High-throughput microfluidic detection system for aerosol dynamic phase transition process

Abstract

The present application relates to the technical field of aerosol phase change detection, and particularly relates to a high-throughput microfluidic detection system for dynamic phase change process of aerosol, which aims at the technical problems that single-channel operation in the prior art leads to low detection efficiency, cannot synchronously compare multiple environmental conditions, and is difficult to track transient phase change process in real time, designs a multi-channel independent regulation and control unit, couples fast temperature switching and humidity simulation functions, and realizes dynamic phase change simulation of aerosol under different environmental conditions based on optical detection technology. Through the multi-channel independent regulation and control combined with the optical detection technology, the present application breaks through the limitation of traditional single-dimensional detection, and compared with the prior art (such as a single temperature control module and offline spectrum analysis), realizes high-throughput dynamic analysis of the whole cycle of aerosol phase change.

Description

Technical Field

[0001] This invention relates to the field of aerosol phase change detection technology, and more specifically, to a high-throughput microfluidic detection system for dynamic phase change processes of aerosols. Background Technology

[0002] Dynamic monitoring of aerosol phase transition processes is of great significance to environmental science, climate change research, and air pollution control. The phase transition characteristics directly affect the optical properties, chemical activity, and environmental effects of aerosols. Accurately capturing the dynamic phase transition characteristics of aerosols under different environmental conditions is of great scientific importance for understanding atmospheric chemical processes and optimizing pollution control strategies.

[0003] Existing technologies for aerosol phase change detection have significant limitations: Firstly, traditional detection systems often employ single-channel experimental designs, simulating only a single environmental condition in a single experiment. To compare the effects of different temperature and humidity parameters, multiple repeated experiments are required, leading to low detection efficiency and the introduction of errors due to batch variations. Secondly, their environmental control capabilities are insufficient, making it difficult to accurately reproduce complex and dynamic real-world environmental scenarios (such as diurnal temperature fluctuations and sudden humidity changes). Furthermore, they lack high-resolution monitoring capabilities for critical phase change zones (such as specific temperature and humidity ranges), resulting in the easy omission of transient phase change details. Simultaneously, some detection methods rely on contact sampling or offline spectral analysis, failing to achieve real-time tracking and in-situ analysis of the phase change process, thus hindering in-depth research into phase change mechanisms.

[0004] To this end, this invention proposes a high-throughput microfluidic detection system for aerosol dynamic phase change processes. By coupling multi-channel independent control with optical detection technology, efficient and accurate monitoring of aerosol phase change processes can be achieved. Summary of the Invention

[0005] To address the shortcomings of existing technologies, the present invention aims to provide a high-throughput microfluidic detection system for aerosol dynamic phase change processes.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A high-throughput microfluidic detection system for aerosol dynamic phase change processes includes a microfluidic chip module, a dynamic environment control module, an optical detection module, and a data acquisition and analysis module.

[0008] Microfluidic chip module: The microfluidic chip module contains 48 independent channels, which are divided into three categories: typical environment channels, extended environment channels, and comparative verification channels;

[0009] Dynamic environment control module: It adopts a distributed architecture of "main controller + 32 intelligent sub-controllers", including dynamic temperature control unit and dynamic humidity control unit, which are used to control the environmental parameters of the channel in real time.

[0010] Optical detection module: Includes a dual-wavelength dynamic scattering detection unit, which measures the refractive index of aerosols and performs phase inversion through dual-wavelength laser scattering;

[0011] Data acquisition and analysis module: High-speed data acquisition card synchronously acquires 48 channels of environmental parameters and optical signals; Phase transition vector comparison unit supports quantitative comparison of phase transition characteristic parameters under typical and extended environments.

[0012] Furthermore, the specific implementation methods for dynamic environmental control are as follows:

[0013] S21. Collect basic parameter sequences for typical scenarios;

[0014] S22. Based on typical environmental parameters, extend the parameters to form an extended parameter sequence;

[0015] S23. Assign parameter sequences to different channel types.

[0016] Furthermore, parameter extension is performed based on typical environmental parameters, using the following specific methods:

[0017] Scene feature enhancement and extension: Enhancement processing is performed on the core environmental features of typical scenes to obtain the environmental parameter curves for feature enhancement;

[0018] Critical Interval Encryption Extension: Focusing on sensitive intervals with high phase transition rates in typical scenarios, this extension increases the temporal resolution of the interval and generates high-density sampled environmental parameter curves.

[0019] Dynamic pattern variation extension: Keeping the average temperature and humidity and fluctuation range of typical scenarios unchanged, changing the dynamic change pattern, and obtaining the environmental parameter curve of pattern variation;

[0020] Cross-scene feature fusion extension: Extract the core environmental features of different typical scenarios and cross-fuse them to obtain the environmental parameter curve of feature fusion.

[0021] Furthermore, the refractive index of the aerosol was measured and phase inversion was performed using dual-wavelength laser scattering. The specific steps are as follows:

[0022] S31. Calibrate and initialize the laser optical path;

[0023] S32. A multi-angle detector array is used to receive laser scattered light of two wavelengths;

[0024] S33. Calculate the complex refractive index of aerosol particles based on Mie scattering theory;

[0025] S34. Based on the complex refractive index, invert the aerosol phase state.

[0026] Furthermore, the complex refractive index of aerosol particles is measured, specifically including:

[0027] After receiving multi-angle scattered light intensity data, the data acquisition and analysis module establishes a measurement model based on Mie scattering theory: given the incident laser wavelength and particle size parameters obtained through previous detection, the least squares method is used to fit the measured scattered light intensity distribution with the theoretically calculated scattered light intensity distribution, and the complex refractive index of the aerosol particles, including the real part, is obtained by inversion. and the virtual part More specifically, the theoretical scattered light intensity distribution is first calculated using the initially assumed refractive index value, and then compared with the measured value. The refractive index value is continuously adjusted through iterative optimization until the deviation between the theoretical distribution and the measured distribution is less than a preset threshold. The refractive index value obtained at this time is the measurement result.

[0028] Furthermore, based on the aforementioned complex refractive index, the aerosol phase state is inverted, specifically including:

[0029] First, we analyze the ratio of the real parts of the refractive index at 632 nm and 450 nm. Secondly, observe the imaginary part of the refractive index. The changing trend can be used as an auxiliary basis for judging phase transition.

[0030] Furthermore, specific methods for quantitatively comparing phase transition characteristic parameters under typical and extended environments include:

[0031] Based on the phase inversion results of the optical detection module, the phase transition process is divided into three stages: the pre-phase transition stage, the mid-phase transition stage, and the post-phase transition stage.

[0032] For the three-stage data of the three types of channels, a core vector is defined, covering environmental parameters and optical detection characteristics, including: the core vector before phase transition. Core vector in phase transition Core vector after phase transition ,in, This represents the average temperature and humidity. This represents the standard deviation of temperature and humidity fluctuations. This represents the average refractive index before / after the phase transition; Indicates the rate of change of the parameter; Indicates the duration of the phase transition; This represents the average temperature and humidity after the phase change. This represents the standard deviation of the refractive index fluctuation after the phase transition.

[0033] Using the core vector of a typical channel as a benchmark, the influence of extended features on phase transition is located by the vector difference between the extended channel and the typical channel; the environmental basis difference is quantified by the Euclidean distance between vectors before and after phase transition; and the Manhattan distance of the rate of change vector during phase transition is used to... Poor correlation analysis was used to analyze the relationship between enhancement features and phase transition rate.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] 1. High-throughput multi-channel parallel detection improves efficiency and comparison accuracy: The system integrates 48 independent channels, divided into typical environment channels (reproducing 8 types of real-world scene parameters), extended environment channels (enhancing / mutating environmental features), and comparison verification channels (isolating single variables). A "main controller + 32-channel intelligent sub-controller" architecture enables independent adjustment of multi-channel environmental parameters. Compared to traditional single-channel modes, it eliminates the need for repeated experiments to simultaneously compare phase transition differences under multiple environmental conditions, significantly improving detection efficiency while eliminating batch errors and achieving precise quantitative comparison of phase transition characteristics.

[0036] 2. Optical Real-Time Detection Technology Enables Non-Contact Precise Phase Transition Identification: Employing a dual-wavelength (632nm and 450nm) laser scattering detection unit, the complex refractive index of aerosols is inverted based on Mie scattering theory. Precise phase inversion is achieved through the changing trends of the real-to-imaginary part ratio of the refractive index (solid > 1.04, liquid < 1.03). Combined with a high-speed data acquisition card, real-time tracking of the entire phase transition cycle is realized. This non-contact detection method avoids sample interference and solves the problem of traditional offline analysis failing to capture transient phase transitions, providing high-fidelity data support for the analysis of phase transition mechanisms. Attached Figure Description

[0037] Figure 1 This is a block diagram of a high-throughput microfluidic detection system for dynamic phase change processes in aerosols.

[0038] Figure 2 This is a flowchart illustrating the measurement of aerosol refractive index and phase inversion according to the present invention. Detailed Implementation

[0039] Example, refer to Figure 1 The high-throughput microfluidic detection system for aerosol dynamic phase change processes in this embodiment includes:

[0040] Microfluidic chip module: The microfluidic chip module contains 48 independent channels, all of which have independent dynamic environmental parameter adjustment capabilities. It adopts a 4×12 matrix layout structure, with a chip size of 100mm×150mm and a channel spacing of 1mm. Each channel group consists of a sample injection area, a dynamic control chamber, and an optical detection window.

[0041] The 48 independent channels are divided into three categories: typical environment channels, extended environment channels, and comparative verification channels. There are 8 typical environment channels, which reproduce the historical environmental parameter sequences of 8 typical scenarios: polar, temperate, tropical, urban, industrial, plateau, marine, and desert. There are 32 extended environment channels, which generate extended parameter sequences based on typical environmental parameters through scene feature enhancement, critical interval encryption, dynamic mode variation, and cross-scenario fusion algorithms. There are 8 comparative verification channels, which set a gradient for the change of a single parameter to isolate the influence of a single variable on the phase transition.

[0042] Dynamic environmental control module: Adopting a distributed architecture of "main controller + 32-channel intelligent sub-controller", including dynamic temperature control unit and dynamic humidity control unit; the dynamic temperature control unit is equipped with a micro semiconductor cooling / heating integrated module for each channel, and achieves dynamic curve tracking through adaptive algorithms (such as PID control), with a temperature change rate adjustment range of 0.5℃ / s-25℃ / s; the dynamic humidity control unit adopts 32 independent gas mixing units, and adjusts the ratio of dry gas to saturated humidity gas through high-speed proportional valves, with a change rate adjustment range of 0.1%RH / s-10%RH / s;

[0043] The specific implementation methods for dynamic environmental control are as follows:

[0044] S21. Collection of Typical Environmental Parameters: Historical monitoring data from meteorological stations in eight typical global scenarios (polar, temperate, tropical, urban, industrial, plateau, marine, and desert) were collected, covering a time span of nearly 10 years. Data types included minute-by-minute temperature (-40℃-45℃) and relative humidity (10%-95%). The raw data was cleaned to remove outliers (such as values ​​exceeding the physical reasonable range), and short-term noise was smoothed using a moving average method. Finally, eight sets of basic parameter sequences for typical scenarios were formed. Each set of sequences included temperature-time curves and humidity-time curves, with a duration of 24 hours.

[0045] S22. Environmental Parameter Extension Generation Strategy: Based on 8 typical environmental parameters, 4 sets of extended parameter sequences are generated using 4 types of algorithms, resulting in a total of 32 sets of extended parameter sequences, which are then assigned to 32 extended environmental channels.

[0046] Scene feature enhancement and expansion: The core environmental features of typical scenes are enhanced, such as lower humidity values ​​in desert scenes, higher base temperatures in industrial scenes, higher base humidity in marine scenes, and lower base temperatures in polar scenes; Function: By amplifying the core environmental features of typical scenes, the influence of these features on aerosol phase change can be verified in an extreme manner.

[0047] Critical Interval Encryption Extension: Focusing on sensitive intervals where phase transitions are frequent in typical scenarios, such as urban scenarios where humidity is 60%-80% and deliquescence is frequent, the temporal resolution of this interval is increased. times (in this embodiment) This generates a high-density sampled humidity fluctuation curve. In temperate scenarios, the temperature range of 0℃-10℃ is a sensitive zone for freezing / thawing. The change process in this temperature range is sampled more densely to accurately capture the impact of temperature fluctuations on phase transitions with finer precision (0.1℃ in this embodiment). Function: Improves parameter control precision and data sampling density in sensitive zones where phase transitions are frequent, and can capture transient phase transition details that are easily ignored under conventional resolution.

[0048] Dynamic mode variation extension: Keeping the average temperature and humidity and fluctuation range of typical scenarios unchanged, the dynamic change mode is changed. The available modes include step change, periodic fluctuation, random fluctuation, pulse change, acceleration mode, deceleration mode, etc. Function: By changing the dynamic change mode of environmental parameters, the influence of dynamic characteristics such as change rate and fluctuation frequency on phase transition can be independently verified.

[0049] Cross-scene feature fusion extension: Extract core environmental features from different typical scenarios and cross-fuse them, such as fusing the low temperature features of a plateau scenario with the high humidity features of a marine scenario to generate a "low temperature and high humidity" coupling curve; fusing the rapid temperature change features of an industrial scenario with the humidity fluctuation features of an urban scenario to generate a "rapid temperature change + humidity fluctuation" curve; Function: Simulate complex composite environmental conditions in real environments through cross-scene feature fusion.

[0050] S23, Multi-channel parameter allocation logic:

[0051] Typical environmental channels: The 24-hour dynamic temperature and humidity curves of 8 typical scenarios are strictly reproduced respectively;

[0052] Extended environment channel: includes 4 sets of scene feature enhancement extensions, 4 sets of critical interval encryption extensions, 4 sets of dynamic mode mutation extensions, and 4 sets of cross-scene feature fusion extension parameters. Each set of extension parameters corresponds to the feature extension of a typical scene.

[0053] Comparative experimental channels: Four groups each were set up for temperature as a single variable (typical scene temperature mean ± 10℃ gradient, humidity fixed value as typical scene mean) and humidity as a single variable (typical scene humidity mean ± 20% gradient, temperature fixed value as typical scene mean).

[0054] Optical detection module: Includes a dual-wavelength dynamic scattering detection unit, using 632nm and 450nm lasers, which are split into 48 sub-beams by a microlens array and focused on each channel detection window;

[0055] like Figure 2 As shown, the specific steps for measuring the refractive index and phase inversion of aerosols using dual-wavelength laser scattering are as follows:

[0056] S31. Laser Optical Path Calibration and Initialization: First, the obtained particle size parameters are detected. Second, in the dual-wavelength dynamic scattering detection unit, the 632nm and 450nm lasers are expanded and collimated, then split into 48 sub-beams by a microlens array. Each sub-beam is perpendicularly incident on the detection window of the corresponding channel through a focusing lens. After the system starts, optical path calibration is performed first. By adjusting the angle of the reflector, the two laser beams are focused at the same point at the detection window, and the spot diameter is controlled within 50μm to ensure the accuracy of scattering signal acquisition.

[0057] S32. Multi-angle scattered light signal acquisition: When aerosol particles enter the detection area, two laser beams of different wavelengths irradiate the particles, causing scattering. The scattered light is received by a multi-angle detector array consisting of eight detection nodes set within a range of 15°-150°. Each node in the detector array is equipped with dedicated filters for 632nm and 450nm wavelengths, enabling simultaneous separate acquisition of the two wavelengths of scattered light.

[0058] S33. Aerosol Refractive Index Inversion Calculation: After receiving multi-angle scattered light intensity data, the data acquisition and analysis module establishes a calculation model based on Mie scattering theory. Mie scattering theory describes the interaction between electromagnetic waves and spherical particles, and the scattered light intensity distribution is closely related to the particle size, refractive index, and incident light wavelength. Given the incident laser wavelengths (632nm and 450nm) and the particle size parameters obtained through previous detection, the least squares method is used to fit the measured scattered light intensity distribution with the theoretically calculated scattered light intensity distribution, thus inverting the complex refractive index of the aerosol particles, including the real part. (Reflecting the change in the speed of light propagation within the particle) and the imaginary part (Reflects the light absorption characteristics of particles). In the specific calculation process, the theoretical scattered light intensity distribution is first calculated using the initially assumed refractive index value, and then compared with the measured value. The refractive index value is continuously adjusted through iterative optimization until the deviation between the theoretical distribution and the measured distribution is less than a preset threshold (in this embodiment, the threshold is a deviation ≤3%). The refractive index value obtained at this time is the measurement result.

[0059] S34. Aerosol Phase Inversion Based on Refractive Index: Based on the inverted dual-wavelength refractive index parameters, the phase state of the aerosol is further inverted using Mie scattering theory. First, the ratio of the real parts of the refractive index at 632 nm and 450 nm is analyzed. Because solid aerosols have an ordered molecular arrangement, this ratio is usually... Liquid aerosol molecules are arranged randomly, and their ratios are generally... The ratio of mixed aerosols is in Range fluctuations. Secondly, observe the imaginary part of the refractive index. The changing trend, for example, when organic aerosols change from solid to liquid, their absorption of 450nm blue light increases, and the imaginary part... The refractive index will increase significantly, and this characteristic can be used as an auxiliary criterion for judging phase transitions. Finally, the phase results obtained from refractive index inversion are cross-validated with Raman spectral characteristics. When the deviation exceeds 5%, the parameters in the refractive index calculation model are automatically corrected to ensure that the phase inversion accuracy is ≥95%.

[0060] Data acquisition and analysis module: includes a high-speed data acquisition card and a phase transition vector comparison unit; the phase transition vector comparison unit supports quantitative comparison of phase transition characteristic parameters (start time, critical parameters, duration, etc.) under typical and extended environments;

[0061] High-speed data acquisition card: A high-speed data acquisition card with a total sampling rate of ≥32MHz, which can simultaneously acquire 48 sets of environmental parameters (temperature, humidity) and optical signals (dual-wavelength scattered light intensity, Raman spectrum).

[0062] Phase transition vector comparison unit: Dual-wavelength dynamic scattering detection unit acquires refractive index data from 48 channels in real time ( and This process simultaneously records the temperature and humidity parameters output by the dynamic environmental control module, with timestamps accurate to 1ms. It enables quantitative comparison of phase transition processes across three channels, accurately uncovering the intrinsic influence of environmental parameters such as temperature and humidity on core characteristics like phase transition initiation time, duration, and phase stability. The specific process includes:

[0063] Based on the phase inversion results from the optical detection module, the phase transition process is divided into three stages:

[0064] Pre-phase transition stage: Focusing on the environmental pre-evolution characteristics before phase transition triggering ( (The phase transition initiation time is determined by the first time the ratio of the real parts of the refractive index exceeds the threshold).

[0065] Phase transition stages: Capturing the transient process of phase transition ( The stable point where the real part of the refractive index fluctuates ≤ ±0.001, i.e., the phase transition termination time.

[0066] Post-phase transition stage: Analyze the steady-state characteristics after the phase transition is completed;

[0067] For the three-stage data of the three types of channels, as shown in Table 1, a core vector is defined, covering environmental parameters and optical detection characteristics (where the mean and standard deviation are calculated indicators from multiple experiments):

[0068]

[0069] Table 1

[0070] Three types of channel comparison methods:

[0071] a. Typical Environmental Channel vs. Extended Environmental Channel: Using the typical channel as a benchmark, the influence of extended features on phase transition is located through the vector difference between the extended and typical channels; the difference in environmental basis is quantified by the Euclidean distance of vectors before / after phase transition; and the Manhattan distance of the rate of change vector during phase transition is used to... Poor correlation analysis was used to analyze the relationship between enhancement characteristics and phase transition rate;

[0072] b. Typical Environment Channel vs. Comparative Validation Channel

[0073] By utilizing the univariate gradient of the contrast channel, the independent effects of temperature and humidity on the phase transition process are separated using the controlled variable method. The temperature and humidity sensitivity coefficients are calculated to quantify the strength of the influence of a single variable on the phase transition characteristics: taking pre-phase transition analysis as an example:

[0074] Temperature sensitivity coefficient: fixed hour, Change and the mean temperature difference before phase transition The ratio;

[0075] ;

[0076] Humidity sensitivity coefficient: fixed hour, Changes in mean humidity before phase transition The ratio;

[0077] ;

[0078] Through the detailed description of the above embodiments, the high-throughput microfluidic detection system for aerosol dynamic phase change processes of the present invention, by designing multi-channel independent control units and coupling rapid temperature switching and humidity simulation functions, and based on optical detection technology, realizes the simulation and detection of dynamic phase change of aerosols under different environmental conditions. By comparing and analyzing the detection results, the core influencing parameters of typical scenarios can be identified; and the impact of environmental parameters on the phase change progress and duration in different scenarios can be quantified.

[0079] The above formulas are all dimensionless calculations, and the preset parameters in the formulas should be set by those skilled in the art according to the actual situation.

[0080] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0081] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0082] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0083] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0084] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0085] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0086] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A high-throughput microfluidic detection system for aerosol dynamic phase change processes, characterized in that, include: The microfluidic chip module contains 48 channels, which are divided into three categories: typical environment channels, extended environment channels, and comparative verification channels. The dynamic environment control module includes a main controller and an intelligent distributed control architecture, including a dynamic temperature control unit and a dynamic humidity control unit, which are used to control the environmental parameters of the channel in real time. The optical detection module measures the refractive index of aerosols and performs phase inversion using dual-wavelength laser scattering; The data acquisition and analysis module includes a high-speed data acquisition card and a phase transition vector comparison unit. The high-speed data acquisition card is used to simultaneously acquire environmental parameters and optical signals from multiple channels; the phase transition vector comparison unit is used for quantitative comparison of phase transition characteristic parameters under typical and extended environments.

2. The high-throughput microfluidic detection system for aerosol dynamic phase change processes according to claim 1, characterized in that, The specific steps for implementing dynamic environmental regulation include: S21. Collect basic parameter sequences for typical scenarios; S22. Based on typical environmental parameters, extend the parameters to form an extended parameter sequence; S23. Assign parameter sequences to different channel types.

3. The high-throughput microfluidic detection system for aerosol dynamic phase change processes according to claim 2, characterized in that, The process of forming the extended parameter sequence includes: Scene feature enhancement and extension: Enhancement processing is performed on the core environmental features of typical scenes to obtain the environmental parameter curves for feature enhancement; Critical Interval Encryption Extension: Focusing on sensitive intervals with high phase transition rates in typical scenarios, this feature improves the temporal resolution of these intervals and generates high-density sampled environmental parameter curves. Dynamic pattern variation extension: Keeping the average temperature and humidity and fluctuation range of typical scenarios unchanged, changing the dynamic change pattern, and obtaining the environmental parameter curve of pattern variation; Cross-scene feature fusion extension: Extract the core environmental features of different typical scenarios and cross-fuse them to obtain the environmental parameter curve of feature fusion.

4. The high-throughput microfluidic detection system for aerosol dynamic phase change processes according to claim 1, characterized in that, Measuring the refractive index of aerosol particles specifically includes: After receiving multi-angle scattered light intensity data, the data acquisition and analysis module establishes a measurement model based on Mie scattering theory: given the incident laser wavelength and particle size parameters obtained through previous detection, the least squares method is used to fit the measured scattered light intensity distribution with the theoretically calculated scattered light intensity distribution, and the complex refractive index of the aerosol particles, including the real part, is obtained by inversion. and the virtual part More specifically, the theoretical scattered light intensity distribution is first calculated using the initially assumed refractive index value, and then compared with the measured value. The refractive index value is continuously adjusted through iterative optimization until the deviation between the theoretical distribution and the measured distribution is less than a preset threshold. The refractive index value obtained at this time is the measurement result.

5. The high-throughput microfluidic detection system for aerosol dynamic phase change processes according to claim 4, characterized in that, Based on the aforementioned complex refractive index, the aerosol phase state is inverted, specifically including: First, we analyze the ratio of the real parts of the refractive index at 632 nm and 450 nm. ,in This represents the real part of the refractive index corresponding to a 632nm laser. The first part represents the real part of the refractive index corresponding to 450nm laser; the second part observes the changing trend of the imaginary part of the refractive index as an auxiliary basis for judging the phase transition.

6. The high-throughput microfluidic detection system for aerosol dynamic phase change processes according to claim 1, characterized in that, Specific methods for quantitatively comparing phase transition characteristic parameters under typical and extended environments include: Based on the phase inversion results of the optical detection module, the phase transition process is divided into three stages: the pre-phase transition stage, the mid-phase transition stage, and the post-phase transition stage. For the three-stage data of the three types of channels, a core vector is defined, covering environmental parameters and optical detection features; Using the core vector of a typical channel as a benchmark, the influence of extended features on phase transition is located by the vector difference between the extended channel and the typical channel; the environmental basis difference is quantified by the Euclidean distance between vectors before and after phase transition; and the Manhattan distance of the rate of change vector during phase transition is used to... Poor correlation analysis was used to analyze the relationship between enhancement features and phase transition rate.

7. The high-throughput microfluidic detection system for aerosol dynamic phase change processes according to claim 6, characterized in that, The core vector definition method is as follows: Core vector before phase transition Core vector in phase transition Core vector after phase transition ,in, and These represent the average values ​​of temperature and humidity, respectively. and These represent the standard deviations of temperature and humidity fluctuations, respectively. and These represent the average refractive index before and after the phase transition, respectively. Indicates the rate of change of the parameter; Indicates the duration of the phase transition; and These represent the average values ​​of temperature and humidity after the phase transition, respectively. It represents the standard deviation of the refractive index fluctuation after the phase transition.

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