Dual frequency comb spectrum enabled attachment device for mobile devices

Abstract

A system for functionally integrating dual-frequency comb spectroscopy (DFCS) into and / or integrating with a mobile device. The system includes: an attachment device configured to couple to a mobile device; and DFCS enabling hardware configured to enable the mobile device to perform DFCS-based molecular analysis of a sample region including a sample, wherein the DFCS enabling hardware includes: a laser emitter configured to generate two optical frequency combs that emit laser light at uniformly spaced intervals across a broad spectrum of optical frequencies, wherein (i) the laser light is directed toward the sample region, and (ii) at least a portion of the laser light is received by at least one photodetector configured to: (a) receive at least a portion of the laser light after interaction with the sample, and (b) generate signal output data based on one or more wavelengths or absorption properties corresponding to a portion of the laser light.

Description

[0001] Cross-references to related applications

[0002] This application claims priority to U.S. Provisional Application No. 63 / 743,896, filed January 10, 2025, entitled “DUAL FREQUENCY COMBSPECTROSCOPY ENABLING ATTACHMENT FOR MOBILE DEVICES,” the disclosure of which is incorporated herein by reference in its entirety. Background Technology

[0003] Spectroscopy can be used for the real-time detection and analysis of chemical compounds, gases, and other substances. However, traditional spectroscopic equipment is often bulky, expensive, and / or requires specialized training to operate, limiting its ease of use and portability. As an alternative, chemical assay kits can be portable and easy to use, but often lack the precision and real-time analytical capabilities offered by spectroscopic techniques such as dual-frequency comb spectroscopy (DFCS). While more compact than traditional spectroscopic equipment, portable spectrometers can still be expensive and may not offer the same level of integration and ease of use as DFCS-enabled devices. The applicant has recognized many of the drawbacks associated with conventional spectroscopic equipment. Summary of the Invention

[0004] The various implementations described herein relate to components, devices, and systems for functionally integrating dual-frequency comb spectroscopy (DFCS) into and / or integrating with mobile devices.

[0005] According to some embodiments, a system includes: an attachment device configured to couple to a mobile device; and DFCS enabling hardware configured to enable the mobile device to perform DFCS-based molecular analysis of a sample region including a sample, wherein the DFCS enabling hardware includes: a laser emitter configured to generate two optical frequency combs that emit laser light at uniformly spaced intervals on a broad spectrum of optical frequencies, wherein: (i) the laser light is directed toward the sample region, and (ii) at least a portion of the laser light is received by at least one photodetector configured to: (a) receive at least a portion of the laser light after interaction with the sample, and (b) generate signal output data based on one or more wavelengths or absorption properties corresponding to a portion of the laser light; and an optical coupling system configured to direct the laser light from the laser emitter into the sample region.

[0006] In some embodiments, the two optical frequency combs include a pump comb and a probe comb, which are combined to focus light onto a sample region. In some embodiments, a portion of the laser is reflected from the sample, and at least one photodetector includes an internal photodetector positioned adjacent to the laser emitter and configured to receive a portion of the laser at near range. In some embodiments, a portion of the laser is transmitted through the sample, and at least one photodetector includes an external photodetector positioned at a distance from the laser emitter and configured to receive a portion of the laser at a distance covering the sample region. In some embodiments, at least one photodetector includes: an internal photodetector positioned adjacent to the laser emitter and configured to detect reflected light from the sample; and an external photodetector positioned at a distance from the laser emitter and configured to detect transmitted light passing through the sample. In some embodiments, a first signal output generated by the external photodetector is combined with a second signal output generated by the internal photodetector. In some implementations, a combination of a first signal output and a second signal output is provided to a phase-locked loop (PLL) control system to generate one or more PLL signals, and the one or more PLL signals are used to generate particulate matter concentration data, which is provided to a monitoring system.

[0007] In some embodiments, the DFCS-enabled hardware is communicatively coupled to a mobile device via a data cable or wireless connection. In some embodiments, the mobile device is configured to measure the concentration of one or more substances in a sample by analyzing signal output data using a machine learning algorithm. In some embodiments, the machine learning algorithm includes at least one of the following: linear regression, logistic regression, decision tree, support vector machine (SVM), Naive Bayes, k-nearest neighbor (KNN), k-means, random forest, recurrent neural network (RNN), convolutional neural network (CNN), transformer, generative adversarial network (GAN), or artificial neural network. In some embodiments, the one or more substances include chemical compounds or gases. In some embodiments, the attachment device includes a strip-shaped shape factor. In some embodiments, the attachment device includes an inline attachment shape factor configured to mate with a data port of the mobile device. In some embodiments, the attachment device includes a skateboard-type shape factor having a mounting device configured to receive and attach to at least a portion of the mobile device. In some embodiments, the sample includes a gas, liquid, or solid.

[0008] The foregoing illustrative description of the invention, as well as other exemplary objects and / or advantages of this disclosure, and the ways in which these objects and / or advantages are achieved, are further explained in the following detailed description and accompanying drawings. Attached Figure Description

[0009] The description of the illustrative embodiments can be read in conjunction with the accompanying drawings. It should be understood that, for the sake of simplicity and clarity, the elements illustrated in the figures are not necessarily drawn to scale unless otherwise described. For example, unless otherwise described, the dimensions of some elements may be exaggerated relative to others. Embodiments incorporating the teachings of this disclosure are shown and described with reference to the accompanying drawings presented herein, in which:

[0010] Figure 1 An example gas detection system according to some embodiments of the present disclosure is described;

[0011] Figure 2A and Figure 2B Example configurations for passive / short-range detection according to some embodiments of this disclosure are depicted;

[0012] Figure 3A and Figure 3B Example configurations for environmental monitoring according to some embodiments of this disclosure are described;

[0013] Figure 4 The shape factor of an example bar shape according to some embodiments of this disclosure is depicted;

[0014] Figure 5 Example inline attachment form factors are depicted according to some embodiments of this disclosure;

[0015] Figure 6 The form factor of an example skateboard-type attachment device according to some embodiments of this disclosure is described; and

[0016] Figure 7 A flowchart illustrating an example process for performing substance detection according to some example embodiments of the present disclosure is depicted. Detailed Implementation

[0017] Some embodiments of this disclosure will be described more fully below with reference to the accompanying drawings, which illustrate some, but not all, embodiments of this disclosure. In fact, this disclosure may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will meet applicable legal requirements. Similar reference numerals always refer to similar elements.

[0018] As used herein, terms such as “front,” “rear,” and “top,” in the examples provided below, are used for illustrative purposes to describe the relative positions of certain parts or portions of parts. Furthermore, as will be apparent to those skilled in the art based on this disclosure, the terms “substantially” and “approximately” indicate that the referenced element or associated description is accurate within applicable engineering tolerances.

[0019] As used herein, the term “comprising” means including but not limited to, and should be interpreted in the manner in which it is typically used in the patent context. The use of broader terms such as “comprising,” “including,” and “having” should be understood to provide support for narrower terms such as “consisting of,” “substantially composed of,” and “substantially constituted by.”

[0020] The phrases “in one embodiment”, “according to one embodiment”, etc., generally mean that the specific feature, structure or characteristic following the phrase may be included in at least one embodiment of this disclosure, and may be included in more than one embodiment of this disclosure (importantly, such phrases do not necessarily refer to the same embodiment).

[0021] As used herein, the terms “example” or “exemplary” mean “serving as an example, instance, or illustration.” Any specific implementation described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other specific implementations.

[0022] If this specification states that a component or feature is "may", "can", "may", "should", "will", "preferably", "possibly", "usually", "optionally", "for example", "often", or "maybe" (or other such language) included or has a characteristic, then the specific component or feature does not need to be included or have that characteristic. Such a component or feature may be optionally included in some embodiments, or it may be excluded.

[0023] As described above, there are many drawbacks associated with conventional spectroscopic equipment. Various exemplary embodiments of this disclosure overcome these challenges and provide various technological advances and improvements. Specifically, various embodiments of this disclosure address the need for portable and accurate real-time molecular analysis. In some embodiments, DFCS-enabled hardware for providing real-time molecular analysis capabilities to mobile devices is disclosed.

[0024] According to various embodiments of this disclosure, a spectroscopic subsystem is provided, including an attachment device for coupling to a mobile device. In some embodiments, the spectroscopic subsystem includes DFCS-enabled hardware that provides DFCS capabilities to the mobile device. In some embodiments, the DFCS-enabled hardware allows an end user to perform DFCS-based molecular analysis directly from the mobile device coupled to the attachment device. In some embodiments, the DFCS capabilities include real-time detection and identification of various substances associated with medical diagnostics, food safety, or environmental monitoring. In some embodiments, the mobile device is configured to use the spectroscopic subsystem to analyze or detect chemical compounds, gases, or other substances in real time.

[0025] In some embodiments, the DFCS enabling hardware includes a DFCS enabling chip and / or circuitry that provides improved resolution and sensitivity spectra in a compact, portable format via a mobile device, without the size limitations of conventional spectrometers. Therefore, the DFCS enabling hardware can be configured with a mobile device to perform on-the-go analysis without costly dedicated equipment and / or training. When used in tandem with other software solutions, DFCS-integrated mobile devices can provide comprehensive data analytics, predictive maintenance, and enhanced safety measures for industrial and commercial applications. Various embodiments of this disclosure utilize the accuracy and wide bandwidth of DFCS to provide precise detection and identification of a wide range of substances. In some embodiments, the DFCS functionality integrated into mobile devices is applicable to various industries, including healthcare, environmental monitoring, and industrial safety.

[0026] Generally speaking, the term mobile device can refer to, for example, one or more mobile phones, tablets, phablets, laptops, laptop computers, etc., and / or any combination of devices or entities adapted to perform the functions, operations, and / or processes described herein.

[0027] Mobile devices may include antennas, transmitters (e.g., radios), receivers (e.g., radios), and processing elements (e.g., complex programmable logic devices (CPLDs), microprocessors, multi-core processors, coprocessor entities, application-specific instruction set processors (ASIPs), microcontrollers, and / or controllers) that correspondingly provide signals to and receive signals from the transmitters and receivers. The signals correspondingly provided to and received from the transmitters and receivers may include signaling information / data according to the air interface standard of the applicable wireless system. In this regard, mobile devices may be able to operate using one or more air interface standards, communication protocols, modulation types, and access types. More specifically, mobile devices may operate according to any of a plurality of wireless communication standards and protocols. In certain implementations, mobile devices can operate according to multiple wireless communication standards and protocols, such as New Radio (NR), 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 Evolution of GSM (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, etc.

[0028] Through these communication standards and protocols, mobile devices can communicate with a variety of other entities using concepts such as Unstructured Supplemental Service Data (USSD), Short Message Service (SMS), Multimedia Messaging Service (MMS), Dual-Tone Multi-Frequency Signaling (DTMF), and / or Subscriber Identity Module Dialer (SIM Dialer). Mobile devices can also download changes, add-ons, and updates, such as to their firmware, software (e.g., including executable instructions, applications, program modules), and operating system.

[0029] According to one implementation, the mobile device may include location determination aspects, devices, modules, functionalities, and / or similar terms used interchangeably herein. For example, the mobile device may include outdoor positioning aspects, such as location modules adapted to acquire, for example, latitude, longitude, altitude, geocoding, flight path, direction, heading, speed, Universal Time (UTC), date, and / or various other information / data. In one implementation, the location module may acquire data by identifying the number of satellites in the field of view and the relative positions of those satellites, sometimes referred to as ephemeris data, using (e.g., using a Global Positioning System (GPS)). The satellites can be a variety of different satellite systems, including Low Earth Orbit (LEO) satellite systems, Department of Defense (DOD) satellite systems, the European Union Galileo positioning system, the Chinese BeiDou Navigation Satellite System, the Indian Regional Navigation Satellite System, etc. Various coordinate systems may be used to collect this data, such as decimal degrees (DD); degrees, minutes, and seconds (DMS); Universal Transverse Mercator (UTM); Universal Polar Spherical (UPS) coordinate system; etc. Alternatively, location information / data can be determined by triangulation of the mobile device's location with various other systems, including cellular towers, Wi-Fi access points, etc. Similarly, mobile devices may include indoor positioning aspects, such as location modules adapted to acquire information / data such as latitude, longitude, altitude, geocoding, flight path, bearing, heading, speed, time, date, and / or various other information / data. Some indoor systems may use various location or location technologies, including radio frequency identification (RFID) tags, indoor beacons or transmitters, Wi-Fi access points, cellular towers, nearby computing devices (e.g., smartphones, laptops), etc. For example, such technologies may include iBeacon, Gimbal proximity beacons, Bluetooth Low Energy (BLE) transmitters, NFC transmitters, etc. These indoor positioning aspects can be used in a variety of settings to determine the location of a person or object, accurate to inches or centimeters.

[0030] Mobile devices may also include a user interface (which may include an output device coupled to the processing element (e.g., a display, speaker, haptic instrument, etc.)) and / or a user input interface (coupled to the processing element). For example, as described herein, a user interface may be a user application, browser, user interface, and / or similar terms used interchangeably herein that perform on and / or are accessible via the mobile device to interact with and / or display information / data. A user input interface may include any of a plurality of input devices (or interfaces) that allow the mobile device to receive codes and / or data, such as a keypad (hard or soft keyboard), a touch display, a voice / speech or motion interface, or other input devices. In some embodiments that include a keypad, the keypad may include (or cause to be displayed) conventional numeric keys (0-9) and associated keys (#, ... Other keys, including a full set of letter keys or a set of keys that can be enabled to provide a full set of alphanumeric keys. In addition to providing input, the user input interface can also be used to activate or deactivate certain functions, such as screen savers and / or sleep modes.

[0031] Mobile devices may also include volatile storage devices or memories and / or non-volatile storage devices or memories, which may be embedded and / or removable. For example, non-volatile memory may be read-only memory (ROM); programmable read-only memory (PROM); erasable programmable read-only memory (EPROM); electrically erasable programmable read-only memory (EEPROM), such as flash memory; etc. Volatile memory may be random access memory (RAM), dynamic random access memory (DRAM), static random access memory (SRAM), synchronous dynamic random access memory (SDRAM), cache memory (including various levels), register memory, etc. Volatile and non-volatile storage devices or memories may store databases, database instances, database management systems, data, applications, programs, program modules, scripts, source code, object code, bytecode, compiled code, interpreted code, machine code, executable instructions, etc., to implement the functions of the mobile device. As indicated, this may include user applications residing on the mobile device or accessible through a browser or other user interface.

[0032] In various implementations, mobile devices may be embodied as artificial intelligence (AI) computing entities. Therefore, mobile devices may be configured to provide and / or receive information / data from a user via input / output mechanisms such as a display, camera, speaker, voice-activated input, etc. In some implementations, the AI ​​computing entity may include one or more predefined and executable program algorithms stored in onboard memory and / or accessible via a network. In various implementations, the AI ​​computing entity may be configured to retrieve and / or execute one or more of these predefined program algorithms upon the occurrence of a predefined triggering event.

[0033] Figure 1 An example gas detection system 100 according to some embodiments of this disclosure is depicted. The gas detection system 100 includes a mobile device 102 coupled to DFCS-enabled hardware 104, which allows the mobile device 102 to perform DFCS-based molecular analysis of a sample region including an exhaust gas sample 110. In some embodiments, the DFCS-enabled hardware 104 includes a spectroscopic subsystem comprising various components, such as a laser emitter / light source and / or an optical coupling system, coupled to the mobile device 102 via attachment devices and / or mounting equipment. The DFCS-enabled hardware 104 may be communicatively coupled to the mobile device 102 via a data cable (e.g., Universal Serial Bus (USB) C) and / or a wireless connection (e.g., Near Field Communication, Bluetooth, etc.).

[0034] The DFCS-enabled hardware 104 may include a laser emitter utilizing two optical frequency combs that emit laser light (e.g., emitted light 108) at precisely, uniformly spaced intervals across a broad spectrum of optical frequencies. For example, a mode-locked laser or an optical parametric oscillator may be used to generate the two optical frequency combs. In some embodiments, the optical frequency combs include a pump comb and a probe comb, which are combined to focus light onto a sample region. For example, the emitted light 108 from the optical frequency combs may interact with the exhaust gas sample 110, resulting in various types of nonlinear interactions, such as four-wave mixing. The exhaust gas sample 110 may include a gas, liquid, and / or solid. After interacting with the exhaust gas sample 110, the transmitted light and / or reflected light (e.g., including at least a portion of the emitted light 108 transmitted through and / or reflected from the exhaust gas sample 110) can be collected and analyzed by an external photodetector 112, for example, using heterodyne detection to measure the phase and amplitude information of the transmitted light, which can be used to characterize the exhaust gas sample 110.

[0035] External photodetector 112 may be configured to receive, detect, and / or interact with a laser emitter at a distance covering a sample area. For example, external photodetector 112 may detect at least a portion of the emitted light 108 after it has passed through the exhaust gas sample 110 (e.g., transmitted light) and / or been reflected from the exhaust gas sample (e.g., reflected light 114). DFCS-enabled hardware 104 may also include an optical coupling system. The optical coupling system may include components such as lenses and optical fibers that guide and / or couple the emitted light 108 from the laser emitter of DFCS-enabled hardware 104 into (e.g., the environment including the exhaust gas sample 110) for desired analysis, thereby ensuring efficient interaction between the emitted light 108 and the exhaust gas sample 110. DFCS-enabled hardware 104 also includes an internal photodetector / reflector 106 configured to receive, detect, and / or interact with a laser emitter at close range, which allows for more accurate tracking and monitoring of the exhaust gas sample 110.

[0036] Figure 2A and Figure 2B Example configurations for passive / short-range detection according to some embodiments of this disclosure are depicted. A mobile device 212 (e.g., mobile device 102) (e.g., via DFCS-enabled hardware 104) is configured with an internal photodetector 202 (e.g., internal photodetector / light reflector 106) and a laser emitter / receiver 204 adjacent to the internal photodetector 202. In doing so, short-range detection of the exhaust gas sample 206 can be provided by the internal photodetector 202 and / or the laser emitter / receiver 204, which detects and / or captures emitted light from the laser emitter / receiver 204, which is reflected from the exhaust gas sample 206 to a location adjacent to the laser emitter / receiver 204.

[0037] In some embodiments, the camera and / or photodetector of the laser emitter / receiver 204 are additionally and / or alternatively (e.g., separate from the internal photodetector 202) configured to measure the scattering / turbidity of the exhaust gas sample 206. In some embodiments, the camera and / or photodetector of the laser emitter / receiver 204 may directly measure at least a portion of the emitted light from the laser emitter of the laser emitter 204 that does not penetrate the exhaust gas sample 206 to aid signal processing (e.g., for signal normalization).

[0038] The internal photodetector 202 and / or laser emitter / receiver 204 can generate and / or provide a signal output representing one or more wavelengths and / or absorption properties measured from the detected and / or captured emitted light. This signal output can be used by a phase-locked loop (PLL) control system to generate and / or provide a PLL signal 208, which can then be used to generate particulate matter concentration data 210 and / or provide this particulate matter concentration data to a monitoring system. In some embodiments, the signal output generated and / or provided by the internal photodetector 202 and / or laser emitter / receiver 204 can be provided to and processed by a machine learning algorithm to measure the concentrations of multiple substances in the exhaust gas sample 206. Various embodiments of this disclosure enable artificial intelligence and / or machine learning algorithms for the analysis of exhaust gas sample 206 (e.g., via signal output generated and / or provided by internal photodetector 202 and / or laser emitter / receiver 204), including but not limited to linear regression, logistic regression, decision tree, support vector machine (SVM), naive Bayes, k-nearest neighbor (KNN), k-means, random forest, recurrent neural network (RNN), convolutional neural network (CNN), transformers, generative adversarial networks (GAN), artificial neural networks, etc., to generate predictive models.

[0039] Figure 3A and 3B Example configurations for environmental detection according to some embodiments of this disclosure are depicted. A mobile device 312 (e.g., mobile device 102) (e.g., via DFCS-enabled hardware 104) is configured with a laser emitter / receiver 304 positioned at a distance from an external photodetector 302 (e.g., external photodetector 112). In doing so, environmental detection of the exhaust gas sample 306 can be provided by the external photodetector 302 and / or the laser emitter / receiver 304, which detects and / or captures emitted light from the laser emitter / receiver 304 that passes through the exhaust gas sample 306 toward the external photodetector 302. Similar to laser emitter / receiver 204, the camera and / or photodetector of laser emitter / receiver 304 are additionally and / or alternatively (e.g., separate from external photodetector 302) configured to measure the scattering / turbidity of exhaust gas sample 206 and / or the emitted light from the laser emitter of laser emitter 304 that does not pass through at least a portion of exhaust gas sample 306.

[0040] External photodetector 302 can generate a signal output representing the wavelength and absorption performance measured from the detected and / or captured emitted light. The signal output generated by external photodetector 302 can be combined with a signal output generated by laser emitter / receiver 304 and / or internal photodetector (e.g., internal photodetector 202), wherein the signal output generated by the internal photodetector represents one or more wavelengths and / or absorption performance of the light detected by the internal photodetector (e.g., adjacent to the laser emitter / receiver). The combination of signal outputs generated by external photodetector 302, laser emitter / receiver 304, and / or internal photodetector can be provided to and used by a PLL control system to generate and / or provide a PLL signal 308, which can then be used to generate particulate matter concentration data 310 and / or provide the particulate matter concentration data to a monitoring system.

[0041] In some embodiments, the signal output generated and / or provided by the external photodetector 302, laser emitter / receiver 304, and / or internal photodetector can be provided to and processed by a machine learning algorithm to measure the concentration of multiple substances in the exhaust gas sample 306. Various embodiments of this disclosure enable artificial intelligence and / or machine learning algorithms for the analysis of the exhaust gas sample 306 (e.g., via the signal output generated and / or provided by the external photodetector 302, laser emitter / receiver 304, and / or internal photodetector), including but not limited to linear regression, logistic regression, decision tree, SVM, Naive Bayes, KNN, k-means, random forest, RNN, CNN, transformers, GAN, artificial neural networks, etc., to generate predictive models.

[0042] According to various embodiments of this disclosure, the DFCS enabling hardware (e.g., DFCS enabling hardware 104) includes Figure 4 , Figure 5 or Figure 6 Any of the shape factors described in the figure.

[0043] Figure 4 A shape factor 400 for an example bar shape according to some embodiments of this disclosure is depicted. The shape factor 400 of the bar shape includes DFCS-enabled hardware 404 communicatively coupled to a mobile device 402 via a data cable 406.

[0044] Figure 5An example inline attachment form factor 500 according to some embodiments of the present disclosure is depicted. The inline attachment form factor 500 includes DFCS enabling hardware 504, which is inline attached and / or mated to the data port of a mobile device 502 via a data connector 506 to establish a wired connection.

[0045] Figure 6 An example skateboard-type attachment form factor 600 according to some embodiments of the present disclosure is depicted. The skateboard-type attachment form factor 600 includes DFCS-enabled hardware 604 attached to a mounting device 606. The mounting device 606 is configured to receive and attach to at least a portion of a mobile device 602 such that the DFCS-enabled hardware 604 is securely attached to a surface (e.g., the rear) of the mobile device 602. The DFCS-enabled hardware 604 can be communicatively coupled to a data port of the mobile device 602 to establish a wired connection, or communicatively coupled to the mobile device 602 via a wireless connection.

[0046] Figure 7 A flowchart illustrating an example process 700 for performing substance detection according to some example embodiments of the present disclosure is depicted. It should be noted that each block of the flowchart, and combinations of blocks in the flowchart, can be implemented by various components such as hardware, firmware, circuitry, and / or other devices associated with the execution of software including one or more computer program instructions. For example, Figure 7 One or more steps / operations described herein may be embodied by computer program instructions, which may be stored in the non-transitory memory of an apparatus employing embodiments of the present disclosure and executed by the processor component of the apparatus. For example, these computer program instructions may instruct the processor component to function in a particular manner, such that the instructions stored in the computer-readable storage memory produce an article of art whose execution implements the function specified in the flowchart block.

[0047] In some embodiments, process 700 begins at step / operation 702, at which point laser light is generated. In some embodiments, the laser light comprises a spectrum of optical frequencies. For example, the spectrum of optical frequencies may comprise a pattern of uniformly spaced spectral lines. The laser light may be emitted in a direction toward the exhaust gas sample and may interact with the exhaust gas sample and / or be reflected from one or more light reflectors.

[0048] In some embodiments, after step / operation 702, the example process proceeds to step / operation 704, where signal output data is received from one or more photodetectors. One or more photodetectors (e.g., internal and / or external photodetectors) can detect light from a laser and generate signal output data based on one or more wavelengths and / or absorption properties measured from at least a portion of the laser detected by the one or more photodetectors (e.g., light transmitted through and / or reflected from the exhaust gas sample).

[0049] In some implementations, after step / operation 704, the example process proceeds to step / operation 706, where the presence or concentration of one or more substances is determined based on the signal output data. The signal output data can be continuously monitored and collected to provide real-time information about the presence or concentration of various substances in the exhaust gas sample. The collected data can be analyzed (e.g., via machine learning algorithms) to identify any hazardous substances and to determine appropriate actions (e.g., alarms, messages, system control actions) based on the analysis. For example, the wavelength and / or absorption properties corresponding to the output signal can be compared with a reference spectrum, and based on the comparison, the concentration of one or more substances in the exhaust gas sample can be determined accurately in real time.

[0050] It should be understood that this disclosure is not limited to the specific embodiments disclosed, and 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 in a general and descriptive sense only, and not for purposes of limitation, unless otherwise described.

Claims

1. A system comprising: An attachment device configured to couple with a mobile device; and Dual-frequency comb spectroscopy (DFCS) enabling hardware, configured to enable the mobile device to perform DFCS-based molecular analysis including a sample region of the sample, wherein the DFCS enabling hardware includes: A laser emitter configured to generate two optical frequency combs that emit laser light at uniformly spaced intervals across a broad spectrum of optical frequencies, wherein: (i) The laser is guided toward the sample region, and (ii) At least a portion of the laser is received by at least one photodetector, the at least one photodetector being configured to: (a) Receiving at least a portion of the laser light after interacting with the sample, and (b) Generating signal output data based on one or more wavelengths or absorption properties corresponding to said portion of the laser; and An optical coupling system configured to direct the laser from the laser emitter into the sample region.

2. The system according to claim 1, wherein: (i) The portion of the laser is reflected from the sample, and (ii) The at least one photodetector includes an internal photodetector positioned adjacent to the laser emitter and configured to receive a portion of the laser at near range.

3. The system according to claim 1, wherein: (i) The portion of the laser light is transmitted through the sample, and (ii) The at least one photodetector includes an external photodetector positioned at a distance from the laser emitter and configured to receive a portion of the laser over a distance covering the sample area.

4. The system according to claim 1, wherein the at least one photodetector comprises: An internal photodetector is positioned adjacent to the laser emitter and configured to detect reflected light from the sample; and An external photodetector is positioned at a distance from the laser emitter and configured to detect transmitted light passing through the sample.

5. The system of claim 4, wherein the first signal output generated by the external photodetector is combined with the second signal output generated by the internal photodetector.

6. The system according to claim 5, wherein: (i) The combination of the first signal output and the second signal output is provided to the phase-locked loop (PLL) control system to generate one or more PLL signals, and (ii) The one or more PLL signals are used to generate particulate matter concentration data, which is provided to the monitoring system.

7. The system of claim 1, wherein the mobile device is configured to measure the concentration of one or more substances in the sample by analyzing the signal output data using a machine learning algorithm.

8. The system according to claim 7, wherein the machine learning algorithm includes at least one of the following: linear regression algorithm, logistic regression algorithm, decision tree algorithm, support vector machine (SVM) algorithm, naive Bayes algorithm, k nearest neighbor (KNN) algorithm, k-means algorithm, random forest algorithm, recurrent neural network (RNN), convolutional neural network (CNN), transformer, generative adversarial network (GAN) or artificial neural network.

9. The system of claim 7, wherein the one or more substances comprise chemical compounds or gases.

10. The system of claim 1, wherein the attachment device comprises a strip-shaped shape factor, an inline attachment shape factor, or a skateboard-shaped shape factor, the inline attachment shape factor being configured to mate with a data port of the mobile device, the skateboard-shaped shape factor having a mounting device configured to receive and attach to at least a portion of the mobile device.

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