CN106018510B - A kind of explosive vapors recognition detection method based on photoelectric respone - Google Patents

Abstract

The invention relates to a method for identifying and detecting explosive vapor based on photoelectric response. The device involved in the method is composed of a sensor, a light source, a power supply, an ammeter, a signal processor and an alarm. The light source is used to irradiate a single sensor with fast photoelectric response, and the photocurrent change caused by the adsorption of explosive vapor on the surface of the sensor sensitive material is measured, and the data is processed by principal component analysis, linear discriminant analysis, artificial neural network and other pattern recognition methods to realize The standard database of the response of the sensor array to different types of explosive vapors, by comparing the data processing results of suspected explosives with the database, finally achieves the purpose of identifying and detecting explosive vapors. The method utilizes the photoelectric properties of the sensitive material to improve the detection limit while simplifying the structure of the sensor array, and utilizes the sensitive gas-sensitive response and fast photoelectric response of the sensitive material to realize the purpose of quickly identifying explosives.

Description

A Photoelectric Response Based Explosive Vapor Recognition and Detection Method

technical field

The invention relates to the field of explosive detection, in particular to a method for identifying and detecting explosive vapor based on photoelectric response. It specifically involves the use of light in a certain wavelength range to irradiate sensitive materials with fast photoelectric response. By periodically switching the light source and changing the light intensity, the photocurrent changes caused by the adsorption of explosive molecules on the sensor under different light intensities are measured. Principal component analysis, Pattern recognition methods such as linear discriminant analysis and artificial neural network are used for data processing to realize the standard database of the response of sensor arrays to different types of explosive vapors, and compare the established standard databases to detect and classify suspected trace explosive vapors. This method can simplify the sensor array structure while improving the detection limit, and realize the purpose of quickly identifying explosives.

Background technique

Terrorist bombings seriously endanger social stability and national security. The detection of hidden explosives has always been a problem of great concern in the field of public security at home and abroad. Standard explosives such as trinitrotoluene (TNT), dinitrotoluene (DNT), p-nitrotoluene (PNT), picric acid (PA), RDX, urea (Urea), black powder ( BP), ammonium nitrate (AN), pentaerythritol tetranitrate (PETN), cyclotetramethylenetetranitramine (HMX), triacetone triperoxide and sulfur, because of their low vapor pressure and high explosiveness, they are the most The most common and widely used class of explosives. There are many types of non-standard explosives with complex components. The raw materials come from industrial raw materials, agricultural fertilizers and daily necessities, such as ammonium nitrate, urea, sulfur, etc., and their detection is very difficult.

Due to the wide variety of explosives, it is very difficult to achieve a completely specific detection. At the same time, building sensors that respond specifically to each type of explosive is more difficult. Therefore, it is necessary to introduce a sensor array in the detection and identify it through data processing. Sensor arrays are a detection method that mimics the way mammals recognize substances through their senses of smell and taste. By establishing a series of cross-reactive sensor units, and then recording the response behavior of all units to the same substance, and using pattern recognition methods such as principal component analysis, linear discriminant analysis, and neural networks for data processing, different substances can be distinguished. Therefore, whether it is an optical sensor or an electrical sensor, a sensor array is often introduced to realize material identification.

The construction process of the sensor array needs to introduce a series of sensor units. In order to ensure the effect of clustering and classification in the data processing process, the number of sensors must be sufficient. This brings about two problems: first, the structure of the sensing system becomes complicated; second, more synthesis or modification steps are required, and the construction process is complicated. Therefore, how to simplify the sensor array structure is an urgently needed technology.

When the incident photon energy (hv) is greater than or equal to the forbidden band width (E _g ) of the semiconductor, the electrons in the valence band will absorb the incident photons, transition from the valence band to the conduction band, and form a photocurrent. When the gas is adsorbed on the material, it will significantly change the photocurrent magnitude. In addition, the light intensity can tune the response characteristics of the sensing material. Therefore, the response properties of photosensitive materials can be controlled by light intensity, thus simplifying the sensor array.

Contents of the invention

The object of the present invention is to provide a method for identifying and detecting explosive vapor based on photoelectric response, the device involved in the method is composed of a sensor, a light source, a power supply, an ammeter, a signal processor and an alarm, using periodic switches and Change the light source with changing light intensity, irradiate a single sensor with fast photoelectric response, measure the photocurrent change caused by the adsorption of explosive vapor on the surface of the sensor sensitive material, and analyze the data through principal component analysis, linear discriminant analysis, neural network and other pattern recognition methods After processing, the standard database of the response of the sensor array to different types of explosive vapors is obtained. By comparing the data processing results of suspected explosives with the database, the purpose of identifying and detecting explosive vapors is finally achieved. This method utilizes the optoelectronic properties of sensitive materials, through light source intensity changes and periodic switching, resulting in differences in the response of optoelectronic materials to explosives, and a single sensor realizes the function of array sensing, which improves the detection limit and simplifies the structure of the sensor array , using the sensitive gas response and fast photoelectric response of sensitive materials to achieve the purpose of quickly identifying explosives.

A method for identifying and detecting explosive vapor based on photoelectric response according to the present invention, the devices involved in the method are composed of sensors, light sources, power supplies, ammeters, signal processors and alarms, sensors (1), light sources (2 ), the power supply (3), the ammeter (4) are connected in series with the signal processor (5), the signal processor (5) is connected with the alarm (6), and the light source (2) that can be periodically switched and changed light intensity is used , irradiated on a single sensor (1) with fast photoelectric response, the sensitive material on the sensor (1) is silicon-zinc oxide p-n junction, silicon nanowire/graphene Schottky junction, titanium dioxide/graphene Schottky junction, Silicon-ZnO core-shell nanowire arrays/graphene Schottky junctions, silicon nanowires/metal Schottky junctions, silicon nanowire arrays, titanium dioxide-modified silicon nanowire arrays/graphene Schottky junctions, or gold nanoparticles Modified silicon nanowire array/graphene Schottky junction, the specific operation is carried out according to the following steps:

a. A light source (2) is installed above the sensitive material of the sensor (1), and the light source (2) is a light-emitting diode (LED), Xe lamp, Hg lamp or laser light source with a wavelength range between 200-800nm;

b. Put the sensor (1) in the air, at room temperature, by periodically turning on and off the light source (2) at an interval of 1ms-2s and changing the light intensity, where the number of light intensities is 3-10, measured The photocurrent of the sensitive material under different light intensities to obtain the baseline current value;

c. Place the sensor (1) in a series of different explosive vapors with known concentrations at room temperature, and periodically turn on and off the light source (2) at an interval of 1ms-2s and change the light intensity, where the number of light intensities is 3 -10, measure the photocurrent of sensitive material under different light intensities, demarcate explosive vapor, calculate the response size of sensor (1) under every kind of light intensity respectively to different explosive vapor, response size is defined as (I _Explosive -I _air )/I _air , wherein I _explosive is the photocurrent size of sensitive material in explosive vapor, and I _air is the photocurrent size of sensitive material in air;

d. Obtain a standard database of sensor (1) arrays responding to different types of explosive vapors through principal component analysis, neural network models or linear discriminant analysis data processing methods;

e. Place the sensor (1) in the atmosphere to be tested, and change the light intensity by periodically turning on and off the light source (2) at an interval of 1ms-2s, wherein the number of light intensities is 3-10, and the measured sensitivity of the sensitive material is For the photocurrent under different light intensities, use the same data processing method as in step d to process the response signal, and compare it with the database in step d to obtain whether there is explosive vapor and what kind of explosive vapor exists.

The light source (2) described in step a is a light emitting diode.

The measurement signal in step c is not only photocurrent, but also photovoltage and resistance.

According to the invention, a method for identifying and detecting explosive vapor based on photoelectric response, the sensitive materials in this method are not limited to silicon-zinc oxide p-n junction, silicon nanowire/graphene Schottky junction, titanium dioxide/graphene Schottky junction base junction, Si-ZnO core-shell nanowire array/graphene Schottky junction, silicon nanowire/metal Schottky junction, silicon nanowire array, titanium dioxide modified silicon nanowire array/graphene Schottky junction or Silicon nanowire arrays/graphene Schottky junctions modified by gold nanoparticles, all materials with fast photoelectric response characteristics to explosive vapors fall within the protection scope of the present invention;

The detection range of explosives in the method for identifying and detecting explosives vapor based on photoelectric response according to the present invention can be adjusted according to the range of establishing an explosives database;

The light intensity number and light intensity in the photoelectric response-based explosive vapor identification and detection method of the present invention can be adjusted according to actual conditions;

A method for identifying and detecting explosives vapor based on photoelectric response according to the present invention has the following advantages compared with the prior art: use a light source that can be switched periodically and change light intensity to irradiate a single object with fast photoelectric response On the sensor, measure the photocurrent change caused by the adsorption of explosive vapor on the surface of the sensitive material of the sensor, and process the data through principal component analysis, linear discriminant analysis, neural network and other pattern recognition methods to obtain a standard database of the response of the sensor array to different explosive vapors , by comparing the data processing results of suspected explosives with the database, the purpose of identifying and detecting explosive vapors is finally achieved. In this method, the variation of the light source intensity and the periodic switch cause the difference in the response of the photoelectric material to the explosive, and a single sensor realizes the function of array sensing, which improves the detection limit and simplifies the structure of the sensor array, thereby realizing the identification of explosives. Purpose. Utilize the sensitive gas sensitive response and fast photoelectric response of sensitive materials to achieve the purpose of rapid identification and detection of explosives. This method makes up for the shortcomings of the traditional sensor array's complex structure and cumbersome construction process, and provides reference and ideas for the construction of new sensor arrays.

Description of drawings

Fig. 1 is the detection structure schematic diagram of the present invention;

Fig. 2 is the photoelectric response curve of the present invention to air at room temperature under the irradiation of 8 kinds of light intensity with silicon-zinc oxide core-shell nanowire array/graphene Schottky junction as the sensitive material;

Fig. 3 is the photoelectric response curve of TNT room temperature saturated vapor with silicon-zinc oxide core-shell nanowire array/graphene Schottky junction as sensitive material in the present invention under the irradiation of 8 kinds of light intensity;

Fig. 4 is the photoelectric response curve of the present invention to ammonium nitrate room temperature saturated vapor with silicon-zinc oxide core-shell nanowire array/graphene Schottky junction as the sensitive material under the irradiation of 8 kinds of light intensity;

Fig. 5 is that the present invention uses silicon-zinc oxide core-shell nanowire array/graphene Schottky junction as sensitive material, room temperature condition p-trinitrotoluene (TNT), dinitrotoluene (DNT), p-nitrotoluene ( PNT), picric acid (PA), RDX, ammonium nitrate, black powder (BP) and ammonium nitrate (AN) 8 kinds of explosives, the curves of photoelectric response of saturated vapor with light intensity;

Fig. 6 is that the present invention uses silicon-zinc oxide core-shell nanowire array/graphene Schottky junction as sensitive material, adopts the data processing method of principal component analysis, trinitrotoluene (TNT), dinitrotoluene (DNT) , p-nitrotoluene (PNT), picric acid (PA), RDX, urea (Urea), black powder (BP), ammonium nitrate (AN) 8 kinds of room temperature saturated vapor database of explosives.

Detailed ways

The present invention is described in detail below in conjunction with accompanying drawing and embodiment:

Example 1

A method for identifying and detecting explosive vapor based on photoelectric response according to the present invention, the devices involved in the method are composed of sensors, light sources, power supplies, ammeters, signal processors and alarms, sensor 1, light source 2, power supply 3 , the ammeter 4 is connected in series with the signal processor 5, the signal processor 5 is connected with the alarm 6, and the light source 2 that can be periodically switched and changed in light intensity is used to irradiate a single sensor 1 with a fast photoelectric response, and on the sensor 1 The sensitive materials are silicon-zinc oxide p-n junction, silicon nanowire/graphene Schottky junction, titanium dioxide/graphene Schottky junction, silicon-zinc oxide core-shell nanowire array/graphene Schottky junction, silicon nanowire Wire/metal Schottky junction, silicon nanowire array, titanium dioxide modified silicon nanowire array/graphene Schottky junction or gold nanoparticle modified silicon nanowire array/graphene Schottky junction, the specific operation is as follows conduct:

a. A light-emitting diode (LED) light source 2 is installed above the silicon-zinc oxide core-shell nanowire array/graphene Schottky junction sensitive material of the sensor 1, and the light source 2 is a light irradiation sensitive material with a wavelength of 468nm;

b. Put the sensor 1 in the air, at room temperature, turn on and off the light source 2 periodically at intervals of 2s and change the light intensity, the light intensity is 1W/m ² , 2W/m ² , 3W/m ² , 4W/m ² , 5W/m ² , 6W/m ² , 7W/m ² , 8W/m 2 , measured silicon-zinc oxide core-shell nanowire array/graphene Schottky junction sensitive material under different light intensities Photocurrent to obtain the baseline current value (Fig. 2);

c. Place sensor 1 at room temperature in a series of known concentrations of trinitrotoluene (TNT), dinitrotoluene (DNT), p-nitrotoluene (PNT), picric acid (PA), RDX ), urea (Urea), black powder (BP) and ammonium nitrate (AN) saturated vapor, the light source 2 is periodically turned on and off at intervals of 2s and the light intensity is changed, the light intensity is 1W/m ² , 2W/ m ² , 3W/m ² , 4W/m ² , 5W/m ² , 6W/m ² , 7W/m ² , 8W/m , measured silicon-zinc oxide core-shell nanowire array/graphene Schottky junction The photoelectric current of the sensitive material under different light intensities is used to calibrate the explosive vapor. The photoelectric response curve of the sensitive material to the TNT room temperature saturated vapor is shown in (Fig. 3), and the photoelectric response curve of the sensitive material to the room temperature ammonium nitrate saturated vapor is shown in (Fig. 4); Calculate the response size of sensor 1 to different explosive vapors under each light intensity respectively, and the response size is defined as (I _explosive -I _air )/I _air , wherein I _explosive is the amount of sensitive material in explosive vapor Photocurrent size, Iair is the photocurrent size of sensitive material in _air ; room temperature condition p-trinitrotoluene (TNT), dinitrotoluene (DNT), p-nitrotoluene (PNT), picric acid (PA), The curves of photoelectric response of saturated vapor of 8 explosives including RDX, urea, black powder (BP) and ammonium nitrate (AN) as a function of light intensity are shown in Fig. 5;

d. Through the principal component analysis data processing method, the sensor 1 array p-trinitrotoluene (TNT), dinitrotoluene (DNT), p-nitrotoluene (PNT), picric acid (PA), RDX ), urea (Urea), black powder (BP) and ammonium nitrate (AN) explosive vapor response standard database (Figure 6);

e. Put the sensor 1 in the saturated vapor of trinitrotoluene (TNT) at room temperature, turn on and off the light source 2 periodically at intervals of 2s and change the light intensity, the light intensity is 1W/m ² , 2W/m ² , 3W respectively /m ² , 4W/m ² , 5W/m ² , 6W/m ² , 7W/m ² , 8W/m, measured silicon-zinc oxide core-shell nanowire array/graphene Schottky junction sensitive material The photocurrent under the light intensity is processed by principal component analysis, and compared with the database in step d, it is found that the suspected substance is consistent with trinitrotoluene (TNT).

Example 2

The device involved in the method is the same as in Example 1, and the specific operations are carried out in the following steps:

a. A light source 2 is installed above the silicon-zinc oxide sensitive material of the sensor 1, and the light source 2 with a wavelength of 468nm irradiates the sensitive material;

b. Put the sensor 1 in the air, at room temperature, turn on and off the light source 2 periodically at an interval of 1 ms and change the light intensity, the light intensity is 1W/m ² , 2W/m ² , 3W/m ² , 4W/m ² , 5W/m ² , 6W/m ² , measure the photocurrent of the silicon-zinc oxide sensitive material under different light intensities, and obtain the baseline current value;

c. Place the sensor 1 at room temperature respectively in trinitrotoluene (TNT), dinitrotoluene (DNT), picric acid (PA), RDX, pentaerythritol tetranitrate (PETN) and cyclotetraethylene In the saturated vapor of methyltetranitramine (HMX), by periodically turning on and off the light source 2 at an interval of 1ms and changing the light intensity, the light intensity is 1W/m ² , 2W/m ² , 3W/m ² , 4W /m ² , 5W/m ² , 6W/m ² , measure the photocurrent of the silicon-zinc oxide sensitive material under different light intensities, and calibrate the explosive vapor; combine with step b to calculate the sensor temperature under each light intensity To the response size of different explosive vapors, the response size is defined as (I _explosive -I _air )/I _air , wherein I _explosive is the photocurrent size of the sensitive material in the explosive vapor, and I _air is the sensitive material in the air The magnitude of the photocurrent in

D, by the data processing method of principal component analysis, obtain sensor 1 array p-trinitrotoluene (TNT), dinitrotoluene (DNT), picric acid (PA), RDX (RDX), pentaerythritol tetranitrate ( PETN) and cyclotetramethylenetetranitramine (HMX) explosives vapor response standard database;

e. Place the sensor 1 in the saturated vapor of cyclotetramethylene tetranitramine (HMX) at room temperature, and change the light intensity by periodically turning on and off the light source 2 at an interval of 1ms. The light intensity is 1W/m ² and 2W respectively. /m ² , 3W/m ² , 4W/m ² , 5W/m ² , 6W/m ² , measured the photocurrent of the silicon-zinc oxide sensitive material under different light intensities, processed the data by principal component analysis, and Comparing the database in step d, it is found that the suspected substance is consistent with cyclotetramethylene tetranitramine (HMX), which proves the existence of cyclotetramethylene tetranitramine (HMX).

Example 3

The device involved in the method is the same as in Example 1, and the specific operations are carried out in the following steps:

a. A laser light source 2 is installed above the titanium dioxide-modified silicon nanowire array/graphene Schottky junction sensitive material of the sensor 1, and the light source 2 has a wavelength of 532nm to irradiate the sensitive material;

b. Put the sensor 1 in the air, at room temperature, turn on and off the light source 2 periodically at an interval of 100ms and change the light intensity, the light intensity is 1W/m ² , 2W/m ² , 3W/m ² , 4W/m ² , 5W/m ² , 6W/m ² , 7W/m ² , 8W/m ² , 9W/m ² , 10W/m ² , measured on titanium dioxide-modified silicon nanowire arrays/graphene Schott The photocurrent of the base junction sensitive material under different light intensities is obtained to obtain the baseline current value;

c. Place the sensor 1 in the saturated vapor of black powder (BP), dinitrotoluene (DNT), picric acid (PA), RDX and triacetone triperoxide respectively at room temperature. Periodically turn on and off the light source 2 and change the light intensity, the light intensity is 1W/m ² , 2W/m ² , 3W/m ² , 4W/m ² , 5W/m ² , 6W/m ² , 7W/m ² , 8W/m ² , 9W/m ² , 10W/m ² , measure the photocurrent of titanium dioxide modified silicon nanowire array/graphene Schottky junction sensitive material under different light intensities, and calibrate the explosive vapor ; In conjunction with step b, calculate the response size of the sensor to different explosive vapors under each kind of light intensity respectively, and the response size is defined as (I _explosive -I _air )/I _air , wherein I _explosive is sensitive material in explosive vapor The photocurrent size in, I _air is the photocurrent size of sensitive material in air;

d. Through the data processing method of linear discriminant analysis, the sensor 1 array is obtained for black powder (BP), dinitrotoluene (DNT), picric acid (PA), RDX and triacetone triperoxide explosives A standard database of vapor responses;

e. Place the sensor 1 in the saturated vapor of triacetone triperoxide at room temperature, and change the light intensity by periodically turning on and off the light source 2 at an interval of 100ms. The light intensity is 1W/m ² , 2W/m ² , 3W/ m ² , 4W/m ² , 5W/m ² , 6W/m ² , 7W/m ² , 8W/m ² , 9W/m ² , 10W/m ² , measured on titanium dioxide-modified silicon nanowire arrays/graphene The photocurrent of the Schottky junction sensitive material under different light intensities is processed by linear discriminant analysis method, and compared with the database in step d, it is found that the suspected substance is consistent with triperoxytriacetone, which proves the existence of triacetone triperoxide .

Example 4

The device involved in the method is the same as in Example 1, and the specific operations are carried out in the following steps:

a. An LED light source 2 is installed above the silicon nanowire array/graphene Schottky junction sensitive material of the sensor 1, and the light source 2 with a wavelength of 367nm irradiates the sensitive material;

b. Put the sensor 1 in the air, at room temperature, turn on and off the light source 2 periodically at an interval of 500ms and change the light intensity, the light intensity is 2W/m ² , 3W/m ² , 4W/m ² , 5W/m ² , 6W/m ² , 7W/m ² , 8W/m ² , measure the photocurrent of the silicon nanowire array/graphene Schottky junction sensitive material under different light intensities, and obtain the baseline current value;

c. Place the sensor 1 in the saturated vapor of trinitrotoluene (TNT), dinitrotoluene (DNT), picric acid (PA), black powder (BP) and triacetone triperoxide respectively at room temperature, and pass through it for 500ms To periodically turn on and off the light source 2 and change the light intensity, the light intensity is 2W/m ² , 3W/m ² , 4W/m ² , 5W/m ² , 6W/m ² , 7W/m ² , 8W/m 2 m ² , measure the photocurrent of the silicon nanowire array/graphene Schottky junction sensitive material under different light intensities, and calibrate the explosive vapor; combined with step b, calculate the sensor’s response to different explosions under each light intensity The response size of material vapor, response size is defined as (I _explosive -I _air )/I _air , and wherein I _explosive is the photocurrent size of sensitive material in explosive vapor, and I _air is the photocurrent of sensitive material in air size;

d. Through the data processing method of the neural network model, the explosion of trinitrotoluene (TNT), dinitrotoluene (DNT), picric acid (PA), black powder (BP) and triacetone triperoxide in sensor 1 array is obtained Standard database for vapor response of substances;

e. Place the sensor 1 in the saturated vapor of black powder (BP) at room temperature, and change the light intensity by periodically turning on and off the light source 2 at an interval of 500ms. The light intensity is 2W/m ² , 3W/m ² , 4W/ m ² , 5W/m ² , 6W/m ² , 7W/m ² , 8W/m ² , measured the photocurrent of silicon nanowire array/graphene Schottky junction sensitive material under different light intensities, using neural network The model processes the data and compares it with the database in step d, and finds that the suspected substance matches the black powder (BP), which proves the existence of black powder (BP).

Example 5

The device involved in the method is the same as in Example 1, and the specific operations are carried out in the following steps:

a. An LED light source 2 is installed above the gold nanoparticle-modified silicon nanowire/graphene Schottky junction sensitive material of the sensor 1, and the light source 2 has a wavelength of 300nm to irradiate the sensitive material;

b. Put the sensor 1 in the air, at room temperature, turn on and off the light source 2 periodically at intervals of 1s and change the light intensity, the light intensity is 4W/m ² , 5W/m ² , 6W/m ² , measured the photocurrent of the silicon nanowire/graphene Schottky junction sensitive material modified by gold nanoparticles under different light intensities, and obtained the baseline current value;

c. Place the sensor 1 in the saturated vapor of RDX, urea, ammonium nitrate (AN) and pentaerythritol tetranitrate (PETN) at room temperature, and open and close periodically at intervals of 1s Light source 2 and change the light intensity, the light intensity is 4W/m ² , 5W/m ² , 6W/m ² respectively. It is measured that the gold nanoparticle modified silicon nanowire/graphene Schottky junction sensitive material The photocurrent of the explosive vapor is calibrated; in conjunction with step b, calculate the response size of the sensor to different explosive vapors under each light intensity respectively, and the response size is defined as (I _explosive -I _air )/I _air , where I _explosive is the photocurrent size of the sensitive material in the explosive vapor, and I _air is the photocurrent size of the sensitive material in the air;

d, through the data processing method of the neural network model, obtain the standard database of sensor 1 array to RDX, urea (Urea), ammonium nitrate (AN) and pentaerythritol tetranitrate (PETN) explosive vapor response;

e. Put the sensor 1 in the saturated vapor of urea at room temperature, turn on and off the light source 2 periodically at intervals of 1s and change the light intensity. The light intensity is 4W/m ² , 5W/m ² , and 6W/m ² respectively. Obtain the photocurrent of the silicon nanowire/graphene Schottky junction sensitive material modified by gold nanoparticles under different light intensities, use the neural network model to process the data, and compare the database in step d, and find that the suspected substance is consistent with urea, Prove the presence of urea.

Example 6

The device involved in the method is the same as in Example 1, and the specific operations are carried out in the following steps:

a. A Hg lamp light source 2 is installed above the titanium dioxide/graphene Schottky junction sensitive material of the sensor 1, and gratings, filters, lenses, and reflectors are added in the optical path, and the light source 2 has a wavelength of 500nm to irradiate the sensitive material;

b. Put the sensor 1 in the air, at room temperature, turn on and off the light source 2 periodically at intervals of 1.5s and change the light intensity, the light intensity is 2W/m ² , 3W/m ² , 4W/m ² , 5W/m ² , 6W/m ² , 7W/m ² , 8W/m ² , 9W/m ² , 10W/m ² Photocurrent, get the baseline current value;

c. Place the sensor 1 at room temperature respectively in trinitrotoluene (TNT), RDX, urea (Urea), ammonium nitrate (AN), pentaerythritol tetranitrate (PETN) and cyclotetramethylene tetranitrate In the saturated vapor of nitramine (HMX) and sulfur, by periodically turning on and off the light source 2 at an interval of 1.5s and changing the light intensity, the light intensity is 2W/m ² , 3W/m ² , 4W/m ² , 5W /m ² , 6W/m ² , 7W/m ² , 8W/m ² , 9W/m ² , 10W/m ² , measured the photocurrent of titanium dioxide/graphene Schottky junction sensitive material under different light intensities, The explosive vapor is calibrated; in conjunction with step b, calculate the response size of the sensor to different explosive vapors under each light intensity respectively, and the response size is defined as (I _explosive -I _air )/I _air , where the I _explosive is The photocurrent size of sensitive material in explosive vapor, Iair is the photocurrent size of sensitive material in _air ;

d. Through the data processing method of principal component analysis, the sensor array is obtained for trinitrotoluene (TNT), RDX, urea (Urea), ammonium nitrate (AN), pentaerythritol tetranitrate (PETN) and ring Standard database for vapor response of tetramethylenetetranitramine (HMX), sulfur explosives;

e. Place the sensor 1 in the saturated vapor of sulfur at room temperature, and change the light intensity by periodically turning on and off the light source 2 at an interval of 1.5s. The light intensity is 2W/m ² , 3W/m ² , 4W/m ² , 5W/m ² , 6W/m ² , 7W/m ² , 8W/m ² , 9W/m ² , 10W/m ² , measured the photocurrent of titanium dioxide/graphene Schottky junction sensitive material under different light intensities , using the principal component analysis method to process the data, and comparing the database in step d, it is found that the suspected substance is consistent with sulfur, which proves the presence of sulfur.

Example 7

The device involved in the method is the same as in Example 1, and the specific operations are carried out in the following steps:

a. A laser light source 2 is installed above the silicon nanowire array sensitive material of the sensor 1, and the light source 2 has a wavelength of 350nm to irradiate the sensitive material;

b. Put the sensor 1 in the air, at room temperature, turn on and off the light source 2 periodically at intervals of 2s and change the light intensity, the light intensity is 3W/m ² , 4W/m ² , 5W/m ² , 6W/m ² , 7W/m ² , 8W/m ² , measure the photocurrent of the silicon nanowire array sensitive material under different light intensities, and obtain the baseline current value;

c. Place the sensor 1 respectively in the saturated vapor of urea (Urea), ammonium nitrate (AN) and pentaerythritol tetranitrate (PETN) at room temperature, by periodically turning on and off the light source 2 at intervals of 2s and changing the light intensity, The light intensities are 3W/m ² , 4W/m ² , 5W/m ² , 6W/m ² , 7W/m ² , and 8W/m ² , and the photocurrents of silicon nanowire array sensitive materials under different light intensities were measured. , the explosive vapor is calibrated; combined with step b, calculate the response size of the sensor to different explosive vapors under each light intensity, and the response size is defined as (I _explosive -I _air )/I _air , where I _explosive is the photocurrent size of the sensitive material in the explosive vapor, and I _air is the photocurrent size of the sensitive material in the air;

d, through the data processing method of principal component analysis, obtain the standard database of sensor 1 array to urea (Urea), ammonium nitrate (AN) and pentaerythritol tetranitrate (PETN) explosive vapor response;

e. Place the sensor 1 in the saturated vapor of pentaerythritol tetranitrate (PETN) at room temperature, and change the light intensity by periodically switching on and off the light source 2 at an interval of 2s. The light intensity is 3W/m ² , 4W/m ² , 5W/m ² , 6W/m ² , 7W/m ² , 8W/m ² , measure the photocurrent of the silicon nanowire array sensitive material under different light intensities, process the data by principal component analysis, and compare step d In the database, it was found that the suspected substance matched with pentaerythritol tetranitrate (PETN), which proved the existence of pentaerythritol tetranitrate (PETN).

Example 8

The device involved in the method is the same as in Example 1, and the specific operations are carried out in the following steps:

a. An Xe light source 2 is installed above the sensitive material of the silicon nanowire array of the sensor 1, gratings, filters, lenses, and reflectors are added in the optical path, and the light source 2 has a wavelength of 500nm to irradiate the sensitive material;

b. Put the sensor 1 in the air, at room temperature, turn on and off the light source 2 periodically at intervals of 2s and change the light intensity, the light intensity is 3W/m ² , 4W/m ² , 5W/m ² , 6W/m ² , 7W/m ² , measure the photocurrent of the silicon nanowire array sensitive material under different light intensities, and obtain the baseline current value;

c. Place the sensor 1 in the saturated vapor of RDX, ammonium nitrate (AN), pentaerythritol tetranitrate (PETN), cyclotetramethylenetetranitramine (HMX) and urea respectively at room temperature. 2s is to periodically turn on and off the light source 2 and change the light intensity. The light intensity is 3W/m ² , 4W/m ² , 5W/m ² , 6W/m ² , and 7W/m ² . The measured silicon nanowire array The photocurrent of the sensitive material under different light intensities is used to calibrate the explosive vapor; in conjunction with step b, calculate the response size of the sensor to different explosive vapors under each light intensity respectively, and the response size is defined as (I _explosive -I _Air )/I _air , wherein I _explosive is the photocurrent size of sensitive material in explosive vapor, and I _air is the photocurrent size of sensitive material in air;

d. Through the data processing method of linear discriminant analysis, the standard database of sensor 1 array's response to ammonium nitrate, RDX, HMX, PETN, urea explosive vapor is obtained;

e. Place the sensor 1 in RDX saturated vapor at room temperature, and change the light intensity by periodically turning on and off the light source 2 at an interval of 2s. The light intensity is 3W/m ² , 4W/m ² , and 5W respectively. /m ² , 6W/m ² , 7W/m ² , measured the photocurrent of silicon nanowire array sensitive material under different light intensities, processed the data with linear discriminant analysis method, and compared the database in step d, found that suspected The object is consistent with RDX, which proves the existence of RDX.

Example 9

The device involved in the method is the same as in Example 1, and the specific operations are carried out in the following steps:

a. An LED light source 2 is installed above the silicon nanowire/gold Schottky junction sensitive material of the sensor 1, and gratings, filters, lenses, and reflectors are added in the optical path, and the light source 2 has a wavelength of 200nm to irradiate the sensitive material;

b. Put the sensor 1 in the air, at room temperature, turn on and off the light source 2 periodically at an interval of 50ms and change the light intensity, the light intensity is 1W/m ² , 2W/m ² , 3W/m ² , 4W/m ² , 5W/m ² , 6W/m ² , 7W/m ² , 8W/m ² , 9W/m ² , 10W/m ² Photocurrent under different light intensities to obtain the baseline current value;

c. Place sensor 1 at room temperature respectively in trinitrotoluene (TNT), dinitrotoluene (DNT), picric acid (PA), RDX, urea (Urea), black powder (BP), In the saturated vapor of ammonium nitrate (AN), pentaerythritol tetranitrate (PETN), cyclotetramethylenetetranitramine (HMX) and triacetone triperoxide, by periodically turning on and off the light source 2 at intervals of 50 ms and changing Light intensity, the light intensity is 1W/m ² , 2W/m ² , 3W/m ² , 4W/m ² , 5W/m ² , 6W/m ² , 7W/m ² , 8W/m ² , 9W/m 2 ^2. 10W/m ² , measure the photocurrent of the silicon nanowire/gold Schottky junction sensitive material under different light intensities, and calibrate the explosive vapor; combined with step b, calculate the sensor’s response to different light intensities. The response size of the explosive vapor, the response size is defined as (I _explosive -I _air )/I _air , wherein I _explosive is the photocurrent size of sensitive material in explosive vapor, and I _air is the photocurrent of sensitive material in air Photocurrent size;

d. Through the data processing method of principal component analysis, the sensor 1 array p-trinitrotoluene (TNT), dinitrotoluene (DNT), picric acid (PA), RDX, urea (Urea), A standard database of explosive vapor responses for black powder (BP), ammonium nitrate (AN), pentaerythritol tetranitrate (PETN), cyclotetramethylene tetranitramine (HMX) and triacetone triperoxide;

e. Place the sensor 1 in the saturated vapor of sodium chloride at room temperature, and change the light intensity by periodically turning on and off the light source 2 at an interval of 50ms. The light intensity is 1W/m ² , 2W/m ² , and 3W/m ² . , 4W/m ² , 5W/m ² , 6W/m ² , 7W/m ² , 8W/m ² , 9W/m ² , 10W/m ² The photocurrent under different light intensities is processed by the principal component analysis method, and compared with the database in step d, it is found that the suspected substance does not match the explosive substance in the standard library, which proves that there is no explosive substance.

Example 10

The device involved in the method is the same as in Example 1, and the specific operations are carried out in the following steps:

a. An LED light source 2 is installed above the silicon nanowire/silver Schottky junction sensitive material of the sensor 1, and gratings, filters, lenses, and reflectors are added in the optical path, and the light source 2 has a wavelength of 800nm to irradiate the sensitive material;

b. Put the sensor 1 in the air, at room temperature, turn on and off the light source 2 periodically at an interval of 800ms and change the light intensity, the light intensity is 1W/m ² , 2W/m ² , 3W/m ² , 4W/m ² , 5W/m ² , 6W/m ² , 7W/m ² , 8W/m ² , 9W/m ² , 10W/m ² Photocurrent under different light intensities to obtain the baseline current value;

c. Place sensor 1 at room temperature respectively in trinitrotoluene (TNT), dinitrotoluene (DNT), picric acid (PA), RDX, urea (Urea), black powder (BP), In the saturated vapor of ammonium nitrate (AN), pentaerythritol tetranitrate (PETN), cyclotetramethylenetetranitramine (HMX) and triacetone triperoxide, by periodically turning on and off the light source at an interval of 800ms and changing the light Intensity, the light intensity is 1W/m ² , 2W/m ² , 3W/m ² , 4W/m ² , 5W/m ² , 6W/m ² , 7W/m ² , 8W/m ² , 9W/m ² , 10W/m ² , measure the photocurrent of the silicon nanowire/silver Schottky junction sensitive material under different light intensities, and calibrate the explosive vapor; combined with step b, calculate the sensor’s response to different light intensities The response size of explosive vapor, response size is defined as (I _explosive -I _air )/I _air , and wherein I _explosive is the photocurrent size of sensitive material in explosive vapor, and I _air is the light of sensitive material in air Current size;

d. Through the data processing method of the neural network model, the sensor 1 array p-trinitrotoluene (TNT), dinitrotoluene (DNT), picric acid (PA), RDX, urea (Urea), A standard database of explosive vapor responses for black powder (BP), ammonium nitrate (AN), pentaerythritol tetranitrate (PETN), cyclotetramethylene tetranitramine (HMX) and triacetone triperoxide;

e. Put the sensor 1 in the saturated vapor of monosodium glutamate at room temperature, turn on and off the light source 2 periodically at an interval of 800ms and change the light intensity, the light intensity is 1W/m ² , 2W/m ² , 3W/m ² , 4W /m ² , 5W/m ² , 6W/m ² , 7W/m ² , 8W/m ² , 9W/m ² , 10W/m ² Under the strong photocurrent, the neural network model is used to process the data, and compared with the database in step d, it is found that the suspected object does not match the explosive in the standard library, which proves that there is no explosive.

In the embodiments of the present invention, for those of ordinary skill in the art, some modifications can be made to the present invention without departing from the principle of the present invention, such as including the use of optical elements such as gratings, filters, lenses (groups) to detect light Process the optical components and optical paths, increase the types of explosives to obtain a standard database to detect explosives not included in this embodiment, increase the number of light intensities, change the intensity of light intensity, replace photoelectric sensitive materials, use other light sources, and collect resistance and voltage change signal.

Claims (1)

1. A method for identifying and detecting explosive vapor based on photoelectric response, characterized in that the device involved in the method is made up of sensor, light source, power supply, ammeter, signal processor and alarm, sensor (1), light source (2 ), the power supply (3), the ammeter (4) are connected in series with the signal processor (5), the signal processor (5) is connected with the alarm (6), and the light source (2) that can switch periodically and change the light intensity is used , irradiated on a single sensor (1) with fast photoelectric response, the sensitive material on the sensor (1) is silicon-zinc oxide p-n junction, silicon nanowire/graphene Schottky junction, titanium dioxide/graphene Schottky junction, Silicon-ZnO core-shell nanowire arrays/graphene Schottky junctions, silicon nanowires/metal Schottky junctions, silicon nanowire arrays, titanium dioxide-modified silicon nanowire arrays/graphene Schottky junctions, or gold nanoparticles Modified silicon nanowire array/graphene Schottky junction, the specific operation is carried out according to the following steps: a. A light source (2) is installed above the sensitive material of the sensor (1), and the light source (2) is a light-emitting diode (LED), Xe lamp, Hg lamp or laser light source with a wavelength range of 200-800 nm; b. Put the sensor (1) in the air, at room temperature, by periodically turning on and off the light source (2) at an interval of 1 ms-2 s and changing the light intensity, measure the sensitivity of the sensitive material under different light intensities Photocurrent, wherein the number of light intensities is 3-10, and the baseline current value is obtained; c. Place the sensor (1) in a series of different explosive vapors with known concentrations at room temperature, and periodically turn on and off the light source (2) at an interval of 1 ms-2 s and change the light intensity, where the light intensity number 3-10, measure the photocurrent of sensitive materials under different light intensities, calibrate the explosive vapor, and calculate the response of the sensor (1) to different explosive vapors under each light intensity, and the response is defined as (I _explosive -I _air )/I _air , wherein I _explosive is the photocurrent size of the sensitive material in the explosive vapor, and I _air is the photocurrent size of the sensitive material in the air; d. Through principal component analysis, neural network model or linear discriminant analysis data processing method, obtain the standard database of sensor (1) response to different types of explosive vapor; e. Place the sensor (1) in the atmosphere to be tested, and measure the photocurrent of the sensitive material under different light intensities by periodically turning on and off the light source (2) at an interval of 1 ms-2 s and changing the light intensity. The number of light intensities is 3-10, and the response signal is processed by the same data processing method as in step d, and compared with the database in step d, to obtain whether there is explosive vapor and what kind of explosive vapor exists.