller 200) may be configured to determine an optimal sample volume, including the volume of sample fluid that the device 10 (e.g., a fluid composition sensor) should sample during the sampling collection operation, by aspirating fluid through the device 10 so that the determined optimal sample volume is directed toward the collection medium 106 (e.g., across the surface of the collection medium). In some embodiments, the volume of fluid passing through the device 10 may be measured (e.g., by a fluid flow sensor); however, in other embodiments, the volume of fluid passing through the device 10 may be estimated (e.g., based on a known fluid flow rate) while the pump is in an operating configuration and for a certain amount of time. As described herein, the optimal sample volume determined by the sensor sampling optimization circuit 209 may be associated with a sample collection operation performed by the fluid sampling device 10 (e.g., by a fluid composition sensor) and may include the fluid sample volume of the fluid sample received (e.g., sampled) by the fluid composition sensor during the sample collection operation, wherein, at the end of the sample collection operation (e.g., after the entire fluid sample with the optimal sample volume has been sampled by the fluid composition sensor), the plurality of particles collected at the collection medium of the fluid composition sensor define particle loading conditions that are at least substantially similar to predetermined optimal particle loading conditions. For example, in various embodiments, the sensor sampling optimization circuit 209 may be configured to perform one or more of the operations described herein by reference block 1010, such as Figure 5 As shown. For example, in various embodiments, the sensor sampling optimization circuit 209 may be configured to compare a predetermined optimal particle loading condition as described herein with a first particle loading condition including a first particle coverage value defined by a first plurality of particles at the collection medium in the first instance, and further estimate the optimal sample volume to be sampled during the sample collection operation based on an identified first elapsed time (e.g., a known amount of time elapsed between the start of the sample collection operation and the first instance) and a known volumetric flow rate of the fluid composition sensor.

[0080] In various embodiments, the sensor sampling optimization circuit 209 may be further configured to update one or more operating characteristics of the fluid composition sensor at least in part based on an optimal sample volume, such that the sample collection operation is at least partially defined by the optimal sample volume. For example, in various embodiments, when determining the optimal sample volume for the sample collection operation based at least in part on first particle loading data, the sensor sampling optimization circuit 209 may be configured to update operating characteristics of the fluid composition sensor, such as the total sample volume associated with the sample collection operation, to reflect the determined optimal sample volume. For example, as described herein, updating the total sample volume associated with the sample collection operation may include providing one or more updated executable instructions for performing the sample collection operation to one or more circuits (e.g., processor 202, memory 201, etc.) of the controller 200, such that the total sample volume is at least substantially similar to the optimal sample volume received (e.g., sampled) by the fluid composition sensor during the sample collection operation. For example, in various embodiments, the sensor sampling optimization circuit 209 may be configured to perform one or more operations of the operations described herein, such as... Figure 5 As shown.

[0081] In addition to or alternatively, in various embodiments, the fluid sampling device 10 (e.g., sensor sampling optimization circuitry 209 of controller 200) may be configured to determine an optimal sample duration by aspirating fluid through the device 10 to guide a determined optimal sample volume toward the collection medium 106 (e.g., through the surface of the collection medium), the optimal sample duration including the amount of time during which the device 10 (e.g., pump 112) should continue performing the sampling collection operation. As described herein, the optimal sample duration may be based at least in part on received first particle loading data identifying first particle loading conditions defined by first plurality of particles captured at a first instance. In various embodiments, the sensor sampling optimization circuitry 209 may be configured to perform one or more of the operations described herein in reference block 1014, such as Figure 5 As shown. For example, in various embodiments, the sensor sampling optimization circuit 209 may be configured to compare a predetermined optimal particle loading condition as described herein with a first particle loading condition including a first particle coverage value defined by a first plurality of particles at the collection medium in the first instance, and further estimate the optimal sample duration for which the sample collection operation should be performed based on an identified first elapsed time (e.g., a known amount of time elapsed between the start of the sample collection operation and the first instance).

[0082] In various embodiments, when determining the optimal sample volume, optimal sample duration, etc., at an instance (e.g., a first instance) during a sample collection operation, the sensor sampling optimization circuit 209 may be configured to perform a comparison of such determined optimal values ​​(e.g., optimal sample volume and / or optimal sample duration) with one or more corresponding operational characteristics associated with the sample collection operation. For example, in various embodiments, the sensor sampling optimization circuit 209 may be configured to compare the optimal sample volume, as described herein, based on first particle data captured at the first instance, with the total sampling volume associated with the sample collection operation at the first instance (e.g., the amount of fluid sampled between the start of the sample collection operation and the first instance). As another non-limiting example, an exemplary method may include comparing the optimal sample duration, as described herein, based on first particle data captured at the first instance, with a first elapsed time associated with the sample collection operation at the first instance. In various embodiments, the sensor sampling optimization circuit 209 may be configured to determine whether the optimal sample volume and / or optimal sample duration, determined at least in part based on the first particle data captured at the first instance, are at least substantially similar to, for example, a corresponding operational characteristic associated with the sample collection operation as measured at the first instance.

[0083] In various embodiments, the sensor sampling optimization circuit 209 may be configured to determine an optimal sample volume and / or optimal sample duration, at least in part based on first particle data captured at a first instance, that is not at least substantially similar to (e.g., at least substantially different from) the corresponding operational characteristics associated with the sample collection operation as measured at the first instance. In this exemplary case, the sensor sampling optimization circuit 209 may be configured to perform an iterative optimization process, wherein the sensor sampling optimization circuit 209 is configured to repeat one or more of the operations described above to determine a second optimal sample volume and / or a second optimal sample duration, at least in part based on second particle loading data generated using second particle data captured by the fluid composition sensor at a second instance during the sample collection operation. For example, in various embodiments, the sensor sampling optimization circuit 209 may be configured to perform one or more of the iterative operations described herein in reference blocks 1026 and 1028, such as Figure 5As shown, this includes determining a second optimal sample volume associated with a sample collection operation based at least in part on a second particle loading condition defined by a second plurality of particles captured at the collection medium in a second instance, and updating one or more operating characteristics of the fluid composition sensor at least in part based on the second optimal sample volume, such that the sample collection operation is at least in part defined by the second optimal sample volume. The sensor sampling optimization circuit 209 may be configured to facilitate the dynamic determination of an optimal sample volume (e.g., and / or optimal sample duration) for a particular sample collection operation, at least substantially in real time, based at least in part on iterative particle data collected by the fluid composition sensor. For example, in various embodiments, the sensor sampling optimization circuit 209 may be configured to determine the optimal sample volume and / or optimal sample duration for each of a plurality of intermittent instances within the sample collection operation, and thus dynamically update the corresponding operating characteristics associated with the sample collection operation until the sensor sampling optimization circuit 209 determines that the optimal sample volume and / or optimal sample duration is at least substantially similar to the corresponding operating characteristics associated with the sample collection operation as measured at the particular instance.

[0084] In various embodiments, the sensor sampling optimization circuit 209 may be configured to stop the sample collection operation performed by the fluid composition sensor of the fluid sampling device 10 when it is determined that the optimal sample volume and / or optimal sample duration are at least substantially equal to the total sample volume received by the fluid composition sensor and / or the elapsed sample time at a specific instance during the sample collection operation, respectively. For example, in various embodiments, when it is determined that the first total sample volume sampled by the fluid composition sensor between the start of the sample collection operation and the first instance is at least substantially equal to the optimal sample volume, the sensor sampling optimization circuit 209 may be configured to generate and / or transmit one or more signals to cause the fluid composition sensor 100 to stop the sample collection operation, as described herein. As another non-limiting example provided for illustrative purposes, when it is determined that the second elapsed sample time elapsed between the start of the sample collection operation and the second instance is at least substantially equal to the second optimal sample duration estimated by the sensor sampling optimization circuit 209, the sensor sampling optimization circuit 209 may be configured to generate and / or transmit one or more signals to cause the fluid composition sensor 100 to stop the sample collection operation, as described herein. In various implementations, the sensor sampling optimization circuit 209 may be configured to perform one or more of the operations described in reference block 1030 herein, such as Figure 5 As shown.

[0085] As described herein, in various embodiments, the sensor sampling optimization circuit 209 may be configured to regulate the operation of the fluid composition sensor 100, for example, by adjusting one or more operating characteristics of the fluid composition sensor 100 associated with the sample collection operation (e.g., pump on / off configuration, pump volumetric flow rate, sample duration, total sample volume, etc.). For example, the sensor sampling optimization circuit 209 may be configured to regulate the operation of the pump 112 of the fluid composition sensor 100, for example, by adjusting (e.g., stopping) one or more operating characteristics of the pump 112 before a certain amount of particles has been captured in the collection medium 106, which degrades the measurement accuracy of future captured particles (e.g., because the collection medium is sufficiently filled with particles that make it impossible to identify newly captured particles and / or to accurately locate the edges of these particles). For example, the sensor sampling optimization circuit 209 may be configured to regulate the operation of the fluid sampling device 10 (e.g., the fluid composition sensor), for example, by updating one or more stored settings, executable instructions, etc., associated with the sample collection operation performed by the fluid composition sensor. For example, in various embodiments, the sensor sampling optimization circuit 209 may be configured to generate and / or transmit one or more signals to enable, restart, and / or stop the sample collection operation of the fluid composition sensor 100, as described herein, wherein during the sample collection operation, the sensor 100 may receive a volume of fluid containing a plurality of particles, and facilitates the engagement of the collection medium 106 with the received volume of fluid such that at least a portion of the plurality of particles within the volume of fluid may be disposed at and / or therein. For example, in various embodiments, the sensor sampling optimization circuit 209 may be configured to transmit one or more signals to switch the pump 112 from an "off" configuration to an "on" operation configuration. Conversely, in various embodiments, the sensor sampling optimization circuit 209 may be configured to transmit one or more signals to switch the pump 112 from an "on" operation configuration to an "off" configuration. As a non-limiting example, the sensor sampling optimization circuit 209 may be configured to transmit one or more signals to cause the pump 112 to switch from an "on" operating configuration to an "off" configuration, at least in part, based on the determination that the total sampling volume received by the fluid composition sensor during the sample collection operation is at least substantially equal to the optimal sample volume. As another non-limiting example, the sensor sampling optimization circuit 209 may be configured to transmit one or more signals to cause the pump 112 to switch from an "on" operating configuration to an "off" configuration, at least in part, based on the determination that the elapsed sample time (e.g., the amount of pump operating time during the sample collection operation) is at least substantially equal to the optimal sample duration.

[0086] In various embodiments, the sensor sampling optimization circuit 209 may be configured to facilitate fluid composition sensor configuration where sample collection operation is paused during imaging and / or analysis of multiple particles captured at the collection medium. For example, in various embodiments, the sensor sampling optimization circuit 209 may be configured to emit one or more signals to switch the pump 112 from an "on" operating configuration to an "off" configuration at a first instance, enabling the imaging device of the sensor 100 to capture first particle data during the non-operating state. In this exemplary case, when the first particle data is processed, the first particle loading data includes the generated first particle loading conditions, and the first particle loading data is received, the sensor sampling optimization circuit 209 may process the received first particle loading data to determine an optimal sample volume and / or optimal sample duration based on the received first particle loading data that are at least substantially different from the first total sampling volume and / or the first elapsed sample time as measured at the first instance. In this exemplary case, the sensor sampling optimization circuit 209 may be configured to emit one or more signals to switch the pump 112 from an "off" operating configuration to an "on" configuration to facilitate the fluid composition sensor continuing to perform sample collection operations. In various implementations, the sensor sampling optimization circuit 209 can be configured to describe a discontinuous sample collection operation defined by one or more intermittent pauses by using input variables (such as, for example, total sampling volume, elapsed sample time, etc.) to determine an optimal sample volume and / or optimal sample duration, the input variables being defined by values ​​collected only during the operating run of the sensor and not over the entire sample duration starting at the initial instance of the sample collection operation.

[0087] In various implementations, at least a portion of one or more operations described above with respect to controller 200 may be performed by one or more external devices, such as, for example, a client device 21 that executes a mobile application and communicates with fluid sampling device 10, such as... Figure 3BAs shown. For example, in various embodiments, the fluid sampling device may be configured to transmit particle data captured by a fluid composition sensor (e.g., an imaging device) at one or more instances during a sample collection operation, such that at least a portion of the data processing operations described above with respect to the various circuits of the exemplary controller 200 (e.g., particle imaging circuit 206, particle type identification circuit 207, particulate matter mass concentration calculation circuit 208, and / or sensor sampling optimization circuit 209) may be performed at least partially by an external device (e.g., client device 21, cloud-based management computing entity, etc.). As a non-limiting example, the fluid sampling device 10 may be configured to capture first particle data using a fluid composition sensor (e.g., an imaging device) and transmit the first particle data to a client device 21 executing a mobile application, the client device being configured to execute one or more executable instructions to analyze the first particle data captured by the fluid composition sensor (e.g., a first particle image) and generate particle loading data, which includes identified particle loading conditions as captured in the first particle data, defined by a plurality of particles present in the collection medium 106 at the first instance. As described herein, in various embodiments, external devices may be configured to transmit data generated based at least in part on particle data received from fluid sampling device 10 back to the fluid sampling device (e.g., controller 200) for further processing and / or analysis.

[0088] Figure 4 Various data flows between exemplary devices according to some embodiments discussed herein are further illustrated schematically. Figure 4 As shown herein, the exemplary fluid sampling device 10 can be configured to transmit one or more data signals to one or more external devices, such as, for example, one or more client devices 20 (e.g., 21, 22, 23), management computing entity 40, etc. For example, various components of the fluid sampling device 10 can electronically communicate with, for example, one or more client devices 20 (e.g., 21, 22, 23) and management computing entity 40 via various wireless or wired communication networks 50, as described herein. As described herein, the exemplary fluid sampling device 10 (e.g., fluid composition sensors and / or controllers as described herein) can be used to perform sample collection operations at least in part based on one or more control signals, as described herein.

[0089] In various embodiments, the fluid sampling device 10 may be configured to communicate with one or more external devices, such as, for example, a client device 21 executing a mobile application and / or a management computing entity 40. In various embodiments, the client device may include, but is not limited to, a smartphone, tablet, laptop, wearable device (e.g., a smartwatch), personal computer, etc. The client device may execute an "application" to interact with one or more components of the fluid sampling device 10, such as, for example, a fluid composition sensor and / or a controller, as described herein. In various embodiments, the management computing entity 40 may include computing devices, such as, for example, a server, a cloud-based computing entity, etc. In various embodiments, the management computing entity 40 may be accessible to authorized individuals and / or client devices. The management computing entity 40 may be configured to store and / or transmit data associated with the fluid sampling device 10, such as, for example, particle data captured by the fluid composition sensor and / or optimal sample volume associated with sample collection operations.

[0090] In various implementations, the fluid sampling device 10 can communicate electronically with one or more client devices 20 (e.g., 21, 22, 23) and management computing entity 40 via the same or different wireless or wired networks 50, including, for example, wired or wireless personal area networks (PANs), local area networks (LANs), metropolitan area networks (MANs), wide area networks (WANs), etc. For example, in various implementations, one or more communication networks 50 described herein may use any of a variety of protocols, such as General Packet Radio Service (GPRS), Universal Mobile Telecommunications System (UMTS), Code Division Multiple Access 2000 (CDMA2000), CDMA2000 1X (1xRTT), Wideband Code Division Multiple Access (WCDMA), Global System for Mobile Communications (GSM), Enhanced Data Rate GSM Evolution (EDGE), Time Division Synchronous Code Division Multiple Access (TD-SCDMA), Long Term Evolution (LTE), Evolved Universal Terrestrial Radio Access Network (E-UTRAN), Evolved Data Optimization (EVDO), High Speed ​​Packet Access (HSPA), High Speed ​​Downlink Packet Access (HSDPA), IEEE 802.11 (Wi-Fi), Wi-Fi Direct, 802.16 (WiMAX), Ultra Wideband (UWB), Infrared (IR) protocol, Near Field Communication (NFC) protocol, Wibree, Bluetooth protocol, Wireless Universal Serial Bus (USB) protocol, and / or any other wireless protocol. In addition, although... Figure 4 Some system entities may be shown as separate, independent entities, but various implementations are not limited to such exemplary specific architectures.

[0091] As described herein, an exemplary fluid sampling device 10 (e.g., a fluid composition sensor and / or controller as described herein) can be used to perform at least a portion of a sample collection operation. In various embodiments, the fluid sampling device 10 can be configured to transmit particle data captured by the fluid sampling device (e.g., an imaging device of the fluid composition sensor) to an external device to facilitate one or more data processing operations performed by the external device. In various embodiments, when the fluid composition sensor captures first particle data including a first particle image, the fluid sampling device 10 can be configured to transmit the first particle data to one or more client devices 20 executing a mobile application, such that the first particle data can be processed by the client devices 20 via one or more executable actions, instructions, etc., defining the mobile application. For example, client devices (e.g., 21, 22, 23) can be configured to receive particle data from the fluid sampling device 10 via a wireless communication network 50 and perform one or more data processing operations to determine particle loading conditions defined by a plurality of particles captured at a collection medium based on the first particle data (e.g., a first particle image) at a first instance. As described herein, in various embodiments, particle loading conditions defined by a plurality of particles associated with the first particle data may include a particle coverage value determined at least in part by using a first particle image captured at a first instance. For example, in various embodiments, particle loading conditions such as, for instance, particle coverage values ​​may be defined at least in part by comparative data, images, particle characteristics, etc., of the first particle coverage values ​​of a plurality of particles captured at a collection medium at a first instance during a sample collection operation. In various embodiments, client devices 21, 22, 23 may be configured to display at an interface associated with them at least a portion of the particle data captured by the exemplary fluid sampling device 10, including, as a non-limiting example, particle images, particle loading data, etc. In various embodiments, client devices 21, 22, 23 receiving the first particle data captured by the fluid sampling device at a first instance during a sample collection operation may be configured to determine one or more particle loading conditions at the first instance using one or more algorithms defined by executable instructions, such as, for example, a particle coverage value defined by a plurality of particles captured at a collection medium of a fluid composition sensor. For example, client devices 21, 22, and 23 may be configured to generate first particle loading data identifying first particle loading conditions, at least in part, based on first particle data captured by fluid sampling device 10 (e.g., a fluid composition sensor). In various embodiments, when generating particle loading data identifying particle loading conditions including particle coverage values, an external device (e.g., client devices 21, 22, and 23) may transmit particle loading data including particle loading conditions to fluid sampling device 10.

[0092] Furthermore, in various embodiments, the management computing entity 40 may be similarly configured to receive particle data from the fluid sampling device 10 via a wireless communication network 50 and perform one or more data processing operations to determine particle loading conditions defined by a plurality of particles captured at the collection medium based on the first particle data (e.g., a first particle image) at the first instance. In various embodiments, at least a portion of the particle data captured by the exemplary fluid sampling device 10 may be displayed at an interface associated therewith, including, as a non-limiting example, particle images, particle loading data, etc. In various embodiments, the management computing entity 40, which receives the first particle data captured by the fluid sampling device at the first instance during a sample collection operation, may be configured to determine one or more particle loading conditions at the first instance via one or more cloud-based operations defined at least in part by one or more executable instructions and / or algorithms, such as, for example, a particle coverage value defined by a plurality of particles captured at the collection medium of a fluid composition sensor. For example, the customer management computing entity 40 may be configured to generate first particle loading data identifying the first particle loading conditions based at least in part on the first particle data captured by the fluid sampling device 10 (e.g., a fluid composition sensor). In various implementations, when generating particle loading data that identifies particle loading conditions including particle coverage values, an external device (e.g., management computing entity 40) can transmit particle loading data including particle loading conditions to fluid sampling device 10.

[0093] In various embodiments, the fluid sampling device 10 may be configured to transmit one or more data signals (such as, for example, one or more control signals and / or one or more information signals) to an external device (such as, for example, one or more client devices 20, management computing entity 40, etc.) in communication with it. For example, in various embodiments, the fluid sampling device 10 may be configured to transmit control signals to the external device, as described herein, thereby providing one or more executable instructions associated with the various particle data transmitted therewith for processing and / or execution by the external device. Furthermore, in various embodiments, the fluid sampling device 10 may be configured to transmit indication signals to the external device, as described herein, to provide one or more messages, acknowledgments, signals, etc., configured to communicate with the external device that has completed a sample collection operation. Alternatively or additionally, in various embodiments, the fluid sampling device 10 may be configured to transmit indication signals to the external device, as described herein, to provide various data associated with the sample collection operation, such as, for example, particle data captured at one or more instances during the sample collection operation, the results of the sample collection operation, and / or performance metrics (e.g., one or more operating characteristics of a fluid composition sensor measured during the execution of the sample collection operation), etc.

[0094] like Figure 5 As shown, an exemplary method 1000 of operating a fluid sampling device may include, at block 1002, receiving a fluid sample comprising a plurality of particles via a volume of fluid at a fluid composition sensor of the exemplary fluid sampling device. In various embodiments, the fluid sample may represent a sample volume of fluid that defines at least a portion of a volume of fluid and includes a plurality of particles therein. In various embodiments, the volume of fluid partially defined by the fluid sample may correspond to fluid in the surrounding environment, such that particles present in the fluid sample may be defined by one or more particle characteristics (e.g., particle concentration) that are at least substantially similar to those of the fluid in the surrounding environment. For example, in various embodiments, the plurality of particles received by the exemplary fluid composition sensor may represent a plurality of particles present in the fluid in the surrounding environment. In various embodiments, the exemplary fluid composition sensor configured to receive the fluid sample may include the exemplary fluid composition sensor 100 as described herein with reference to FIG3 and / or any other suitable particle imaging sensor capable of measuring the particulate contents within one or more volumes of fluid using one or more particle imaging operations. As described herein, the exemplary fluid composition sensor may be configured to receive one or more particles of a plurality of particles within the fluid sample at a collection medium disposed therein. In various implementations, the collection medium may include an adhesive material medium (e.g., a viscous gel), a liquid medium, a solid or quasi-solid surface, a heated medium, etc.

[0095] Furthermore, at block 1004, first particle data associated with a plurality of particles collected from within a fluid sample can be captured via a fluid compensation sensor. As described herein, in various embodiments, the first particle data associated with the fluid sample can be captured by a fluid compensation sensor. In various embodiments, the plurality of particles within the fluid sample can be received by a fluid composition sensor. In various embodiments, the first particle data can be captured by a fluid composition sensor configured to detect, measure, and / or characterize one or more particle characteristics (e.g., particulate matter concentration, particle number, particle size, etc.) associated with the plurality of particles within the fluid sample and / or facilitate the determination of particle loading conditions defined by the plurality of particles collected at the collection medium. As described herein, the first particle data can be captured by the fluid composition sensor at a first instance, wherein the first instance occurs in the middle of a sample collection operation performed by the fluid composition sensor (e.g., the first particle data is captured at an instance during the sample duration of the sample collection operation). For example, as described herein, the first particle data associated with multiple particles and captured by the fluid composition sensor may include particle images captured by the imaging device of the fluid composition sensor using one or more particle imaging techniques, such as, for example, lensless holography, fluorescence imaging, optical microscopy, etc. Furthermore, in various embodiments, the fluid composition sensor may use one or more image focusing techniques such as computational techniques (e.g., angular spectral propagation) and / or mechanical techniques (e.g., optomechanical adjustment) to capture additional first particle data.

[0096] In various embodiments, the first particle data captured by the fluid composition sensor may further include additional particle data generated at least in part based on captured particle images associated with a plurality of particles, such as, for example, particle load data, particle type data, particulate matter concentration data, particle number data, particle size data, particle coverage value data, etc. Alternatively, in various embodiments, such additional particle data generated by the fluid composition sensor (e.g., a controller) at least in part based on the captured first particle images of the first particle data may be generated by an external device communicating with a fluid sampling device and configured to perform one or more particle analysis operations, such as, for example, a client device configured to communicate with a fluid sampling device and perform one or more particle analysis operations via a mobile application, as described herein. For example, at block 1006, exemplary method 1000 may include: transmitting the first particle data captured by the fluid composition sensor to an external device. In various embodiments, the external device may include a client device configured to receive the first particle data from the fluid sampling device and perform one or more particle analysis operations based on the received first particle data, such as, for example, a smartphone, tablet, personal computer, physical server, remote computing platform (e.g., a cloud-based server), etc.

[0097] In various embodiments, an external device may perform one or more data processing operations to determine particle loading conditions defined by a plurality of particles captured at a collection medium based on first particle data (e.g., a first particle image) at a first instance. As described herein, in various embodiments, particle loading conditions defined by a plurality of particles associated with the first particle data may include a particle coverage value determined at least in part by using the first particle image captured at the first instance. For example, in various embodiments, particle loading conditions such as, for example, a particle coverage value may be defined at least in part by comparative data, images, particle characteristics, etc., that define the first particle coverage value of the plurality of particles captured at the collection medium at the first instance as a predetermined optimal particle coverage value. For example, in various embodiments, a predetermined optimal particle loading condition including a predetermined optimal particle coverage value may include a stored data value indicating a particle coverage value that is at least substantially optimized. In such an exemplary case, the first particle loading condition may be defined by the particle coverage value and / or the ratio of the first particle coverage value to the predetermined optimal particle coverage condition. In addition to or alternatively, as described herein, in various embodiments, the first particle loading condition may include a particle coverage value generated at least in part based on a first particle image, as a comparison value, fraction, ratio, coefficient, etc., defined as a percentage of a predetermined optimal particle coverage condition exhibited by the captured particle data (e.g., particle images). In such exemplary cases, as a non-limiting example, a first particle image showing a particle coverage percentage less than the predetermined optimal particle loading condition may be assigned a particle coverage value of at least substantially less than 100%, a first particle image showing a particle coverage percentage at least substantially equal to the predetermined optimal particle coverage condition may be assigned a particle coverage value of 100%, and a first particle image showing a particle coverage percentage greater than the predetermined optimal particle coverage condition may be assigned a particle coverage value of at least greater than 100%. The particle coverage value at a first instance of a plurality of particles captured at the collection medium of the fluid composition sensor may be determined by an external device using one or more algorithms defined by executable instructions corresponding to generating first particle loading data based on the first particle data captured by the fluid sampling device. In various implementations, when generating particle loading data that identifies particle loading conditions including particle coverage values, an external device can transmit particle loading data including particle loading conditions to a fluid sampling device.

[0098] Referring now to reference box 1008, exemplary method 1000 may further include: receiving first particle loading data that identifies a first particle loading condition associated with the first particle data. For example, as described above with reference box 1006, in various embodiments, wherein the first particle data is transmitted to an external device configured to perform one or more analytical operations to identify particle loading conditions, the first particle loading data may be received by the fluid sampling device from the external device to which the first particle data is transmitted. Alternatively or additionally, in various embodiments, a fluid composition sensor and / or controller of the fluid sampling device may receive the first particle loading data from one or more other components of the fluid sampling device in communication with it.

[0099] Furthermore, at block 1010, method 1000 further includes determining an optimal sample volume associated with the sample collection operation based at least in part on identified first particle loading conditions defined by a plurality of particles captured at the collection medium at a first instance during the sample collection operation. In various embodiments, the optimal sample volume may include a fluid sample volume configured such that, when sampled by an exemplary fluid composition sensor, the plurality of particles received from the fluid sample volume by the collection medium define particle loading conditions at least substantially similar to predetermined optimal particle loading conditions. For example, the optimal sample volume associated with the sample collection operation may include the fluid sample volume of a fluid sample received (e.g., sampled) by the fluid composition sensor during the sample collection operation, wherein, at the end of the sample collection operation (e.g., after the entire fluid sample with the optimal sample volume has been sampled by the fluid composition sensor), the plurality of particles collected at the collection medium of the fluid composition sensor define particle loading conditions at least substantially similar to predetermined optimal particle loading conditions. As another example, the optimal sample volume associated with a sample collection operation can be defined by a sample fluid volume estimated by the fluid sampling device as comprising multiple particles that will result in particle data being captured by the fluid composition sensor at the end of the sample collection operation, the sample fluid volume being defined at least in part by particle loading conditions at the collection medium of the fluid composition sensor that are at least substantially similar to predetermined optimal particle loading conditions.

[0100] In various embodiments, the optimal sample volume may be determined at least in part based on the identified first particle loading conditions and the known elapsed time associated with the sample collection operation. In various embodiments, the first elapsed time associated with the first instance of the sample collection operation may be determined by the fluid sampling device (e.g., a controller) in such a way as to identify the duration of operation of the fluid composition sensor between the initial instance of the sample collection operation and the first instance of capturing first particle data (e.g., the amount by which the pump and / or fan of the fluid composition sensor has been operated to facilitate particle collection by the sensor). Furthermore, in various embodiments, the volumetric flow rate of the fluid sample moving through the fluid composition sensor may be at least in part based on the known / determined operating characteristics of the fan or pump (e.g., operating power) such that a first total sampling volume at the first instance can be identified (e.g., the amount of fluid sampled by the fluid composition sensor between the start of the sample collection operation and the first instance). In various embodiments, the first elapsed time may be at least substantially proportional to the first total sampling volume, such that the optimal sample volume may be determined at least in part based on a comparison of the ratio of the first particle loading conditions to the optimal particle loading conditions (e.g., the ratio of the first particle coverage value to a predetermined optimal particle coverage value) to the ratio of the first total sampling volume to the optimal sample volume. For example, in various embodiments, the optimal sample volume may be at least substantially between 1 L and 1000 L (e.g., between 2 L and 150 L). As a non-limiting example, in various embodiments, the first elapsed time may be at least substantially proportional to the first total sampling volume. In such exemplary cases, the relative particle characteristics can be determined using the following formula:

[0101]

[0102] For example, the exemplary relationship described above, in which the first elapsed time is at least substantially proportional to the first total sampling volume, can define the operation of the fluid composition sensor in exemplary cases where the volumetric flow rate of the fluid composition sensor is at least substantially constant and the operation of the pump / fan of the fluid composition sensor is at least substantially continuous, such that the sample collection operation is not interrupted in instances prior to the first instance. In various embodiments, such as exemplary cases where the fluid flow rate of the fluid composition sensor varies at one or more instances during the sample collection operation, one or more calculations can be performed to illustrate exemplary cases where the first elapsed time is not at least substantially proportional to the first total sampling volume.

[0103] Furthermore, at block 1012, method 1000 further includes updating one or more operating characteristics of the fluid composition sensor, at least in part, based on an optimal sample volume, such that the sample collection operation is at least partially defined by the optimal sample volume. In various embodiments, the operation of the fluid composition sensor may be defined by one or more operating characteristics such as, for example, on / off configuration, volumetric flow rate, power consumption, runtime, total sample volume, etc. In various embodiments in which the fluid composition sensor is configured to perform a sample collection operation, one or more operating characteristics of the fluid composition sensor may be associated with the sample collection operation, such that the sample collection operation is defined by the one or more operating characteristics associated therewith. For example, in various embodiments, the sample collection operation may be at least partially defined by one or more operating characteristics of the fluid composition sensor corresponding to the sample duration and / or total sample volume. As described herein, the sample duration and / or total sample volume of the sample collection operation can be updated and / or adjusted by updating one or more operating characteristics of the fluid composition sensor, such as, for example, runtime and / or total sample volume. In various embodiments, when determining the optimal sample volume for a sample collection operation based at least in part on first particle loading data, the operating characteristics of the fluid composition sensor, such as the total sample volume associated with the sample collection operation, may be updated to reflect the determined optimal sample volume. For example, updating the total sample volume associated with the sample collection operation may include providing one or more update executable instructions to the fluid composition sensor (e.g., a controller) that will perform the sample collection operation, such that the total sample volume is at least substantially similar to the optimal sample volume received (e.g., sampled) by the sensor.

[0104] As another non-limiting example, a sample collection operation can be optimized by dynamically determining an optimal sample duration based on particle data captured during the sample collection operation and accordingly calibrating one or more operating characteristics of the fluid composition sensor. For example, as shown in block 1014, method 1000 may include determining an optimal sample duration associated with the sample collection operation based at least in part on identified first particle loading conditions defined by a plurality of particles captured at a collection medium at a first instance during the sample collection operation. In various embodiments, the optimal sample duration may include an amount of operating time sufficient for the fluid composition sensor to receive a plurality of particles at the collection medium, the amount of operating time defining particle loading conditions at least substantially similar to predetermined optimal particle loading conditions. For example, the optimal sample duration associated with the sample collection operation may include a sample duration defined by a time length (such as, for example, the amount of operating time of the fluid composition sensor) that is long enough that, when that time length has elapsed since the fluid composition sensor began the sample collection operation (e.g., since the initial instance of the sample collection operation), the plurality of particles received by the collection medium during that time length define particle loading conditions at least substantially similar to predetermined optimal particle loading conditions. As another example, the optimal sample duration associated with a sample collection operation may be defined by the sample duration of the sample collection operation, which the sample sampling device estimates will result in particle data captured by the fluid composition sensor after the optimal sample duration has elapsed (e.g., at the end of the sample collection operation), which is at least partially defined by particle loading conditions that are at least substantially similar to the predetermined optimal particle loading conditions.

[0105] In various embodiments, the optimal sample duration may be determined at least in part based on the identified first particle loading conditions and the known elapsed time associated with the sample collection operation. In various embodiments, the first elapsed time associated with the first instance of the sample collection operation may be determined by a fluid sampling device (e.g., a controller) by identifying the duration of operation of the fluid composition sensor between the initial instance of the sample collection operation and the first instance of capturing first particle data (e.g., the amount by which the pump and / or fan of the fluid composition sensor has been operated to facilitate particle collection operations by the sensor). In various embodiments, the optimal sample duration may be determined at least using the ratio of the first particle loading conditions to optimal particle loading conditions (e.g., the ratio of the first particle coverage value to a predetermined optimal particle coverage value) and the first elapsed time. For example, in various embodiments, the first elapsed time may be at least substantially proportional to the first particle loading conditions (e.g., the first particle coverage value). For example, in various embodiments, the optimal sample duration may be at least substantially between 1 second and 30 minutes (e.g., between 10 seconds and 5 minutes). In such exemplary cases, the relative particle characteristics can be determined using the following formula:

[0106]

[0107] Furthermore, at block 1016, method 1000 may include: updating one or more operating characteristics of a fluid composition sensor, at least in part, based on an optimal sample duration, such that the sample collection operation is at least partially defined by the optimal sample duration. As described herein, in various embodiments, the sample duration of the sample collection operation may be updated and / or adjusted by updating one or more operating characteristics of the fluid composition sensor, such as, for example, runtime and / or total sample volume. In various embodiments, when determining the optimal sample duration of the sample collection operation, at least in part, based on first particle loading data, operating characteristics of the fluid composition sensor, such as the total sample volume associated with the sample collection operation, may be updated to reflect the determined optimal sample duration. For example, updating the sample duration associated with the sample collection operation may include: providing one or more update executable instructions to the fluid composition sensor (e.g., a controller) that will perform the sample collection operation, such that the sample duration is at least substantially similar to the optimal sample duration. Furthermore, in various embodiments, updating the sample duration associated with the sample collection operation may include: identifying the remaining time between the first instance and the optimal sample duration, and providing one or more update executable instructions to the fluid composition sensor (e.g., a controller) to continue performing the sample collection operation for the identified remaining amount of time.

[0108] Referring now to block 1018, exemplary method 1000 may include: receiving additional plurality of particles via a volume of fluid at a collection medium of a fluid composition sensor, wherein a first plurality of particles and the additional plurality of particles together define a second plurality of particles. For example, in various embodiments, the additional plurality of particles received at the collection medium of the exemplary fluid composition sensor may represent a plurality of particles present within a portion of a fluid sample received by the fluid composition sensor at a time after a first instance during a sample collection operation.

[0109] Furthermore, at block 1020, second particle data associated with a second plurality of particles collected from the fluid sample can be captured at a second instance via a fluid compensation sensor. As described herein, in various embodiments, the second particle data associated with the fluid sample can be captured by a fluid compensation sensor at a second instance following the first instance. As described herein, the second particle data can be captured at a second instance by a fluid composition sensor, wherein the second instance occurs in the middle of a sample collection operation performed by the fluid composition sensor after the first instance (e.g., the second particle data is captured at an instance during the sample duration of the sample collection operation). For example, in various embodiments, the second plurality of particles received by the fluid composition sensor can be defined by a collection of particles received at the collection medium of the fluid composition sensor between the start of the sample collection operation and the second instance. Thus, as described herein, the second plurality of particles can be defined by a first plurality of particles (e.g., a plurality of particles received between the start of the sample collection operation and the first instance) and an additional plurality of particles (e.g., a plurality of particles received between the first instance and the second instance). As described herein, in various embodiments, the second particle data associated with and captured by the fluid composition sensor may include second particle images captured by the imaging device of the fluid composition sensor using one or more particle imaging techniques, such as, for example, lensless holography, fluorescence imaging, optical microscopy, etc.

[0110] Blocks 1022 and 1024 of the exemplary method 1000 correspond to various operations related to the second particle data, which are at least substantially similar to the steps previously described with respect to the first particle data at blocks 1006 and 1008, including: transmitting the second particle data to an external device, and receiving second particle loading data that identifies the second particle loading conditions associated with the second particle data.

[0111] Furthermore, blocks 1026 and 1028 of the exemplary method 1000 correspond to various operations related to a second optimal sample volume, which are at least substantially similar to the steps previously described with respect to the first particle data at blocks 1010 and 1012, including: determining a second optimal sample volume associated with a sample collection operation based at least in part on a second particle loading condition defined by a second plurality of particles captured at the collection medium in a second instance, and updating one or more operating characteristics of the fluid composition sensor based at least in part on the second optimal sample volume, such that the sample collection operation is at least in part defined by the second optimal sample volume.

[0112] In various embodiments, when determining the optimal sample volume, optimal sample duration, etc., at an instance during a sample collection operation, such determined values ​​may be compared with one or more operational characteristics associated with the sample collection operation. For example, in various embodiments, an exemplary method may include comparing an optimal sample volume determined based on first particle data captured at a first instance as described herein with the total sampling volume associated with the sample collection operation at the first instance. As another non-limiting example, an exemplary method may include comparing an optimal sample duration determined based on first particle data captured at a first instance as described herein with a first elapsed time associated with the sample collection operation at the first instance. Furthermore, an exemplary method may include comparing a second optimal sample volume determined based on second particle data captured at a second instance as described herein with the total sampling volume associated with the sample collection operation at the second instance. In various embodiments, when determining that the determined optimal values ​​(such as, for example, optimal sample volume and / or optimal sample duration) are at least substantially similar to operational characteristics associated with the sample collection operation as measured at the current instance, an exemplary method may include stopping the sample collection operation.

[0113] For example, in various embodiments, as shown in block 1030, exemplary method 1000 may further include stopping the sample collection operation when a second optimal sample volume is determined to be at least substantially equal to the total sampling volume received by the fluid composition sensor. In various embodiments, stopping the sample collection operation of the fluid composition sensor may include adjusting the operation of the fluid composition sensor, such as by adjusting one or more operating characteristics of the fluid composition sensor 100 (e.g., pump on / off configuration, pump volumetric flow rate, etc.), to cause the fluid composition sensor to stop performing the sample collection operation (e.g., stop receiving fluid samples). For example, one or more operating characteristics of the fluid composition sensor may be adjusted to reconfigure the sensor's pump / fan to an "off" setting, thereby causing the fluid composition sensor to stop sampling the fluid sample and defining an end instance of the sample collection operation. As described herein, stopping the sample collection operation at the identified optimal sample duration and / or after the fluid composition sensor has sampled the optimal sample volume may result in stopping the sample collection operation before a certain amount of particles are collected, which may degrade the measurement accuracy of future captured particles (e.g., because the collection medium is sufficiently filled with particles that make it impossible to identify newly captured particles and / or accurately locate the edges of these particles). For example, as described herein, particle loading conditions that lead to increased inaccuracies due to measurement errors caused by prolonged collection of multiple particles can be avoided by stopping the sample collection operation after the identified optimal sample duration and / or after the fluid composition sensor has sampled the optimal sample volume. Additionally or alternatively, in various embodiments, the sample collection operation can be stopped (e.g., by stopping the operation of the pump of the fluid composition sensor) based on determining that the optimal sample volume and / or optimal sample duration associated with the sample collection operation has exceeded (e.g., at least substantially greater than) corresponding predetermined thresholds (such as, for example, a maximum sample collection operation duration threshold and / or a maximum sample collection operation volume threshold).

[0114] In various implementations, exemplary methods as described herein may include repeating at least a portion of the steps described herein with respect to method 1000, such as, for example, the operations described with reference to blocks 1002 to 1012 and / or the operations described with reference to blocks 1018 to 1028, such that the method includes iteratively capturing multiple particle data at multiple sequential instances during a sample collection operation to facilitate the dynamic determination of the optimal sample volume (e.g., and / or optimal sample duration) for a particular sample collection operation, at least substantially in real time. In various embodiments, when the determined optimal value associated with particle data captured at a particular instance during a sample collection operation (such as, for example, a second optimal sample volume and / or a second optimal sample duration determined based on second particle data associated with a second instance) is not at least substantially similar to the corresponding operational characteristics associated with the sample collection operation as measured at the particular instance (e.g., the total sampling volume at the second instance and / or the second elapsed time at the second instance), the exemplary method may further continue to repeat one or more operations described herein, such as, for example, one or more operations described with reference to blocks 1002 to 1012, in order to facilitate the iterative nature of the dynamic process of optimizing the total sampling volume of the sample collection operation based at least in part on real-time data captured by the fluid composition sensor.

[0115] Many modifications and other embodiments will occur to those skilled in the art to which this disclosure pertains, which have the benefits of the teachings presented in the foregoing description and associated drawings. Therefore, it should be understood that this disclosure is not limited to the specific embodiments disclosed, and that modifications and other embodiments are intended to be included within the scope of the appended claims. Although specific terminology is used herein, it is used only in a general and descriptive sense and not for purposes of limitation.

Claims

1. A fluid sampling device, the device comprising: A fluid composition sensor configured to receive a fluid sample and capture a plurality of particles from the fluid sample at a collection medium, wherein the fluid composition sensor is configured to generate particle data associated with the plurality of particles using particle imaging operations; and The controller is configured to: The optimal sample volume to be sampled during the sample collection operation is determined based at least in part on a comparison between the particle loading conditions defined by the plurality of particles captured at the collection medium by the first instance during the sample collection operation and the predetermined optimal particle loading conditions, and based on the identified amount of time elapsed between the start of the sample collection operation and the first instance and the volumetric flow rate of the fluid composition sensor. Update one or more operating characteristics of the fluid composition sensor such that the sample collection operation is at least partially defined by the optimal sample volume, and The one or more operating characteristics include pump on / off configuration and pump volumetric flow rate, and the particle loading conditions are defined at least in part by: the spatial arrangement of a plurality of particles disposed at the collection medium, particle coverage value, average gray level of all pixels in the captured image, particle mass, total light intensity, amount of collected particles, or calculated particle density.

2. The fluid sampling apparatus of claim 1, wherein the controller is configured to determine an updated sample duration corresponding to the optimal sample volume; wherein updating the one or more operating characteristics of the fluid composition sensor such that the sample collection operation is at least partially defined by the optimal sample volume includes: Update one or more operating characteristics of the fluid composition sensor such that the sample collection operation is at least partially defined by the duration of the updated sample.

3. The fluid sampling device of claim 1, wherein the particle data generated by the fluid composition sensor includes a first particle image captured by an imaging device of the fluid composition sensor using lensless holography.

4. The fluid sampling apparatus of claim 1, wherein the controller is configured to transmit the particle data to an external device and receive particle loading data associated with the plurality of particles from the external device, wherein the received particle loading data is at least partially defined by the particle loading conditions.

5. The fluid sampling device of claim 1, wherein the controller is configured to cause the fluid composition sensor to stop the sample collection operation when the optimal sample volume is determined to be at least substantially equal to the total sampling volume received by the fluid composition sensor.

6. The fluid sampling apparatus of claim 1, wherein the fluid composition sensor is configured to capture multiple particle data at each of a plurality of instances defined by a set time interval during a sample collection operation.

7. The fluid sampling apparatus of claim 1, wherein the particle data generated by the fluid composition sensor includes first particle data generated at a first instance during the sample collection operation and second particle data generated at a second instance during the sample collection operation, wherein the second instance follows the first instance; and wherein the controller is configured to: The first optimal sample volume is determined at least in part based on the first particle loading conditions defined by the plurality of particles captured at the first instance in the collection medium; and The second optimal sample volume is determined at least in part based on the second particle loading conditions defined by the plurality of particles captured at the collection medium in the second instance.

8. The fluid sampling device according to claim 7, wherein the controller is configured to: When determining the second optimal sample volume, one or more operating characteristics of the fluid composition sensor are updated such that the sample collection operation is at least partially defined by the second optimal sample volume.

9. The fluid sampling apparatus of claim 1, wherein the optimal sample volume is determined at least in part based on the elapsed sample time and the flow rate of the fluid composition sensor.

10. The fluid sampling device according to claim 1, wherein the controller is configured to: Multiple particles within a fluid sample are quantified and classified based on imaging of multiple particles received by the collection medium of the fluid composition sensor, thereby monitoring multiple particles from the fluid sample.

11. The fluid sampling device according to claim 1, further comprising: An impactor nozzle configured to minimize reflection of a portion of a light beam emitted from an illumination source during particle imaging operations.

12. The fluid sampling device of claim 1, wherein the collection medium is a replaceable collection medium.

13. The fluid sampling apparatus of claim 1, wherein the optimal sample duration is dynamically determined, at least in part, based on particle data iteratively captured by a fluid composition sensor that performs at least a portion of the sample collection operation.

14. A fluid sampling device, the device comprising: A fluid composition sensor configured to receive a fluid sample and capture a plurality of particles from the fluid sample at a collection medium, wherein the fluid composition sensor is configured to generate particle data associated with the plurality of particles using particle imaging operations; and The controller is configured to: The optimal sample duration to be sampled during the sample collection operation is determined based at least in part on a comparison between the particle loading conditions defined by the plurality of particles captured at the collection medium by the first instance during the sample collection operation and the predetermined optimal particle loading conditions, and based on the identified amount of time elapsed between the start of the sample collection operation and the first instance and the volumetric flow rate of the fluid composition sensor. as well as Update one or more operating characteristics of the fluid composition sensor such that the sample collection operation is at least partially defined by the optimal sample duration, and The one or more operating characteristics include pump on / off configuration and pump volumetric flow rate, and the particle loading conditions are defined at least in part by: the spatial arrangement of a plurality of particles disposed at the collection medium, particle coverage value, average gray level of all pixels in the captured image, particle mass, total light intensity, amount of collected particles, or calculated particle density.

15. A method for optimizing sample collection operations using a fluid sampling device according to any of the preceding claims, the method comprising: A fluid sample containing multiple particles is received via a certain volume of fluid; First particle data associated with the plurality of particles received from the fluid sample is captured at the first instance during the sample collection operation; The optimal sample volume or the optimal sample duration to be sampled during the sample collection operation is determined based at least in part on a comparison between the particle loading conditions defined by the plurality of particles and the predetermined optimal particle loading conditions, and based on the identified amount of time elapsed between the start of the sample collection operation and the first instance and the volumetric flow rate of the fluid composition sensor. One or more operational characteristics associated with the sample collection operation are updated at least in part based on the optimal sample volume or the optimal sample duration, such that the sample collection operation is at least in part defined by the optimal sample volume or the optimal sample duration; and The one or more operating characteristics include pump on / off configuration and pump volumetric flow rate, and the particle loading conditions are defined at least in part by: the spatial arrangement of a plurality of particles disposed at the collection medium, particle coverage value, average gray level of all pixels in the captured image, particle mass, total light intensity, amount of collected particles, or calculated particle density.

16. The method of claim 15, wherein the fluid sample is received at the collection medium by the fluid composition sensor, and wherein the first particle data is generated by the fluid composition sensor.

17. The method of claim 16, wherein the first particle data generated by the fluid composition sensor includes a first particle image captured by an imaging device of the fluid composition sensor using lensless holography.

18. The method of claim 15, further comprising: The sample collection operation is stopped when the optimal sample volume or the optimal sample duration is determined to be at least substantially equal to the corresponding operational characteristic associated with the sample collection operation measured at the first instance.

19. The method according to claim 15, further comprising: Second particle data associated with the plurality of particles received from the fluid sample is captured at a second instance during the sample collection operation, wherein the second instance follows the first instance; as well as The second optimal sample volume or the second optimal sample duration is determined at least in part based on the second particle loading conditions, which are identified at least in part based on the second particle data.

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