Portable automated microscope system for rapid detection of multiple microplastics

Abstract

This invention provides a portable automated microscope system for the field, real-time detection and classification of plastics and fibers in environmental and industrial water samples. The system includes a sample introduction module for receiving water samples, a filtration module including a membrane holder and at least one filter membrane for retaining plastics and fibers, and a dye application module comprising a dye matrix containing at least one lipophilic fluorescent dye and at least one hydrophilic (water-soluble) colorimetric dye. It also includes a microscope imaging module comprising an optical system with autofocus, a controllable light source providing visible and ultraviolet light, and a digital camera for acquiring images of the retained plastics and fibers. A control module coordinates the operation of the system components, and a computation module receives and analyzes the acquired images, identifies regions of interest, extracts color, size, and morphological features, and classifies the plastics and fibers using image analysis methods.

Description

Cross-reference to related applications

[0001] This application claims priority to U.S. Provisional Patent Application No. 63 / 771,785, filed March 14, 2025, the entire contents of which are incorporated herein by reference. Technical Field

[0002] This invention relates to the field of characterization and detection technology. More specifically, this invention relates to a portable automated system for on-site, real-time detection and classification of plastics and fibers. Background Technology

[0003] The growing global concern about plastic pollution in terrestrial and marine environments has driven the development of numerous technologies for the detection and analysis of microplastics and microfibers. Currently, methods for identifying and classifying plastics and fibers in environmental and industrial samples mainly rely on manual inspection or laboratory analysis systems. These methods are typically time-consuming, equipment-dependent, and unsuitable for rapid, large-scale, or on-site monitoring.

[0004] In the field of microplastic detection, a widely used technique is staining with Nile Red. Nile Red is a fluorescent dye that preferentially binds to various hydrophobic materials such as plastic polymers. Therefore, staining with Nile Red allows microplastics to be visualized under a fluorescence microscope. For example, Maes et al., in their paper "Nile Red Staining in Microplastic Analysis—A Proposal for a Reliable and Rapid Assessment Method" (Environmental Monitoring & Assessment, 2017), presented an improved Nile Red staining scheme to enhance the speed and reliability of microplastic detection. Although this method has certain advantages over traditional staining techniques, it still relies on laboratory procedures, manual processing, and subsequent analysis.

[0005] Another commonly used method is vibrational spectroscopy, particularly Fourier transform infrared (FTIR) spectroscopy and Raman spectroscopy. These techniques identify microplastics by detecting characteristic vibrational signals of molecules. FTIR spectroscopy is generally suitable for larger microplastic particles, while Raman spectroscopy, with its higher spatial resolution, is better suited for smaller particles. Löder and Gerdts, in "Analysis of environmental microplastics by vibrational microspectroscopy: FTIR, Raman or both?" (Analytical and Bioanalytical Chemistry, 2015), compared these techniques and pointed out that the combined application of FTIR and Raman spectroscopy can improve identification accuracy. However, these methods still have significant practical limitations, including high equipment costs, operational complexity, long analysis times, and high dependence on specialized personnel.

[0006] While existing microplastic detection technologies are effective for analysis, several major drawbacks remain. First, traditional detection methods are time-consuming, typically taking hours or even days from start to finish, making them unsuitable for rapid or high-throughput screening. Second, most detection systems are limited to laboratory environments, lacking portability and making them unsuitable for on-site monitoring in remote areas, marine environments, or industrial settings. Third, advanced spectroscopic equipment such as Fourier transform infrared (FTIR) and Raman spectroscopy are expensive and complex to operate, limiting their widespread application. Finally, many existing technologies rely on retrospective analysis, requiring sampling before offline detection, thus delaying the acquisition of actionable data and hindering real-time decision-making. Summary of the Invention

[0007] The purpose of this invention is to provide a system and method to solve the above-mentioned technical problems.

[0008] According to a first aspect of the present invention, a portable automated microscope system is provided for detecting and classifying plastics and fibers in environmental and / or industrial water samples. Specifically, the portable automated microscope system includes the following components: a sample introduction module for receiving environmental and / or industrial water samples; a filtration module including a membrane holder and at least one filter membrane for retaining plastics and fibers in the water sample; a dye application module for applying a dye matrix to the retained plastics and fibers, wherein the dye matrix includes at least one lipophilic fluorescent dye and at least one hydrophilic (water-soluble) colorimetric dye; a microscope imaging module including an optical system comprising an objective lens and a linear actuator or stepper motor for automatic focusing; a controllable light source for providing visible and ultraviolet light; and a digital camera for acquiring images of the retained plastics and fibers; a control module for controlling the operation of the sample introduction module, filtration module, dye application module, and microscope imaging module; and a computing module including a processor and a memory, wherein the computing module is configured to: receive images acquired from the digital camera; identify regions of interest corresponding to the plastics and fibers; extract color, size, and morphological features from the regions of interest; and classify the plastics and fibers based on color, size, and morphological features using image analysis methods. It is worth noting that this system can perform on-site and real-time detection and classification of plastics and fibers in environmental and / or industrial water samples.

[0009] According to one embodiment, the membrane holder accommodates at least one filter membrane, and the pore size of the at least one filter membrane is from about 100 micrometers to about 5000 micrometers.

[0010] According to another embodiment, the filter membrane is made of polyvinylidene fluoride (PVDF), cellulose acetate, or nylon.

[0011] According to another embodiment, the portable automated microscope system also includes a backwashing module for cleaning the filter membrane.

[0012] According to another embodiment, the system is operatively connected to a computing device having a display, and the computing device is available for a user to operate and monitor the system.

[0013] According to another embodiment, the sample introduction module includes a pump for delivering the water sample.

[0014] According to another embodiment, the lipophilic fluorescent dye preferentially binds to the surface of a hydrophobic polymer, wherein the lipophilic fluorescent dye may be selected from Nile Red and / or structurally related lipophilic fluorescent molecules.

[0015] According to another embodiment, the hydrophilic (water-soluble) colorimetric dye includes one or more water-soluble dyes, selected from Safranin T and Acid Blue 9.

[0016] According to another embodiment, the calculation module generates and displays quantitative analysis results of the detected plastics and fibers.

[0017] According to another embodiment, the computing module includes a convolutional neural network for classifying the plastic and fibers.

[0018] According to a second aspect of the present invention, a method for detecting and classifying plastics and fibers in environmental and / or industrial water samples using a portable automated microscope system is provided. The method includes the following steps: introducing an environmental and / or industrial water sample into the system; filtering the water sample using a filter membrane to trap plastics and fibers on the filter membrane; adding a dye matrix to the trapped plastics and fibers, wherein the dye matrix comprises at least one lipophilic fluorescent dye and at least one hydrophilic colorimetric dye; irradiating the trapped plastics and fibers with visible light and / or ultraviolet light; acquiring one or more images of the trapped plastics and fibers using a digital camera; transmitting the acquired images to a computing module; analyzing the images through the computing module to identify regions of interest, wherein the regions of interest correspond to the plastics and fibers, and extracting color, size, and morphological features from the regions of interest, classifying the plastics and fibers based on the extracted features and generating a classification result; and outputting the classification result to identify the types of plastics and / or fibers present in the environmental and / or industrial water sample.

[0019] According to one embodiment, the filtration step includes passing the water sample through the filter membrane, wherein the filter membrane is selected from a plurality of replaceable filter membranes, and the pore size of the filter membrane is from about 100 micrometers to about 5000 micrometers.

[0020] According to another embodiment, the dye matrix is ​​dissolved in a solvent system comprising water and an organic solvent, wherein the organic solvent is selected from one or more of methanol, ethanol, acetone and dimethyl sulfoxide.

[0021] According to another embodiment, the method further includes adjusting the pH of the dye matrix to approximately 9-14 before applying the dye matrix to the retarded plastic and fiber.

[0022] According to another embodiment, the method further includes heating the dye matrix and the trapped plastic and fiber in a temperature range of 25°C to 80°C for 5-300 minutes prior to the irradiation step.

[0023] According to another embodiment, the classification step further includes distinguishing between cotton fibers, polyester fibers, polyamide 6, polyamide 66, polypropylene, acrylonitrile-butadiene-styrene copolymer, polymethyl methacrylate, and polyvinyl chloride.

[0024] According to another embodiment, the method further includes cleaning the filter membrane by backwashing after image acquisition is completed. Attached Figure Description

[0025] Referring to the accompanying drawings, embodiments of the present invention will be described in more detail below, wherein:

[0026] Figure 1 A prototype of a portable automated microscope system according to an embodiment of the present invention is shown;

[0027] Figure 2 Preliminary results are shown for cotton fibers (black) and polyester fibers (blue);

[0028] Figures 3A-3B It shows the effect in visible light ( Figure 3A ) and 254 nm ultraviolet light ( Figure 3B The staining results of six types of plastics under irradiation and heating at pH 10; and

[0029] Figure 4 A block diagram of a prototype portable automated microscope system according to an embodiment of the present invention is shown. Detailed Implementation

[0030] In the following description, systems and / or methods for detecting and classifying plastics and fibers in environmental and / or industrial water samples are listed as preferred embodiments. It will be apparent to those skilled in the art that modifications, including additions and / or substitutions, can be made without departing from the scope and spirit of the invention. Specific details may be omitted below to avoid obscuring the invention; however, this document is prepared to enable those skilled in the art to practice the techniques disclosed herein without excessive experimentation.

[0031] This invention provides an innovative and practical technical solution applicable to the identification and classification of plastics and fibers from various sources, including industrial wastewater, environmental water samples, and marine microplastic contamination samples. By combining advanced staining methods with a portable automated microscope system, this invention enables rapid, accurate, and low-cost detection of microplastics and microfibers directly at the sampling site. More specifically, this system is suitable for in-situ and real-time analysis, eliminating the need for complex laboratory infrastructure, thus significantly reducing detection time and operating costs.

[0032] More specifically, this invention introduces a novel staining method using a special dye matrix that interacts with the surface properties and chemical characteristics of different plastics and fiber materials. Under controlled detection conditions (e.g., pH adjustment, heat treatment, and dual-mode light source irradiation), this dye matrix produces distinguishable colorimetric and fluorescence responses to different polymer types and fiber materials, thereby achieving accurate differentiation and classification of plastics and fibers. Therefore, it can be effectively applied even in complex environmental or industrial environments. By combining this staining method with a portable automated microscope system, real-time visualization, image acquisition, and automated classification and analysis of contaminants can be reliably achieved.

[0033] The system's portable and automated design facilitates large-scale detection and rapid screening in environments such as marine areas, coastal zones, industrial emissions, and wastewater treatment. Therefore, this invention is particularly suitable for applications such as marine microplastic pollution monitoring, industrial emission regulation, environmental impact assessment, and sustainable development management. Through its in-situ analysis capabilities, this system provides regulatory agencies, environmental management departments, and industrial operators with an effective tool for real-time compliance monitoring and pollution control.

[0034] Furthermore, in regions where relevant regulatory systems are not yet fully developed or are still under construction, this invention also provides a feasible technical basis for formulating and implementing plastic emission standards. By enabling rapid on-site detection and classification, this invention can provide effective evidence, contributing to the formulation of relevant policies, improving enforcement efficiency, and promoting environmental responsibility management of industrial activities. Overall, this invention provides a faster, more efficient, and highly deployable detection platform, contributing to global environmental protection, pollution control, and sustainable development.

[0035] As used herein, the term "lipophilic fluorescent dye" refers to a fluorescent compound that has a higher affinity for hydrophobic or nonpolar materials than for water, such that the dye preferentially binds to, adsorbs onto, or interacts with hydrophobic polymeric materials (including plastic particles and synthetic polymer surfaces, such as synthetic fibers) in an aqueous or mixed solvent system. When excited by light of an appropriate wavelength, the lipophilic fluorescent dye emits a fluorescence signal detectable by an optical imaging system. In some embodiments, when the lipophilic fluorescent dye binds to the hydrophobic polymeric material under visible light illumination, it can also provide detectable optical contrast.

[0036] In this invention, the lipophilic fluorescent dye provides fluorescence contrast to polymer particles and / or synthetic fibers by preferentially binding to relatively hydrophobic regions on polymer chains and / or polymer surfaces. This binding is due to hydrophobic interactions, π-π interactions, or van der Waals forces between the dye molecules and the polymer chains and plastic surfaces. Non-limiting examples of lipophilic fluorescent dyes include Nile Red, BODIPY derivatives, and other hydrophobic fluorescent molecules that emit fluorescence upon excitation under visible or ultraviolet light.

[0037] As used herein, the term "hydrophilic colorimetric dye" refers to a water-soluble dye that preferentially remains in the aqueous phase under dyeing conditions and contains one or more polar and / or ionizable groups, enabling it to interact with polar functional groups (e.g., polar regions on polymer or fiber surfaces) on hydrophilic fibers, hydrophilic surfaces, and / or trapped materials. In addition to the aforementioned polar and / or ionizable groups, hydrophilic colorimetric dyes may also contain aromatic groups and / or other relatively hydrophobic chromophore structures (e.g., phenyl-containing structures), thereby binding to polymeric materials through hydrophobic interactions and / or π-π interactions; however, the polar and / or ionizable groups make the dye more selective for trapped materials containing polar and / or ionizable functional groups and / or relatively more polar polymer surfaces compared to non-polar plastic materials (e.g., the dye preferentially binds to microplastics with higher polar bond density and / or polar surface functional groups). Non-limiting examples of hydrophilic colorimetric dyes include saffron T, acid blue 9, and other water-soluble dyes that produce a distinct color contrast under visible light.

[0038] As used herein, the term "visible light illumination" refers to broadband visible light (e.g., white light) used for reflection- and / or transmission-based imaging to obtain absorption and / or color contrast. Although broadband visible light may contain wavelengths capable of exciting fluorescent molecules, the configuration of the imaging device can ensure that the acquired signal is primarily a reflected and / or transmitted signal, rather than a fluorescence signal. In contrast, the term "visible light excitation" refers to illumination excitation at a selected wavelength or a limited band of the visible spectrum (e.g., approximately 400–800 nm, or a narrow band centered on a selected wavelength) to produce fluorescence imaging, typically using a combination of excitation and emission filters to ensure that the detected signal is primarily a fluorescence emission signal.

[0039] In some embodiments, the hydrophilic colorimetric dye can provide color contrast features in images acquired under visible light illumination (e.g., broadband white light illumination for reflection- and / or transmission-based imaging, where the acquired signal is primarily non-fluorescent), and this color contrast is associated with the polarity and / or ionizable functional groups on the retentate. In some embodiments, under ultraviolet and / or visible light excitation conditions for fluorescence imaging, the hydrophilic colorimetric dye can produce unique optical responses and / or alter the apparent fluorescence response of the retentate (e.g., through spectral overlap, energy transfer, and / or fluorescence quenching).

[0040] In this invention, the hydrophilic colorimetric dye can be used alone and / or as a pre-dye prior to the application of the lipophilic fluorescent dye to impart a color contrast image of polarized and / or ionizable sites of the retentate under visible light irradiation. In some embodiments, when subsequently used in combination with the lipophilic fluorescent dye, the hydrophilic colorimetric dye can also modulate the composite optical response under UV and / or visible light excitation conditions.

[0041] In some embodiments, the lipophilic fluorescent dye and the hydrophilic colorimetric dye are used together as a dye matrix. The lipophilic fluorescent dye stains nonpolar polymer regions, thus preferentially providing fluorescence contrast, while the hydrophilic colorimetric dye stains polar functional groups, thus preferentially providing color contrast. Furthermore, in some embodiments, the hydrophilic colorimetric dye interacts with the lipophilic fluorescent dye to further distinguish optical responses under visible light irradiation and ultraviolet and / or visible light excitation. This combined dye matrix scheme enables simultaneous detection and classification using a single imaging workflow.

[0042] In some embodiments, regardless of whether lipophilic fluorescent dyes, hydrophilic colorimetric dyes, or mixtures thereof are used, the intercepted material is distinguished and / or classified by interpreting a combination of image features available under various imaging conditions. Image features include, but are not limited to: (1) color and / or intensity contrast under visible light illumination; (2) fluorescence response under ultraviolet light excitation; (3) fluorescence response under visible light excitation; and (4) morphology and size-related features (e.g., particle shape, aspect ratio, texture, and size distribution). In some embodiments, this classification is based on a comprehensive interpretation of multiple features, rather than relying solely on a single feature.

[0043] According to a first aspect of the invention, a portable automated microscope system is provided for detecting and classifying plastics and fibers in environmental and / or industrial water samples. The system is configured for in-situ and real-time analysis, enabling rapid identification and classification of microplastics and microfibers directly at the sampling site without relying on laboratory equipment. Figure 4 As shown, the system 10 includes a sample introduction module 101, a filtering module 102, a dye application module 103, a microscope imaging module 104, a control module 105, and a calculation module 106.

[0044] The sample introduction module 101 is configured to receive environmental and / or industrial water samples and may include a pump (not shown) for delivering the water sample to the system interior. In some embodiments, the pump delivers the water sample from a sample container, conduit, or collection container to the filtration module 102 at a controlled and continuous flow rate. The filtration module 102 includes a membrane holder 102b that can accommodate one of a plurality of replaceable filter membranes 102a to trap plastics and fibers in the water sample while allowing the liquid phase to pass through. In some embodiments, the pore size of the filter membrane 102a ranges from 100 micrometers to 5000 micrometers to effectively trap plastics and fibers of different sizes. The filter membrane 102a may be made of polyvinylidene fluoride, cellulose acetate, or nylon, thereby providing good chemical compatibility, mechanical strength, and stable filtration performance. The filter module 102 can also be operatively connected to a backwashing module (not shown), which can flow back-flowing cleaning liquid through the filter membrane 102a after analysis to remove trapped material for subsequent use.

[0045] The dye application module 103 adds a dye matrix to the filter membrane 102 to trap plastics and fibers. The dye matrix includes at least one lipophilic fluorescent dye and at least one hydrophilic (water-soluble) colorimetric dye to visualize the plastics and fibers based on fluorescence and color. In some embodiments, the lipophilic fluorescent dye includes Nile Red, which can selectively dye hydrophobic polymer materials; while the colorimetric dye may include one or more of Saffron T and Acid Blue 9, which can produce distinguishable color characteristics when interacting with different types of fibers or plastics. The dye application module 103 can automatically and controllably apply a predetermined volume of dye matrix to the filter membrane 102 to ensure that the trapped plastics and fibers are uniformly dyed.

[0046] The microscope imaging module 104 images the dyed plastic and fibers on the filter membrane 102a. The microscope imaging module 104 includes an optical system 104a, which includes an objective lens and a linear actuator or stepper motor for automatic focusing, enabling precise and repeatable focusing without manual intervention. The microscope imaging module 104 also includes a controllable light source 104b to provide visible and ultraviolet light for imaging under different illumination conditions, thereby enhancing contrast and revealing dye-specific fluorescence or colorimetric responses. A digital camera 104c, operatively connected to the optical system 104, is used to acquire images of the trapped plastic and fibers. In some embodiments, the digital camera 104c is a high-resolution camera capable of acquiring still images and / or video streams of the dyed material.

[0047] The control module 105 is configured to control the operation of the sample introduction module 101, the filtration module 102, the dye application module 103, and the microscope imaging module 104. The control module 105 may include a microcontroller, a programmable logic controller, or other control circuitry, and is programmed to execute a preset process including sample delivery, filtration, dye application, light source control, autofocus adjustment, and image acquisition. By coordinating the control of each module, the system 10 can complete an automated and repeatable detection process with minimal human intervention.

[0048] The system 10 also includes a computing module 106, which includes a processor 106a and a memory 106b. The computing module 106 receives images acquired by the digital camera 104c and performs image analysis on the images. During operation, the computing module 106 identifies regions of interest (ROIs) in the images corresponding to plastics and fibers, extracts color features, size features, and morphological features from these ROIs, and processes the extracted features using image analysis methods to classify the plastics and fibers. In some embodiments, the computing module 106 includes a convolutional neural network that classifies plastics and fibers based on learned visual patterns, color features, and morphological features. The computing module can also be configured to generate and display a quantitative analysis report of the detected plastics and fibers, which may include information such as quantity statistics, size distribution, material category, and spatial distribution.

[0049] In some embodiments, the portable automated microscope system 10 is also connected to a computing device with a display, such as a laptop, tablet, or embedded display terminal, so that the user can operate and monitor the system 10. Through the display, the user can view real-time images, adjust operating parameters, initiate analysis processes, and view classification results and reports. The computing module 106 can be integrated inside the system housing or used as an external computing device, communicating with the system via wired or wireless communication.

[0050] By integrating a sample introduction module 101, a filtration module 102, a dye application module 103, a microscope imaging module 104, a control module 105, and a calculation module 106, this system 10 can perform on-site and real-time detection and classification of plastics and fibers in environmental and / or industrial water samples. By combining fluorescent dyes and colorimetric dyes, automated imaging, and advanced image analysis technology, it achieves rapid, accurate, and repeatable identification of plastics and fibers, making the system particularly suitable for applications such as environmental monitoring, industrial wastewater analysis, marine pollution assessment, and microplastic monitoring.

[0051] According to a second aspect of the invention, a method is provided for detecting and classifying plastics and fibers in environmental and / or industrial water samples using a portable automated microscope system. In the operation, firstly, an environmental and / or industrial water sample is introduced into the system through a sample introduction interface; subsequently, the water sample is introduced into a filtration module, allowing it to pass through a filter membrane, thereby trapping plastics and fibers in the water on the surface of the filter membrane and discharging the liquid phase. In some embodiments, the filtration step includes selecting one of a plurality of replaceable filter membranes to filter the water sample, the filter membrane having a pore size range of approximately 100 micrometers to approximately 5000 micrometers, thereby achieving a balance between the trapping of microplastics / microfibers and the throughput suitable for automated field operation.

[0052] After filtration, the dye matrix is ​​directly applied to the trapped plastics and fibers on the filter membrane. The dye matrix comprises at least one lipophilic fluorescent dye and at least one hydrophilic (water-soluble) colorimetric dye. In a preferred embodiment, the dye matrix comprises Nile Red as a lipophilic fluorescent dye and Saffron T and Acid Blue 9 as water-soluble colorimetric dyes. The dye matrix is ​​dissolved in a solvent system containing water and one or more organic solvents selected from methanol, ethanol, acetone, dimethyl sulfoxide, or any combination thereof to ensure good solubility, penetration, and interaction of the dye with the trapped plastics and fibers. In some embodiments, before applying the dye matrix to the trapped material, the pH of the dye matrix is ​​adjusted to approximately 9-14 by adding an alkaline reagent to enhance the selective interaction between the dye and the trapped material (plastics and fibers). In a further embodiment, before irradiation, the dye matrix and the trapped material are co-heated to a temperature range of 25°C-80°C and maintained for 5-300 minutes to promote the diffusion, adsorption, and binding of the dye on the plastic surface, thereby improving dyeing contrast and classification accuracy.

[0053] After dye application and optional pH adjustment and heat treatment, the trapped plastics and fibers are irradiated with visible and / or ultraviolet light using a controlled light source of a portable automated microscope system. Subsequently, one or more images are acquired using a digital camera integrated into the microscope's imaging module. The acquired images are transmitted to a computing module including a processor and memory. This computing module analyzes the images to identify regions of interest corresponding to the plastics and fibers, extracts color, size, and morphological features from these regions of interest, and classifies the plastics and fibers based on the extracted features using image analysis methods (e.g., machine learning or convolutional neural network algorithms). The computing module generates classification results identifying the types of plastics and / or fibers in environmental and / or industrial water samples and outputs these results to a user interface, display device, or external device.

[0054] In some embodiments, the classification step includes distinguishing cotton fibers, polyester fibers, polyamide 6, polyamide 66, polypropylene, acrylonitrile-butadiene-styrene copolymer, polymethyl methacrylate, and polyvinyl chloride based on color characteristics, fluorescence response characteristics, and morphological characteristics observed under visible and / or ultraviolet light irradiation. After image acquisition and analysis, the method further includes backwashing the filter membrane, i.e., applying a reverse flow of cleaning liquid to the filter membrane to remove trapped particles and residual dyes, thereby preparing it for subsequent sample testing and ensuring stable performance between multiple testing cycles.

[0055] By combining these adjustable filtration, multi-dye staining, pH adjustment and heat treatment, dual-mode illumination, automated imaging and intelligent image analysis steps, the method of the present invention can achieve real-time on-site detection and classification of plastics and fibers in environmental and / or industrial water samples with high sensitivity, high specificity and high operating efficiency.

[0056] Example

[0057] Example 1: Portable Automated Microscope System

[0058] This invention provides a novel portable automated microscope system suitable for field detection and classification of plastics and fibers. The system can identify microplastics and fibers in environmental and industrial samples in real time, making it particularly suitable for sample types such as laundry wastewater and marine water samples. The system integrates a staining module, a portable microscope with image acquisition capabilities, an automated liquid sampler, and an AI-based image processing unit for material classification. The system's structure and operating procedures are described in detail below.

[0059] This system enables in-situ and real-time analysis of microplastics and microfibers in environmental and industrial samples. For example... Figure 1 As shown, the system comprises several core components, including a microscope imaging module, an autosampler, a controller, and a computing module. Each component will be described in detail below to ensure completeness and adequacy.

[0060] This microscope imaging module is the core unit for imaging, optical detection, and analysis. It includes an optical system comprising a microscope objective and a focusing support. More specifically, the optical system includes a high-magnification variable-focus objective with a magnification range of 10x to 1500x, enabling high-resolution imaging of microplastics and fibers. Focusing of the objective is achieved via a linear actuator or stepper motor controlled by a computational module, thus enabling autofocus and improving image sharpness. The objective has a field of view range of 30° to 120°, providing a larger observation area and facilitating the acquisition of images of microplastics and fibers trapped on the filter membrane.

[0061] The microscope imaging module also includes an illumination system comprising an adjustable LED light source with a wavelength range of 200 nm to 800 nm, supporting both visible and near-ultraviolet illumination. This light source has a maximum brightness of approximately 1000 lumens to ensure sufficient brightness during imaging, and its intensity can be controlled via a pulse-width modulation (PWM) dimming system for precise and dynamic brightness adjustment. Furthermore, the illumination system can operate in multiple illumination modes, including continuous mode, pulsed mode, and strobe mode, to further optimize image contrast and sharpness for different sample types and detection requirements.

[0062] The system also includes a light source controller for real-time adjustment of light intensity and switching between different lighting modes. This controller can be integrated into the computing module and connected to a software interface, allowing users to adjust lighting parameters via a graphical user interface (GUI). In some embodiments, the light source controller can automatically adjust lighting settings based on sample characteristics or image analysis feedback.

[0063] The microscope imaging module also includes a high-resolution digital camera with a resolution of at least 1920x1080 pixels and a frame rate of 30-60 frames per second. This camera connects to the processing unit via a high-speed interface (e.g., USB 3.0, USB-C, or wireless communication). The camera supports both automatic and manual exposure control to optimize imaging under different lighting conditions. During operation, the camera acquires high-resolution images of the filter membrane surface and subsequently processes the images using artificial intelligence-based image analysis algorithms.

[0064] This autosampler automatically introduces samples, performs staining, and manages the filtration process. It includes a sample introduction module, a filtration module, and a dye application module. The sample introduction module introduces wastewater or other liquid samples into the sample chamber. This module is connected to a pump (including but not limited to a peristaltic pump, diaphragm pump, or electromagnetic pump) to adjust the sample flow rate, which is adjustable from approximately 10 mL / min to 600 mL / min to accommodate different sample volumes and analytical requirements. The sample introduction module also includes a drain outlet for discharging the processed sample after filtration, staining, and analysis.

[0065] The dye application module includes a dye injector equipped with a precision nozzle, capable of delivering dye volumes ranging from approximately 1 ml to 50 ml. The dye injection process is automated via a stepper motor-driven injection pump, achieving an injection accuracy of approximately ±0.1 ml. According to a preset detection procedure, the system can inject multiple dyes simultaneously or sequentially, thereby achieving selective dyeing of plastics and fibers.

[0066] The filtration module includes a membrane holder and one or more filter membranes. The membrane holder accommodates one of a plurality of replaceable filter membranes to trap microplastics and microfibers from the sample. The filter membrane can be made of polyvinylidene fluoride, cellulose acetate, nylon, or other suitable materials, with a pore size ranging from about 100 micrometers to about 5000 micrometers. The membrane holder promotes uniform distribution of liquid on the membrane surface and reduces clogging during operation.

[0067] The autosampler also includes a backwashing system to clean the filter membrane after each analytical cycle. This backwashing removes particles and residues trapped on the filter membrane surface by flowing clean water in the opposite direction. This cleaning process extends the lifespan of the filter membrane and prepares it for subsequent analyses.

[0068] The system includes a control module, which comprises a microcontroller, an embedded controller, and / or a programmable logic controller. In some embodiments, the control module includes an Arduino control board, a Raspberry Pi control unit, or an industrial-grade programmable logic controller. The control module can execute pre-programmed control sequences to control the operation of components such as pumps, valves, actuators, lighting systems, and cameras. The control module communicates with system components via one or more serial communication protocols, including but not limited to I... 2 C, SPI, UART, CAN or Ethernet protocols.

[0069] The system also includes a computing module implemented on a connected computer, industrial computer, or embedded processor. This computing module provides a graphical user interface on a display device, allowing users to control parameters such as focus, zoom, light intensity, and image exposure, and monitor the sample loading, dye application, filtering, and image acquisition processes. The computing module can also execute artificial intelligence-based image processing algorithms to automatically identify and classify plastics and fibers based on color, morphological, size, and shape features. After processing, the acquired images further identify regions of interest, classify polymer types (including but not limited to polyethylene terephthalate, polypropylene, and acrylonitrile-butadiene-styrene copolymers), and generate analysis reports for user viewing.

[0070] In some embodiments, the control module is operatively connected to the computing module to exchange control signals and data to coordinate fluid processing, imaging, and analysis operations.

[0071] During operation, a wastewater sample ranging from approximately 50 ml to 500 ml is introduced into the system through the inlet. The sample is driven into the system by a pump (e.g., a peristaltic pump or diaphragm pump) at a controlled flow rate of approximately 10 ml / min to 600 ml / min. The sample is then guided into a membrane holder, which houses one of a plurality of replaceable filter membranes selected from those with pore sizes ranging from approximately 100 micrometers to approximately 5000 micrometers. As the sample passes through the membrane, microplastics and fibers larger than the pore size are trapped on the membrane surface, and the filtered water is discharged through the outlet.

[0072] After filtration, the dye matrix is ​​injected into the membrane holder using an automated precision syringe pump with a volume accuracy of approximately ±0.1 mL. The total volume of the introduced dye matrix ranges from approximately 1 mL to 100 mL. The dye matrix comprises 50% to 99% by volume of component A, and 0.5% to 50% by volume of components B and C, respectively. Preferably, component A is Nile Red, but other dyes with the same function, such as organic dyes or fluorescent dyes, may also be included. Preferably, components B and C are Saffron T and Acid Blue 9, respectively, but may also be selected from xanthracene dyes, phthalocyanine dyes, quinoline dyes, or other dyes with the same function. The concentration of the dye matrix ranges from approximately 0.1 mmol / L to 10 mol / L.

[0073] The dye matrix is ​​prepared using a solvent system comprising a mixture of water and one or more organic solvents selected from methanol, ethanol, acetone, dimethyl sulfoxide, or combinations thereof, with a volume ratio of organic solvent to water ranging from approximately 1:99 to 99:1. In fiber detection, approximately 1-20 mL of the thoroughly mixed dye solution is directly added dropwise to the fiber-containing area and incubated for approximately 10-30 seconds. In plastic detection, the pH of the dye solution is first adjusted to approximately 9-14 using an alkaline substance selected from potassium hydroxide, sodium hydroxide, calcium hydroxide, or other similarly functional substances; subsequently, the mixture of plastic and dye solution is heated to approximately 25°C to 80°C for approximately 5-300 minutes to enhance the interaction between the dye and microplastics, followed by detection under ultraviolet and visible light in the wavelength range of approximately 200 nm to 800 nm.

[0074] After the staining process is completed, the filter membrane is placed within the field of view of the microscope, and one or more images of the filter membrane surface are acquired using a digital camera integrated with the microscope. In one embodiment, the camera has a resolution of approximately 1920x1080 pixels and a frame rate ranging from approximately 30 to 60 frames per second. The acquired images are automatically transmitted to a connected AI-based image analysis system via a data interface (e.g., USB 3.0, USB-C, or wireless connectivity including Wi-Fi). The images can be stored in standard digital image formats, including but not limited to TIFF, JPEG, or PNG formats, for subsequent processing and analysis.

[0075] This AI-based image analysis system uses machine learning algorithms (including convolutional neural networks) to process acquired images. The system automatically identifies one or more regions of interest (ROIs) in each image and segments these ROIs, dividing them into different color-coded regions based on detected dye signals. Each pixel or group of pixels is assigned a color code corresponding to a specific material type; for example, under 254 nm ultraviolet light, polyvinyl chloride (PVC) appears blue, and nylon-6 appears orange. Based on the segmented image data and color-coded information, the system uses a pre-trained classification model to classify the detected particles. The classification categories include, but are not limited to: polyester, cellulose acetate, polymethyl methacrylate (PMMA), acrylonitrile-butadiene-styrene copolymer, PVC, or other material types.

[0076] Example 2: Detection and classification of cotton fibers and polyester microfibers using a portable automated microscope system.

[0077] Using the portable automated microscope system according to the present invention, a specially formulated dye matrix comprising Nile Red, Saffron T, and Acid Blue 9 was employed to evaluate the performance of detecting and classifying cotton and polyester microfibers in water samples. The results of this embodiment show that, compared to traditional manual microscope-based detection methods, this system provides rapid, reliable, and real-time analytical capabilities for mixed fiber samples.

[0078] A homogeneous fiber suspension was prepared by dispersing 10 mg of polyester microfibers and 10 mg of cotton microfibers in 100 mL of water. The dye matrix concentration used for dyeing was 1 mmol / L, with a total volume of 10 mL. The solvent system was a mixture of water and methanol at a volume ratio of 9:1. Notably, the dye matrix included 95% Nile Red as component A; 2.5% Saffron T as component B; and 2.5% Acid Blue 9 as component C.

[0079] Analysis and detection are performed using a portable automated microscope system according to the present invention. The system includes at least an optical imaging system, an adjustable LED light source, a light source controller, and a high-resolution digital camera. The system also includes an autosampler for automatically injecting samples and dyes; a membrane holder equipped with at least one filter membrane with a pore size of 100 micrometers; a backwashing system for cleaning the filter membrane; and a control and image analysis software platform for automatic operation and data processing.

[0080] The prepared 100 mL fiber suspension is introduced into the system through the inlet. A pump drives the sample through the system and filters it through a membrane, trapping cotton and polyester microfibers on the membrane surface. The filtrate is then discharged through the outlet. After filtration, an autosampler draws 5 mL from a 10 mL dye matrix reservoir and injects it into the membrane holder, where the dye solution is evenly distributed on the trapped microfiber surface. The dyed fibers are then incubated for approximately 15 seconds to allow sufficient interaction between the dye components and the fibers.

[0081] like Figure 2 As shown, after the dyeing process is completed, the filter membrane is placed under a microscope, and images of the dyed fibers are acquired using a high-resolution camera at a resolution of 1920x1080 pixels. The acquired images are automatically transmitted to control software for further processing and analysis. An artificial intelligence-based image analysis module identifies regions of interest in the images and classifies the fibers according to their color and morphological characteristics. In this embodiment, cotton fibers appear as black areas, while polyester fibers appear as blue areas, thus achieving a clear distinction between the two fiber types.

[0082] After image acquisition and analysis are completed, the system initiates a backwashing process, using reverse-flowing clean water to remove residual fibers and dyes from the filter membrane surface. The backwashing process lasts approximately 30 seconds to 1 minute. Once completed, the filter membrane surface is clean and ready for the next batch of sample analysis.

[0083] The analysis results show that this portable automated microscope system can classify cotton microfibers and polyester microfibers with high precision. In the analyzed samples, the identification accuracy of cotton fibers was 100%, and the identification accuracy of polyester fibers was also 100%, demonstrating that the combination of a multi-component dye matrix and an AI-based image analysis module can accurately distinguish between natural and synthetic fibers.

[0084] The entire analysis process includes sample filtration, dye application, incubation, image acquisition, AI-based image analysis, and backwashing to clean the filter membrane, taking approximately five minutes in total. This demonstrates that the system of this invention has short processing time capabilities, enabling rapid, real-time analysis, representing a significant improvement over traditional, time-consuming manual microscopic detection methods and laboratory analysis methods.

[0085] Figure 2 This image displays a representative picture acquired by the system after dyeing and imaging. In this image, cotton fibers appear as black areas, and polyester fibers appear as blue areas, creating a clear visual contrast that enables the image processing algorithm to reliably perform automatic classification.

[0086] The above results show that the portable automated microscope system of the present invention can quickly and accurately detect and classify microfibers in water samples. By combining an artificial intelligence-based image analysis module with a specially formulated dye matrix, automated and high-throughput detection can be achieved, significantly reducing user intervention and completing the entire detection and classification process within minutes, thus significantly improving operational efficiency. This makes the system particularly suitable for applications such as on-site environmental monitoring, industrial wastewater analysis, and real-time pollution control.

[0087] Example 3: Detection and classification of six types of plastics using a portable automated microscope system.

[0088] Using the portable automated microscope system according to the present invention, a dye matrix comprising Nile Red, Saffron T, and Acid Blue 9 was employed to evaluate the detection and classification performance of six plastics in water samples. Experimental results showed that, under alkaline conditions and after heating and ultraviolet irradiation, the various plastics exhibited different color responses, thus allowing for accurate visual identification and automatic classification. The evaluated plastics included polyamide 6, polyamide 66, polymethyl methacrylate, polypropylene, acrylonitrile-butadiene-styrene copolymer, and polyvinyl chloride.

[0089] The amount of each plastic sample used was 50 mg. The dye matrix concentration was 5 mmol / L, the total volume was 100 mL, and the solvent system was a mixture of water and methanol at a volume ratio of 19:1. The dye matrix included: 90%-95% Nile Red as component A; 2.5%-5% Saffron T as component B; and 2.5%-5% Acid Blue 9 as component C.

[0090] Analysis and detection are performed using a portable automated microscope system according to the present invention. The system includes an optical imaging system, an LED light source, a light source controller, and a high-resolution digital camera. The system also includes an autosampler for automatically injecting samples and dyes; a membrane holder equipped with at least one 100-micron pore size filter membrane; a backwashing system for cleaning the filter membrane; and a control and image analysis software platform for automated operation and data processing.

[0091] During the dyeing process, six plastic samples (50 mg each of polyamide 6, polyamide 66, polymethyl methacrylate, polypropylene, acrylonitrile-butadiene-styrene copolymer, and polyvinyl chloride) were placed in a reactor, and a dye matrix solution (100 mL) was added to the reactor, ensuring all plastic particles were completely submerged. Next, the pH of the dye solution was adjusted to approximately 10 using potassium hydroxide. The reactor was then placed in a heating device and reacted at 50°C for 15 minutes to enhance the interaction between the dye and the polymer. After the reaction was complete, the mixture containing the dye solution and dyed plastic particles was directly transferred to a filtration system without rinsing.

[0092] The mixture was introduced into a membrane holder and filtered through a 100-micron pore size filter membrane. The filter membrane retained all dyed plastic particles, while the dye solution was discharged through the system outlet. After filtration, the filter membrane containing the dyed plastic was placed under a portable automated microscope. High-resolution images were acquired under visible light and 254 nm ultraviolet light (e.g., ...). Figure 3A and Figure 3B As shown in the image, the camera has a resolution of 1920x1080 pixels and a frame rate of 30-60 frames per second. The acquired images are transmitted to a connected computing module for further processing.

[0093] The image was analyzed using an AI-based image analysis system. The system automatically identified the regions of interest for each plastic particle and extracted the color, size, and shape features of each region. Based on the unique color features produced by the dye matrix under visible and ultraviolet light conditions, the AI ​​system classified the particles into polyamide 6, polyamide 66, polymethyl methacrylate, polypropylene, acrylonitrile-butadiene-styrene copolymer, and polyvinyl chloride.

[0094] After image acquisition and analysis are completed, the system initiates a backwashing step to clean the filter membrane. The system uses reverse-flowing clean water to flush away residual particles from the membrane within approximately 30 seconds to 1 minute, preparing the system for subsequent analysis.

[0095] The results showed that all types of plastics exhibited clear and distinguishable color responses. For example, polyamide 6 appeared distinctly red under visible light, polyamide 66 appeared red under visible light, polypropylene appeared light pink under visible light, acrylonitrile-butadiene-styrene copolymer appeared light pink under visible light, polymethyl methacrylate appeared yellow under ultraviolet light, and polyvinyl chloride appeared blue under 254 nm ultraviolet light. Figure 3A Showing representative images of polyamide 6, polyamide 66, polymethyl methacrylate, polypropylene, and acrylonitrile-butadiene-styrene copolymer under visible light; Figure 3B A representative image of polyvinyl chloride under 254 nm ultraviolet light irradiation is shown.

[0096] The entire process, including dye application, pH adjustment, heating reaction, filtration, image acquisition, image analysis, and cleaning of the filter membrane, takes less than 30 minutes in total.

[0097] This embodiment demonstrates that a portable automated microscope system, combined with a multi-component dye matrix, can accurately and efficiently detect and classify various types of plastics based on color response. By combining chemical staining, automated filtration, high-resolution imaging, and AI-based classification analysis, a rapid, reliable, and low-cost method for plastic identification is achieved. This method is particularly suitable for on-site environmental monitoring, industrial wastewater analysis, and large-scale plastic pollution screening.

[0098] According to the embodiments disclosed in this invention, the functional units and modules of a portable automated microscope system, as well as / or application methods, can be implemented using computing devices, computer processors, or electronic circuits, including but not limited to application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), microcontrollers, graphics processing units (GPUs), and other programmable logic devices configured or programmed according to this disclosure. Based on the teachings of this disclosure, those skilled in the art of software or electronics can easily write computer instructions or software code that run on computing devices, computer processors, or programmable logic devices.

[0099] All or part of the methods according to the embodiments can be executed on one or more computing devices, including server computers, personal computers, laptops, and mobile computing devices (such as smartphones and tablets).

[0100] Embodiments may include computer storage media, transient and non-transient storage devices storing computer instructions or software code, which can be used to program or configure computing devices, computer processors, or electronic circuits to perform any steps of the present invention. Storage media, transient and non-transient storage devices may include, but are not limited to, floppy disks, optical disks, Blu-ray discs, DVDs, CD-ROMs and magneto-optical disks, read-only memory, random access memory, flash memory devices, or any medium or device suitable for storing instructions, code, and / or data.

[0101] According to various embodiments, each functional unit and module may also be implemented in a distributed computing environment and / or cloud computing environment, wherein all or part of the machine instructions are executed in a distributed manner by one or more processing devices interconnected through communication networks (such as intranets, wide area networks, local area networks, the Internet and other forms of data transmission media).

[0102] As used herein, the terms “approximately,” “substantially,” “essentially,” and “about / approximately” are used to describe and explain a small variation. When used in conjunction with an event or situation, the term can refer to the exact occurrence of the event or situation, or approximately the occurrence of the event or situation. The term “about,” as used herein with respect to a given value or range, typically refers to a range of ±10%, ±5%, ±1%, or ±0.5% of the given value or interval, which can be understood herein as ranging from one endpoint to another or between two endpoints. Unless otherwise stated, all ranges disclosed in this disclosure include endpoints. Furthermore, unless explicitly stated otherwise, the term “a” as used herein should be understood to include one or more; when referring to the same numerical value or characteristic as “about / approximately,” the term may refer to a value within ±10%, ±5%, ±1%, or ±0.5% of the numerical average.

[0103] The above description is provided for the purpose of illustrating and describing the invention, and is not intended to be exhaustive or to limit the invention to the precise forms disclosed. Many modifications and variations will be apparent to those skilled in the art.

[0104] The above embodiments were chosen and described in order to best explain the principles of the invention and its practical application, thereby enabling other skilled in the art to understand the various embodiments of the invention and the various modifications suitable for the intended particular use.

Claims

1. A portable automated microscope system for detecting and classifying plastics and fibers in environmental and / or industrial water samples, characterized in that, include: The sample introduction module is used to receive environmental and / or industrial water samples; A filtration module comprising a membrane holder and at least one filter membrane for retaining plastics and fibers in the water sample; A dye application module is used to apply a dye matrix to the truncated plastic and fiber, wherein the dye matrix comprises at least one lipophilic fluorescent dye and at least one hydrophilic colorimetric dye; Microscope imaging module, which includes An optical system, which includes an objective lens and a linear actuator or stepper motor for automatic focusing; A controllable light source used to provide visible and ultraviolet light; as well as A digital camera for capturing images of the intercepted plastic and fibers; The control module is used to control the operation of the sample introduction module, the filtration module, the dye application module, and the microscope imaging module; and A computing module, comprising a processor and memory, is configured as follows: Receive the image from the digital camera; Identify regions of interest corresponding to plastics and fibers; Extract color, size, and shape features from the region of interest; as well as Plastics and fibers are classified based on their color, size, and morphological characteristics using image analysis methods. The system can perform on-site and real-time detection and classification of plastics and fibers in environmental and / or industrial water samples.

2. The portable automated microscope system of claim 1, wherein the membrane holder accommodates the at least one filter membrane, and the at least one filter membrane has a pore size ranging from 100 micrometers to 5000 micrometers.

3. The portable automated microscope system according to claim 2, wherein the filter membrane is made of polyvinylidene fluoride, cellulose acetate or nylon.

4. The portable automated microscope system according to claim 1, wherein the portable automated microscope system further includes a backwashing module for cleaning the filter membrane.

5. The portable automated microscope system of claim 1, wherein the system is operatively connected to a computing device having a display, and the computing device is available for operation and monitoring by a user.

6. The portable automated microscope system of claim 1, wherein the sample introduction module includes a pump for delivering the water sample.

7. The portable automated microscope system according to claim 1, wherein the calculation module generates and displays quantitative analysis results of the detected plastics and fibers.

8. The portable automated microscope system of claim 1, wherein the computing module includes a convolutional neural network for classifying the plastics and fibers.

9. The portable automated microscope system of claim 1, wherein the dye matrix is ​​dissolved in a solvent system comprising water and an organic solvent, wherein the organic solvent comprises one or more of methanol, ethanol, acetone and dimethyl sulfoxide.

10. A method for detecting and classifying plastics and fibers in environmental and / or industrial water samples using a portable automated microscope system, characterized in that, include: Introduce environmental and / or industrial water samples into the system; The water sample is filtered using a filter membrane to trap plastics and fibers on the filter membrane. A dye matrix is ​​added to the truncated plastic and fiber, wherein the dye matrix comprises at least one lipophilic fluorescent dye and at least one hydrophilic colorimetric dye; Irradiate the trapped plastic and fibers with visible light and / or ultraviolet light; One or more images of the trapped plastic and fibers are captured using a digital camera; Transmit one or more images to the computing module; The analysis of the one or more images by the calculation module includes: To identify regions of interest, wherein the regions of interest correspond to plastics and fibers; Extract color, size, and shape features from the region of interest; and Based on the aforementioned color, size, and morphological characteristics, plastics and fibers are classified and classification results are generated. Output the classification results to identify the types of plastics and / or fibers present in the environmental and / or industrial water samples.

11. The method of claim 10, wherein the filtration step further comprises passing the water sample through the filter membrane, wherein the filter membrane is selected from a plurality of replaceable filter membranes, and the pore size of the filter membrane is in the range of 100 micrometers to 5000 micrometers.

12. The method according to claim 10, wherein the dye matrix is ​​dissolved in a solvent system comprising water and an organic solvent, wherein, The organic solvent is selected from one or more of methanol, ethanol, acetone and dimethyl sulfoxide.

13. The method of claim 10, further comprising adjusting the pH of the dye matrix to approximately 9-14 before applying the dye matrix to the retarded plastic and fiber.

14. The method according to claim 10, further comprising heating the dye matrix and the trapped plastic and fiber at a temperature range of 25°C to 80°C for 5-300 minutes prior to the irradiation step.

15. The method of claim 10, wherein the classification includes distinguishing between cotton fibers, polyester fibers, polyamide 6, polyamide 66, polypropylene, acrylonitrile-butadiene-styrene copolymer, polymethyl methacrylate, and polyvinyl chloride.

16. The method according to claim 10, further comprising cleaning the filter membrane by backwashing after the image acquisition is completed.

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