Fibrous MXene Synapse-Based Biomimetic Tactile Nervous System for Multimodal Perception and Memory.

Biomimetic tactile nervous system (BTNS) inspired by organisms has motivated extensive attention in wearable fields due to its biological similarity, low power consumption, and perception-memory integration. Though many works about planar-shape BTNS are developed, few researches could be found in the field of fibrous BTNS (FBTNS) which is superior in terms of strong flexibility, weavability, and high-density integration. Herein, a FBTNS with multimodal sensibility and memory is proposed, by fusing the fibrous poly lactic acid (PLA)/Ag/MXene/Pt artificial synapse and MXene/EMIMBF4 ionic conductive elastomer. The proposed FBTNS can successfully perceive external stimuli and generate synaptic responses. It also exhibits a short response time (23 ms) and low set power consumption (17 nW). Additionally, the proposed device demonstrates outstanding synaptic plasticity under both mechanical and electrical stimuli, which can simulate the memory function. Simultaneously, the fibrous devices are embedded into textiles to construct tactile arrays, by which biomimetic tactile perception and temporary memory functions are successfully implemented. This work demonstrates the as-prepared FBTNS can generate biomimetic synaptic signals to serve as artificial feeling signals, it is thought that it could offer a fabric electronic unit integrating with perception and memory for Human-Computer interaction, and has great potential to build lightweight and comfortable Brain-Computer interfaces.

Stabilizing the Ferroelectric Phase of Hf_{0.5}Zr_{0.5}O_{2} Thin Films by Charge Transfer.

Ferroelectric hafnia-based thin films have attracted significant interest due to their compatibility with complementary metal-oxide-semiconductor technology (CMOS). Achieving and stabilizing the metastable ferroelectric phase in these films is crucial for their application in ferroelectric devices. Recent research efforts have concentrated on the stabilization of the ferroelectric phase in hafnia-based films and delving into the mechanisms responsible for this stability. In this study, we experimentally demonstrate that stabilization of the ferroelectric phase in Hf_{0.5}Zr_{0.5}O_{2} (HZO) can be controlled by the interfacial charge transfer and the associated hole doping of HZO. Using the meticulously engineered charge transfer between an La_{1-x}Sr_{x}MnO_{3} buffer layer with variable Sr concentration x and an HZO film, we find the optimal x=0.33 that provides the required hole doping of HZO to most efficiently stabilize its ferroelectric phase. Our theoretical modeling reveals that the competition of the hole distribution between the threefold and fourfold coordinated oxygen sites in HZO controls the enhancement or reduction of the ferroelectric phase. Our findings offer a novel strategy to stabilize the ferroelectric phase of hafnia-based films and provide new insights into the development of ferroelectric devices compatible with CMOS.

Integrated Navigation Algorithm Based on Multiple Fading Factors Kalman Filter.

An integrated navigation algorithm based on a multiple fading factors Kalman filter (MFKF) is proposed to solve the problems that the Kalman filtering (KF) algorithm easily brings about diffusion when the model becomes a mismatched or noisy, and the MFKF accuracy is reduced when the fading factor is overused. Based on the innovation covariance theory, the algorithm designs an improved basis for judging filtering anomalies and makes the timing of the introduction of the fading factor more reasonable by switching the filtering state. Different from the traditional basis of filter abnormality judgment, the improved judgment basis adopts a recursive way to continuously update the estimated value of the innovation covariance to improve the estimation accuracy of the innovation covariance, and an empirical reserve factor for the judgment basis is introduced to adapt to practical engineering applications. By establishing an inertial navigation system (INS)/global navigation satellite system (GNSS) integrated navigation model, the results show that the average positioning accuracy of the proposed algorithm is improved by 26.52% and 7.48%, respectively, compared with the KF and MFKF, and shows better robustness and self-adaptability.

Unusual Hole and Electron Midgap States and Orbital Reconstructions Induced Huge Ferroelectric Tunneling Electroresistance in BaTiO3/SrTiO3.

Oxide heterostructures have attracted a lot of interest because of their rich exotic phenomena and potential applications. Recently, a greatly enhanced tunneling electroresistance (TER) of ferroelectric tunnel junctions (FTJs) has been realized in such heterostructures. However, our understanding on the electronic structure of resistance response with polarization reversal and the origin of huge TER is still lacking. Here, we report on electronic structures, particularly at the interface and surface, and the control of the spontaneous polarization of BaTiO3 films by changing the termination of a SrTiO3 substrate. Interestingly, unusual electron and hole midgap states are concurrently formed and accompanied by orbital reconstructions, which determine the ferroelectric polarization orientation in the BaTiO3/SrTiO3. Such unusual midgap states, which yield a strong electronic screening effect, reduce the ferroelectric barrier width and height, and pin the ferroelectric polarization, lead to a dramatic enhancement of the TER effect. The midgap states are also observed in BaTiO3 films on electron-doped Nb/SrTiO3 revealing its universality. Our result provides new insight into the origin of the huge TER effect and opens a new route for designing ferroelectric tunnel junction-based devices with huge TER through interface engineering.

Atomic-Scale Control of Magnetism at the Titanite-Manganite Interfaces.

Complex oxide thin-film heterostructures often exhibit magnetic properties different from those known for bulk constituents. This is due to the altered local structural and electronic environment at the interfaces, which affects the exchange coupling and magnetic ordering. The emergent magnetism at oxide interfaces can be controlled by ferroelectric polarization and has a strong effect on spin-dependent transport properties of oxide heterostructures, including magnetic and ferroelectric tunnel junctions. Here, using prototype La2/3Sr1/3MnO3/BaTiO3 heterostructures, we demonstrate that ferroelectric polarization of BaTiO3 controls the orbital hybridization and magnetism at heterointerfaces. We observe changes in the enhanced orbital occupancy and significant charge redistribution across the heterointerfaces, affecting the spin and orbital magnetic moments of the interfacial Mn and Ti atoms. Importantly, we find that the exchange coupling between Mn and Ti atoms across the interface is tuned by ferroelectric polarization from ferromagnetic to antiferromagnetic. Our findings provide a viable route to electrically control complex magnetic configurations at artificial multiferroic interfaces, taking a step toward low-power spintronics.

Vacancy-Induced Synaptic Behavior in 2D WS2 Nanosheet-Based Memristor for Low-Power Neuromorphic Computing.

Memristors with nonvolatile memory characteristics have been expected to open a new era for neuromorphic computing and digital logic. However, existing memristor devices based on oxygen vacancy or metal-ion conductive filament mechanisms generally have large operating currents, which are difficult to meet low-power consumption requirements. Therefore, it is very necessary to develop new materials to realize memristor devices that are different from the mechanisms of oxygen vacancy or metal-ion conductive filaments to realize low-power operation. Herein, high-performance and low-power consumption memristors based on 2D WS2 with 2H phase are demonstrated, which show fast ON (OFF) switching times of 13 ns (14 ns), low program current of 1 µA in the ON state, and SET (RESET) energy reaching the level of femtojoules. Moreover, the memristor can mimic basic biological synaptic functions. Importantly, it is proposed that the generation of sulfur and tungsten vacancies and electron hopping between vacancies are dominantly responsible for the resistance switching performance. Density functional theory calculations show that the defect states formed by sulfur and tungsten vacancies are at deep levels, which prevent charge leakage and facilitate the realization of low-power consumption for neuromorphic computing application.

A New Memristor with 2D Ti3 C2 Tx MXene Flakes as an Artificial Bio-Synapse.

Two-dimensional (2D) materials have attracted extensive research interest in academia due to their excellent electrochemical properties and broad application prospects. Among them, 2D transition metal carbides (Ti3 C2 Tx ) show semiconductor characteristics and are studied widely. However, there are few academic reports on the use of 2D MXene materials as memristors. In this work, reported is a memristor based on MXene Ti3 C2 Tx flakes. After electroforming, Al/Ti3 C2 Tx /Pt devices exhibit repeatable resistive switching (RS) behavior. More interestingly, the resistance of this device can be continuously modulated under the pulse sequence with 10 ns pulse width, and the pulse width of 10 ns is much lower than that in other reported work. Moreover, on the nanosecond scale, the transition from short-term plasticity to long-term plasticity is achieved. These two properties indicate that this device is favorable for ultrafast biological synapse applications and high-efficiency training of neural networks. Through the exploration of the microstructure, Ti vacancies and partial oxidation are proposed as the origins of the physical mechanism of RS behavior. This work reveals that 2D MXene Ti3 C2 Tx flakes have excellent potential for use in memristor devices, which may open the door for more functions and applications.

Monoclinic Lu2-xSmxWO6-Based White Light-Emitting Phosphors: From Ground-Excited-States Calculation Prediction to Experiment Realization.

Through ground state and constrained density function calculations, Sm3+ ions luminescence in self-activated monoclinic Lu2WO6 was originated from intra 4f → 4f transitions, not inter 5d → 4f transitions. Theoretically the white luminescence was obtained by combining red and blue-green emissions of 4f energy levels and W-O charge transfer transitions. Experimentally, pure and Sm3+ doping Lu2WO6 powders were synthesized using solid phase reaction calcined in air atmosphere. By the analysis of X-ray photoelectron spectroscopy and Rietveld refinement, element Sm charge state was trivalent, and Sm3+ doping was concentration-dependent selectively doping in three Lu sites. With the increase of Sm3+ concentrations, the color coordinates changed gradually from blue (0.17, 0.17) through white light (0.33, 0.25) toward orange (0.44, 0.32) in the visible spectral under 325 nm excitation. On the basis of the theoretical prediction and experimental preparation, a white emission LED lamp was produced using a 365 nm ultraviolet chip and Lu1.99Sm0.01WO6 phosphor. The present design method can be applied to select excellent activators from a large number of rare-earth (Re) ions like Sm3+ and Eu3+/2+ or non-Re ions like Bi3+ and Mn4+ in various matrixes.

Flexible Graphene Electrodes for Prolonged Dynamic ECG Monitoring.

This paper describes the development of a graphene-based dry flexible electrocardiography (ECG) electrode and a portable wireless ECG measurement system. First, graphene films on polyethylene terephthalate (PET) substrates and graphene paper were used to construct the ECG electrode. Then, a graphene textile was synthesized for the fabrication of a wearable ECG monitoring system. The structure and the electrical properties of the graphene electrodes were evaluated using Raman spectroscopy, scanning electron microscopy (SEM), and alternating current impedance spectroscopy. ECG signals were then collected from healthy subjects using the developed graphene electrode and portable measurement system. The results show that the graphene electrode was able to acquire the typical characteristics and features of human ECG signals with a high signal-to-noise (SNR) ratio in different states of motion. A week-long continuous wearability test showed no degradation in the ECG signal quality over time. The graphene-based flexible electrode demonstrates comfortability, good biocompatibility, and high electrophysiological detection sensitivity. The graphene electrode also combines the potential for use in long-term wearable dynamic cardiac activity monitoring systems with convenience and comfort for use in home health care of elderly and high-risk adults.

An Ultrasensitive Biomimetic Optic Afferent Nervous System with Circadian Learnability.

The optic afferent nervous system (OANS) plays a significant role in generating vision and circadian behaviors based on light detection and signals from the endocrine system. However, the bionic simulation of this photochemically mediated behavior is still a challenge for neuromorphic devices. Herein, stimuli of neurotransmitters at ultralow concentrations and illumination are coupled to artificial synapses with the aid of biofunctionalized heterojunction and tunneling to successfully simulate a circadian neural response. Furthermore, the mechanisms underlying the photosensitive synaptic current in response to stimuli are described. Interestingly, this OANS is demonstrated to be capable of mimicking normal and abnormal circadian learnability by combining the measured synaptic current with a three-layer spike neural network. Strong theoretical and experimental evidence, as well as applications, are provided for the proposed biomimetic OANS to demonstrate that it can reproduce biological circadian behavior, thus establishing it as a promising candidate for future neuromorphic intelligent robots.

CircEYA3 aggravates intervertebral disc degeneration through the miR-196a-5p/EBF1 axis and NF-κB signaling.

Intervertebral disc degeneration (IDD) is a well-established cause of disability, and extensive evidence has identified the important role played by regulatory noncoding RNAs, specifically circular RNAs (circRNAs) and microRNAs (miRNAs), in the progression of IDD. To elucidate the molecular mechanism underlying IDD, we established a circRNA/miRNA/mRNA network in IDD through standardized analyses of all expression matrices. Our studies confirmed the differential expression of the transcription factors early B-cell factor 1 (EBF1), circEYA3, and miR-196a-5p in the nucleus pulposus (NP) tissues of controls and IDD patients. Cell proliferation, apoptosis, and extracellular mechanisms of degradation in NP cells (NPC) are mediated by circEYA3. MiR-196a-5p is a direct target of circEYA3 and EBF1. Functional analysis showed that miR-196a-5p reversed the effects of circEYA3 and EBF1 on ECM degradation, apoptosis, and proliferation in NPCs. EBF1 regulates the nuclear factor kappa beta (NF-кB) signalling pathway by activating the IKKß promoter region. This study demonstrates that circEYA3 plays an important role in exacerbating the progression of IDD by modulating the NF-κB signalling pathway through regulation of the miR196a-5p/EBF1 axis. Consequently, a novel molecular mechanism underlying IDD development was elucidated, thereby identifying a potential therapeutic target for future exploration.

Development and Validation of a Machine Learning Algorithm to Predict the Risk of Blood Transfusion after Total Hip Replacement in Patients with Femoral Neck Fractures: A Multicenter Retrospective Cohort Study.

OBJECTIVE: Total hip arthroplasty (THA) remains the primary treatment option for femoral neck fractures in elderly patients. This study aims to explore the risk factors associated with allogeneic blood transfusion after surgery and to develop a dynamic prediction model to predict post-operative blood transfusion requirements. This will provide more accurate guidance for perioperative humoral management and rational allocation of medical resources. METHODS: We retrospectively analyzed data from 829 patients who underwent total hip arthroplasty for femoral neck fractures at three third-class hospitals between January 2017 and August 2023. Patient data from one hospital were used for model development, whereas data from the other two hospitals were used for external validation. Logistic regression analysis was used to screen the characteristic subsets related to blood transfusion. Various machine learning algorithms, including logistic regression, SVA (support vector machine), K-NN (k-nearest neighbors), MLP (multilayer perceptron), naive Bayes, decision tree, random forest, and gradient boosting, were used to process the data and construct prediction models. A 10-fold cross-validation algorithm facilitated the comparison of the predictive performance of the models, resulting in the selection of the best-performing model for the development of an open-source computing program. RESULTS: BMI (body mass index), surgical duration, IBL (intraoperative blood loss), anticoagulant history, utilization rate of tranexamic acid, Pre-Hb, and Pre-ALB were included in the model as well as independent risk factors. The average area under curve (AUC) values for each model were as follows: logistic regression (0.98); SVA (0.91); k-NN (0.87) MLP, (0.96); naive Bayes (0.97); decision tree (0.87); random forest (0.96); and gradient boosting (0.97). A web calculator based on the best model is available at: (https://nomo99.shinyapps.io/dynnomapp/). CONCLUSION: Utilizing a computer algorithm, a prediction model with a high discrimination accuracy (AUC > 0.5) was developed. The logistic regression model demonstrated superior differentiation and reliability, thereby successfully passing external validation. The model's strong generalizability and applicability have significant implications for clinicians, aiding in the identification of patients at high risk for postoperative blood transfusion.

Optical-Electrical Coordinately Modulated Memristor Based on 2D Ferroelectric RP Perovskite for Artificial Vision Applications.

Traditional artificial vision systems built using separate sensing, computing, and storage units have problems with high power consumption and latency caused by frequent data transmission between functional units. An effective approach is to transfer some memory and computing tasks to the sensor, enabling the simultaneous perception-storage-processing of light signals. Here, an optical-electrical coordinately modulated memristor is proposed, which controls the conductivity by means of polarization of the 2D ferroelectric Ruddlesden-Popper perovskite film at room temperature. The residual polarization shows no significant decay after 109-cycle polarization reversals, indicating that the device has high durability. By adjusting the pulse parameters, the device can simulate the bio-synaptic long/short-term plasticity, which enables the control of conductivity with a high linearity of ≈0.997. Based on the device, a two-layer feedforward neural network is built to recognize handwritten digits, and the recognition accuracy is as high as 97.150%. Meanwhile, building optical-electrical reserve pool system can improve 14.550% for face recognition accuracy, further demonstrating its potential for the field of neural morphological visual systems, with high density and low energy loss.

Stochastic neuro-fuzzy system implemented in memristor crossbar arrays.

Neuro-symbolic artificial intelligence has garnered considerable attention amid increasing industry demands for high-performance neural networks that are interpretable and adaptable to previously unknown problem domains with minimal reconfiguration. However, implementing neuro-symbolic hardware is challenging due to the complexity in symbolic knowledge representation and calculation. We experimentally demonstrated a memristor-based neuro-fuzzy hardware based on TiN/TaOx/HfOx/TiN chips that is superior to its silicon-based counterpart in terms of throughput and energy efficiency by using array topological structure for knowledge representation and physical laws for computing. Intrinsic memristor variability is fully exploited to increase robustness in knowledge representation. A hybrid in situ training strategy is proposed for error minimizing in training. The hardware adapts easier to a previously unknown environment, achieving ~6.6 times faster convergence and ~6 times lower error than deep learning. The hardware energy efficiency is over two orders of magnitude greater than field-programmable gate arrays. This research greatly extends the capability of memristor-based neuromorphic computing systems in artificial intelligence.

Approaching the Ideal Linearity in Epitaxial Crystalline-Type Memristor by Controlling Filament Growth.

Brain-inspired neuromorphic computing has attracted widespread attention owing to its ability to perform parallel and energy-efficient computation. However, the synaptic weight of amorphous/polycrystalline oxide based memristor usually exhibits large nonlinear behavior with high asymmetry, which aggravates the complexity of peripheral circuit system. Controllable growth of conductive filaments is highly demanded for achieving the highly linear conductance modulation. However, the stochastic behavior of the filament growth in commonly used amorphous/polycrystalline oxide memristor makes it very challenging. Here, the epitaxially grown Hf0.5Zr0.5O2-based memristor with the linearity and symmetry approaching ideal case is reported. A layer of Cu nanoparticles is inserted into epitaxially grown Hf0.5Zr0.5O2 film to form the grain boundaries due to the breaking of the epitaxial growth. By combining with the local electric field enhancement, the growth of filament is confined in the grain boundaries due to the fact that the diffusion of oxygen vacancy in crystalline lattice is more difficult than that in the grain boundaries. Furthermore, the decimal operation and high-accuracy neural network are demonstrated by utilizing the highly linear and multi-level conductance modulation capacity. This method opens an avenue to control the filament growth for the application of resistance random access memory (RRAM) and neuromorphic computing.

7D High-Dynamic Spin-Multiplexing.

Multiplexing technology creates several orthogonal data channels and dimensions for high-density information encoding and is irreplaceable in large-capacity information storage, and communication, etc. The multiplexing dimensions are constructed by light attributes and spatial dimensions. However, limited by the degree of freedom of interaction between light and material structure parameters, the multiplexing dimension exploitation method is still confused. Herein, a 7D Spin-multiplexing technique is proposed. Spin structures with four independent attributes (color center type, spin axis, spatial distribution, and dipole direction) are constructed as coding basic units. Based on the four independent spin physical effects, the corresponding photoluminescence wavelength, magnetic field, microwave, and polarization are created into four orthogonal multiplexing dimensions. Combined with the 3D of space, a 7D multiplexing method is established, which possesses the highest dimension number compared with 6 dimensions in the previous study. The basic spin unit is prepared by a self-developed laser-induced manufacturing process. The free state information of spin is read out by four physical quantities. Based on the multiple dimensions, the information is highly dynamically multiplexed to enhance information storage efficiency. Moreover, the high-dynamic in situ image encryption/marking is demonstrated. It implies a new paradigm for ultra-high-capacity storage and real-time encryption.

Hardware implementation of memristor-based artificial neural networks.

Artificial Intelligence (AI) is currently experiencing a bloom driven by deep learning (DL) techniques, which rely on networks of connected simple computing units operating in parallel. The low communication bandwidth between memory and processing units in conventional von Neumann machines does not support the requirements of emerging applications that rely extensively on large sets of data. More recent computing paradigms, such as high parallelization and near-memory computing, help alleviate the data communication bottleneck to some extent, but paradigm- shifting concepts are required. Memristors, a novel beyond-complementary metal-oxide-semiconductor (CMOS) technology, are a promising choice for memory devices due to their unique intrinsic device-level properties, enabling both storing and computing with a small, massively-parallel footprint at low power. Theoretically, this directly translates to a major boost in energy efficiency and computational throughput, but various practical challenges remain. In this work we review the latest efforts for achieving hardware-based memristive artificial neural networks (ANNs), describing with detail the working principia of each block and the different design alternatives with their own advantages and disadvantages, as well as the tools required for accurate estimation of performance metrics. Ultimately, we aim to provide a comprehensive protocol of the materials and methods involved in memristive neural networks to those aiming to start working in this field and the experts looking for a holistic approach.

Effect of Anterior Cervical Decompression Fusion and Partial Resection of Uncinate Vertebra Joint on Cervical Sagittal Sequence in Patients with Non-Single-Segment Radiculopathy and its Correlation with Curative Effect: A Retrospective Analysis.

OBJECTIVE: Analyze the effect of preservation or resection of the partial uncinate joint on the sagittal sequence of the cervical vertebrae in patients with non-single-segment radiculopathy and the correlation between the sagittal sequence of the cervical vertebrae and the long-term effect after surgery, we explored whether it is necessary to perform partial resection of the uncinate joint in patients with cervical spondylotic radiculopathy undergoing anterior cervical decompression and fusion (ACDF). METHODS: The study retrospectively analyzed 96 patients with cervical spondylotic radiculopathy with more than two segments from August 2016 to January 2021, who underwent ACDF (ACDF group, 45 patients) or ACDF combined with partial uncinate joint resection (ACDF + UT group, 51 patients). Partial resection of the uncinate joint indicated removal of part of the uncinate joint and osteophyte based on the compression of the nerve root during surgery, whereas the uncinate joints in the ACDF group were retained completely. The imaging data and functional scores of the two groups were recorded before surgery, 1 month after surgery, and at the last follow-up. A paired t-test or rank sum test was applied to analyze the data. In addition, the correlation between the imaging parameters and functional scores was validated using the Pearson's test. RESULTS: All 96 patients successfully completed the surgery and were followed up for at least 12 months, with an average follow-up time of 14 months. At the last follow-up, the pain visual analog scale (VAS), neck disability index (NDI), and neck pain and disability scale (NPAD) scores of the two groups were significantly lower than those before surgery, and the Japanese Orthopaedic Association (JOA) score was significantly higher than that before surgery. At the last follow-up, compared with the ACDF+UT group, the NDI and NPAD scores in the ACDF group decreased more significantly (p < 0.05), and C2-7SVA, △C2-7SVA (the difference between C2-7 SVA at last follow-up and before operation), and T1S values decreased significantly (p < 0.05). The C2-7 Cobb angle was positively correlated with the JOA score and T1S (p < 0.05) and negatively correlated with the VAS, NDI, and NPAD scores and CGH-C7SVA (p < 0.05). C2-7SVA was positively correlated with CGH-C7SVA and T1S (p < 0.05). CONCLUSION: Patients with non-single-segmental cervical spondylotic radiculopathy and ACDF with or without uncinate joint resection can have effective improvement in the clinical effect and sagittal balance; however, partial uncinate joint resection has a certain negative impact on the long-term reconstruction of sagittal balance and long-term effects in patients after surgery.

Strain engineering on electronic structure, effective mass and charge carrier mobility in monolayer YBr3.

In recent years, two-dimensional materials have significant prospects for applications in nanoelectronic devices due to their unique physical properties. In this paper, the strain effect on the electronic structure, effective mass, and charge carrier mobility of monolayer yttrium bromide (YBr3) is systematically investigated using first-principles calculation based on density functional theory. It is found that the monolayer YBr3undergoes energy band gap reduction under the increasing compressive strain. The effective mass and charge carrier mobility can be effectively tuned by the applied compressive strain. Under the uniaxial compressive strain along the zigzag direction, the hole effective mass in the zigzag direction (mao1_h) can decrease from 1.64m0to 0.45m0. In addition, when the uniaxial compressive strain is applied, the electron and hole mobility can up to ∼103cm2V-1s-1. The present investigations emphasize that monolayer YBr3is expected to be a candidate material for the preparation of new high-performance nanoelectronic devices by strain engineering.

Superlow Power Consumption Memristor Based on Borphyrin-Deoxyribonucleic Acid Composite Films as Artificial Synapse for Neuromorphic Computing.

Memristor synapses based on green and pollution-free organic materials are expected to facilitate biorealistic neuromorphic computing and to be an important step toward the next generation of green electronics. Metalloporphyrin is an organic compound that widely exists in nature with good biocompatibility and stable chemical properties, and has already been used to fabricate memristors. However, the application of metalloporphyrin-based memristors as synaptic devices still faces challenges, such as realizing a high switching ratio, low power consumption, and bidirectional conductance modulation. We developed a memristor that improves the resistive switching (RS) characteristics of Zn(II)meso-tetra(4-carboxyphenyl) porphine (ZnTCPP) by combining it with deoxyribonucleic acid (DNA) in a composite film. The as-fabricated ZnTCPP-DNA-based device showed excellent RS memory characteristics with a sufficiently high switching ratio of up to ∼104, super low power consumption of ∼39.56 nW, good cycling stability, and data retention capability. Moreover, bidirectional conductance modulation of the ZnTCPP-DNA-based device can be controlled by modulating the amplitudes, durations, and intervals of positive and negative pulses. The ZnTCPP-DNA-based device was used to successfully simulate a series of synaptic functions including long-term potentiation, long-term depression, spike time-dependent plasticity, paired-pulse facilitation, excitatory postsynaptic current, and human learning behavior, which demonstrates its potential applicability to neuromorphic devices. A two-layer artificial neural network was used to demonstrate the digit recognition ability of the ZnTCPP-DNA-based device, which reached 97.22% after 100 training iterations. These results create a new avenue for the research and development of green electronics and have major implications for green low-power neuromorphic computing in the future.