Brain-like calculation-oriented bionic dynamic nanofluidic ion device and regulation and control method

Abstract

The invention discloses a bionic dynamic nanofluidic ion device for brain-like calculation and a regulation and control method. The bionic dynamic nanofluidic ion device comprises a nano-scale nanofluidic channel and a flexible support layer, the nanofluid channel can be dynamically bent, and the inner surface of the channel is provided with dynamically-adjustable distributed charges. The bionic dynamic nanofluidic ion device can regulate and control the current, capacitance and conductivity of the bionic nanofluidic ion device by applying stress, pulse voltage intensity, pulse voltage quantity and positive and negative pulse voltage, thereby realizing regulation, memory and storage of information. The bionic nanofluidic ion device has the read-write capability of positive voltage conductance and the erasing capability of negative voltage conductance under the action of voltage pulses. The bionic nanofluidic ion device has the advantages of high plasticity, high parallel processing, adaptive learning, ultralow energy consumption and the like in the brain-like calculation process.

Description

Technical Field

[0001] The present invention belongs to the field of bionic nanocurrent ionics, and specifically relates to the fields of nanotechnology, ionic devices and brain-like computing. Background Art

[0002] Brain-like computing aims to simulate and understand the information processing principles and mechanisms of the human brain in order to achieve a more efficient, intelligent and adaptive computing system. It requires high-performance electronic devices to support the goal of imitating the brain to achieve efficient computing and solve the current problems faced by artificial intelligence. At present, ionic devices, as a new type of electronic device, have broad application prospects in this field, but the following problems currently faced by them limit their development and application, such as stability issues: ion channels and ion transport materials in ionic devices need to have good reliability and long-term stability. However, some ionic materials may be inactivated, drifted or degraded under long-term use or high temperature conditions, limiting the life and stability of ionic devices. Responsiveness issues: The speed of ion transmission is usually slow. Compared with traditional electronic devices, ionic devices have a longer response time, which may limit their performance and efficiency in some application scenarios. Manufacturing technology issues: The manufacturing and integration of ionic devices face some challenges. On the one hand, it is still difficult to manufacture consistent and controllable nanoscale ion channels and ion transport materials; on the other hand, the integration issues between ionic devices and traditional electronic devices, such as interface matching, signal conversion and interface optimization, also need to be solved. Energy consumption problem: Although ion devices have potential energy efficiency advantages in some aspects, some ion transport processes still require high energy consumption. In addition, the power consumption and energy consumption of ion devices are closely related to factors such as their size, structure and operating conditions. Scaling problem: The large-scale production and commercial application of ion devices still face some challenges. The process from the laboratory stage to commercial production requires overcoming technical difficulties, reducing costs, ensuring reliability and sustainability, etc. Therefore, there is an urgent need to develop new ion devices that are low-cost, stable, highly controllable and low in energy consumption. Summary of the invention

[0003] The main purpose of the present invention is to provide a bionic dynamic nanocurrent ion device for brain-like computing. Bionic nanocurrent ion devices, as a new type of electronic device developed based on bionic nanocurrent ionology, provide a new direction for solving the above problems. The nanocurrent ion channels in the specific organism realize the organic combination of regulation, memory and storage of the information conduction process based on dynamic ion transmission regulation, and establish a bridge between the biological brain and the artificial brain for the development of signal transmission and translation media for brain-like computing, so as to solve the bottleneck problem of current artificial intelligence in the field of human-computer interaction and the high efficiency and energy consumption of current electronic devices in the brain-like computing process. Therefore, the bionic nanocurrent ion device regulates ion transmission by imitating the structure and function of nanoscale ion channels in the organism to realize the processing, memory and storage conversion of information. On the one hand, it helps to solve the problems of poor controllability, low computing efficiency and high energy consumption faced by traditional ion devices in the field of brain-like computing, and on the other hand, it promotes the development of bionic nanocurrent nanocurrent ion devices in the fields of brain-like computing.

[0004] The present invention provides a bionic dynamic nanoflow ion device, which comprises a nanoflow channel and a flexible supporting material. The inner diameter of the nanoflow channel is 1 to 500 nm, the inner surface of the channel has an electric charge, the nanoflow channel will dynamically bend under the condition of applying external pressure, and the nanoflow channel contains an electrolyte solution.

[0005] The nanocurrent channel in the bionic nanocurrent ion device undergoes dynamic bending under external pressure, and at the same time, protons change the dynamic confined space to achieve dynamic regulation of channel current, conductance and capacitance.

[0006] The bionic nanocurrent ion device generates a current memory hysteresis curve and a conductance memory hysteresis curve under the action of an alternating voltage.

[0007] The bionic nanocurrent ion device has the capability of reading and writing with positive voltage conductance and erasing with negative voltage conductance under the action of voltage pulse.

[0008] Preferably, the inner diameter of the nanoflow channel in the bionic nanoflow ion device is 1 to 10 nm.

[0009] Preferably, the flexible support material in the biomimetic nanocurrent ion device is one or more of polydimethylsiloxane, epoxy resin, polyvinyl alcohol, polyester or polyethylene naphthalate. The preferred curing temperature of the flexible support material is 50 to 100° C. and the curing time is 2 to 24 hours.

[0010] Preferably, the nanoflow channel matrix structure in the bionic nanoflow ion device is one or more of single-walled carbon nanotubes, double-walled carbon nanotubes, multi-walled carbon nanotubes, titanium dioxide nanotubes, titanate nanotubes, and titanium oxide nanotubes. The inner diameter of the nanotube channel is preferably 10 to 500 nm. In another preferred embodiment of the present invention, the nanoflow channel material includes one or more of single-walled carbon nanotubes, double-walled carbon nanotubes, multi-walled carbon nanotubes, titanium dioxide nanotubes, titanate nanotubes, and titanium oxide nanotubes. The inner diameter of the nanoflow channel is 1 to 10 nm.

[0011] Preferably, the ion activation power of the bionic nanoflow ion nanoflow channel is 50 to 300 W, and the activation time is 5 to 30 min.

[0012] Preferably, the interior of the biomimetic nanoflow ion nanoflow channel is modified with an organic polyelectrolytic polymer.

[0013] Preferably, the organic polyelectrolytic polymer includes one or more of polydopamine, polyacrylamide, polyacrylic acid and polystyrene sulfonic acid, with a concentration of 0.1 g / L to 5 g / L, and a reaction time of 4 to 24 h.

[0014] Optionally, the nanochannel modification method includes but is not limited to free radical polymerization, chemical grafting, interface bonding, layer-by-layer polymerization, chemical deposition, etc.

[0015] In a preferred embodiment of the present invention, the number of nanoflow channels in the bionic nanoflow ion device is one or more.

[0016] In a preferred embodiment of the present invention, the concentration range of the electrolyte solution is 0.1 mM to 1000 mM.

[0017] In a preferred embodiment of the present invention, the applied voltage intensity is +0.1 to +10.0V.

[0018] In a preferred embodiment of the present invention, the applied pressure is 1000-100000 Pa.

[0019] The initiator of the organic polyelectrolytic polymer includes inorganic initiators such as ammonium persulfate, potassium persulfate, sodium persulfate, and organic initiators such as benzoyl peroxide, di-tert-butyl peroxide, methyl ethyl ketone peroxide, and the like, with a concentration of 0.01 g / L to 1 g / L.

[0020] Another object of the present invention is to provide a control method based on a biomimetic dynamic nanoflow ion device, wherein the inner surface of the nanoflow channel is in an electrolyte solution, with a positive or negative charge, and the current, conductance, capacitance, etc. of the biomimetic dynamic nanoflow ion device are controlled by applying stress, adjusting proton concentration, pulse voltage, and AC voltage, thereby imitating the control mechanisms such as the presynaptic potential, postsynaptic potential, and synaptic weight of brain neurons to achieve efficient brain-like computing. Preferably, the inner diameter of the nanoflow channel of the biomimetic dynamic nanoflow ion device is 1 to 10 nm.

[0021] In a preferred embodiment of the present invention, the conductivity of the bionic nanocurrent ion device can be regulated by applying pressure, proton concentration, pulse voltage intensity, pulse voltage number, positive and negative pulse voltage, so as to realize adaptive learning, highly parallel processing, ultra-low energy consumption, plasticity and multimodal perception of brain-like computing.

[0022] Optionally, the bionic nanocurrent ion device achieves an accuracy rate of over 90% in the brain-like computing process.

[0023] The advantages of the bionic nanocurrent ion device of the present invention compared with the current ion devices are:

[0024] 1. The present invention is based on a bionic dynamic nanochannel in a nanoscale confined space, which can imitate the ion transmission behavior between neuronal synapses and realize the transmission, memory and storage of information.

[0025] 2. The bionic dynamic nanochannel of the present invention can imitate the dynamic bending function of biological channels and realize dynamic regulation of ion transport.

[0026] 3. The bionic nanocurrent ion device of the present invention has the ability to read and write with positive voltage conductance and erase with negative voltage conductance under the action of voltage pulses, realizing memory and forgetting in the brain-like computing process.

[0027] 4. The bionic nanocurrent ion device of the present invention has a high recognition efficiency of over 90% in handwritten digit recognition in the brain-like computing process based on neural networks.

[0028] 5. The bionic nanocurrent ion device of the present invention can regulate the conductivity of the bionic nanocurrent ion device by applying stress, pulse voltage intensity, pulse voltage number, positive and negative pulse voltage, so as to achieve adaptive learning, anti-forgetting characteristics, ultra-low energy consumption, plasticity and multimodal perception of brain-like computing. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0030] Figure 1 Schematic diagram of the bending and recovery process of the bionic dynamic nanoflow channel under external pressure.

[0031] Figure 2 This is the current-voltage memory hysteresis curve of the bionic dynamic nanocurrent ion device in Example 1.

[0032] Figure 3 This is the conductance-voltage memory hysteresis curve of the bionic dynamic nanocurrent ion device in Example 1.

[0033] Figure 4 This is the resistance writing curve of the bionic dynamic nanocurrent ion device of Example 2 under the action of pulse voltage.

[0034] Figure 5 This is the resistance erasure curve of the bionic dynamic nanocurrent ion device of Example 2 under the action of pulse voltage.

[0035] Figure 6 This is the brain-like computing result of the bionic dynamic nanocurrent ion device in handwritten digit recognition in Example 2.

[0036] Figure 7 The dynamic neural network constructed based on the bionic dynamic nanocurrent ion device in Example 2 has dynamic anti-forgetting characteristics compared with conventional neural networks. DETAILED DESCRIPTION

[0037] The present invention is further explained below in conjunction with the accompanying drawings and specific embodiments. The accompanying drawings of the present invention are only for illustration to make it easier to understand the present invention, and their specific proportions can be adjusted according to design requirements.

[0038] (1) Nanotube channel device preparation

[0039] A 1-cm-wide square silicon wafer was used as the growth substrate, which was then backwashed with acetone and alcohol twice and blown dry. The carbon nanotube array growth substrate was modified, the development conditions were screened, and the conditions and parameters such as the evaporation of metal particles were improved and optimized. Nanotube nanoflow channel arrays of different materials and inner diameters were obtained by chemical vapor deposition. The arrays were then activated using a plasma activation device. A flexible polymer was then poured into the gaps on the outer wall of the nanotubes and cured at high temperature. Finally, the cryo-ultrathin sectioning method was used to remove the excess polymer and nanotubes at both ends of the channel to obtain a bidirectionally conductive nanoflow channel component.

[0040] (2) Performance characterization and results of nanotube nanofluidic channel devices

[0041] ① Characterization and results of device memory performance: The controllable ion transport test was characterized by electrochemical methods. The designed control system is divided into two modules, with a symmetrical structure on the left and right. A bionic nanocurrent ion device is placed in the middle, and a silver / silver chloride electrode is placed on the left and right sides of the upper part. A pressure sensor is connected to the right side of the component, and then different concentrations of electrolyte solutions are injected into the inside. Under different pH proton concentration conditions, the current and conductivity changes of the device are measured by applying 100Hz and 0.1V AC voltages to the electrodes, respectively. Then, a +1V pulse voltage is applied to the electrode for 10s, and then a +0.1V reading voltage is applied for 10s at an interval of 10s for a pulse writing process; a -1V pulse voltage is applied for 10s, and then a -0.1V reading voltage is applied for 10s at an interval of 10s for a pulse erasing process. Finally, a +0.1V pulse voltage is applied to the device electrode for 5 times, and then a +1V pulse voltage is applied once, and then a pulse voltage of +0.1V is applied instead.

[0042] ② Device brain-like computing characterization and results: Finally, the device is embedded as a neural network node in the neural network brain-like computing process to perform handwritten digit recognition operations.

[0043] Example 1

[0044] (1) Preparation of nanofluidic channels of bionic nanofluidic ion devices

[0045] A 1-cm-wide square silicon wafer was used as the growth substrate, which was then backwashed with acetone and alcohol twice and blown dry. The carbon nanotube array growth substrate was modified, the development conditions were screened, and the conditions and parameters such as the evaporation of metal particles were improved and optimized. A multi-walled carbon nanotube nanoflow channel array with an inner diameter of 100 nm was obtained by chemical vapor deposition. The array was then activated for 5 minutes using a plasma activation device at a power of 200 W. Polyepoxy resin was then poured into the gaps on the outer wall of the carbon nanotubes and cured at 60°C for 3 hours. Finally, the excess epoxy resin and carbon nanotubes at both ends of the channel were removed by the cryosectioning method to obtain a bidirectionally conductive nanoflow channel component.

[0046] (2) Polyelectrolyte modification of the inner surface of the nanochannel of biomimetic nanofluidic ion devices

[0047] The inner surface of the nanoflow channel was modified by free radical polymerization. Specifically, the flexible carbon nanotube channel was cleaned with ethanol and immersed in 0.1M dilute hydrochloric acid for 24 hours to remove residual products on the surface. Then, it was placed in a plasma cleaner with an activation power of 150W and plasma activated for 5 minutes. Subsequently, it was immediately placed in a Tris buffer mixed solution containing 1.5g / L dopamine and 0.5g / L polyethyleneimine and ultrasonicated for 4 minutes and then left to stand for 12 hours. Then, the unreacted dopamine and polyethyleneimine were removed with pure water. The device was added to 4g / L polyacrylic acid and 1g / L ammonium persulfate solution for 12 hours to obtain the prepared bionic nanoflow ion channel device.

[0048] (3) Performance characterization and results of bionic nanocurrent ion devices

[0049] ① Characterization and results of device memory performance: Electrochemical methods were used to test and characterize the controllable ion transport. The designed control system is divided into two modules, with a symmetrical structure on the left and right. A bionic nanocurrent ion device is placed in the middle, and a silver / silver chloride electrode is placed on each of the upper left and right sides. A pressure sensor is connected to the right side of the component, and then a 0.1mM electrolyte solution is injected inside. Under pH=4 conditions, the current and conductance changes of the device were measured by applying 100Hz and +0.1V AC voltages to the electrodes respectively, generating a memory hysteresis curve, showing a memory function; subsequently, a +1V pulse voltage was applied to the electrode for 10s, and then a +0.1V read voltage was applied for 10s after an interval of 10s, which was a pulse writing process. The results showed that the conductivity of the device increased from 156nS to 276nS, showing that the conductivity increased with the increase in the number of pulses; a -1V pulse voltage was applied for 10s, and then a -0.1V read voltage was applied for 10s after an interval of 10s, which was a pulse erasing process. The results showed that the conductivity of the device decreased from 376nS to 156nS, showing that the conductivity of the device gradually decreased with the increase in the number of pulse erasing processes, and the device exhibited storage and erasing functions. Finally, a +0.1V pulse voltage was applied to the device electrode 5 times, and the conductivity was 123nS. A +1V pulse voltage was applied once, and then a pulse voltage of +0.1V was applied, and the conductivity was changed to 136nS. The conductivity of the device on the surface of the test structure increased after applying a 1V pulse voltage, and the device showed a memory enhancement effect.

[0050] ② Characterization and results of brain-like computing of the device: Finally, the device was embedded into the neural network brain-like computing process as a neural network node to perform handwritten digit recognition operations. The results showed that the device's computing accuracy reached 81%.

[0051] Example 2

[0052] (1) Preparation of nanofluidic channels of bionic nanofluidic ion devices

[0053] A 1-cm-wide square silicon wafer was used as the growth substrate, which was then backwashed with acetone and alcohol twice and blown dry. A multi-walled carbon nanotube nanoflow channel array with an inner diameter of 200 nm was obtained by modifying the growth substrate of the carbon nanotube array, screening the development conditions, and improving and optimizing the parameters such as the evaporation of metal particles. The array was then activated for 5 minutes at 150°C using a plasma activation device, and then polyepoxy resin was poured into the gaps on the outer wall of the carbon nanotubes and cured at 60°C for 18 hours. Finally, the excess polydimethylsiloxane and carbon nanotubes at both ends of the channel were removed by the cryosectioning method to obtain a bidirectionally conductive nanoflow channel component.

[0054] (2) Polyelectrolyte modification of the inner surface of the nanochannel of biomimetic nanofluidic ion devices

[0055] The inner surface of the nanoflow channel was modified by a chemical grafting method. Specifically, the flexible carbon nanotube channel was first cleaned with ethanol and immersed in 0.1M dilute hydrochloric acid for 24 hours to remove residual products on the surface. It was then placed in a plasma cleaner with an activation power of 75W and plasma activated for 15 minutes. It was then immediately placed in a Tris buffer mixed solution containing 2g / L dopamine and ultrasonicated for 10 minutes and then left to stand for 24 hours. Unreacted dopamine was then removed with pure water. The device was added to 2g / L sodium styrene sulfonate polyacrylic acid and 0.01g / L potassium persulfate solution for 6 hours to obtain the prepared bionic nanoflow ion channel device.

[0056] (3) Performance characterization and results of bionic nanocurrent ion devices

[0057] ① Characterization and results of device memory performance: The controllable ion transport test was characterized by electrochemical methods. The designed control system is divided into two modules, with a symmetrical structure on the left and right. A bionic nanocurrent ion device is placed in the middle, and a silver / silver chloride electrode is placed on each of the upper left and right sides. A pressure sensor is connected to the right side of the component, and a pressure of 10,000 Pa is applied, and then a 1mM electrolyte solution is injected inside. Under pH=4 conditions, the current and conductance changes of the device were measured by applying 100Hz and +0.1V AC voltages to the electrodes respectively, generating a memory hysteresis curve, which showed a memory function; then a +1V pulse voltage was applied to the electrode for 10s, and then a +0.1V read voltage was applied for 10s after an interval of 10s, which was a pulse writing process. The results showed that the conductivity of the device increased from 95nS to 176nS, which showed that the conductivity increased with the increase in the number of pulses; a -1V pulse voltage was applied for 10s, and then a -0.1V read voltage was applied for 10s after an interval of 10s, which was a pulse erasing process. The results showed that the conductance of the device decreased from 176nS to 96nS, which showed that the conductivity of the device gradually decreased with the increase in the number of pulse erasing processes, and the device exhibited storage and erasing functions. Finally, the device electrode was subjected to a +0.1V pulse voltage five times, with a conductivity of 83nS. A +1V pulse voltage was then applied once, followed by a pulse voltage of +0.1V. The conductivity was changed to 119nS. The conductivity of the device on the surface of the test structure was enhanced after applying the +1V pulse voltage, and the device showed a memory enhancement effect.

[0058] ② Characterization and results of brain-like computing of the device: Finally, the device was embedded into the neural network brain-like computing process as a neural network node to perform handwritten digit recognition operations. The results showed that the device's computing accuracy reached 89%.

[0059] Example 3

[0060] (1) Preparation of nanofluidic channels of bionic nanofluidic ion devices

[0061] A 1-cm-wide square silicon wafer was used as the growth substrate, which was then backwashed with acetone and alcohol twice and blown dry. A multi-walled carbon nanotube nanoflow channel array with an inner diameter of 8 nm was obtained by modifying the growth substrate of the carbon nanotube array, screening the development conditions, and improving and optimizing the condition parameters such as the evaporation of metal particles. The array was then activated for 30 minutes using a plasma activation device at 50 W, and then polyepoxy resin was poured into the gaps on the outer wall of the carbon nanotubes and cured at 50°C for 24 hours. Finally, the cryosectioning method was used to remove the excess polydimethylsiloxane and carbon nanotubes at both ends of the channel to obtain a bidirectionally conductive nanoflow channel component.

[0062] (2) Polyelectrolyte modification of the inner surface of the nanochannel of biomimetic nanofluidic ion devices

[0063] First, the flexible carbon nanotube channels were cleaned with ethanol and immersed in 0.1M dilute hydrochloric acid for 24 hours to remove residual products on the surface. Then, they were placed in a plasma cleaner with an activation power of 50W and plasma activated for 25 minutes. Subsequently, they were immediately placed in a Tris buffer mixed solution containing 2g / L polyethyleneimine and ultrasonicated for 4 minutes and then left to stand for 12 hours. Unreacted dopamine and polyethyleneimine were then removed with pure water. The device was added to 0.1g / L acrylic acid and 0.2g / L di-tert-butyl peroxide solution for 24 hours to obtain the prepared bionic nanoflow ion channel device.

[0064] (3) Performance characterization and results of bionic nanocurrent ion devices

[0065] ① Characterization and results of device memory performance: Electrochemical methods were used to test and characterize the controllable ion transport. The designed control system is divided into two modules, with a symmetrical structure on the left and right. A bionic nanocurrent ion device is placed in the middle, and a silver / silver chloride electrode is placed on each of the upper left and right sides. A pressure sensor is connected to the right side of the component, and a pressure of 1000Pa is applied, and then a 10mM electrolyte solution is injected inside. Under pH=4 conditions, the current and conductance changes of the device were measured by applying 100Hz and +0.1V AC voltages to the electrodes respectively, generating a memory hysteresis curve, showing a memory function; subsequently, a +1V pulse voltage was applied to the electrode for 10s, and then a +0.1V read voltage was applied for 10s after an interval of 10s, which was a pulse writing process. The results showed that the conductivity of the device increased from 76nS to 141nS, showing that the conductivity increased with the increase in the number of pulses; a -1V pulse voltage was applied for 10s, and then a -0.1V read voltage was applied for 10s after an interval of 10s, which was a pulse erasing process. The results showed that the conductivity of the device decreased from 140nS to 74nS, showing that the conductivity of the device gradually decreased with the increase in the number of pulse erasing processes, and the device exhibited storage and erasing functions. Finally, a 0.1V pulse voltage was applied to the device electrode five times, and the conductivity was 63nS. A +1V pulse voltage was applied once, and then a pulse voltage of 0.1V was applied, and the conductivity was changed to 78nS. The conductivity of the device on the surface of the test structure increased after applying the +1V pulse voltage, and the device showed a memory enhancement effect.

[0066] ② Characterization and results of brain-like computing of the device: Finally, the device was embedded into the neural network brain-like computing process as a neural network node to perform handwritten digit recognition operations. The results showed that the device's computing accuracy reached 96%.

[0067] Example 4

[0068] (1) Preparation of nanofluidic channels of bionic nanofluidic ion devices

[0069] A 1-cm-wide square silicon wafer was used as the growth substrate, which was then backwashed with acetone and alcohol twice and blown dry. A titanium oxide nanotube nanoflow channel array with an inner diameter of 500 nm was obtained by modifying the carbon nanotube array growth substrate, screening the development conditions, and improving and optimizing the conditions and parameters such as the evaporation of metal particles. The array was then activated for 12 minutes using a plasma activation device at 300 W. Polyepoxy resin was then poured into the gaps on the outer wall of the carbon nanotubes and cured at 100°C for 12 hours. Finally, the excess polydimethylsiloxane and carbon nanotubes at both ends of the channel were removed by the cryosectioning method to obtain a bidirectionally conductive nanoflow channel component.

[0070] (2) Polyelectrolyte modification of the inner surface of the nanochannel of biomimetic nanofluidic ion devices

[0071] First, the flexible carbon nanotube channels were cleaned with ethanol and immersed in 0.1M dilute hydrochloric acid for 24 hours to remove residual products on the surface. Then, they were placed in a plasma cleaner with an activation power of 300W and plasma activated for 5 minutes. Subsequently, they were immediately placed in a Tris buffer mixed solution containing 0.5g / L dopamine and 1.5g / L polyethyleneimine and ultrasonicated for 10 minutes and then left to stand for 24 hours. Unreacted dopamine and polyethyleneimine were removed with pure water, and the device was added to 0.75g / L styrene sulfonic acid and 0.9g / L benzoyl peroxide solution for 24 hours to obtain the prepared bionic nanoflow ion channel device.

[0072] (3) Performance characterization and results of bionic nanocurrent ion devices

[0073] ① Characterization and results of device memory performance: The controllable ion transport test was characterized by electrochemical methods. The designed control system is divided into two modules, with a symmetrical structure on the left and right. A bionic nanocurrent ion device is placed in the middle, and a silver / silver chloride electrode is placed on each of the upper left and right sides. A pressure sensor is connected to the right side of the component, and a pressure of 1000Pa is applied, and then a 1mM electrolyte solution is injected inside. Under pH=4 conditions, the current and conductance changes of the device were measured by applying 100Hz and +0.1V AC voltages to the electrodes respectively, generating a memory hysteresis curve, showing a memory function; subsequently, a +1V pulse voltage was applied to the electrode for 10s, and then a +0.1V read voltage was applied for 10s after an interval of 10s, which was a pulse writing process. The results showed that the conductivity of the device increased from 156nS to 193nS, showing that the conductivity increased with the increase in the number of pulses; a -1V pulse voltage was applied for 10s, and then a -0.1V read voltage was applied for 10s after an interval of 10s, which was a pulse erasing process. The results showed that the conductivity of the device decreased from 190nS to 154nS, showing that the conductivity of the device gradually decreased with the increase in the number of pulse erasing processes, and the device exhibited storage and erasing functions. Finally, a +0.1V pulse voltage was applied to the device electrode five times, and the conductivity was 151nS. A +1V pulse voltage was applied once, and then a pulse voltage of +0.1V was applied, and the conductivity was changed to 169nS. The conductivity of the device on the surface of the test structure was enhanced after applying the +1V pulse voltage, and the device showed a memory enhancement effect.

[0074] ② Characterization and results of brain-like computing of the device: Finally, the device was embedded into the neural network brain-like computing process as a neural network node to perform handwritten digit recognition operations. The results showed that the device's computing accuracy reached 91%.

[0075] Example 5

[0076] (1) Preparation of nanofluidic channels of bionic nanofluidic ion devices

[0077] A 1-cm-wide square silicon wafer was used as the growth substrate, which was then backwashed with acetone and alcohol twice and blown dry. A titanium dioxide nanotube nanoflow channel array with an inner diameter of 100 nm was obtained by modifying the carbon nanotube array growth substrate, screening the development conditions, and improving and optimizing the conditions and parameters such as the evaporation of metal particles. It was then activated for 8 minutes using a plasma activation device at a power of 120 W, and then polyethylene naphthalate was poured into the gaps on the outer wall of the titanium dioxide nanotubes and cured at 70°C for 12 hours. Finally, the cryosectioning method was used to remove the excess polydimethylsiloxane and titanium dioxide nanotubes at both ends of the channel to obtain a bidirectionally conductive nanoflow channel component.

[0078] (2) Polyelectrolyte modification of the inner surface of the nanochannel of biomimetic nanofluidic ion devices

[0079] First, the flexible carbon nanotube channels were cleaned with ethanol and immersed in 0.1M dilute hydrochloric acid for 24 hours to remove residual products on the surface. Then, they were placed in a plasma cleaner with an activation power of 60W and plasma activated for 18 minutes. Subsequently, they were immediately placed in a Tris buffer mixed solution containing 1.9g / L dopamine and 0.1g / L polyethyleneimine and ultrasonicated for 2 minutes and then left to stand for 4 hours. Unreacted dopamine and polyethyleneimine were removed with pure water, and the device was added to 2.5g / L acrylic acid and 0.05g / L methyl ethyl ketone peroxide solution for 18 hours to obtain the prepared bionic nanoflow ion channel.

[0080] (3) Performance characterization and results of bionic nanocurrent ion devices

[0081] ① Characterization and results of device memory performance: Electrochemical methods were used to test and characterize the controllable ion transport. The designed control system is divided into two modules, with a symmetrical structure on the left and right. A bionic nanocurrent ion device is placed in the middle, and a silver / silver chloride electrode is placed on each of the upper left and right sides. A pressure sensor is connected to the right side of the component, and a pressure of 50,000 Pa is applied, and then a 10mM electrolyte solution is injected inside. Under pH=4 conditions, the current and conductance changes of the device were measured by applying 100Hz and +0.1V AC voltages to the electrodes respectively, generating a memory hysteresis curve, showing memory function; then a +1V pulse voltage was applied to the electrode for 10s, and then a 0.1V read voltage was applied for 10s after an interval of 10s, which was a pulse writing process. The results showed that the conductivity of the device increased from 91nS to 115nS, showing that the conductivity increased with the increase in the number of pulses; a -1V pulse voltage was applied for 10s, and then a -0.1V read voltage was applied for 10s after an interval of 10s, which was a pulse erasing process. The results showed that the conductivity of the device decreased from 116nS to 90nS, showing that the conductivity of the device gradually decreased with the increase in the number of pulse erasing processes, and the device exhibited storage and erasing functions. Finally, a +0.1V pulse voltage was applied to the device electrode five times, with a conductivity of 78nS. A +1V pulse voltage was applied once, followed by a pulse voltage of +0.1V. The conductivity was changed to 96nS. The conductivity of the device on the surface of the test structure was enhanced after applying the +1V pulse voltage, and the device showed a memory enhancement effect.

[0082] ② Characterization and results of brain-like computing of the device: Finally, the device was embedded into the neural network brain-like computing process as a neural network node to perform handwritten digit recognition operations. The results showed that the device's computing accuracy reached 94%.

[0083] Comparative Example 1

[0084] (1) Preparation of nanofluidic channels of bionic nanofluidic ion devices

[0085] A 1-cm-wide square silicon wafer was used as the growth substrate, which was then backwashed with acetone and alcohol twice and blown dry. The carbon nanotube array growth substrate was modified, the development conditions were screened, and the parameters such as the evaporation of metal particles were improved and optimized. A multi-walled carbon nanotube nanoflow channel array with an inner diameter of 8 nm was obtained by chemical vapor deposition. The array was then activated for 10 minutes using a plasma activation device at a power of 100 W. Polydimethylsiloxane was then poured into the gaps on the outer wall of the carbon nanotubes and cured at 70°C for 24 hours. Finally, the cryo-ultrathin sectioning method was used to remove the excess polydimethylsiloxane and carbon nanotubes at both ends of the channel to obtain a bidirectionally conductive nanoflow channel component.

[0086] (2) Performance characterization and results of carbon nanotube channel devices

[0087] ① Characterization and results of device memory performance: The controllable ion transport test was characterized by electrochemical methods. The designed control system is divided into two modules, with a symmetrical structure on the left and right. A bionic nanocurrent ion device is placed in the middle, and a silver / silver chloride electrode is placed on the left and right sides of the upper part. A pressure sensor is connected to the right side of the component to apply different pressures, and then a 100mM electrolyte solution is injected inside. Under pH = 4 conditions, the current and conductivity changes of the device were measured by applying 100Hz and 0.1V AC voltages to the electrodes respectively. The results showed that the current-voltage relationship was linear, and no memory hysteresis curve was generated. Then, a +1V pulse voltage was applied to the electrode for 10s, and then a +0.1V reading voltage was applied for 10s after an interval of 10s for a pulse writing process. The research results showed that during a pulse process, the current was maintained at 12μA and the conductivity was 1.2×10 -5 S, and does not increase with the increase of the number of pulse processes. The results show that its conductivity does not change; applying a -1V pulse voltage for 10s, and then applying a -0.1V read voltage for 10s after an interval of 10s is a pulse erase process. The current remains at 12μA, and its conductivity is 1.2×10 -5 S, and does not increase with the increase of the number of pulse processes, the device shows no information storage and erasure function. Finally, the device electrode was subjected to a +0.1V pulse voltage for 5 times, a +1V pulse voltage was applied once, and then a pulse voltage of +0.1V was applied. The test results showed that the device conductivity did not change after applying a +1V pulse voltage, and the device showed no memory enhancement effect.

[0088] ② Characterization and results of brain-like computing of the device: Finally, the device was embedded into the neural network brain-like computing process as a neural network node to perform handwritten digit recognition operations. The results showed that the device’s computing accuracy reached 0%, which is mainly because the device has no memory, information storage and regulation functions.

[0089] Comparative Example 2

[0090] (4) Preparation of nanofluidic channels of bionic nanofluidic ion devices

[0091] A 1-cm-wide square silicon wafer was used as the growth substrate, which was then backwashed with acetone and alcohol twice and blown dry. A multi-walled carbon nanotube nanoflow channel array with an inner diameter of 8 nm was obtained by modifying the growth substrate of the carbon nanotube array, screening the development conditions, and improving and optimizing the parameters such as the evaporation of metal particles. The array was then activated for 30 minutes using a plasma activation device at 50 W, and then polyepoxy resin was poured into the gaps on the outer wall of the carbon nanotubes and cured at 60°C for 24 hours. Finally, the cryosectioning method was used to remove the excess polydimethylsiloxane and carbon nanotubes at both ends of the channel to obtain a bidirectionally conductive nanoflow channel component.

[0092] (5) Polyelectrolyte modification of the inner surface of the nanochannel of biomimetic nanofluidic ion devices

[0093] First, the flexible carbon nanotube channels were cleaned with ethanol and immersed in 0.1M dilute hydrochloric acid for 24 hours to remove residual products on the surface. Then, they were placed in a plasma cleaner with an activation power of 50W and plasma activated for 25 minutes. Subsequently, they were immediately placed in a Tris buffer mixed solution containing 2g / L polyethyleneimine and ultrasonicated for 4 minutes and then left to stand for 12 hours. Unreacted dopamine and polyethyleneimine were then removed with pure water. The device was added to 0.1g / L acrylic acid and 0.2g / L di-tert-butyl peroxide solution for 24 hours to obtain the prepared bionic nanoflow ion channel device.

[0094] (6) Performance characterization and results of bionic nanocurrent ion devices

[0095] Device memory performance characterization and results: Ion controllable transport test characterization by electrochemical method. Designed control system

[0096] It is divided into two modules, with a symmetrical structure on the left and right. A bionic nanocurrent ion device is placed in the middle. A silver / silver chloride electrode is placed on each of the upper left and right sides. A pressure sensor is connected to the right side of the component. No pressure is applied, and then a 0.1mM electrolyte solution is injected into the inside. Under pH=7 conditions, the current and conductivity changes of the device are measured by applying 100Hz and +0.1V AC voltages to the electrodes respectively. No memory hysteresis curve appears, indicating that there is no memory function; then a +1V pulse voltage is applied to the electrode for 10s, and then a +0.1V reading voltage is applied for 10s after an interval of 10s. This is a pulse writing process. The results show that the conductivity of the device remains unchanged at 76nS. A -1V pulse voltage is applied for 10s, and then a -0.1V reading voltage is applied for 10s after an interval of 10s. This is a pulse erasing process. The results show that the conductivity of the device is stable at 74nS, indicating that the device has no storage and erasing functions. Finally, a 0.1V pulse voltage was applied to the device electrode five times, and the conductivity was 63nS. A +1V pulse voltage was applied once, and then a pulse voltage of 0.1V was applied, and the conductivity was changed to 64nS. The conductivity of the device on the surface of the test structure remained almost unchanged after applying the +1V pulse voltage, and the device had no memory enhancement effect.

[0097] Device brain-like computing characterization and results: Finally, the device is embedded into the neural network brain-like computing process as a neural network node

[0098] In the experiment, handwritten digit recognition was performed, and the results showed that the device had an accuracy of 0%, which was mainly because the device had no memory, information storage and regulation functions.

[0099] The above description is only a preferred embodiment of the present invention, and therefore cannot be used to limit the scope of the present invention. That is, equivalent changes and modifications made according to the patent scope of the present invention and the contents of the specification should still fall within the scope of the present invention.

Claims

1. A bionic dynamic nanocurrent ion device for brain-like computing, characterized by: include: The invention comprises a nano-flow channel and a flexible supporting material. The inner diameter of the nano-flow channel is 1 to 500 nm. The inner surface of the channel has electric charge. The nano-flow channel can bend dynamically under the condition of applying external pressure. The nano-flow channel contains an electrolyte solution.

2. The bionic dynamic nanocurrent ion device for brain-like computing according to claim 1, characterized in that: The nanoflow channel undergoes dynamic bending under external pressure, and at the same time, protons change the dynamic confinement space to achieve dynamic regulation of channel current, conductance and capacitance.

3. The bionic dynamic nanocurrent ion device for brain-like computing according to claim 1, characterized in that: The bionic nanocurrent ion device generates a current memory hysteresis curve and a conductance memory hysteresis curve under the action of an alternating voltage.

4. The bionic dynamic nanocurrent ion device for brain-like computing according to claim 1, characterized in that: The bionic nanocurrent ion device has the capability of reading and writing with positive voltage conductance and erasing with negative voltage conductance under the action of voltage pulse.

5. The bionic dynamic nanocurrent ion device for brain-like computing according to claim 1, characterized in that: The flexible support material includes at least one or more of polydimethylsiloxane, epoxy resin, polyvinyl alcohol, polyester or polyethylene naphthalate.

6. The bionic dynamic nanocurrent ion device for brain-like computing according to claim 5, characterized in that: The curing temperature of the flexible support material is 50 to 100° C., and the curing time is 2 to 24 hours.

7. The bionic dynamic nanocurrent ion device for brain-like computing according to claim 1, characterized in that: The nano-flow channel matrix structure is one or more of single-walled carbon nanotubes, double-walled carbon nanotubes, multi-walled carbon nanotubes, titanium dioxide nanotubes, titanate nanotubes, and titanium oxide nanotubes.

8. The bionic dynamic nanocurrent ion device for brain-like computing according to claim 1, characterized in that: The nanoflow channel of the bionic nanoflow ion device is modified with an organic polyelectrolytic polymer.

9. The bionic dynamic nanocurrent ion device for brain-like computing according to claim 8, characterized in that: The organic polyelectrolytic polymer includes one or more of polydopamine, polyacrylamide, polyacrylic acid and polystyrene sulfonic acid.

10. A control method based on a bionic dynamic nanocurrent ion device, wherein the nanocurrent channel of the bionic dynamic nanocurrent ion device has an inner diameter of 1 to 10 nm, and the inner surface of the nanocurrent channel is in an electrolyte solution with a positive or negative charge. The current, conductance, and capacitance of the bionic dynamic nanocurrent ion device are controlled by applying stress, adjusting proton concentration, pulse voltage, and AC voltage, thereby simulating the control mechanism of the presynaptic potential, postsynaptic potential, and synaptic weight of brain neurons.

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