CN117517299A - H 2 S colorimetric/electrical sensor and deep learning colorimetric/electrical dual-sensing system - Google Patents
H 2 S colorimetric/electrical sensor and deep learning colorimetric/electrical dual-sensing system Download PDFInfo
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Abstract
The invention relates to a H 2 S colorimetric/electrical sensor and deep learning colorimetric/electrical dual-sensing system adopting H 2 Synthesis of H by combining S-sensitive nanofiber membrane with PET flexible interdigital electrode 2 The S colorimetric/electrical sensor can have two response characteristics of colorimetric and resistance change. The deep learning colorimetric/electrical dual-sensing system which visually displays visual response and simultaneously records resistance response is obtained by combining the color-changing monitoring platform and the resistance response module, the effects of 'instant shooting and instant measuring', rapid and accurate detection of gas concentration are achieved, the detection advantages of strong applicability, wide application range, visualization and high accuracy can be realized through a dual-sensing response complementary mechanism, and the system can be used for detecting the concentration of gasCan realize a large range of concentration H 2 S detection has high resolution, short response time, high selectivity, low detection limit of 0.1ppm, good stability and concentration detection accuracy up to 99.8%.
Description
Technical Field
The invention belongs to the technical field of gas sensors, and particularly relates to an H-type sensor 2 S colorimetric/electrical sensor and deep learning colorimetric/electrical dual-sensing system.
Background
Hydrogen sulfide (H) 2 S) is a colorless, flammable and toxic gas with a bad egg taste. Long-term contact with H at low concentrations (15 to 50 ppm) 2 S can stimulate mucous membrane and cause headache, dizziness and nausea, high concentration>200ppm）H 2 S can lead to choking, coma, or loss of consciousness. Therefore, it is important to develop a monitoring system that is responsive to hydrogen sulfide gas and has high sensitivity.
The colorimetric gas sensor is a sensing device which generates a visual sensing signal through color change so as to identify gas to be detected, and has the advantages of low power consumption, low detection limit, small volume, convenience in operation and capability of providing visual response. Colorimetric sensors are currently in common use that are constructed based on films, gels, nanofibers, fabrics, etc. to detect various harmful gases such as ammonia, phosgene, hydrogen sulfide, volatile organic compounds, etc.
CN109856122a discloses a colorimetric type hydrogen sulfide sensor, which synthesizes solid gel by using a gellan gum-silver nano solution synthesized in green and an agar solution, has a high sensitivity response to hydrogen sulfide gas, and gradually changes the color from yellow to colorless when reacting with hydrogen sulfide. But at present H 2 The color change of the S colorimetric gas sensor needs to be observed by a large instrument, and if the information generated by the color change is difficult to capture only by naked eyes, the detection precision is low. Therefore, the current colorimetric gas sensor only completes partial detection work and does not form a complete H 2 S monitoring system is difficult to break through the use restriction of certain environment, especially under the environment of strong light interference and dark no light, and the colorimetry sensing function is limited. The electrical sensing has better stability and selectivity under different light environments, and the combination of colorimetric sensing and electrical sensing can solve the problemAnd the detection work is difficult to carry out in the end light environment. Meanwhile, the double-sensing system combined with deep learning can avoid the dependence of high-power consumption instruments in the detection process, and realize an ultra-low energy consumption (i.e. without a large spectrometer, a color difference meter and the like) and high-precision colorimetric-electrical double-function integrated sensing system, thereby forming a complete gas sensing system.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides an H 2 S colorimetric/electrical sensor and deep learning colorimetric/electrical dual-sensing system. The invention uses H 2 Synthesis of H by combining S-sensitive nanofiber membrane with PET flexible interdigital electrode 2 The S colorimetric/electrical sensor is combined with the color-changing monitoring platform and the resistance response module to obtain the deep learning colorimetric/electrical dual-sensing system capable of intuitively showing visual response, achieves the effects of 'instant shooting and instant measuring', rapid and accurate detection of gas concentration, and achieves the detection advantages of real visualization, ultra-low power consumption and high precision.
To achieve the above object, a first aspect of the present invention provides an H 2 S colorimetric/electrical sensor comprising H 2 S-sensitive nanofiber membrane and PET flexible interdigital electrode, H 2 S sensitive nanofiber membrane is fixed on the PET flexible interdigital electrode through electrostatic spinning process, and H is formed by the steps of 2 The S sensitive nanofiber membrane is Pb (CH) 3 CO 2 ) 2 PVA nanofiber. Will H 2 The S-sensitive nanofiber membrane is combined with the PET flexible interdigital electrode, so that the applicability and application universality of the sensor can be improved according to the dual response characteristics of the colorimetric-resistance sensor.
Further, the H 2 The S-sensitive nanofiber membrane is a compact reticular structure formed by interlacing nanofibers, the diameter of the nanofibers is 250-400nm, and the H 2 The thickness of the S sensitive nanofiber membrane is 0.5-0.7mm.
In particular, the hydrogen sulfide sensing sensitivity of the nanofiber membrane is closely related to the microstructure thereof, the large specific surface area and the pore structure formed by interlacing fibers are key influencing factors of the nanofiber membrane on the sensitivity of the nanofiber membrane to gas, and the smaller fiber size can generate more pore structures, so that the reaction is causedThe method can be quicker and more sufficient, and further improves the sensitivity of the device. In addition, the fiber film has a certain thickness, can ensure that the color of the PET flexible interdigital electrode is not interfered in the color changing process, and under the thickness, H 2 The gas sensing performance of S is optimal.
In a second aspect, the invention provides a process for preparing H 2 A method of S colorimetric/electrical sensor comprising the steps of:
(1) Dissolving lead acetate trihydrate, naF and sodium dodecyl sulfate in deionized water to obtain a lead acetate solution;
(2) Slowly adding polyvinyl alcohol into the lead acetate solution, and heating and stirring in a water bath to obtain an electrostatic spinning solution;
(3) The electrostatic spinning solution is processed by an electrostatic spinning process to form H 2 The form of the S-sensitive nanofiber membrane is fixed on a PET flexible interdigital electrode to obtain the H 2 S colorimetric/electrical sensor.
Further, the lead acetate trihydrate: naF: sodium dodecyl sulfate: the mass ratio of the polyvinyl alcohol is (0.8-1.2) g: (25-35) mg: (10-20) mg: (2.2-2.6) g, wherein the heating and stirring temperature in the step (2) is 80-90 ℃ and the time is 8-10h.
Further, the electrostatic spinning process in the step (3) is as follows: a20-, 22-or 24-gauge metal needle was used, a 20+ -1.5 kV static voltage was applied at the needle, the pushing speed of the microinjection pump was 1+ -0.2 mL/h, and the receiver was 10+ -0.5 cm from the needle. The environmental humidity is 35 percent plus or minus 5 percent and the temperature is 25 plus or minus 2 ℃ in the spinning process; the spinning time is 8-10h. The micro-morphology of various surfaces of the nanofiber, such as a net shape, a porous structure and an open structure, can be changed by adjusting the preparation process and parameters of the sensor, and the micro-morphology of the nanofiber membrane influences the gas-sensitive performance of the sensor.
A third aspect of the present invention provides a deep learning colorimetric/electrical dual sensing system comprising an air chamber, H 2 S colorimetric/electrical sensor, resistance response module, color-changing monitoring platform, analysis module and terminal;
the H is 2 S colorimetric/Electrical sensor is H provided previously 2 S colorimetric/electrical sensor located in the atmosphereInside the chamber for real-time detection of H 2 S gas obtains a gas concentration signal, and converts the gas concentration signal into a color change signal and a resistance change signal;
the resistance response module and the H 2 The S colorimetric/electrical sensor is connected and used for collecting, storing and displaying resistance change signals output by the sensor in real time;
the color-changing monitoring platform is used for collecting, storing and displaying color-changing signals output by the sensor in real time;
the analysis module is mounted on the terminal and is used for analyzing and processing color change signals acquired by the color change monitoring platform and outputting H based on analysis and processing results 2 S concentration; the terminal is used for displaying H 2 S concentration detection results.
Further, the color-changing monitoring platform comprises a light supplementing unit, a collecting unit, a storage unit and a display unit, wherein the light supplementing unit is an LED light supplementing lamp, the collecting unit is an integrated camera module and is used for collecting color change signals output by a sensor in real time, the storage unit is used for storing the collected color change signals, and the display unit is used for displaying the color change signals. In a specific embodiment, the color-changing monitoring platform captures the color of the sensor in H by using an integrated camera module 2 And shooting a color-changing sample under the S environment by using a color-changing image under a unified light source, controlling the sample specification to be consistent, and then returning to a shooting view in real time by using a user graphical interface designed by a Tking library of the Python, wherein the color-changing image can be shot manually or automatically and stored in a local database.
Further, the analysis module utilizes a color segmentation algorithm to segment color change signals collected by the color change monitoring platform, calculates color difference value delta E of the color change signals by introducing a color difference formula, and is based on H designed by a multi-layer perceptron algorithm 2 The S concentration prediction model is calculated to give H 2 S concentration. In the specific implementation process, a color segmentation algorithm is introduced to further segment a color change signal, namely a color response result, the degree of color change of a sensor area is reflected visually, a CIE color difference formula is introduced, and the condition that a color difference meter is not externally connected is avoidedUnder the condition, the color response result is digitized, and simultaneously, the measured H is rapidly given out through a multi-layer perceptron prediction model 2 S gas concentration.
Furthermore, the deep learning colorimetric/electrical dual-sensing system utilizes a resistance response module to form a colorimetric-electrical dual-function integrated sensing system, so that H can be detected simultaneously 2 Resistance response value change of S colorimetric/electrical sensor for detecting H 2 S gas can avoid detection work stop in special environments with strong light interference and dark no light.
Further, the detection range of the deep learning colorimetric/electric dual-sensing system is 0-100ppm, the detection limit and resolution are 0.1ppm, the colorimetric response time is less than 4s, and the deep learning colorimetric/electric dual-sensing system can keep stable performance in 180 days and can work normally in strong light interference and dark scenes.
Compared with the prior art, the invention has the following beneficial effects:
(1) The invention uses H 2 Synthesis of H by combining S-sensitive nanofiber membrane with PET flexible interdigital electrode 2 The S colorimetric/electrical sensor is combined with the color-changing monitoring platform and the analysis module to obtain a deep learning colorimetric/electrical dual-sensing system capable of intuitively showing visual response, achieves the effect of 'instant shooting and instant measuring', rapidly and accurately detects the concentration of gas, and achieves the real detection advantages of visualization, ultra-low power consumption and high precision; and the resistance response module is also added to be used as response compensation of the deep learning colorimetric/electrical dual-sensing system so as to ensure the completeness of the deep learning colorimetric/electrical dual-sensing system.
(2) The deep learning colorimetric/electrical dual-sensing system can realize large-range concentration H 2 S is detected, the detection limit is low, the response time is short, the selectivity is high, the resolution of 0.1ppm is realized, the stability is good, and the concentration detection accuracy can reach 99.8 percent.
Drawings
FIG. 1a is a schematic diagram of a process flow of a deep learning colorimetric/electrical dual-sensor system of the present invention; FIG. 1b is a concentration detection flow of the deep learning colorimetric/electrical dual sensing system of the present invention;
FIG. 2a is H 2 SEM image of S-sensitive nanofiber membrane at 1000 x; FIG. 2b is H 2 SEM image of S-sensitive nanofiber membrane at 10000 magnification; FIG. 2c is H 2 SEM images of S-sensitive nanofiber membranes after one week in an environment with 50% relative humidity;
FIG. 3 is a schematic diagram of the concentration detection results of the deep learning colorimetric/electrical dual-sensor system of the present invention;
FIG. 4a is a graph showing the result of processing an image based on a Euclidean distance segmentation algorithm; FIG. 4b is a graph showing the result of image processing based on the Manhattan multichannel distance segmentation algorithm; FIG. 4c is a graph showing the result of image processing based on the Manhattan single channel distance segmentation algorithm;
FIG. 5 is H 2 S colorimetric/electrical sensors are respectively used for different H under LED, natural light and night low light level 2 Color change results in an S concentration environment;
FIG. 6 is H 2 S colorimetric/electrical sensor at different H 2 Color difference delta E change curve under S concentration environment;
FIG. 7a is a diagram of H based on 3-layer perceptron algorithm design 2 Training the S concentration prediction model; FIG. 7b is a graph showing the gas concentration prediction results of a cold model and a warm model based on the Manhattan multi-channel distance color segmentation algorithm; FIG. 7c is a graph of test accuracy for a cold model; FIG. 7d is a graph of test accuracy for a warm model; FIG. 7e is a graph showing the loss versus the cold model versus the warm model;
FIG. 8a is H 2 S colorimetric/Electrical sensor on NH 3 、SO 2 、NO 2 、H 2 And H 2 S, a color difference delta E change result under the gas environment; FIG. 8b is H 2 S color difference delta E change results of the colorimetric/electrical sensor under two mixed gas environments; FIG. 8c is H 2 The color difference delta E of the S colorimetric/electrical sensor changes within 180 days;
FIG. 9a is H 2 S colorimetric/electrical sensor at different concentrations of H 2 Resistance response results in the S environment; FIG. 9b is H 2 S colorimetric/Electrical sensor on NH 3 、SO 2 、NO 2 、H 2 And H 2 Resistance response results under the S gas environment; FIG. 9c is H 2 S colorimetric/Electrical sensor over 180 daysResistance change results; FIG. 9d is H 2 S, comparing a resistance response and a chromatic aberration delta E response result of the colorimetric/electrical sensor;
FIG. 10a is H 2 S colorimetric/Electrical sensor with 0-5ppm H 2 FIG. 10b shows the color difference ΔE change in the S environment at 0.1-0.5ppm H in FIG. 10a 2 An enlarged schematic diagram of a color difference delta E change result in an S environment; FIG. 10c is H 2 S colorimetric/Electrical sensor with 50.1-50.9ppm H 2 A color difference delta E change difference schematic diagram under the S environment; FIG. 10d is H 2 S colorimetric/Electrical sensor with 0-5ppm H 2 FIG. 10e shows the resistance change in the S environment at 0.1-0.5ppm H in FIG. 10d 2 An enlarged schematic diagram of the resistance change result in the S environment; FIG. 10f is H 2 S colorimetric/Electrical sensor with 50.1-50.9ppm H 2 And (5) a schematic diagram of resistance change variability in the S environment.
Detailed Description
The principles and features of the present invention are described below in connection with examples, which are set forth only to illustrate the present invention and not to limit the scope of the invention. FIG. 1a is a schematic diagram of a process flow of a deep learning colorimetric/electrical dual-sensor system of the present invention; FIG. 1b is a concentration detection flow of the deep learning colorimetric/electrical dual-sensor system of the present invention.
Example 1
H (H) 2 S colorimetric/electrical sensor comprising H 2 S-sensitive nanofiber membrane and PET flexible interdigital electrode, H 2 The thickness of the S-sensitive nanofiber membrane is 0.6mm.
The preparation method comprises the following steps:
s1 to 1g (CH 3 COO) 2 Pb·3H 2 O, 30mg NaF,15mg Sodium Dodecyl Sulfate (SDS) were added to 20mL deionized water and magnetically stirred at 27℃for 1.5h to obtain a clear lead acetate solution;
s2 slowly add 2.4g polyvinyl alcohol (PVA) to lead acetate solution, 85 o C, stirring in a water bath for 9 hours, taking out and standing at room temperature for 30 minutes after stirring is finished, and obtaining electrostatic spinning solution;
s3, sucking the obtained electrostatic spinning solution into a disposable syringe, and carrying out static electricity by using a 22-gauge metal needleSpinning: applying 20kV static voltage at the needle, wherein the pushing speed of the microinjection pump is 1mL/h, the distance between the receiver and the needle is 10cm, the ambient humidity in the spinning process is 35%, and the temperature is 25% o C, spinning for 9H, with H 2 The form of the S-sensitive nanofiber membrane is fixed on a PET flexible interdigital electrode through an electrostatic spinning process, and the H is obtained 2 S colorimetric/electrical sensor.
FIG. 2 is H 2 As can be seen from the SEM image of the S-sensitive nanofiber membrane in fig. 2a, the nanofibers are randomly distributed and the fibers are interlaced to form a dense network, and as can be seen from fig. 2b, the nanofibers have a diameter of 250-400nm. Due to addition of (CH) 3 COO) 2 Pb·3H 2 O increases ion concentration in spinning solution, increases solution conductivity, increases instability of solution jet in electric field, and makes sub-nanometer fiber split from main fiber, thereby forming spider-web structure in electrostatic spinning, and smaller fiber size can generate more pore structure, so that reaction can be faster and more sufficient, and sensitivity of device is improved. In addition, to verify H 2 Stability of S-sensitive nanofiber membranes, H 2 The S-sensitive nanofiber membrane was left in an atmospheric environment (relative humidity 50%) for 1 week, and as can be seen from fig. 2c, no precipitation of CH was observed on the nanofibers (CH 3 COO) 2 Pb particles, indicating H 2 The S-sensitive nanofiber membrane has good stability.
Example 2
H (H) 2 S colorimetric/electrical sensor comprising H 2 S-sensitive nanofiber membrane and PET flexible interdigital electrode, H 2 The S-sensitive nanofiber membrane is a compact reticular structure formed by interlacing nanofibers, and the membrane thickness is 0.5mm.
The preparation method comprises the following steps:
s1 will be 0.8g (CH 3 COO) 2 Pb·3H 2 O, 25mg NaF and 10mg SDS are added into 20mL deionized water, and the mixture is magnetically stirred at 25 ℃ for 1.5. 1.5h to obtain clear lead acetate solution;
s2 slowly add 2.2g PVA to lead acetate solution, 80 o C, stirring in water bath for 8h, taking out and standing at room temperature for 25min after stirring is finished, and obtainingAn electrostatic spinning solution;
s3, sucking the obtained electrostatic spinning solution into a disposable injector, and carrying out electrostatic spinning by using a metal needle head with the number of 20: a static voltage of 18.5kV is applied to the needle, the pushing speed of the microinjection pump is 0.8mL/h, the distance between a receiver and the needle is 9.5cm, the environmental humidity in the spinning process is 30%, and the temperature is 23% o C, spinning for 8H, with H 2 The form of the S-sensitive nanofiber membrane is fixed on a PET flexible interdigital electrode through an electrostatic spinning process, and the H is obtained 2 S colorimetric/electrical sensor.
Example 3
H (H) 2 S colorimetric/electrical sensor comprising H 2 S-sensitive nanofiber membrane and PET flexible interdigital electrode, H 2 The S-sensitive nanofiber membrane is a compact reticular structure formed by interlacing nanofibers, and the membrane thickness is 0.7mm.
The preparation method comprises the following steps:
s1 1.2g (CH 3 COO) 2 Pb·3H 2 O, 35mg of NaF and 20mg of SDS are added into 20mL of deionized water, and the mixture is magnetically stirred for 2 hours at 25 ℃ to obtain clear lead acetate solution;
s2 slowly add 2.6g PVA to lead acetate solution, 90 o C, stirring in a water bath for 10 hours, taking out and standing at room temperature for 35 minutes after stirring is finished, and obtaining electrostatic spinning solution;
s3, sucking the obtained electrostatic spinning solution into a disposable injector, and carrying out electrostatic spinning by using a 24-gauge metal needle: applying a static voltage of 21.5kV at the needle, wherein the pushing speed of the microinjection pump is 1.2mL/h, the distance between the receiver and the needle is 10.5cm, the ambient humidity in the spinning process is 40%, and the temperature is 27% o C, spinning for 10H, with H 2 The form of the S-sensitive nanofiber membrane is fixed on a PET flexible interdigital electrode through an electrostatic spinning process, and the H is obtained 2 S colorimetric/electrical sensor.
Deep learning colorimetric/electrical dual-sensing system
The colorimetric/electrical dual-sensing system for deep learning is built by using the colorimetric/electrical sensor in the embodiment 1 and mainly comprises an air chamber and H 2 S colorimetric/electrical sensor, color-changing monitoring platform, analysis module and terminal. H 2 S color comparison/electricityThe learning sensor is positioned in the air chamber and used for detecting H in real time 2 S gas obtains a gas concentration signal, and converts the gas concentration signal into a color change signal and a resistance change signal; the color-changing monitoring platform is used for collecting, storing and displaying color-changing signals output by the sensor in real time; the analysis module is loaded on the terminal and is used for analyzing and processing the color change signals acquired by the color change monitoring platform and outputting H based on the analysis and processing results 2 S concentration; the terminal is used for displaying H 2 S concentration detection results, fig. 3 is a schematic diagram of concentration detection results of the deep learning colorimetric/electrical dual-sensing system.
The deep learning colorimetric/electrical dual-sensing system also comprises a resistance response module and H 2 S colorimetric/electrical sensor connection for real-time acquisition, storage and display of H 2 Resistance change signal output by S colorimetric/electrical sensor for detecting H 2 S gas.
The color-changing monitoring platform comprises a light supplementing unit, a collecting unit, a storage unit and a display unit, wherein the light supplementing unit is an LED light supplementing lamp, the collecting unit is an integrated camera module and is used for collecting color change signals output by the sensor in real time, the storage unit is used for storing the collected color change signals, and the display unit is used for displaying the color change signals. Specifically, the color-changing monitoring platform captures the sensor in H by using the integrated camera module 2 And the camera and the color-changing sample keep a distance of 15cm under the S environment, the color-changing sample is photographed under a unified light source, the sample specification control is consistent and is 2cm multiplied by 2cm, then a user graphical interface designed by a Tkiner library of the Python can be utilized to return to a photographing view in real time, the color-changing image can be photographed manually or automatically, and the color-changing image is stored in a local database, so that the later algorithm can be conveniently called to process data.
In the specific operation process, H 2 S colorimetric/electrical sensors are arranged in the air chamber, and H with quantitatively increasing concentration is sequentially introduced 2 S gas, observing the color change condition of the sensor in real time through a color change monitoring platform interface, shooting the color change results of the sensor under different concentrations, uniformly storing the color change samples in a local database, wherein the color change samples are all provided with H 2 The label corresponding to the S gas concentration, i.e. the currently known gas concentration. For color-changing sample images without labels, it is necessary to immediately identify the H in the environment in which the sample is located 2 S gas concentration, in order to achieve the measurement effect of' instant shooting, color expression is converted into visual and accurate gas concentration data to inform a user, and rapid gas concentration measurement and calculation needs to be achieved through an analysis module.
The analysis module divides the color change signals acquired by the color change monitoring platform by utilizing a color division algorithm based on Euclidean distance or Manhattan distance (single channel or multi-channel), calculates color difference value delta E of the color change signals by introducing a color difference formula such as CIE 1976, CIE 1995 or CIE DE2000, and designs H based on a multi-layer perceptron algorithm 2 The S concentration prediction model is calculated to give H 2 S concentration. The color difference value delta E can distinguish the chromaticity change degree of the colorimetric/electrical sensor under different gas concentration environments, and the color segmentation algorithm processes a color change signal, namely a color change sample image, by using a cold color set or warm color set model.
In a specific embodiment, a local database of the color-changing monitoring platform is called, 200 color-changing images are divided to serve as training sets of the later-stage prediction model, and 50 color-changing images serve as test sets for verifying model accuracy. The color segmentation algorithm based on Euclidean distance and Manhattan distance is utilized to preprocess a sample image, the pixel mean value calculation can obtain (R, G, B) mean value vectors of a color-changing image, the sample RGB values are converted into LabB color space suitable for a color difference meter, the color difference value delta E of the image can be calculated through CIE color difference formula, so that the chromaticity change of colorimetric/electrical sensors under different gas concentration environments can be distinguished, and the color change from white to dark brown can prove H 2 The presence of S. H based on multi-layer perceptron algorithm design 2 S concentration prediction model can rapidly give out final measurement and calculation result, and finally can realize high-precision, low-cost and cross-platform applicable H 2 S detection, the measurement effect of 'instant beating and obtaining' is truly realized.
The color-changing samples collected during the experiment can be used for training the model after image preprocessing. A color-changing sample image needs to be processed into a color-changing sample imageThe (R, G, B) vector is used as an input to train the concentration prediction model, and this vector is to be used as a "pointer" to the gas concentration value for the color change sample. When calculating the mean value of the color-changing sample image, each pixel of the picture is divided and the color-changing sample image is divided into a plurality of pixels (R i ，G i ，B i ) The value is judged, and the value (R i ，G i ，B i ) Values, thereby deriving a pixel mean vector (R, G, B) of the color change samples. The method is specifically calculated by the following formula:
the color segmentation algorithm is an important step of image preprocessing, and is used for classifying pixels of a color image based on RGB space, namely, region extraction is carried out on a color-changing sample image, and then average colors are designated for segmented regions according to a given color sample set, so that the reaction degree of each region on the sample image can be clearly distinguished. In order to realize effective color segmentation, a similarity measure is introduced, and the Euclidean distance, the Manhattan single-channel distance and the Manhattan multi-channel distance are calculated in sequence as follows:
D(z,α)=ǁz-αǁ=[(z R -α R ) 2 +(z G -α G ) 2 +(z B -α B ) 2 ]，D(z,α)≤D 0
D MR (Z R ,α R )=ǀZ R -α R ǀ
D M (z,α)=ǁz-αǁ L1 =ǀz R -α R ǀ+ǀz G -α G ǀ+ǀz B -α B ǀ，D M ≤ηǁσ‖L1
wherein Z is the current divided pixel point (R i ，G i ，B i ) The value α is the color average (R, G, B) of the divided color regions, denoted below as RGB component, D 0 For the segmentation threshold, ǁ σ||L1 is the standard deviation vector for each of the three channels, η is the standard deviation coefficient, and is typically 1.25.
In color segmentationWhen the image is processed, the selection of different color sample sets has obvious influence on the final effect of segmentation, different color block segmentation results can be presented, and the difference is large. At 20ppm H 2 For example, the color-changing sample image in the S environment is processed by using a segmentation algorithm based on Euclidean distance (the segmentation threshold is 1500), and a given color sample set is respectively a black-white set, a warm color set and a cold color set in sequence, as shown in FIG. 4a; the image was processed using a manhattan distance based segmentation algorithm (ǁ σ|l1 taken to be 1.25) and the set of given color samples were in turn a black-white set, a warm set, and a cool set, respectively, as shown in fig. 4b. It is obvious that the color segmentation based on the manhattan algorithm is more sensitive, the pixel classification is finer, meanwhile, the color segmentation based on the manhattan single channel (given single-channel gray color set, single-channel single color set) is as shown in fig. 4c, and it can be seen that the color corresponding part of the 'Blue' has better segmentation effect on the color-changing sample image, and the color of the color-changing sample is biased to a darker tone (white gradually transits to black brown), so that the cold color sample is better in color sample set selection.
Accurate identification of H for adaptation to various complex environments 2 The invention introduces a CIE color difference calculation formula (CIE 1976, CIE 1995 or CIE DE 2000) and adds a color difference calculation function, so as to replace the work of a color difference meter with larger power consumption in a colorimetric experiment, thereby reducing the detection procedure and calculating the color change degree of an observation sample in real time.
For flexible and rapid measurement of colorimetric/electrical sensors at a certain H 2 And (3) placing the color change degree under the concentration of the S gas in a gas chamber with a color change monitoring platform, and monitoring the color change condition of the sensor in real time through a camera. The initial color of the colorimetric/electrical sensor is defined as a standard color, and an image of sensor discoloration is acquired as a sample color every one second. In order to ensure the detection effect of the system in different illumination environments as far as possible, the cameras are respectively arranged on the LED light supplementing lamp, the natural light and the H of low light level at night 2 Capturing an image of sensor discoloration in an S-environment, as in FIG. 5, and then mapping the imageAnd uploading the image data to the intelligent terminal. In the compiling environment of Spyder, the RGB pixel mean value of the image is calculated by taking Python as a development language, and a color space expression function of RGB value conversion Lab written in advance is called, wherein the core calculation formulas (1) (2) (3) are shown as follows. Since the RGB color space cannot be directly converted into the l×a×b color space, the RGB color space needs to be converted into the XYZ color space by means of the XYZ color space, then the XYZ color space is converted into the l×a×b color space, lab numerical expressions of all image colors are obtained, and finally a function of the CIE DE2000 color difference calculation formula is called to obtain a color difference Δe change curve of the sensor as shown in fig. 6.
（1）
（2）
（3）
The conversion of the image data and the digital data is completed through image preprocessing, and a data set which can be used for training a gas concentration prediction model is obtained after the data is cleaned. Subsequent use of a Multi-layer perceptron algorithm (Multi-Layer Feed Forward Neural Networks) to predict unknown concentrations of H 2 S gas, which shows better stability in processing multidimensional nonlinear data and can rapidly give a prediction result. H designed based on 3-layer perceptron algorithm is shown in FIG. 7a 2 Training process of S concentration prediction model. The Adam optimizer is selected in the training process, the function is easy to realize, the calculation is efficient, the memory requirement is low, and the updating of parameters is not influenced by the gradient expansion transformation.
R = (R 1 ,R 2 ......R 200 ) T ,G = (G 1 ,G 2 ......G 200 ) T ,B = (B 1 ,B 2 ……B 200 ) T Vector of RGB mean value of 200 training samples is used as three-layer perceptronInput of input layer, X 1 ,X 2 ,X 3 Is 3 neuron models of the input layer, is a multiple linear regression process from the input layer to the first hidden layer, and sets the initial weight of the input layer to the first hidden layer as W 1 ,W 2 ,W 3 At this time, the input of the neurons of the first hidden layer is calculated by the formula (4); the first hidden layer to the second hidden layer are nonlinear regression processes, and due to the stability of calculation, the condition that gradient vanishes during random gradient descent optimization calculation is avoided, and a ReLU function (Rectified Linear Unit) is selected as an activation function. The output of the first layer hidden layer neuron can be obtained by combining the activation function, and the output of the second layer hidden layer neuron can be obtained by calculating according to the formula (5), and the input of the second layer hidden layer neuron can be obtained by calculating according to the formula (6). Where m= (100, 50, 20) is the dimension of the three hidden layers, v 1 ，v 2 ……v m The layer-to-next layer weight values are hidden for each layer. And repeatedly calculating to obtain the output gas concentration prediction result of the output layer, which is called Y-p for short.
（4）
（5）
（6）
The trained model needs to be further tested to determine whether unknown concentrations of H can be dosed 2 S gas environment is used for working. Based on a Manhattan multichannel distance color segmentation algorithm (wherein the standard deviation sigma vector of each of three channels is set to be 1.25), a cooling and heating color set is selected to process a color-changing sample image, image data processed by the cooling and heating color set are respectively used for training two original models, and the two trained models are named as a cooling model and a heating model. A test set is set to input a trained model which immediately gives the gas concentration prediction result, as shown in FIG. 7bIn an intuitive finding, the concentration prediction curve of the cold model exhibits a better fit, while the concentration prediction curve of the hot model is relatively more shifted from the centerline. To evaluate the prediction accuracy of the gas prediction model, a coefficient R is determined 2 Is introduced as an index of the quality of the evaluation model. R is R 2 The value of (2) is [0,1 ]]Fluctuation of R 2 The higher the predicted gas concentration value is, the closer the predicted gas concentration value is to the true value, the model prediction accuracy is high, and the performance is more excellent. FIGS. 7c and 7d show the test accuracy of the cold and warm models, respectively, the accuracy of the hot model is relatively low, the accuracy can only reach 82.3%, and the accuracy of the cold model can reach 99.8%, which is significantly higher than that of the cold model, showing excellent H 2 S gas concentration prediction capability. In the model training process, the predicted result Y-p and the known data Y are used as (Y-p) i ，y i ) And an input to calculate the loss of the model. The loss function is defined to calculate the loss once per 500 iterations, as in equation (7), i.e., calculate the mean square error MSE (Mean Squared Error). The loss versus curves for the two models are shown in fig. 7e, and it is evident that the cold model has less loss and can reach the loss minimum of 0.00012 more quickly.
（7）
Therefore, the deep learning colorimetric/electrical dual-sensing system can realize H 2 S short-time safety detection is combined with a real-time color-changing monitoring platform and an analysis module, so that the color-changing degree of a sample can be continuously observed, and H can be accurately judged 2 S gas concentration.
Although the color change monitoring platform can continuously observe the color change process, the color change of the sensor can be flexibly recorded, and the analysis module can rapidly give a gas concentration prediction result for the color change of the sensor. However, as a separate gas sensing system, in the face of possible system detection failures (e.g., damage to the color-changing monitoring platform, or very strong light interference), a "second scheme" of gas detection must be provided as a response compensation to ensure H 2 S gas sensor systemIntegrity of the system. Therefore, the deep learning colorimetric/electrical dual-sensing system of the invention further comprises a resistance response module and H 2 S colorimetric/electrical sensor connection for real-time acquisition, storage and display of H 2 S colorimetric/electrical sensor outputs resistance response value change signals so as to detect H 2 S gas. The PET flexible interdigital electrode can synchronously perform resistance test by leading out a lead through soldering.
Performance test of deep learning colorimetric/electrical dual-sensing system
To verify the single selectivity of the sensor color response, colorimetric/electrical sensors are placed in the air chambers, respectively, and NH is introduced into the air chambers, respectively 3 、SO 2 、NO 2 、H 2 、H 2 S five gases possibly existing in the industrial site, controlling the concentration of the gases to be 100ppm, and observing the gases under the conditions of the same concentration and different gas types by using a color-changing monitoring platform. It can be clearly seen that it is at H 2 The colorimetric/electrical sensor of the S gas environment shows obvious color difference change, but in NH 3 、SO 2 、NO 2 、H 2 The sensor color in the gas environment was hardly changed, and the color difference result calculated by the analysis module also corroborates this point (see fig. 8 a), proving that the colorimetric/electrical sensor pair H 2 The S gas has a color change sensitivity different from other gases. At the same time, H 2 S and NH 3 、SO 2 、NO 2 、H 2 The 4 gases coexist in pairs (the gas concentration is 100 ppm) respectively, the color change difference of the sensor under the interference of complex gas environment is simulated, and the drawing is shown as figure 8b, and the non-H is obvious 2 The influence of the S gas on the color change result of the sensor is almost zero, and the colorimetric nanofiber gas sensor on H under the complex gas environment is reflected 2 High selectivity of S gas. At the same time, the long-term stability of the sensor was tested, and the color response of the sensor at 20ppm was tested and recorded every five days for the first three months, and every month for the last three months, and the results indicated that the sensor color change performance remained stable for 180 days, as shown in fig. 8c.
FIG. 9a shows the colorimetric/electrical sensing in example 1The device is in incremental H 2 Resistance change in S gas concentration environment, it can be seen that H follows 2 The S gas concentration increases stepwise from 0ppm to 100ppm, and the colorimetric/electrical sensor resistance decreases stepwise due to H 2 S gas and H 2 The S-sensitive nanofiber membrane reacts to form PbS large particles to precipitate, original nanofiber gaps are torn and enlarged, the membrane structure becomes loose, and metal sulfide has good conductivity, so that the resistance of the material is reduced. And as can be derived from fig. 9a, the control sensor is controlled at each stage (i.e. different H 2 In S concentration environment) is within 30S, the change of the resistance is stable, the resistance response time of the sensor is within 10-15S except the time that the resistance value is stable. To analyze the selectivity of the sensor, the difference in the resistance response of the sensor to different gases was reflected and exposed to 1000ppm NH 3 、SO 2 、NO 2 、H 2 Gas and 100ppm of H 2 The results are shown in FIG. 9b under S gas atmosphere. Colorimetric/electrical sensor pair H 2 The response value of the S gas is significantly higher than other gases. Resistance response long term stability to sensor (H 2 S concentration of 20 ppm) showed that the sensor resistance performance remained stable for 180 days as shown in fig. 9c. Through experimental observation, the color response of the sensor is significantly more flexible, and as is apparent from fig. 9d, the resistance value is still decreasing as the color change approaches to be stable, which means that the color response speed is faster (the response time is less than 4 s), and the measurement is faster and the energy consumption is lower. So at H 2 In the S gas measurement process, the color response result is mainly used, and the resistance response result is only used as an automatic interlocking result of the colorimetric/electrical dual-sensing system.
FIGS. 10a and 10d show a lower H 2 Color response and resistance response at S gas concentration. As can be seen by enlarging fig. 10b and 10e, the detection limit of the sensing system can be up to 0.1ppm. FIGS. 10c and 10f show a higher H 2 Color response at S gas concentration is different from resistance response. Clearly, the sensing system is able to sensitively detect subtle concentration differences with a resolution that isUp to 0.1ppm.
The foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and scope of the invention are intended to be included within the scope of the invention.
Claims (10)
1.H 2 S colorimetric/electrical sensor, characterized in that the sensor comprises H 2 S-sensitive nanofiber membrane and PET flexible interdigital electrode, H 2 S sensitive nanofiber membrane is fixed on PET flexible interdigital electrode through electrostatic spinning process, and the sensitive nanofiber membrane is Pb (CH) 3 CO 2 ) 2 PVA nanofiber.
2. H according to claim 1 2 S colorimetric/electrical sensor characterized in that the H 2 The S-sensitive nanofiber membrane is a compact reticular structure formed by interlacing nanofibers, the diameter of the nanofibers is 250-400nm, and the H 2 The thickness of the S sensitive nanofiber membrane is 0.5-0.7mm.
3. An H according to any one of claims 1-2 2 The preparation method of the S colorimetric/electrical sensor is characterized by comprising the following steps of:
(1) Dissolving lead acetate trihydrate, naF and sodium dodecyl sulfate in deionized water to obtain a lead acetate solution;
(2) Slowly adding polyvinyl alcohol into the lead acetate solution, and heating and stirring in a water bath to obtain an electrostatic spinning solution;
(3) The electrostatic spinning solution is processed by an electrostatic spinning process to form H 2 The form of the S-sensitive nanofiber membrane is fixed on a PET flexible interdigital electrode to obtain the H 2 S colorimetric/electrical sensor.
4. A method of preparation according to claim 3, wherein the lead acetate trihydrate: naF: sodium dodecyl sulfate: the mass ratio of the polyvinyl alcohol is (0.8-1.2) g: (25-35) mg: (10-20) mg: (2.2-2.6) g, wherein the heating and stirring temperature in the step (2) is 80-90 ℃ and the time is 8-10h.
5. The method according to claim 3, wherein the step (3) comprises the steps of: applying 20+/-1.5 kV static voltage to the needle by using a metal needle with the number of 20, 22 or 24, wherein the pushing speed of the microinjection pump is 1+/-0.2 mL/h, and the distance between a receiver and the needle is 10+/-0.5 cm; the environmental humidity is 35 percent plus or minus 5 percent and the temperature is 25 plus or minus 2 ℃ in the spinning process; the spinning time is 8-10h.
6. The deep learning colorimetric/electrical dual-sensing system is characterized by comprising an air chamber and H 2 S colorimetric/electrical sensor, resistance response module, color-changing monitoring platform, analysis module and terminal;
the H is 2 S colorimetric/electrical sensor is H as defined in any one of claims 1-2 2 S colorimetric/electrical sensor located inside the air chamber for detecting H in real time 2 S gas obtains a gas concentration signal, and converts the gas concentration signal into a color change signal and a resistance change signal;
the resistance response module and the H 2 The S colorimetric/electrical sensor is connected and used for collecting, storing and displaying resistance change signals output by the sensor in real time;
the color-changing monitoring platform is used for collecting, storing and displaying color-changing signals output by the sensor in real time;
the analysis module is mounted on the terminal, analyzes and processes the color change signals acquired by the color change monitoring platform, and outputs H based on analysis and processing results 2 S concentration; the terminal is used for displaying H 2 S concentration detection results.
7. The deep learning colorimetric/electrical dual-sensing system according to claim 6, wherein the color-changing monitoring platform comprises a light supplementing unit, a collecting unit, a storage unit and a display unit, wherein the light supplementing unit is an LED light supplementing lamp, the collecting unit is an integrated camera module and is used for collecting color change signals output by the sensor in real time, the storage unit is used for storing the collected color change signals, and the display unit is used for displaying the color change signals.
8. The deep learning colorimetric/electrical dual-sensing system according to claim 6, wherein the analysis module uses a color segmentation algorithm to segment the color change signal collected by the color change monitoring platform, calculates a color difference value Δe of the color change signal by introducing a CIE color difference formula, and gives H based on a multi-layer perceptron algorithm prediction model 2 S concentration.
9. The deep learning colorimetric/electrical dual-sensing system as claimed in claim 6, wherein the deep learning colorimetric/electrical dual-sensing system forms a colorimetric-electrical dual-function integrated sensing system by using a resistance response module, and can detect H simultaneously 2 Resistance response value change of S colorimetric/electrical sensor for detecting H 2 S gas can avoid detection work stop in the environments of strong light interference and darkness.
10. The deep learning colorimetric/electrical dual-sensing system of claim 6, wherein the detection range of the deep learning colorimetric/electrical dual-sensing system is 0-100ppm, the detection limit and resolution are 0.1ppm, the colorimetric response time is less than 4s, the performance is stable within 180 days, and the deep learning colorimetric/electrical dual-sensing system can work normally under strong light interference and dark scenes.
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