Reconfigurable neuromorphic olfaction sensing memristor system and preparation method thereof

Abstract

The invention discloses a reconfigurable neuromorphic olfactory sensing memristor system and a preparation method thereof, and relates to the technical field of neuromorphic electronics and intelligent sensing, and the system comprises a sensing calculation module which comprises a memristor based on an MXene (at) SnS2 (at) PANI heterostructure, the memristor shows a synaptic characteristic under gas stimulation and shows a neuron characteristic under electric pulse stimulation, and the memristor shows a neuron characteristic under electric pulse stimulation; a sensing signal is output; the signal conditioning and encoding module receives the sensing signal and encodes the sensing signal into a pulse sequence; the neuromorphic calculation module receives the pulse sequence and executes gas concentration identification and gas flow rate identification according to the reservoir calculation network and the pulse neural network; and the decision and execution module generates a control instruction according to an output result of the neuromorphic calculation module so as to trigger a corresponding decision operation. Integrated integration of gas sensing and neuromorphic calculation is realized, and high-efficiency and low-power-consumption intelligent gas monitoring is realized.

Description

Technical Field

[0001] This invention relates to the field of neuromorphic electronics and intelligent sensing technology, and in particular to a reconfigurable neuromorphic olfactory sensing memristor system and its fabrication method. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] The rapid development of the Internet of Things (IoT) and artificial intelligence (AI) has placed higher demands on the intelligence and energy efficiency of terminal sensors. Traditional electronic olfactory systems typically follow the von Neumann architecture, where sensing, analog-to-digital conversion, storage, and processing units are physically separated. This structure leads to frequent data transfer between units, resulting in significant energy consumption and transmission latency, making it difficult to meet the real-time, low-power edge computing requirements.

[0004] Inspired by biological sensory systems, neuromorphic computing aims to mimic the efficient information processing methods of the biological brain. Among these, memristors are considered ideal components for building integrated sensory-computing systems because they can mimic the behavior of biological synapses and neurons.

[0005] In recent years, although some research has attempted to develop neuromorphic gas sensors, most of them are single-function, either only able to achieve synaptic plasticity or only able to simulate neuronal firing, lacking the ability to dynamically reconfigure on a single device according to task requirements. This functional fragmentation limits the flexibility and efficiency of the system in handling complex and variable gas environments (such as simultaneously needing to identify gas type, concentration, and leakage rate). Summary of the Invention

[0006] To address the aforementioned issues, this invention proposes a reconfigurable neuromorphic olfactory sensing memristor system and its fabrication method. This system integrates gas sensing with neuromorphic computation, dynamically switching its computational paradigm based on the type of external stimulus (gas or electrical pulse). This enables efficient and low-power intelligent gas monitoring, solving the problems of separation between gas sensing and information processing, low system energy efficiency, limited functionality, and inability to dynamically reconfigure existing technologies.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a reconfigurable neuromorphic olfactory sensing memristor system, comprising: The sensing and computing module includes a memristor based on the MXene@SnS2@PANI heterostructure. The memristor exhibits synaptic characteristics under gas stimulation and neuronal characteristics under electrical pulse stimulation, so as to output sensing signals. The signal conditioning and encoding module is used to receive the sensed signal and encode it into a pulse sequence. The neuromorphic computing module is used to receive pulse sequences and perform gas concentration identification and gas flow rate identification according to the reservoir computing network and the spiking neural network, respectively. The decision-making and execution module is used to generate control commands based on the output of the neuromorphic computing module, thereby triggering the corresponding decision-making operations.

[0008] As an alternative implementation, the memristor based on the MXene@SnS2@PANI heterostructure includes, from bottom to top, interdigitated electrodes, an MXene@SnS2 nanoflower-shaped thin film, and a PANI thin film; wherein, MXene@SnS2 is an n-type semiconductor and PANI is a p-type semiconductor, and the two form a pn heterojunction. The width of the depletion layer at the interface changes dynamically with gas adsorption / desorption or electrical pulse stimulation, thereby achieving reconfigurable control of the conduction state.

[0009] As an alternative implementation, the operating modes of the system include: When in gas sensing mode, gas molecules interact with PANI, changing the heterojunction band structure. At this time, the memristor exhibits synaptic characteristics under gas stimulation. When in electrical processing mode, the applied voltage pulse resets the heterojunction band structure, and when the pulse integral exceeds the threshold, it generates a neuron-like all-or-no spike discharge. At this time, the memristor exhibits neuronal characteristics under electrical pulse stimulation.

[0010] As an alternative implementation, the fabrication process of the memristor based on the MXene@SnS2@PANI heterostructure includes: LiF was uniformly dispersed in HCl solution, Ti3AlC2 powder was added, and the mixture was reacted in a water bath at 30℃-50℃. After washing and drying, multilayer MXene was obtained. PMMA template microspheres and multilayer MXene were mixed at a mass ratio of 9-11:1. MXene was coated on the surface of PMMA template microspheres by electrostatic adsorption. After centrifugation and drying, PMMA@MXene composite microspheres were obtained. PMMA@MXene composite microspheres were dispersed in a mixed solvent of ethanol and ethylene glycol, and thiourea and tin chloride pentahydrate were added sequentially. After ultrasonic stirring, the mixture was transferred to a high-pressure reactor and reacted at 130℃-170℃. The reaction product was washed, vacuum dried, and then annealed at 300℃-400℃ for 1 hour under an argon atmosphere to obtain MXene@SnS2 nanoflowers. MXene@SnS2 nanospheres were dispersed in ethanol and a uniform film with a thickness of 1 μm was formed on the surface of the interdigitated electrode by spin coating. A DMF solution of PANI was spin-coated onto the MXene@SnS2 film to form a PANI film with a thickness of 50 nm. After air drying, an MXene@SnS2@PANI composite thin film memristor was formed.

[0011] As an alternative implementation method, in the process of preparing PMMA@MXene composite microspheres: after adding Ti3AlC2 powder, the reaction is carried out in a 40°C water bath, and the PMMA template microspheres and multilayer MXene are mixed at a mass ratio of 10:1.

[0012] As an alternative implementation method, the preparation of MXene@SnS2 nanoflowers involves: reacting at 150°C in a high-pressure reactor and annealing at 350°C for 1 hour under an argon atmosphere.

[0013] As an alternative implementation method, spin coating includes: a spin coating speed of 4500-5500 rpm and a spin coating time of 25-35 seconds.

[0014] As an alternative implementation method, the spin coating speed is 5000 rpm and the spin coating time is 30 seconds.

[0015] Secondly, the present invention provides a method for fabricating a reconfigurable neuromorphic olfactory sensor memristor, comprising: LiF was uniformly dispersed in HCl solution, Ti3AlC2 powder was added, and the mixture was reacted in a water bath at 30℃-50℃. After washing and drying, multilayer MXene was obtained. PMMA template microspheres and multilayer MXene were mixed at a mass ratio of 9-11:1. MXene was coated on the surface of PMMA template microspheres by electrostatic adsorption. After centrifugation and drying, PMMA@MXene composite microspheres were obtained. PMMA@MXene composite microspheres were dispersed in a mixed solvent of ethanol and ethylene glycol, and thiourea and tin chloride pentahydrate were added sequentially. After ultrasonic stirring, the mixture was transferred to a high-pressure reactor and reacted at 130℃-170℃. The reaction product was washed, vacuum dried, and then annealed at 300℃-400℃ for 1 hour under an argon atmosphere to obtain MXene@SnS2 nanoflowers. MXene@SnS2 nanoflowers were dispersed in ethanol and a uniform film with a thickness of 1 μm was formed on the surface of the interdigitated electrode by spin coating. A DMF solution of PANI was spin-coated onto the MXene@SnS2 film to form a PANI film with a thickness of 50 nm. After air drying, an MXene@SnS2@PANI composite film memristor was formed. The sensing and computing module, signal conditioning and encoding module, neuromorphic computing module, and decision and execution module are electrically connected to form a reconfigurable neuromorphic olfactory sensing memristor system.

[0016] Thirdly, the present invention provides an application of the reconfigurable neuromorphic olfactory sensor memristor system described in the first aspect in smart home air quality monitoring, industrial hazardous gas leak early warning, or real-time monitoring of harmful gases in warehouse environments.

[0017] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention proposes a reconfigurable neuromorphic olfactory sensing memristor system and its fabrication method. A memristor with an MXene@SnS2@PANI heterostructure is used as the sensing and computing module. Through bandgap engineering, the device achieves a dynamically reconfigurable response to gas and electrical stimuli. In gas mode, the memristor exhibits synaptic plasticity, including short-term plasticity, long-term plasticity, and double-pulse facilitation, which can be used for reservoir calculations to identify gas concentrations. In electrical pulse mode, the memristor exhibits integrative-firing neuronal characteristics, which can be used for spiking neural networks to identify gas flow rates. This system breaks through the bottlenecks of the traditional von Neumann architecture and overturns the discrete architecture of traditional sensing systems. It integrates sensing, information processing, and storage functions into a single memristor device, achieving low-power, low-latency, and highly integrated sensor computing. It eliminates the energy consumption and latency caused by data transfer and integrates wireless communication and actuators, enabling the construction of a complete "perception-decision-execution" closed loop. It is suitable for real-time, intelligent gas monitoring and early warning in scenarios such as smart homes, industrial safety, and environmental monitoring.

[0018] In this invention, the same hardware platform can dynamically switch its functional role (synapse / neuron) according to the type of input signal (gas / electricity), realizing on-demand allocation of hardware resources, greatly enhancing the system's flexibility and adaptability, while simplifying peripheral circuits.

[0019] This invention integrates reservoir computing and spiking neural networks to simultaneously and efficiently process two key types of information: gas concentration (spatial pattern) and gas flow rate (temporal pattern), providing a more comprehensive and accurate environmental perception capability. Furthermore, the system performs intensive computation only when a relevant gas event is detected, resulting in extremely low static power consumption, making it suitable for battery-powered IoT terminal devices.

[0020] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0022] Figure 1 This is a schematic diagram of the overall architecture of the reconfigurable neuromorphic olfactory sensing memristor system provided in Embodiment 1 of the present invention; Figure 2 This is a schematic diagram of the material fabrication of the MXene@SnS2@PANI heterostructure memristor provided in Embodiment 1 of the present invention; Figure 3 The material XRD pattern of the MXene@SnS2@PANI heterostructure memristor provided in Embodiment 1 of the present invention; Figure 4 The material SEM image of the MXene@SnS2@PANI heterostructure memristor provided in Embodiment 1 of the present invention; Figure 5 The material EDS spectrum of the MXene@SnS2@PANI heterostructure memristor provided in Embodiment 1 of the present invention; Figure 6 The material XPS spectrum of the MXene@SnS2@PANI heterostructure memristor provided in Embodiment 1 of the present invention; Figure 7 This is a graph showing the synaptic response recovery time in gas mode provided in Embodiment 1 of the present invention; Figure 8 This is a graph showing the synaptic double-pulse facilitation behavior in gas mode provided in Embodiment 1 of the present invention. Figure 9 This is a schematic diagram illustrating the transition from short-term plasticity to long-term plasticity induced by different numbers of gas pulses, as provided in Embodiment 1 of the present invention. Figure 10 This is a schematic diagram of biological neuron integration and firing in electrical pulse mode provided in Embodiment 1 of the present invention; Figure 11 This is a schematic diagram of neuron-like responses excited by different numbers of electrical pulses in an electrical pulse mode, as provided in Embodiment 1 of the present invention. Figure 12 This is a schematic diagram of the threshold activation characteristics of neurons in electrical pulse mode provided in Embodiment 1 of the present invention; Figure 13 This is a schematic diagram illustrating the gas concentration identification accuracy based on a storage pool computing network provided in Embodiment 1 of the present invention. Figure 14A schematic diagram illustrating the gas flow velocity recognition accuracy based on a pulse neural network provided in Embodiment 1 of the present invention; Figure 15 This is a schematic diagram illustrating the application of the present invention in a smart home scenario, as provided in Embodiment 1 of the present invention. Figure 16 This is a structural block diagram of the system hardware platform provided in Embodiment 1 of the present invention; Figure 17 This is a data flow diagram of the system signal processing and decision-making process provided in Embodiment 1 of the present invention; Figure 18 This is a schematic diagram of the system prototype provided in Embodiment 1 of the present invention. Detailed Implementation

[0023] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0024] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0025] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, unless the context clearly indicates otherwise, the singular form is intended to include the plural form as well. Furthermore, it should be understood that the terms “comprising” and “including”, and any variations thereof, are intended to cover non-exclusive inclusion, for example, a process, method, system, product, or apparatus that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0026] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0027] Example 1 This embodiment provides a reconfigurable neuromorphic olfactory sensing memristor system, such as Figure 1 As shown, the principle of an integrated bioneuromnographic system is used to promote the interaction of multisensory information. Its design concept draws inspiration from the neural excitability regulation mechanism induced by human respiratory behavior. Employing the MXene@SnS2@PANI sensor, it simulates the functional properties of an electronic nose, enabling precise detection of NH3 and airflow changes.

[0028] This reconfigurable neuromorphic olfactory sensing memristor system includes: The sensing and computing module includes a memristor based on the MXene@SnS2@PANI heterostructure for simultaneously sensing gas signals; the memristor exhibits synaptic characteristics under gas stimulation, supporting the computation of the reservoir network, and exhibits neuronal characteristics under electrical pulse stimulation, supporting the computation of the spiking neural network. The signal conditioning and encoding module, connected to the sensing and computing module, is used to preprocess the analog current signal output by the memristor and encode it into a pulse sequence. The neuromorphic computing module integrates a reservoir computing network and a spiking neural network. It receives the encoded pulse sequence and performs gas concentration recognition and gas flow rate recognition according to the reservoir computing network and the spiking neural network, respectively. The decision-making and execution module generates control commands based on the output of the neuromorphic computing module, thereby triggering decision-making operations such as alarms, ventilation, or communication. The wireless communication module is used to transmit system status, gas concentration data, and early warning information to the user terminal.

[0029] In this embodiment, the memristor based on the MXene@SnS2@PANI heterostructure includes, from bottom to top, interdigitated electrodes, an MXene@SnS2 nanoflower-shaped thin film, and a PANI thin film; Among them, MXene@SnS2 is an n-type semiconductor and PANI is a p-type semiconductor. The two form a pn heterojunction. The width of the depletion layer at the interface changes dynamically with gas adsorption / desorption or electrical pulse stimulation, thereby realizing the reconfigurable control of the conduction state.

[0030] In this embodiment, the operating mode of the system can be dynamically switched, specifically including: When in gas sensing mode, gas molecules interact with PANI, changing the heterojunction band structure and causing analog, non-volatile, or volatile changes in the device's conductance. This is used to simulate the short-term and long-term plasticity of biological synapses. At this time, the memristor exhibits synaptic characteristics under gas stimulation. After sensing the gas signal, it extracts the spatiotemporal characteristics of the gas concentration through a reservoir computing network.

[0031] When in electrical processing mode, the applied voltage pulse causes the heterojunction band structure to reset, and when the pulse integral exceeds the threshold, a neuron-like "all-or-none" spike discharge is generated. At this time, the memristor exhibits neuronal characteristics under electrical pulse stimulation. After sensing the gas signal, it identifies dynamic events such as gas flow rate through a spiking neural network.

[0032] like Figure 2 The diagram shows the preparation of the MXene@SnS2@PANI composite material, which includes the following steps: Step 1: Preparation of hollow PMMA@MXene composite microspheres: LiF was uniformly dispersed in HCl solution, Ti3AlC2 powder was added, and the mixture was reacted in a water bath at 30℃-50℃ for 24 hours. After washing and drying, multilayer MXene was obtained. PMMA template microspheres were mixed with multilayer MXene at a mass ratio of 9-11:1. MXene was then coated onto the surface of the PMMA template microspheres by electrostatic adsorption. After centrifugation and drying, PMMA@MXene composite microspheres were obtained.

[0033] As an alternative implementation method, Ti3AlC2 powder is added and the reaction is carried out in a water bath at 40°C.

[0034] As an alternative implementation method, PMMA template microspheres are mixed with multilayer MXene at a mass ratio of 10:1.

[0035] Step 2: Synthesis of MXene@SnS2 nanofloral balls: PMMA@MXene composite microspheres were dispersed in a mixed solvent of ethanol and ethylene glycol, and thiourea and tin chloride pentahydrate were added sequentially. After sonication and stirring, the mixture was transferred to a high-pressure reactor and reacted at 130℃-170℃ for 24 hours. After washing and vacuum drying, the reaction product was annealed at 300 ℃-400 ℃ for 1 hour under an argon atmosphere to remove the PMMA template, resulting in hollow MXene@SnS2 nanoflowers.

[0036] As an alternative implementation method, the mixture is transferred to a high-pressure reactor and reacted at 150°C.

[0037] As an alternative implementation method, annealing is performed at 350 °C for 1 hour under an argon atmosphere.

[0038] Step 3: Construct the sensor computing module: MXene@SnS2 nanoflowers were dispersed in ethanol and a uniform film with a thickness of 1 μm was formed on the surface of the interdigitated electrode by spin coating. Subsequently, a PANI DMF solution was spin-coated onto an MXene@SnS2 film to form a PANI film with a thickness of 50 nm. After air drying, an MXene@SnS2@PANI composite thin film memristor was formed.

[0039] As an alternative implementation method, the spin coating speed is 4500-5500 rpm and the spin coating time is 25-35 seconds.

[0040] Preferably, the spin coating speed is 5000 rpm and the spin coating time is 30 seconds.

[0041] In this embodiment, XRD tests were performed to demonstrate the crystallization effect of the MXene@SnS2@PANI composite material. The test results are as follows: Figure 3 As shown, this demonstrates the successful synthesis of the MXene@SnS2@PANI composite material.

[0042] To verify the microstructure of the MXene@SnS2@PANI hollow microspheres, SEM testing was performed. The test results are as follows: Figure 4 As shown, MXene@SnS2@PANI is a microsphere with uniformly sized nanosheets loaded on its surface. The results of SEM and EDS tests (…) Figure 5 As shown in the figure, the nanosheet structure SnS2 is SnS2 nanosheet.

[0043] XPS testing was performed to verify the composition of MXene@SnS2@PANI. The test results are as follows: Figure 6 As shown, where, Figure 6 (a) in the figure is the total spectrum of XPS elements. Figure 6 (b)-(e) in the figure represent the narrow spectrum scans corresponding to N, O, C, and Ti elements, respectively. The successful synthesis of MXene@SnS2@PANI is confirmed by XRD and XPS test results.

[0044] To demonstrate the response-recovery performance of the MXene / SnS2 / PANI sensor, response-recovery tests were conducted. The test results are as follows: Figure 7 As shown, the response time is 0.9 s and the recovery time is 15.7 s, which means that the MXene / SnS2 / PANI sensor has the characteristics of fast response and recovery.

[0045] An experiment on gas synaptic characteristic detection based on an MXene / SnS2 / PANI sensor was conducted. The specific test conditions were: ammonia pulse detection experiment using 100 ppm ammonia gas in a gas chamber at a temperature of 25℃ and a humidity of 45%. Figure 8 As shown, the device exhibits facilitated behavior under synaptic double-pulse conditions in gas mode. Figure 9 As shown, through multiple gas pulse tests, the synaptic properties change from short-term plasticity to long-term plasticity with increasing number of tests.

[0046] like Figure 10 The diagram illustrates the integration and firing of biological neurons in electrical pulse mode. A neuron, composed of a cell body, dendrites, and axon, is the core unit for information integration and transmission. Action potentials are generated by the diffusion of ions across the cell membrane. When the current flowing through a neuron reaches a predetermined threshold, it transmits an integrated signal to subsequent neurons.

[0047] An experiment was conducted to detect the characteristics of electrical neurons based on the MXene / SnS2 / PANI sensor. The specific test conditions were: pulse detection with a pulse width of 0.3 seconds using a 10V current. Figure 11 As shown, after multiple electrical pulse tests, the device exhibits neuron-like rapid response characteristics. Figure 12 As shown, in continuous testing, the threshold firing characteristics of neurons are simulated when the current signal exceeds a certain threshold.

[0048] To demonstrate the accuracy of the intelligent detection and recognition capabilities of this system. For example... Figure 13 As shown, this system, combined with the storage tank calculation, identifies gas concentrations, achieving an accuracy rate of 91.56% for NH3 concentration identification. Figure 14 As shown, this system uses a pulse neural network to identify gas flow rate with an accuracy of 93.98%.

[0049] like Figure 15 The image shows the application of this intelligent system in a smart home scenario. Multiple intelligent detection devices can monitor indoor gas leaks in real time. For example... Figure 16 The diagram shown is a detailed structural diagram of the hardware platform in this system. Figure 18 This is a schematic diagram of the prototype of this system. To further clarify the internal signal processing and decision-making processes of the system, Figure 17 This demonstrates the process by which the system's internal reservoir calculations and spiking neural network combine to process signals when gas is detected.

[0050] In further embodiments, a method for fabricating the reconfigurable neuromorphic olfactory sensing memristor system described in Embodiment 1 is also provided, comprising the following steps: Step 1: Preparation of hollow PMMA@MXene composite microspheres: LiF was uniformly dispersed in HCl solution, Ti3AlC2 powder was added, and the mixture was reacted in a water bath at 30℃-50℃ for 24 hours. After washing and drying, multilayer MXene was obtained. PMMA template microspheres were mixed with multilayer MXene at a mass ratio of 9-11:1. MXene was then coated onto the surface of the PMMA template microspheres by electrostatic adsorption. After centrifugation and drying, PMMA@MXene composite microspheres were obtained.

[0051] As an alternative implementation method, Ti3AlC2 powder is added and the reaction is carried out in a water bath at 40°C.

[0052] As an alternative implementation method, PMMA template microspheres are mixed with multilayer MXene at a mass ratio of 10:1.

[0053] Step 2: Synthesis of MXene@SnS2 nanofloral balls: PMMA@MXene composite microspheres were dispersed in a mixed solvent of ethanol and ethylene glycol, and thiourea and tin chloride pentahydrate were added sequentially. After sonication and stirring, the mixture was transferred to a high-pressure reactor and reacted at 130℃-170℃ for 24 hours. After washing and vacuum drying, the reaction product was annealed at 300 ℃-400 ℃ for 1 hour under an argon atmosphere to remove the PMMA template, resulting in hollow MXene@SnS2 nanoflowers.

[0054] As an alternative implementation method, the mixture is transferred to a high-pressure reactor and reacted at 150°C.

[0055] As an alternative implementation method, annealing is performed at 350 °C for 1 hour under an argon atmosphere.

[0056] Step 3: Construct the sensor computing module: MXene@SnS2 nanoflowers were dispersed in ethanol and a uniform film with a thickness of 1 μm was formed on the surface of the interdigitated electrode by spin coating. Subsequently, a PANI DMF solution was spin-coated onto an MXene@SnS2 film to form a PANI film with a thickness of 50 nm. After air drying, an MXene@SnS2@PANI composite thin film memristor was formed.

[0057] As an alternative implementation method, the spin coating speed is 4500-5500 rpm and the spin coating time is 25-35 seconds.

[0058] Preferably, the spin coating speed is 5000 rpm and the spin coating time is 30 seconds.

[0059] Step 4: Electrically connect the sensing and computing module, signal conditioning and encoding module, neuromorphic computing module, and decision and execution module to form a reconfigurable neuromorphic olfactory sensing memristor system.

[0060] In further embodiments, an application of the reconfigurable neuromorphic olfactory sensor memristor system as described in Embodiment 1 is also provided in smart home air quality monitoring, industrial hazardous gas leak early warning, or real-time monitoring of harmful gases in warehouse environments.

[0061] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A reconfigurable neuromorphic olfactory sensing memristor system, characterized in that, include: The sensing and computing module includes a memristor based on the MXene@SnS2@PANI heterostructure. The memristor exhibits synaptic characteristics under gas stimulation and neuronal characteristics under electrical pulse stimulation, so as to output sensing signals. The signal conditioning and encoding module is used to receive the sensed signal and encode it into a pulse sequence. The neuromorphic computing module is used to receive pulse sequences and perform gas concentration identification and gas flow rate identification according to the reservoir computing network and the spiking neural network, respectively. The decision-making and execution module is used to generate control commands based on the output of the neuromorphic computing module, thereby triggering the corresponding decision-making operations.

2. The reconfigurable neuromorphic olfactory sensing memristor system as described in claim 1, characterized in that, The memristor based on the MXene@SnS2@PANI heterostructure includes, from bottom to top, interdigitated electrodes, an MXene@SnS2 nanoflower-shaped thin film, and a PANI thin film. MXene@SnS2 is an n-type semiconductor, and PANI is a p-type semiconductor. The two form a pn heterojunction, and the width of the depletion layer at the interface changes dynamically with gas adsorption / desorption or electrical pulse stimulation, thereby achieving reconfigurable control of the conduction state.

3. The reconfigurable neuromorphic olfactory sensing memristor system as described in claim 2, characterized in that, The system's operating modes include: When in gas sensing mode, gas molecules interact with PANI, changing the heterojunction band structure. At this time, the memristor exhibits synaptic characteristics under gas stimulation. When in electrical processing mode, the applied voltage pulse resets the heterojunction band structure, and when the pulse integral exceeds the threshold, it generates a neuron-like all-or-no spike discharge. At this time, the memristor exhibits neuronal characteristics under electrical pulse stimulation.

4. The reconfigurable neuromorphic olfactory sensing memristor system as described in claim 1, characterized in that, The fabrication process of memristors based on the MXene@SnS2@PANI heterostructure includes: LiF was uniformly dispersed in HCl solution, Ti3AlC2 powder was added, and the mixture was reacted in a water bath at 30℃-50℃. After washing and drying, multilayer MXene was obtained. PMMA template microspheres and multilayer MXene were mixed at a mass ratio of 9-11:

1. MXene was coated on the surface of PMMA template microspheres by electrostatic adsorption. After centrifugation and drying, PMMA@MXene composite microspheres were obtained. PMMA@MXene composite microspheres were dispersed in a mixed solvent of ethanol and ethylene glycol, and thiourea and tin chloride pentahydrate were added sequentially. After ultrasonic stirring, the mixture was transferred to a high-pressure reactor and reacted at 130℃-170℃. The reaction product was washed, vacuum dried, and then annealed at 300℃-400℃ for 1 hour under an argon atmosphere to obtain MXene@SnS2 nanoflowers. MXene@SnS2 nanospheres were dispersed in ethanol and a uniform film with a thickness of 1 μm was formed on the surface of the interdigitated electrode by spin coating. A DMF solution of PANI was spin-coated onto the MXene@SnS2 film to form a PANI film with a thickness of 50 nm. After air drying, an MXene@SnS2@PANI composite thin film memristor was formed.

5. A reconfigurable neuromorphic olfactory sensing memristor system as described in claim 4, characterized in that, In the preparation of PMMA@MXene composite microspheres: after adding Ti3AlC2 powder, the reaction was carried out in a 40℃ water bath, and the PMMA template microspheres and multilayer MXene were mixed at a mass ratio of 10:

1.

6. A reconfigurable neuromorphic olfactory sensing memristor system as described in claim 4, characterized in that, In the preparation of MXene@SnS2 nanoflowers: the reaction was carried out in a high-pressure reactor at 150°C, and then annealed at 350°C for 1 hour under an argon atmosphere.

7. A reconfigurable neuromorphic olfactory sensing memristor system as described in claim 4, characterized in that, Spin coating method includes: spin coating speed of 4500-5500 rpm, spin coating time of 25-35 seconds.

8. A reconfigurable neuromorphic olfactory sensing memristor system as described in claim 7, characterized in that, The spin coating speed was 5000 rpm and the spin coating time was 30 seconds.

9. A method for fabricating a reconfigurable neuromorphic olfactory sensing memristor system, characterized in that, include: LiF was uniformly dispersed in HCl solution, Ti3AlC2 powder was added, and the mixture was reacted in a water bath at 30℃-50℃. After washing and drying, multilayer MXene was obtained. PMMA template microspheres and multilayer MXene were mixed at a mass ratio of 9-11:

1. MXene was coated on the surface of PMMA template microspheres by electrostatic adsorption. After centrifugation and drying, PMMA@MXene composite microspheres were obtained. PMMA@MXene composite microspheres were dispersed in a mixed solvent of ethanol and ethylene glycol, and thiourea and tin chloride pentahydrate were added sequentially. After ultrasonic stirring, the mixture was transferred to a high-pressure reactor and reacted at 130℃-170℃. The reaction product was washed, vacuum dried, and then annealed at 300℃-400℃ for 1 hour under an argon atmosphere to obtain MXene@SnS2 nanoflowers. MXene@SnS2 nanoflowers were dispersed in ethanol and a uniform film with a thickness of 1 μm was formed on the surface of the interdigitated electrode by spin coating. A DMF solution of PANI was spin-coated onto the MXene@SnS2 film to form a PANI film with a thickness of 50 nm. After air drying, an MXene@SnS2@PANI composite film memristor was formed. The sensing and computing module, signal conditioning and encoding module, neuromorphic computing module, and decision and execution module are electrically connected to form a reconfigurable neuromorphic olfactory sensing memristor system.

10. The application of a reconfigurable neuromorphic olfactory sensor memristor system as described in any one of claims 1-8 in smart home air quality monitoring, industrial hazardous gas leak early warning, or real-time monitoring of harmful gases in warehouse environments.

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