Xiaobing Yan

Abstract Memristor, based on the principle of biological synapse, is recognized as one of the key devices in confronting the bottleneck of classical von Neumann computers. However, conventional memristors are difficult to continuously adjust the conduction and dutifully mimic the biosynapse function. Here, TiO 2 films with self‐assembled Ag nanoclusters implemented by gradient Ag dopant are employed to achieve enhanced memristor performance. The memristors exhibit gradual both potentiating and depressing conduction under positive and negative pulse trains, which can fully emulate excitation and inhibition of biosynapse. Moreover, comprehensive biosynaptic functions and plasticity, including the transition from short‐term memory to long‐term memory, long‐term potentiation and depression, spike‐timing‐dependent plasticity, and paired‐pulse facilitation, are implemented with the fabricated memristors in this work. The applied pulses with a width of hundreds of nanoseconds timescale are beneficial to realize fast learning and computing. High‐resolution transmission electron microscopy observations clearly demonstrate that Ag clusters redistribute to form Ag conductive filaments between Ag and Pt electrode under electrical field at ON‐state device. The experimental data confirm that the oxides doped with Ag clusters have the potential for mimicking biosynaptic behavior, which is essential for the further creation of artificial neural systems.

Abstract From Deep Blue to AlphaGo, artificial intelligence and machine learning are booming, and neural networks have become the hot research direction. However, due to the size limit of complementary metal–oxide–semiconductor (CMOS) transistors, von Neumann‐based computing systems are facing multiple challenges (such as memory walls). As the number of transistors required by the neural network increases, the development of neural networks based on the von Neumann computer is limited by volume and energy consumption. As the fourth basic circuit element, memristor shines in the field of neuromorphic computing. The new computer architecture based on memristor is widely considered as a substitute for the von Neumann architecture and has great potential to deal with the neural network and big data era challenge. This article reviews existing materials and structures of memristors, neurophysiological simulations based on memristors, and applications of memristor‐based neural networks. The feasibility and advancement of implementing neural networks using memristors are discussed, the difficulties that need to be overcome at this stage are put forward, and their development prospects and challenges faced are also discussed.

Abstract Doped‐HfO 2 thin films with ferroelectricity have attracted great attention due to their potential application in semiconductor industry as negative capacitance and resistance switching memory. Despite Hf 0.5 Zr 0.5 O 2 (HZO) thin films having the most robust ferroelectric properties among all doped‐HfO 2 thin films, the realization of single orthorhombic phase HZO thin films is not achieved, while the direct evidence between the structural–properties relationship of orthorhombic phase HZO and ferroelectricity is not confirmed. In this work, the growth of single orthorhombic phase HZO thin films with decent ferroelectricity and resistive switching behavior is reported. With the aid of advanced structural characterization techniques, the HZO thin film is confirmed to be in the single orthorhombic phase. Next, using scanning probe microscopy techniques and macroscopic ferroelectric measurements, the single phase HZO thin films exhibit ferroelectric properties with a remanent polarization of about 20 µC cm −2 . Interestingly, the HZO thin film shows ferroelectric resistive switching with an R OFF / R ON ratio of about 16 100% with excellent device performance. Furthermore, brain‐like learning behavior is also observed in the HZO thin film. These results may serve to stimulate the study of ferroelectric properties of HZO thin films and their application in the electronic industry.

A summary of the characteristics and switching mechanisms of memristors based on novel 2D materials.

The visual perception system is the most important system for human learning since it receives over 80% of the learning information from the outside world. With the exponential growth of artificial intelligence technology, there is a pressing need for high-energy and area-efficiency visual perception systems capable of processing efficiently the received natural information. Currently, memristors with their elaborate dynamics, excellent scalability, and information (e.g., visual, pressure, sound, etc.) perception ability exhibit tremendous potential for the application of visual perception. Here, we propose a fully memristor-based artificial visual perception nervous system (AVPNS) which consists of a quantum-dot-based photoelectric memristor and a nanosheet-based threshold-switching (TS) memristor. We use a photoelectric and a TS memristor to implement the synapse and leaky integrate-and-fire (LIF) neuron functions, respectively. With the proposed AVPNS we successfully demonstrate the biological image perception, integration and fire, as well as the biosensitization process. Furthermore, the self-regulation process of a speed meeting control system in driverless automobiles can be accurately and conceptually emulated by this system. Our work shows that the functions of the biological visual nervous system may be systematically emulated by a memristor-based hardware system, thus expanding the spectrum of memristor applications in artificial intelligence.

With the exploration of ferroelectric materials, researchers have a strong desire to explore the next generation of non-volatile ferroelectric memory with silicon-based epitaxy, high-density storage, and algebraic operations. Herein, a silicon-based memristor with an epitaxial vertically aligned nanostructures BaTiO3 -CeO2 film based on La0.67 Sr0.33 MnO3 /SrTiO3 /Si substrate is reported. The ferroelectric polarization reversal is optimized through the continuous exploring of growth temperature, and the epitaxial structure is obtained, thus it improves the resistance characteristic, the multi-value storage function of five states is achieved, and the robust endurance characteristic can reach 109 cycles. In the synapse plasticity modulated by pulse voltage process, the function of the spiking-time-dependent plasticity and paired-pulse facilitation is simulated successfully. More importantly, the algebraic operations of addition, subtraction, multiplication, and division are realized by using fast speed pulse of the width ≈50 ns. Subsequently, a convolutional neural network is constructed for identifying the CIFAR-10 dataset, to simulate the performance of the device; the online and offline learning recognition rate reach 90.03% and 92.55%, respectively. Overall, this study paves the way for memristors with silicon-based epitaxial ferroelectric films to realize multi-value storage, algebraic operations, and neural computing chip applications.

Abstract More than 80% of biological learning information is received through the visual system; therefore, artificial vision systems have garnered continual interest in the field of artificial intelligence technologies. Simulating the activities of a range of human vision systems, such as discrimination, memory, and induced muscular activity, which still remains a challenge. The authors develop a high‐speed multifunctional artificial vision system capable of recognizing, memorizing, and actuating self‐protection by combining a Sb 2 Se 3 /CdS‐core/shell (SC) nanorod array optoelectronic memristor, a threshold‐switching memristor, and an electrochemical actuator. When an optoelectronic memristor is activated, it can cause an electrochemical actuator to move, simulating the eye muscle contraction and reproducing the self‐protection response of closing eyes when the human eyes are injured by intense light. Light absorption and charge carrier extraction are advantages of optoelectronic memristors with high‐quality SC nanorod arrays. The device achieves a fast response speed and a large response current of up to 40 µs and 0.8 µA. Artificial vision systems offer a potential technique for bionanotechnology, particularly in the domain of artificial intelligence simulation of biosensor systems.

As key components of the human brain's neural network, synapses and neurons are important processing units that enable highly complex neuromorphic systems. Spiking neural network (SNN) is more powerful and efficient in terms of neuromorphic computing. Moreover, memristor-based neuromorphic computers can implement neural network algorithms more effectively than conventional hardware. However, the investigation on spiking neural network (SNN) based neuromorphic computing is still in the exploratory stage. Herein, a SNN based on ferroelectric Si:HfO2 film (∼ 6.8 nm) memristor was realized. The Si:HfO2-based memristor exhibits lower switching voltage (1.55/− 1.50 V) and super low power consumption (∼ 32.65 fJ). Additionally, it also shows superior conductance tunability and reliable realization of multiple synaptic functions. Especially, the highly linear conductance modulation of the Si:HfO2-based memristor results in a high accuracy of ∼ 96.23 % for handwritten digits. Spatiotemporal model recognition and unsupervised synaptic weight update functions were successfully implemented with the SNN constructed by these synaptic devices and artificial neuron models, which demonstrates the excellent adaptability and versatility of this SNN and paves the way for future neural network studies.

Carbon dots (CDs) are widely utilized in sensing, energy storage, and catalysis due to their excellent optical, electrical and semiconducting properties. However, attempts to optimize their optoelectronic performance through high-order manipulation have met with little success to date. In this study, through efficient packing of individual CDs in two-dimensions, the synthesis of flexible CDs ribbons is demonstrated technically. Electron microscopies and molecular dynamics simulations, show the assembly of CDs into ribbons results from the tripartite balance of π-π attractions, hydrogen bonding, and halogen bonding forces provided by the superficial ligands. The obtained ribbons are flexible and show excellent stability against UV irradiation and heating. CDs ribbons offer outstanding performance as active layer material in transparent flexible memristors, with the developed devices providing excellent data storage, retention capabilities, and fast optoelectronic responses. A memristor device with a thickness of 8 µm shows good data retention capability even after 104 cycles of bending. Furthermore, the device functions effectively as a neuromorphic computing system with integrated storage and computation capabilities, with the response speed of the device being less than 5.5 ns. These properties create an optoelectronic memristor with rapid Chinese character learning capability. This work lays the foundation for wearable artificial intelligence.

Partial substitution of synthetic nitrogen (N) with organic fertilizers (PSOF) is of great significance in improving soil ecosystem functions in systems that have deteriorated due to the excessive application of chemical N fertilizer. However, existing studies typically focus on individual soil functions, neglecting the fact that multiple functions occur simultaneously. It remains unclear how PSOF influences multiple soil functions and whether these impacts are related to soil microbial communities. Here, we examined the impacts of partial substitutions (25%−50%) of chemical N fertilizer with organic form (pig manure or municipal sludge) in a vegetable field on soil multifunctionality, by measuring a range of soil functions involving primary production (vegetable yield and quality), nutrient cycling (soil enzyme activities, ammonia volatilization, N leaching, and N runoff), and climate regulation (soil organic carbon sequestration and nitrous oxide emission). We observed that PSOF improved soil multifunctionality, with a 50% substitution of chemical N fertilizer with pig manure being the best management practice; the result was strongly related to the diversities and network complexities of bacteria and fungi. Random forest analysis further revealed that soil multifunctionality was best explained by the bacterial-fungal network complexity, followed by available phosphorus level and bacterial diversity. The PSOF also shifted the composition of bacterial and fungal communities, with increased relative abundances of dominant bacteria phyla, such as Bacteroidetes, Gemmatimonadetes, and Myxococcota, and fungal phyla, such as Basidiomycota and Olpidiomycota. The observed increases in soil multifunctionality were consistent with significant increases in the relative abundances of keystone taxa such as Blastocladiomycota, Chaetomiaceae, and Nocardiopsaceae. Together, these findings indicate that PSOF can enhance interactions within and among microbial communities and that such practices have the potential to improve soil ecosystem multifunctionality and contribute to the development of sustainable agriculture.

Brain-inspired computing is an emerging field, which intends to extend the capabilities of information technology beyond digital logic. The progress of the field relies on artificial synaptic devices as the building block for brainlike computing systems. Here, we report an electronic synapse based on a ferroelectric tunnel memristor, where its synaptic plasticity learning property can be controlled by nanoscale interface engineering. The effect of the interface engineering on the device performance was studied. Different memristor interfaces lead to an opposite virgin resistance state of the devices. More importantly, nanoscale interface engineering could tune the intrinsic band alignment of the ferroelectric/metal–semiconductor heterostructure over a large range of 1.28 eV, which eventually results in different memristive and spike-timing-dependent plasticity (STDP) properties of the devices. Bidirectional and unidirectional gradual resistance modulation of the devices could therefore be controlled by tuning the band alignment. This study gives useful insights on tuning device functionalities through nanoscale interface engineering. The diverse STDP forms of the memristors with different interfaces may play different specific roles in various spike neural networks.

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.

In this study, a simple TiN/SiO₂/p-Si tunneling junction structure was fabricated <italic>via</italic> thermal oxidation growth on a Si substrate annealed at 600 °C.

Monoelemental two-dimensional (2D) materials (Xenes) aroused a tremendous attention in 2D science owing to their unique properties and extensive applications. Borophene, one emerging and typical Xene, has been regarded as a promising agent for energy, sensor, and biomedical applications. However, the production of borophene is still a challenge because bulk boron has rather intricate spatial structures and multiple chemical properties. In this review, we describe its excellent properties including the optical, electronic, metallic, semiconducting, photoacoustic, and photothermal properties. The fabrication methods of borophene are also presented including the bottom-up fabrication and the top-down fabrication. In the end, the challenges of borophene in the latest applications are presented and perspectives are discussed.

Abstract Li‐CO 2 batteries can not only capture CO 2 to solve the greenhouse effect but also serve as next‐generation energy storage devices on the merits of economical, environmentally‐friendly, and sustainable aspects. However, these batteries are suffering from two main drawbacks: high overpotential and poor cyclability, severely postponing the acceleration of their applications. Herein, a new Co‐doped alpha‐MnO 2 nanowire catalyst is prepared for rechargeable Li‐CO 2 batteries, which exhibits a high capacity (8160 mA h g −1 at a current density of 100 mA g −1 ), a low overpotential (≈0.73 V), and an ultrahigh cyclability (over 500 cycles at a current density of 100 mA g −1 ), exceeding those of Li‐CO 2 batteries reported so far. The reaction mechanisms are interpreted depending on in situ experimental observations in combination with density functional theory calculations. The outstanding electrochemical properties are mostly associated with a high conductivity, a large fraction of hierarchical channels, and a unique Co interstitial doping, which might be of benefit for the diffusion of CO 2 , the reversibility of Li 2 CO 3 products, and the prohibition of side reactions between electrolyte and electrode. These results shed light on both CO 2 fixation and new Li‐CO 2 batteries for energy storage.

MXene Ti3C2, as the emerging member of two-dimensional (2D) materials family, has been applied to memristor and exhibited fast pulse modulation time and miniaturization size. However, the current abrupt behavior of switching processes hampered its further applications as a neuro-bionic device. Here, we present a facile method to improve the electronic performance of 2D MXene Ti3C2-based memristor by Ag nanoparticle doping. Compared to the pure Ti3C2 device, the Ag nanoparticle doping method exhibits a bidirectional continuous current transition behavior. Then, the variation trend of excitatory postsynaptic current (EPSC) was analysis in detail, and the transition rule of short-term potentiation (STP) to long-term potentiation (LTP) was systematically summarized. More interestingly, the energy consumption of generate one spike in unit device is as low as 0.35pj. And the memristor has ability to implement decimal arithmetic operations such as addition and multiplication. Exploration of the microstructure and finite element analysis (FEA) shows that the atomic vacancies capture the metal ions to form the filaments. This work provides a facile method to improve the electronic neuromorphic performance of MXene Ti3C2 memristor, which will considerably promote the diversification development of 2D materials in the field of neuro-morphology chips and greatly enhance the practicability of 2D materials.

Utilizing the instability of the edge atoms of graphene defects, carbon conductive filaments were formed under the regulation of the electric field and the synaptic function was achieved.

Memristors are designed to mimic the brain’s integrated functions of storage and computing, thus breaking through the von Neumann framework. However, the formation and breaking of the conductive filament inside a conventional memristor is unstable, which makes it difficult to realistically mimic the function of a biological synapse. This problem has become a main factor that hinders memristor applications. The ferroelectric memristor overcomes the shortcomings of the traditional memristor because its resistance variation depends on the polarization direction of the ferroelectric thin film. In this work, an Au/Hf0.5Zr0.5O2/p+-Si ferroelectric memristor is proposed, which is capable of achieving resistive switching characteristics. In particular, the proposed device realizes the stable characteristics of multilevel storage, which possesses the potential to be applied to multi-level storage. Through polarization, the resistance of the proposed memristor can be gradually modulated by flipping the ferroelectric domains. Additionally, a plurality of resistance states can be obtained in bidirectional continuous reversibility, which is similar to the changes in synaptic weights. Furthermore, the proposed memristor is able to successfully mimic biological synaptic functions such as long-term depression, long-term potentiation, paired-pulse facilitation, and spike-timing-dependent plasticity. Consequently, it constitutes a promising candidate for a breakthrough in the von Neumann framework.

The rapid development of artificial intelligence (AI), big data analytics, cloud computing, and Internet of Things applications expect the emerging memristor devices and their hardware systems to solve massive data calculation with low power consumption and small chip area. This paper provides an overview of memristor device characteristics, models, synapse circuits, and neural network applications, especially for artificial neural networks and spiking neural networks. It also provides research summaries, comparisons, limitations, challenges, and future work opportunities.

Elemental phosphorus (P) is key to all life forms on earth. Efficient P management and control in natural environments especially in water bodies is of paramount importance to the balance and stability of the ecosystem on both local and global scales. In the past decades, there have been numerous efforts devoted to the P analysis as well as on its efficient removal in water. However, natural occurrence of P species is in diverse forms with different properties, some even yet to be known, posing challenges to current analytic methods and removal technologies. In this review, we make an attempt to clarify the current advances on the analysis of different P species in water as well as the corresponding removal strategies. We hope to provide a new perspective for P management purpose, i.e., linking the P speciation analysis with the removal strategies, and offer a complementary guidance for researchers that are normally specialized in either field. Moreover, we envision future directions in both fields, and address the need for the development of P species-orientated removal strategies with high efficiency and selectivity based on advanced analytic technologies.

In the past few decades, 2D layer materials have gradually become a central focus in materials science owing to their uniquely layered structural qualities and good optoelectronic properties. However, in the development of 2D materials, several disadvantages, such as limited types of materials and the inability to synthesize large-scale materials, severely confine their application. Therefore, further exploration of new materials and preparation methods is necessary to meet technological developmental needs. Organic molecular materials have the advantage of being customizable. Therefore, if organic molecular and 2D materials are combined, the resulting 2D organic materials would have excellent optical and electrical properties. In addition, through this combination, the free design and large-scale synthesis of 2D materials can be realized in principle. Furthermore, 2D organic materials exhibit excellent properties and unique functionalities along with great potential for developing sensors, biomedicine, and electronics. In this review, 2D organic materials are divided into five categories. The preparation methods and material properties of each class of materials are also described in detail. Notably, to comprehensively understand each material's advantages, the latest research applications for each material are presented in detail and summarized. Finally, the future development and application prospects of 2D organic materials are briefly discussed.

Urban lakes were critical in aquatic ecology environments, but how environmental factors affected the distribution and change characteristics of algal communities in urban lakes of Xi'an city was not clearly. Here, we investigated the algal community structure of six urban lakes in Xi'an and evaluated the effects of water quality parameters on algae. The results indicated that the significant differences on physicochemical parameters existed in different urban lakes. The maximum concentration of total phosphorus in urban lakes was (0.18 ± 0.01) mg/L and there was a phenomenon of phosphorus limitation. In addition, 51 genera of algae were identified and Chlorella sp. was the dominant algal species, which was affiliated with Chlorophyta. Network analysis elucidated that each lake had a unique algal community network and the positive correlation was dominant in the interaction between algae species, illustrating that mature microbial communities existed or occupied similar niches. Redundancy analysis illustrated that environmental factors explained 47.35% variance of algal species-water quality correlation collectively, indicating that water quality conditions had a significant influence on the temporal variations of algae. Structural equation model further verified that algal community structure was directly or indirectly regulated by different water quality conditions. Our study shows that temporal patterns of algal communities can reveal the dynamics and interactions of different urban ecosystem types, providing a theoretical basis for assessing eutrophication levels and for water quality management.

Taihu Lake has suffered from eutrophication and algal blooms for decades, primarily due to increasing anthropogenic pollutants from human activities. Extensive research and widespread implementation of water pollution control measures have significantly contributed to the improvement of water quality of Taihu Lake. However, the relevant experience of Taihu Lake pollution control has not been well summarized to provide insight for future lake restoration. This review article seeks to address this gap by first providing a comprehensive overview of Taihu Lake's water quality dynamics over the past thirty years, characterized by two distinct stages: (I) water quality deterioration (1990s–2007); and (II) water total nitrogen (TN) improvement but total phosphorus (TP) fluctuation (2007–current). Subsequently, we conducted a thorough review of the experiences and challenges associated with water pollution control during these two stages. Generally, pollution control practices emphasized point source control but overlooked non-point sources before 2007, possibly due to point sources being easier to identify and manage. Accordingly, the focus shifted from industrial point sources to a combination of industrial point and agricultural non-point sources after 2007 to control water pollution in the Taihu Lake Basin. Numerous studies have delved into non-point source pollution control, including source control, transport intercept, in-lake measures, and the integration of these technologies. Taken together, this paper provides suggestions based on the needs and opportunities of this region. Further research is needed to better understand and model the underlying pollution processes, as well as to increase public participation and improve policy and law implementation, which will assist decision-makers in formulating better water management in Taihu Lake.

Memristor-based neuromorphic computing is beneficial for artificial intelligence to process external information autonomously with high speed and high efficiency. Two-dimensional (2D) layered van der Waals rhenium selenide (ReSe2) has optoelectronic and semiconductor properties, but its ferroelectricity has not been confirmed fully experimentally and the application exploration is currently limited. Here, we experimentally confirmed the room-temperature ferroelectricity of 2D ReSe2 and proposed a reconfigurable ReSe2 memristor that can realize multiple functions. The device can realize the conductance bidirectional regulation, and under the action of electrical signals, it exhibits the high 0.99 and 0.98 linearity and accurate bidirectional update of weights. Under the complementary effect of ReSe2 ferroelectric polarization flipping and interface defects, the device exhibits the memcapacitor and memristor reconfigurable behavior and multiple functions such as visible light perception, logical “OR” calculation, and long/short-term synaptic plasticity. In addition, the six-layer convolutional neural network built based on ReSe2 memristors can perform feature extraction and classification recognition of handwritten digital pictures, and its recognition accuracy can reach 97.04%. In addition to obtain substantial experimental evidence for the ferroelectricity of 2D ReSe2, this work also provides a new avenue for the implementation of ReSe2 ferroelectric memristors in the neuromorphic computing system with the front-end sensing and back-end processing.

Impregnating the cores of triblock-copolymer nanotubes with Fe2O3 renders them superparamagnetic. The resultant polymer/Fe2O3 hybrid nanofibers (see TEM image) have interesting properties in magnetic fields. The nanofibers align themselves in the magnetic-field direction, and mixtures of the nanofibers and nanotubes undergo phase separation.

Reported is a novel and simple method for the preparation of polymer spheres bearing hemispherical surface bumps where one type of polymer chains concentrates. The method is used to produce spheres with a diameter between approximately 30 and approximately 500 nm. Spheres with chain-segregated bumpy surfaces may find applications in drug delivery and other areas.

The effects of the electroforming polarity on the bipolar resistive switching characteristics in SrTiO3−δ thin films have been investigated. The conduction mechanisms of high resistance state and low resistance state are Poole–Frenkel emission and tunneling, respectively. The temperature dependences of the resistance at high and low resistance state are both semiconductorlike. The impact of the polarity of the electroforming voltage on the resistive switching mechanism and the distribution of defects was discussed. A simple model describing the combination of bulk and the interface effect was proposed to explain the resistive switching in this material.

In this paper, a novel bio-molecular device based on DNA with the reversible memristive behavior is fabricated by a spin-coating method. The Au/(DNA)10/Au device shows excellent memristive characteristics including good endurance, long retention time, satisfying ON/OFF current ratio, extraordinary write–read–erase rewritable ability and reproducible write-once read-many times (WORM) memory behavior. More inspiringly, a programmable multistate memory system is achieved under different sweeping voltages, which can significantly enhance the storage density. The introduction of Ag ions decreases the set voltage, which results from the reduction of DNA bandgap and is verified by the UV–Vis and Raman spectra. These results imply that this bio-molecular device has a promising potential for the multilevel nonvolatile memory applications.

Resistive memory (RRAM) based on a solid–electrolyte insulator is a type of critical nanoscale device with promising potential in non-volatile memory, analog circuits and neuromorphic synapse applications.

In this work, we fabricate and report a flexible memristor device with the structure of Ta/Ta2O5-x/Al2O3/InGaZnO4 on a stainless steel substrate, which is robust in emulating the bio-synapse function and anti-pull capacity. The I-V curves show that this device has excellent stability and uniformity in 100 sweep cycles. When applying stimulation voltage pulses, the device conductance is adjusted gradually and can still be modulated after 1000 times of bending. Furthermore, this device demonstrates essential synaptic behaviors, including short-term plasticity, long-term plasticity, and short-term to long-term transition. In addition, under a tension of 200 N, the I-V characteristics have no obvious degeneration and the conduction of the device can still be modulated under pulse trains. The flexible Ta/Ta2O5-x/Al2O3/InGaZnO4 memristor can be a promising candidate for neuromorphic computing applications.

A model based on carbon conductive filaments (CFs) for a memristor based on carbon quantum dots (QDs) is proposed for the first time.

An electrochemical metallization memristor based on Zr_0.5Hf_0.5O₂film and an active Cu electrode with quantum conductance and neuromorphic behavior has been reported in this work.

Phosphonates discharged from wastewater treatment plants (WWTPs) have attracted increasing concerns because of their potential impact on eutrophication and potential risks to aquatic ecosystems. However, very few studies are available on their occurrence and transformation in WWTPs, partly due to the lack of sensitive methods for phosphonate analysis in complex matrices. Herein, based on our recent progress in phosphonate analysis, the occurrence and transformation of phosphonates along the full-scale wastewater treatment processes of two textile dyeing WWTPs were revealed. A set of typical phosphonates, including six phosphonate chelators (PCs) and four potential degradation products of PCs (DP-PCs) were quantified in different units and the final dewatered sludge. Three PCs (2-phosphonobutane-1,2,4-tricarboxylic acid (PBTC), 1-hydroxyethane 1,1-diphosphonic acid (HEDP) and nitrilotris(methylene phosphonic acid) (NTMP)) at upmost mg/L and a considerable amount of four DP-PCs (9.12–608 μg/L) were detected in the influents of both WWTPs. In the subsequent treatment, NTMP could be removed more efficiently than PBTC and HEDP, especially in the coagulation unit, and the dissolved phosphonates were eliminated more readily than other dissolved organic phosphorus fractions. Of particular note, the toxicologically critical DP-PC (i.e., aminomethylphosphonic acid) was produced during the coagulation and biological treatment units. The final precipitation unit seemed essential to ensure satisfactory removal of PCs and DP-PCs. In addition, a significant accumulation of phosphonates in dewatered sludge (up to 7.81 g/kg) and the widespread occurrence of harmful DP-PCs also reminded us to pay more concerns on their potential risks during further sludge disposal in future.

Abstract With the advancement of artificial intelligence technology, more and more biological functions need to be imitated to complete more complex tasks and adapt to a complex external work environment. Memristors, as an excellent candidate for neuromorphic artificial electronic devices with many biological functions, have inspired the interest of researchers because of the advantages of scalability, good retention, and high operating speed. In this work, wide band gap semiconductor materials silicon carbide (SiC) films are prepared as a memristor medium. By adjusting the current compliance, both threshold character and bipolar resistive switching phenomenon are realized in one device with both lower powers for set operation. For the threshold characteristic, this device has mimicked the “threshold,” “inadaptation,” and “relaxation” features of a nociceptor, which will protect the artificial intelligence system to have stronger adaptability to the external environment. For the bipolar resistive switching characteristics, this device demonstrates good stability and retention time, with a switching speed of 18 ns. These bipolar resistance switching characteristics have simulated many synaptic functions. Pulses with hundreds of nanosecond time scale widths are conducive to fast learning and calculation. This device‐based third‐generation SiC semiconductor material will find a broad application in neuromorphic chip systems.

The recognition, memory, and processing of light information are an important link in the development of artificial vision system. However, the traditional image recognition and memory unit of artificial vision systems need a complex circuit structure, which brings great challenges to the development of artificial vision. In this work, a CeOx/ZnO structure optoelectronic memristor based on a simple two-terminal structure was prepared. Through optical and electrical tests, 405 nm visible light recognition, storage, and processing were achieved, and at the same time, the response current has been greatly improved. And the response of 405 nm visible light was verified by using simulating memristor array, indicating potential application in the artificial vision system. Finally, the physical conduction mechanism of the device is explained combining with the adjustment of the height of the CeOx/ZnO interface barrier by photo-generated carriers. It provides an important reference for the simplification of the artificial vision system circuit structure in the future.

Abstract Flexible electronic devices have been extensively studied for their advantages of distinguished portability, conformal contact characteristics, and human‐friendly interfaces compared with conventional bulk Si technology. Flexible electronics can be attached to various surfaces like skin and internal organs for human–machine interaction, which has made significant advances in electronics, medicine, neuromorphic computing, etc. Owing to the innovation and maturation of the thin film preparation process, which have promoted the successful preparation of high‐quality flexible ferroelectric films. Herein, the preparation of flexible ferroelectric devices and the progress of their applications in recent years are reviewed. Prevailing methods for preparing flexible ferroelectric films including the van der Waals heteroepitaxy on flexible substrate, delamination of the ferroelectric films on rigid substrate by chemical etching techniques, and direct use of novel 2D ferroelectric materials etc. are summarized. The research progress of flexible ferroelectric devices applied to memories, sensors, photovoltaic devices, and energy harvesters are also be discussed in detail. Moreover, the potential applications of flexible ferroelectric devices in the field of neuromorphic computing are studied, a new trend which contributes to the further development of brain‐like chip systems. Finally, the challenges and prospects of flexible ferroelectric devices in the future advanced electronics are briefly proposed.

Small water bodies such as interval water-flooded ditches, ponds, and streams serve as important nutrient sinks in many landscapes, especially in the multi-water continuum system. Yet watershed nutrient cycling models often fail to or insufficiently capture these waters, resulting in great uncertainty in quantifying the distributed transfer and retention of nutrients across diverse landscapes in a watershed. In this study, we present a network-based predictive framework of the nutrient transport process in nested small water bodies, which incorporates topology structure, hydrological and biogeochemical processes, and connectivity to perform a nonlinear and distributed scaling of nutrient transfer and retention. The framework was validated and applied to N transport in a multi-water continuum watershed in the Yangtze River basin. We show that the importance of N loading and retention depends on the spatial context of grid source and water bodies because of the great variation in location, connectivity, and water types. Our results demonstrate that hotspots in nutrient loading and retention could be accurately and efficiently identified through hierarchical network effects and spatial interactions. This offers an effective approach for the reduction of watershed-scale nutrient loads. This framework can be used in modeling to identify where and how to restore small water bodies for reduced non-point pollution from agricultural watersheds.

With the advancement of artificial intelligence technology, memristors have aroused the interest of researchers because they can realize a variety of biological functions, good scalability, and high running speed. In this work, the amorphous semiconductor material silicon carbide (SiC) was used as the dielectric to fabricate the memristor with the Ag/SiC/n-Si structure. The device has a power consumption as low as 3.4 pJ, a switching ratio of up to 105, and a lower set voltage of 1.26 V, indicating excellent performance. Importantly, by adjusting the current compliance, the strength of the formed filaments changes, and the threshold characteristic and bipolar resistance switching phenomenon could be simultaneously realized in one device. On this basis, the biological long- and short-term memory process was simulated. Importantly, we have implemented leakage integration and fire models constructed based on structured Ag/SiC/n-Si memristor circuits. This low-power reconfigurable device opens up the possibilities for memristor-based applications combining artificial neurons and synapses.

Near-sensor analog computing systems have received a lot of attention as they can effectively reduce the large amount of redundant data transferred between sensor terminals and computing units, thereby shortening the data processing time and reducing power consumption. However, ensuring the reliability and stability of memristor devices used in the hardware circuits of near-sensor analog computing systems remains a considerable challenge. In this paper, we describe a robust ferroelectric memristor based on Pd/BaTiO3:Nd2O3/La0.67Sr0.33MnO3 grown on a silicon structure with SrTiO3 as the buffer layer. Through optimized growth temperature, the device exhibits a low coercive field voltage (−1–2 V), robust endurance characteristics (>1010 cycles), and a power consumption as low as 0.45 fJ per synaptic event. Also in this study, a near-sensor analog computing system based on an array of pressure sensors and ferroelectric memristors was constructed. It is shown that this system can accurately calculate multiple raw analog pressure signals in real time without the need for peripheral circuitry and that the system can classify object shapes and perform edge detection with a maximum deviation of only about 58.6 nA. This study highlights the great potential of ferroelectric memristors for use as fundamental components of near-sensor analog computing systems.

Different nanotubes were prepared from two triblock copolymers. Chemistry was performed on the nanotubes so that one type contained amino terminal groups and the other bore carboxyl terminal groups. The amino and carboxyl groups were reacted by amidization to join the nanotubes head to tail to yield nanotube multiblocks. The block copolymer nanotube multiblocks (CONATUBLOCs) may be viewed as a macroscopic counterpart of block copolymers. Like block copolymers, the different blocks of the CONATUBLOCs segregated from one another not only in a block-selective solvent mixture but also in the solid state.

Corticotropin-releasing hormone (CRH) plays an important role in neuroendocrine, autonomic and behavioral responses to stressors. In the present study, the effect of chronic unpredictable mild stress (CUMS) on CRH neurons was investigated in rat brain.The rats were exposed to one of the stressors each day for 21 d. Immunostaining was performed to detect the CRH-positive neurons in the paraventricular nucleus (PVN) of the hypothalamus and in amygdala.After the stress protocol, the animals showed a reduction in body weight gain as well as reduced sucrose preference and locomotor activity. Interestingly, the CRH neurons in both PVN and central nucleus of the amygdala (CeA) were stimulated by CUMS. The densities of CRH-containing neurons in both PVN and CeA were significantly higher than those in control group.The CRH systems in PVN and CeA may both contribute to depression-like behaviors during CUMS.

In this paper, the ionic conductivities of La0.54Sr0.44Co0.2Fe0.8O3-δ and La0.6Sr0.4Co0.2Fe0.8O3-δ were measured by electron-blocked alternating current impedance analysis technique. The results show that the oxygen ion conductivity of La0.54Sr0.44Co0.2Fe0.8O3-δ is nearly five times higher than that of La0.6Sr0.4Co0.2Fe0.8O3-δ, which makes La0.54Sr0.44Co0.2Fe0.8O3-δ cathode more conductive than YSZ electrolyte. Consequently, the electrochemical reaction region is extended from the interface between the cathode and the electrolyte to the whole surface of the cathode grains, with a result of the cathode polarization overpotential being decreased and the cell electrical performance being improved. Besides, the XRD results show that both La0.54Sr0.44Co0.2Fe0.8O3-δ and La0.6Sr0.4Co0.2Fe0.8O3-δ begin to react with 8YSZ([Y2O3]0.08·[ZrO2]0.92) at 850 °C, but La0.54Sr0.44Co0.2Fe0.8O3-δ with a faster reaction rate. The thermal expansion experiments manifest that the two LSCFs have approximate thermal expansion coefficients, being about 14 × 10−6–15 × 10−6 K−1 from 500 °C to 700 °C, which is moderately higher than that of 8YSZ.

Abstract The monitor of sea fogs become more important with the rapid development of marine activities. Remote sensing through laser is an effective tool for monitoring sea fogs, but still challengeable for large distance. We demonstrated a Long-distance Lidar for sea fog with superconducting nanowire single-photon detector (SNSPD), which extended the ranging area to a 180-km diameter area. The system, which was verified by using a benchmark distance measurement of a known island, is applied to the Mie scattering weather prediction Lidar system. The fog echo signal distribution in the range of 42.3∼63.5 km and 53.2∼74.2 km was obtained by the Lidar system. Then the fog concentration and the velocity of the fog were deduced from the distribution, which is consistent with the weather prediction. The height of the sea fog is about two hundred meter while the visibility at this height is about 90 km due to the Earth’s radius of curvature. Therefore, the capability of this SNSPD-based Lidar was close to the theoretical limit for sea fog measurements for extremely high signal-to-noise ratio of SNSPD.

The demand for massive deep learning neural networks has driven the development of nanoscale memristor devices, which perform brain-inspired neuromorphic computing.

A rising kind of 2D pnictogens has drawn a great deal of attention in the field of catalytic application owing to their high specific surface area, mechanical properties, biocompatibility, optical and electrical performance.

Memristive devices with high-density and high-speed performance have considerable potential for neuromorphic computing applications in data storage and artificial synapses. However, current memristive devices that are based on conductive filaments, such as silver, are unstable owing to the high mobility and low thermodynamic stability of the filaments. A high-quality SnSe film was deposited using the pulsed laser deposition technology, and high-performance Pd/SnSe/NSTO devices were fabricated. High-stability memristive devices can not only implement simple arithmetic function but also exhibit the centralized distribution of SET/RESET voltage and cell–cell uniformity. The SET/RESET power can achieve approximately 4.1 and 61 μW power. The possibility of Pd filament formation and Pd2+ diffusion in SnSe thin films is first confirmed by combining high-resolution transmission electron microscopy, energy-dispersive spectrometer mapping, and first principle calculation. The formation and destruction process of Pd filaments can simulate the influx and extrusion kinetics of K+, Ca2+, or Na+ in biological synapses and implements considerable synaptic functions. This study thus provides a new idea for improving device performance using different filament materials, which can greatly facilitate the development of neuromorphic computing.

Phosphonates, featuring organic skeletons binding phosphonic acid group, are ubiquitous in wastewaters and natural waters. Highly efficient removal of trace phosphonates is desirable to ensure the water quality and to prevent eutrophication. Commercially available hyper-cross-linked resins modified by amino groups have been proved to exhibit preferable adsorption of various organic acids from aqueous systems via synergetic effect of various interactions (electrostatic interaction, van der Waals interaction, π-π interaction, etc.) but seem relatively ineffective for phosphonates. In this study, we proposed a new strategy via coupling a commercial resin NDA88 with nanoconfinement of Hydrated ferric oxide (HFO) for selective removal of phosphonates. The resultant [email protected] shows an adsorption capacity of 15.0 mg P/g towards a model phosphonate (nitrilotris(methylenephosphonic acid)), higher than those of the NDA88 host (7.9 mg P/g) and the bare HFO (7.4 mg P/g). Remarkably, compared to NDA88 and HFO, [email protected] exhibits much higher resistance to the interference of various anions and other substances at much greater levels, particularly the organic analogue nitrilotriacetic acid and phosphate. Such superior selectivity is believed to result from the cooperative contributions of the host NDA88 as well as the embedded nano-HFO. Moreover, [email protected] shows excellent reusability and satisfactory performance in fixed-bed water treatment. This work provides a rational design of nanocomposite adsorbents for highly selective removal of phosphonates from complicated water matrix.

The effects of land use on riverine N2O emissions are not well understood, especially in suburban zones between urban and rural with distinct anthropogenic perturbations. Here, we investigated in situ riverine N2O emissions among suburban, urban, and rural sections of a typical agricultural-urban gradient river, the Qinhuai River of Southeastern China from June 2010 to September 2012. Our results showed that suburban agriculture greatly increased riverine N concentration compared to traditional agricultural rivers (TAR). The mean total dissolved nitrogen (TDN) concentration was 8.18 mg N L-1 in the suburban agricultural rivers (SUAR), which was almost the same as that in the urban rivers (UR, of 8.50 mg N L-1), compared to that in TAR (0.92 mg N L-1). However, the annual average indirect N2O flux from the SUAR was only 27.15 μg N2O-N m-2 h-1, which was slightly higher than that from the TAR (13.14 μg N2O-N m-2 h-1) but much lower than that from the UR (131.10 μg N2O-N m-2 h-1). Moreover, the average N2O emission factor (EF5r, N2O-N/DIN-N) in the SUAR (0.0002) was significantly lower than those in the TAR (0.0028) and UR (0.0004). The limited indirect N2O fluxes from the SUAR are best explained by the high riverine dissolved organic carbon (DOC) and low dissolved oxygen, which probably reduced the denitrification source N2O by favoring complete denitrification to produce N2 and inhibited the nitrification source N2O, respectively. An exponential decrease model incorporating dissolved inorganic nitrogen and DOC could greatly improve our EF5r predictions in the agricultural-urban gradient river. Given the unprecedented suburban agriculture in the world, more studies in suburban agricultural rivers are needed to further refine the EF5r and better reveal the mechanisms behind indirect N2O emissions as influenced by suburban agriculture.

Lattice oxygen is increasingly attracting the attention of researchers as an active site in metal oxide catalysts. This paper reviews the strategies used in recent years to regulate the activity of lattice oxygen in catalysts and its application in catalytic reactions. It begins by exploring the mechanisms of lattice oxygen in various reactions, then provides a systematic overview of techniques employed to enhance the activity of catalysts, such as inactive A-site cation substitution, B-site cation substitution, the design of shell-core structures, and post-treatment of existing catalysts. Finally, the practical applications of these methods are discussed, including electrocatalytic reactions, advanced oxidation processes, and removal of volatile organic compounds. This review provides technical guidance for lattice oxygen-based metal oxide catalysts in oxygen vacancy modulation and provides a reference for future researchers interested in exploring the transformation of lattice oxygen in the reaction.

Ferroelectric memristors play a critical role in the hardware implementation of artificial synapses and neuromorphic computing because of their reliable nonvolatile storage and adjustable resistance states capabilities. In this study, we demonstrate how a silicon-based vertically aligned nanocomposite structure of Pd/(BaTiO3)0.6(CeO2)0.4/La0.7Sr0.3MnO3/SrTiO3/Si can be used to realize a robust electronic synapse. The results show that the electronic synapse has the characteristics of tunable resistance states that can imitate synaptic behaviors, such as spike-timing-dependent plasticity, paired pulse facilitation, and paired pulse depression. Moreover, the device exhibits good conductance modulation capability, which may provide feasibility in the field of image processing and neural computing.

Reported are the preparation and characterization of Pd/Ni nanoparticles in the core of water-dispersible nanotubes of a triblock copolymer. The preparation involves first the preparation of the triblock nanotubes and then the production of Pd nanoparticles inside the nanotube cores. Ni is introduced in the final step by electroless deposition using Pd as the catalyst. The challenges facing each reaction step are discussed.

Although clinical reports suggest a possible relationship between excess retinoids and the development of depression, the effect of retinoids on mood-related behavior remains controversial. Hyperactivity of the hypothalamus-pituitary-adrenal (HPA) axis plays a key role in the development of affective disorders. The present study aimed to elucidate the effect of retinoid on the activity of HPA axis in rat and whether this goes together with behavioral changes. All-trans retinoic acid (ATRA) was administered to juvenile male rats by daily intraperitoneal injection for 6 weeks. ATRA treatment increased basal serum corticosterone concentration as well as the thickness of adrenal cortex in young rat. Furthermore, the mRNA expression of corticotropin release factor (CRF) and retinoic acid receptor-α (RAR-α) in the hypothalamus was both markedly increased in ATRA-treated rats compared with vehicle. Some behavioral alterations were also observed. ATRA-treated rats showed anxiety-like behavior in elevated-plus maze and decreased spontaneous exploratory activities in novel open field. However, in the sucrose preference test chronic ATRA treatment did not modify behavior in the juvenile animals. Chronic administration of ATRA did not impair physical motor ability in either the prehensile traction or the beam balance/walk test. In conclusion, long-term ATRA administration resulted in hyperactivated HPA axis which was accompanied by several behavioral changes in young rat.

Reproducible and reliable bipolar resistive switching was obtained from memory cells. The current–voltage characteristic of the Ag/STO/Pt cells with a positive voltage applied to the Pt electrode and the results of X-ray photoelectron spectroscopy imply that the electrochemical reaction and the diffusion of ions play a critical role in the resistive switching effect. The temperature dependence of the on-state resistance, combined with the time dependence of the on- and off-state resistances under a constant voltage, provides further evidence that the resistive switching mechanism should be ascribed to the formation and dissolution of the metallic Ag nanofilaments.

Separation and purification of graphene oxide (GO) prepared from chemical oxidation of flake graphite and ultrasonication by capillary electrophoresis (CE) was demonstrated. CE showed the ability to provide high-resolution separations of GO fractionations with baseline separation. The GO fractionations after CE were collected for Raman spectroscopy, atomic force microscopy, and transmission electron microscopy characterizations. GO nanoparticles (unexfoliated GO) or stacked GO sheets migrated toward the anode, while the thin-layer GO sheets migrated toward the cathode. Therefore, CE has to be performed twice with a reversed electric field to achieve a full separation of GO. This separation method was suggested to be based on the surface charge of the GO sheets, and a separation model was proposed. This study might be valuable for fabrication of GO or graphene micro- or nanodevices with controlled thickness.

Further performance improvement is necessary for resistive random access memory (RRAM) to realize its commercialization. In this work, a novel pulse operation method is proposed to improve the performance of RRAM based on Ti/HfO2/Pt structure. In the DC voltage sweep of the RRAM device, the SET transition is abrupt under positive bias. If current sweep with positive bias is utilized in SET process, the SET switching will become gradual, so SET is current controlled. In the negative voltage sweep for RESET process, the change of current with applied voltage is gradual, so RESET is voltage controlled. Current sweep SET and voltage sweep RESET shows better controllability on the parameter variation. Considering the SET/RESET characteristics in DC sweep, in the corresponding pulse operation, the width and height of the pulse series can be adjusted to control the SET and RESET process, respectively. Our new method is different from the traditional pulse operation in which both the width and height of program/erase pulse are simply kept constant which would lead to unnecessary damage to the device. In our new method, in each program or erase operation, a series of pulses with the width/height gradually increased are made use of to fully finish the SET/RESET switching but no excessive stress is generated at the same time, so width/height-controlled accurate SET/RESET can be achieved. Through the operation, the uniformity and endurance of the RRAM device has been significantly improved.

Catering to the general trend of artificial intelligence development, simulating humans' learning and thinking behavior has become the research focus. Second-order memristors, which are more analogous to biological synapses, are the most promising devices currently used in neuromorphic/brain-like computing. However, few second-order memristors based on two-dimensional (2D) materials have been reported, and the inherent bionic physics needs to be explored. In this work, a second-order memristor based on 2D SnSe films was fabricated by the pulsed laser deposition technique. The continuously adjustable conductance of Au/SnSe/NSTO structures was achieved by gradually switching the polarization of a ferroelectric SnSe layer. The experimental results show that the bio-synaptic functions, including spike-timing-dependent plasticity, short-term plasticity and long-term plasticity, can be simulated using this two-terminal devices. Moreover, stimulus pulses with nanosecond pulse duration were applied to the device to emulate rapid learning and long-term memory in the human brain. The observed memristive behavior is mainly attributed to the modulation of the width of the depletion layer and barrier height is affected, at the SnSe/NSTO interface, by the reversal of ferroelectric polarization of SnSe materials. The device energy consumption is as low as 66 fJ, being expected to be applied to miniaturized, high-density, low-power neuromorphic computing.

The Ta/Ta₂O₅/AlN/graphene memristor with silicon-based multilayer graphene films as the bottom electrode has stable electrical characteristics.

This study focused on the fouling characteristics evaluation of the sludge in a membrane bioreactor integrated with microbial fuel cell (MFC-MBR) to reveal the mechanisms of membrane fouling mitigation. The filtration of soluble microbial products (SMPs) in MFC-MBR showed lower flux decline rate than those in the control system (C-MBR). Based on the extended Derjaguin-Landau-Verwey-Overbeek analysis, decreases in free energies of adhesion between the SMPs and clean membrane or SMP-fouled membrane were observed in MFC-MBR. When approaching the clean membrane or SMP-fouled membrane, the SMPs in MFC-MBR had to overcome a higher energy barrier compared to those in C-MBR, indicating the inhibition of adsorption of SMPs on the membrane surface in MFC-MBR. Additionally, sludge flocs in MFC-MBR exhibited lower hydrophobicity and were less negative surface charged in comparison to those in the C-MBR. In MFC-MBR, the sludge flocs approaching the clean membrane, SMP-fouled membrane and cake layer all experienced higher energy barriers and lower secondary energy minimums compared to those in C-MBR, exhibiting the lower potential of cake layer formation. These results confirmed that decreases of the fouling potentials of SMPs and sludge flocs were essential for the membrane fouling mitigation in the MFC-MBR.

Graphdiyne is a newly discovered two-dimensional planar carbon allotrope with highly π-conjugated interactions. This review aims to introduce graphdiyne and describe its similarities and differences with graphene to better understand the graphdiyne.

The drinking water distribution system is important for water supply and it affects the quality of the drinking water. Indoor pipeline water quality is regulated by physical, hydraulic and biological elements, such as indoor temperature and stagnation. In this work, the effects of indoor heating and overnight stagnation on the variation in bacterial community structure and the total cell count were assessed by full-length 16S rRNA gene sequencing and flow cytometry, respectively. The results exhibited that the average intact cell count was 6.99 × 104 cells/mL and the low nucleic acid (LNA) bacteria was 4.48 × 104 cells/mL after stagnation. The average concentration of total and intracellular adenosine triphosphate (ATP) was 3.64 × 10-12 gATP/mL and 3.13 × 10-17 gATP/cell in stagnant water, respectively. The growth of LNA cells played a crucial role in increasing ATP. The dominant phylum observed was Proteobacteria (87.21 %), followed by Actinobacteria (8.25 %). Opportunistic pathogens increased the risk of disease in stagnant water (up to 1.2-fold for Pseudomonas sp. and 5.8-fold for Mycobacterium sp.). Meanwhile, structural equation model (SEM) and redundancy analysis (RDA) also illustrated that water temperature, residual chlorine and Fe significantly affected the abundance and composition of bacterial community. Taking together, these results show response of tap water quality to overnight stagnation and indoor heating, and provide scientific basis for drinking water security management in winter season.

Memristors with threshold switching behavior are increasingly used in the study of neuromorphic computing, which are frequently used to simulate synaptic functions due to their high integration and simple structure. However, building a neuron circuit to simulate the characteristics of biological neurons is still a challenge. In this work, we demonstrate a leaky integrate-and-fire model of neurons, which is presented by a memristor-CMOS hybrid circuit based on a threshold device of a TiN/HfO2/InGaZnO4/Si structure. Moreover, we achieve multiple neural functions based on the neuron model, including leaky integration, threshold-driven fire, and strength-modulated spike frequency characteristics. This work shows that HfO2-based threshold devices can realize the basic functions of spiking neurons and have great potential in artificial neural networks.

The centimeter-scale single crystal α-MoO 3 was developed via oxygen assisted self-standing growth. The Ti/α-MoO 3 /Au memristor simulated synaptic properties and achieved low-energy consumption conductance update.

The memristor based NbNiO 3 nanocrystals can not only improve stability of device, but also be modulated by light and electrical signals. By constructing sensory neurons, they can be used to assist autonomous driving.

Two triblock copolymers of the poly(butyl methacrylate)-block-poly(2-cinnamoyloxyethyl methacrylate)-block-poly(tert-butyl acrylate) or PBMA-b-PCEMA-b-PtBA family were synthesized and characterized. The triblocks had PCEMA to PtBA volume ratios ≥1.3 and PBMA volume fractions ≥0.56. Their bulk morphologies consisted of cylindrical domains with PtBA cores and PCEMA shells dispersed in the PBMA matrix. Nanofibers of PBMA-b-PCEMA-b-PtBA were obtained after cross-linking PCEMA photochemically and separating the cross-linked cylinders from dissolved PBMA chains. PBMA-b-PCEMA nanotubes with PAA-lined channels were obtained after tert-butyl group removal from the PtBA cores by selective hydrolysis.

Raman spectra of collagen and DNA were discussed at different temperatures. The temperature-dependence of Raman intensity was obtained in the region from -150 to 200 degrees C. Four denaturation points at 0, 40, 68 and 90 degrees C of collagen and two peaks at 38 and 82 degrees C for DNA were obtained. The wavenumbers of many vibrational modes were found to increase for lower temperature, but the peak at 1302 cm(-1) of collagen and the peak at 1101 cm(-1) of DNA showed the opposite trend. In all of the vibrational modes of DNA, the bases showed the most sensitive to different temperatures and there is a pronounced shift of bands at 70 degrees C, the starting point of denaturation.

Triblock copolymer nanotubes bearing end-exposed poly(acrylic acid) or PAA core chains were prepared. The exposed PAA chains were reacted by amidization with a large excess of polystyrene spacer chains possessing amino end groups or amino-containing end blocks to graft the spacer chains. The amino groups at the other end of the spacer chains were then reacted with nanospheres bearing surface carboxyl groups to connect the nanotubes to nanospheres. The products from such a coupling reaction ranged from multiarm adduct to surfactant- and dumbbell-like objects. Product control using different strategies was explored. The products may have interesting properties and applications.

Studied by dynamic light scattering (DLS) and transmission electron microscopy (TEM) is the coaggregation of poly(tert-butyl acrylate)-block-poly(2-cinnamoyloxyethyl methacrylate), PtBA−PCEMA, and polystyrene-block-poly(2-cinnamoyloxyethyl methacrylate), PS−PCEMA, in mixtures of chloroform and hexane, where hexane is a precipitant for PCEMA and PS but a good solvent for PtBA. To ensure coaggregation, the PCEMA blocks in the different diblocks were tagged by the H-bonding DNA base pair thymine and adenine. Coaggregation of the associating diblocks resulted in interesting block copolymer aggregation behavior and morphologies.

In this work, based on wide bandgap Ga2O3 films, we demonstrated a fully transparent bipolar resistive random access memory (RRAM) device with very high average transmittance of 91.7% in the visible region. The semiconducting In-Ga-Zn-O (IGZO) films were used as symmetric electrodes to reduce sneak current. Different I-V performance will introduce a change in the overall oxygen vacancy distribution by an opposite polarity of electroforming voltage. The temperature dependent of I-V characteristics will be fitted to the hopping conduction mechanism for both of the high-resistance states (HRS) and low-resistance states (LRS) with semiconducting nature. The activation energy and trap spacing of LRS were lower and shorter than that of HRS. A model of resistive switching mechanism related to correlated barrier hopping theory has been proposed for the fully transparent IGZO/Ga2O3/IGZO RRAM device.

A novel organic memory device ‘Al/silver nanoparticles-deoxyribonucleic acid-cetyltrimethylammonium Bromide/ITO’ (Al/Ag NPs–DNA–CTMA/ITO) was fabricated. The measured I–V curve of the device exhibits unipolar switching. The conductivity and the memristive characteristics are significantly improved by the introduction of Ag nanoparticles, but with a poor stability. Better stability is achieved by annealing the device, which also changes the switching characteristic from unipolar to bipolar. As the annealing temperature is raised, the switching voltage first decreases and then increases, while the current IRESET first increases and then decreases. The range of the optimal annealing temperature is from 383 K to 403 K and the maximum ON/OFF current ratio (ION/IOFF) can reach 104. The switching voltage, the current, and ION/IOFF all increase with the applied voltage amplitude, and VSET and ION/IOFF obey a quadratic and Boltzmann relationship, respectively.

Oxygen vacancies are widely thought to be responsible for resistive switching (RS) effects based on polycrystalline oxides films. It is also well known that grain boundaries (GB) serve as reservoirs for accumulating oxygen vacancies. Here, Ar gas was introduced to enlarge the size of GB and increase the quantity of oxygen vacancies when the Ba0.6Sr0.4TiO3 (BST) films were deposited by pulse laser deposition technique. The experimental results indicate that the RS properties of the device exhibits better in the Ar-introduced BST films than in the O2-grown BST films. High resolution transmission electron microscopy images show that an amorphous region GB with large size appears between two lattice planes corresponding to oxygen vacancies defects in the Ar-introduced BST. Fourier-transform infrared reflectivity spectroscopy results also reveal highly accumulated oxygen vacancies in the Ar-introduced BST films. And we propose that the conduction transport of the cell was dominantly contributed from not ions migration of oxygen vacancies but the electrons in our case according to the value of activation energies of two kinds of films.

Three-dimensional (3D) plasmonic structures have been intensively investigated as high performance surface enhanced Raman scattering (SERS) substrates. Here, we demonstrate a 3D biomimetic SERS substrate prepared by deposition of silver nanoparticles (Ag NPs) on the bioscaffold arrays of cicada wings using laser molecular beam epitaxy. This deposition method can offer a large number of nanoparticles with average diameter of ∼10 nm and nanogaps of sub-10 nm on the surface of chitin nanopillars to generate a high density of hotspots. The prepared 3D Ag/cicada SERS substrate shows a limit of detection (LOD) for Rhodamine 6G as low as 10-7 M, high enhancement factor of 1.09 × 105, and excellent signal uniformity of 6.8%. Moreover, the molecular fingerprints of melamine in infant formula can be directly extracted with an LOD as low as 10 mg L-1, without the need for functional modification. The prepared SERS-active substrate, due to its low cost, high-throughput, and good detection performance, can be widely used in applications such as food safety and environmental monitoring.

Recent years have witnessed dramatic upsurge of artificial intelligence and neural networks in various application areas. The classification accuracy of neural networks is closely related to the number of training samples, the complexity of the neural network structure, and the number of training epochs. To achieve high performance of the neural network, a large number of training samples are usually required. However, since the number of training samples available can be limited in many real scenes, significant overfitting issue commonly arises and seriously deteriorates the training performance of neural networks. Here, we show a dropout neuronal unit based on NbOx volatile memristor, which can enable dropout function in the neural network constructed. The proposed neural network with such dropout neuron is able to suppress the overfitting problem effectively in the training process, and the role of dropout probability is investigated based on MNIST and FashionMNIST datasets. The results clearly imply the high potential of the dropout neuronal unit in building highly efficient neuromorphic computing systems.

Block copolymer nanofibers may be viewed as the macroscopic counterparts of polymer chains or supra−polymer chains. Well-characterized nanofiber fractions with narrow length distributions are required to facilitate their study by techniques such as viscometry and dynamic light scattering. Polystyrene-block-polyisoprene (PS-b-PI) nanofibers were prepared in this study by stirring S2Cl2-treated PS-b-PI films in THF to separate and disperse the cross-linked PI cylindrical domains. Nanofiber fractions were obtained by combining centrifugation fractionation and ultrasonication. These fractions were characterized by transmission electron microscopy to obtain their length distribution functions and static light scattering to yield the weight-average molar masses and z-average radii of gyration. A quantitative analysis of the radius of gyration values revealed that the PS-b-PI nanofibers had a persistence length of ∼200 nm in THF.

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.

This paper proposes a novel CMOS curvature-compensated bandgap reference (BGR) by using a new full compensation technique. The theory behind the proposed full compensation technique is analyzed. The proposed BGR is designed and implemented using 0.15 μm standard CMOS process. Simulation results show that the proposed BGR achieves a temperature coefficient (TC) of 0.84 ppm/°C over the temperature range from −40 °C to 120 °C with a 1.2 V supply voltage. The current consumption of proposed BGR is 51 μA at 27 °C. The line regulation of proposed BGR is 0.023%/V over the supply voltage range from 1.2 V to 1.8 V at 27 °C. In addition, the PSRRs of proposed BGR are −91 dB, −81 dB, −61 dB and −29 dB at DC or 10 Hz, 1 kHz, 10 kHz, and 100 kHz, respectively.

An elegant aptasensor was developed for dual fluorimetric determination of<italic>Salmonella paratyphi A</italic>through DNase I-assisted target recycling enlargement.

Biofilm formation involves the development of extracellular matrix and initially depends on adherence and tropism by flagellar movement. With the widespread development of antibiotic resistance and tolerance of biofilms, there is a growing need for novel anti-infective strategies. No currently approved medications specifically target biofilms. Aptamers are single-stranded nucleic acid molecules that may bind to their targets with high affinity and affect the target functions. We developed a bifunctional conjugate by linking an aptamer targeting bacterial flagella with ampicillin. We investigated its influence on biofilm prevention and dissolution by ultraviolet-visible spectrophotometry, inverted microscopy, and atomic force microscopy. This conjugate had distinctive antibacterial activity. Notably, the conjugate was more active than either component, and thus had a synergistic effect against biofilms.

Most therapeutical nucleic acid aptamers tend to inhibit protein–protein interactions and thereby function as antagonists. Attachment of the influenza virus surface glycoprotein hemagglutinin (HA) to sialic acid-containing host cell receptors (glycan) facilitates the initial stage of viral infection. Inhibition of the attachment may result in an antiviral effect on the proliferation of the influenza virus. To develop therapeutically interesting agents, we selected two single-stranded DNA (ssDNA) aptamers specific to the HA protein of H1N1 influenza virus (A/Puerto Rico/8/1934) through a procedure of systematic evolution of ligands by exponential enrichment. As it showed a higher binding affinity for HA protein (Kd = 78 ± 1 nM), aptamer 1 was tested for its ability to interfere with HA-glycan interactions using chicken red blood cell hemagglutination and microneutralization assays, which demonstrated that it significantly suppressed the viral infection in host cells. These results indicate that the isolated ssDNA aptamer may be developed as an antiviral agent against influenza through appropriate therapeutic formulation.

Nonvolatile stateful logic computing in memristors has tremendous potential to realize the aggregation combined with information storage and processing in the same physical location for breaking the von Neumann bottleneck of traditional computing architecture. Here, we fabricate a monoclinic BiVO4 film with a bandgap of Eg ≈ 2.4 eV and a nanoporous morphology as the memristor storage medium. The device, consisting of a TiN/BiVO4/fluorine-doped tin oxide structure, demonstrated excellent electric- and light-control of resistive switching performance. A Boolean “OR” gate is shown to be operable with an electrical signal and light signal as inputs and the resistance as output. According to the I–V fitting results, the conduction mechanism of the memristor is inferred to be trapped-assisted tunneling model. The large photocurrent is due to trapped electrons in the defects which will be released to the conduction band. The nanoporous structure and suitable bandgap are also beneficial to light absorption and electron detrapping for enlarging photocurrent. This work lays the device foundation for electrical–optical controlling logic functions in memristor devices.

Low-dimensional carbon materials, such as conducting graphene, semiconducting C60 and their hybrids, have recently received a great deal of attention for the potential applications in low-cost flexible and wearable nanoelectronics, due to their remarkable mechanical, electrical and optoelectronic properties. While graphene exhibits intrinsically weak absorption and C60 is typically associated with low carrier mobility and short exciton diffusion length, the marriage of two materials is a synergetic route to overcome these technical short-comings. Here, we fabricate a van der Waals bonded graphene/C60 hybrid on a plastic substrate to demonstrate a highly sensitive flexible photodetector. Intimate electronic coupling across the all-carbon interface allows highly efficient interfacial charge transfer, which effectively dissociates the electron-hole pairs and leads to significant photoresponse across ultraviolet to near-infrared (∼104 A/W @ 405 nm). Thanks to remarkable absorption of C60, it enables the detection of weak signals including those from a lighter and fluorescent lighting. Simultaneously, the photodetector exhibits extraordinary mechanical flexibility, allowing excellent electric conductivity and stable light detection under quite large tensile strain. Furthermore, the flexible devices still exhibit a satisfactory photoresponse after 6 months, indicating the excellent environmental robustness. This scalable all-caron hybrids may provide a viable route to produce the high-performance flexible optoelectronic devices.

Threshold switching (TS) devices are finding increasing use in the hardware implementation of neuromorphic network computing. Here, a simple structured Ag/amorphous Si/Pt TS device with a switching ratio of ∼105 is prepared, with turn-on and turn-off speeds as high as ∼20 ns and ∼16 ns, respectively. We use this TS device to construct a leaky integration-and-firing artificial neuron that emulates key biological neuron features like threshold-driven firing, all-or-nothing spiking, refractory period, intensity-modulated frequency response, and conductance-modulated frequency response. These results suggest that Si film-based TS device artificial neurons have significant potential for building high-speed artificial neural networks.

Nowadays, memristors are extremely similar to biological synapses and can achieve many basic functions of biological synapses, making them become a new generation of research hotspots for brain-like neurocomputing. In this work, we prepare a memristor based on two-dimensional α-In2Se3 nanosheets, which exhibits excellent electrical properties, faster switching speeds, and continuous tunability of device conduction. Meanwhile, most basic bio-synapse functions can be implemented faithfully, such as short-term memory (STM), long-term memory (LTM), four different types of spike-timing-dependent plasticity (STDP), and paired-pulse facilitation (PPF). More importantly, we systematically study three effective methods to achieve LTM, in which the reinforcement learning can be faithfully simulated according to the Ebbinghaus forgetting curve. Therefore, we believe this work will promote the development of learning functions for brain-like computing and artificial intelligence.

Recently, rapid progress has been made in the application of organic-inorganic halide perovskites in electronic devices, such as memristors and artificial synaptic devices. Organic-inorganic halide perovskite is considered as a promising candidate for the next generation of computing devices due to its ion migration property and advantages in manufacturing. In this work, a two-dimensional (2D)-3D organic-inorganic hybrid perovskite memristor was studied, using the stacking structure of indium tin oxide (ITO)/FA1−yMAyPbI3−xClx/(PEA)2PbI4/Au. The results show that this new type of memristor has novel resistance switching characteristics, such as scanning-rate-dependent current switching property, good current-voltage (I–V) curve repeatability, and ultralow energy consumption. A defect-modulated electron tunneling mechanism is demonstrated using the p-i-n junction model, and it is proven that the conductance state of the memristive device is determined by the defect concentration in the perovskite film near the electrode sides. In addition to the good memristive properties, this 2D-3D perovskite memristor can also function well as an artificial synapse, and its internal defect movement can faithfully simulate the inflow and extrusion of Ca2+ in biological synapses. Moreover, this perovskite-based artificial synapse has ultra-low power consumption due to the switchable p-i-n structure in organic-inorganic halide perovskites. Our finding highlights the immense application potential of the 2D-3D perovskite memristor in the future neuromorphic computing system.

Neuromorphic computing based on memristors capable of in-memory computing is promising to break the energy and efficiency bottleneck of well-known von Neumann architectures. However, unstable and nonlinear conductance updates compromise the recognition accuracy and block the integration of neural network hardware. To this end, we present a highly stable memristor with self-assembled vertically aligned nanocomposite (VAN) SrTiO3:MgO films that achieve excellent resistive switching with low set/reset voltage variability (4.7%/-5.6%) and highly linear conductivity variation (nonlinearity = 0.34) by spatially limiting the conductive channels at the vertical interfaces. Various synaptic behaviors are simulated by continuously modulating the conductance. Especially, convolutional image processing using diverse crossbar kernels is demonstrated, and the artificial neural network achieves an overwhelming recognition accuracy of up to 97.50% for handwritten digits. Even under the perturbation of Poisson noise (λ = 10), 6% Salt and Pepper noise, and 5% Gaussian noise, the high recognition accuracies are retained at 95.43%, 94.56%, and 95.97%, respectively. Importantly, the logic memory function is proven experimentally based on the nonvolatile properties. This work provides a material system and design idea to achieve high-performance neuromorphic computing and logic operation.

Reported in this paper is the preparation of ε-Co nanocrystals coated by a monolayer of poly(acrylic acid)-block-polystyrene or PAA−PS. This method is adopted from one using oleic acid as the surfactant. The replacement of oleic acid by PAA−PS as the surfactant during Co nanoparticle preparation yields Co/PAA−PS particles that can be solvent-cast to yield bulk films. For the multidentate nature of the PAA binding block, the PAA−PS coating is resistant toward solvent rinsing. The thickness of the coating can be increased by increasing the length of the PS block.

The emerging two-terminal memristor with a conductance-adjustable function under external stimulation is considered a strong candidate for use in artificial memory and electronic synapses. However, the stability, uniformity, and power consumption of memristors are still challenging in neuromorphic computing. Here an Au/SnSe/graphene/SiO2/Si memristor was fabricated, incorporating two-dimensional graphene with high thermal conductivity. The device not only exhibits excellent electrical characteristics (e.g., high stability, good uniformity and a high ROFF/RON ratio), but also can implement biological synaptic functions such as paired-pulse facilitation, short-term plasticity and long-term plasticity. Its set and reset power values can be as low as 16.7 and 2.3 nW, respectively. Meanwhile, the resistance switching mechanism for the device, which might be associated with the formation and rupture of a filamentary conducting path consisting of Sn vacancies, was confirmed by high-resolution transmission electron microscopy observations. The proposed device is an excellent candidate for use in high-density storage and low-power neuromorphic computing applications.

A memristor is very important for the development of an artificial neuromorphic system. However, the breakthrough of the limit of a work region for memristors remains challenging. Herein, a BiVO4 nanoparticle is proposed to be a high-performance artificial synapse for a neuromorphic system. A BiVO4-based artificial synapse exhibits superior bidirectional analog switching properties. Furthermore, the fundamental neurobiological synaptic functions in the BiVO4-based artificial synapse can be achieved, such as potentiation, a depression, nonlinear transmission, spike-time-dependent plasticity, pair-pulse facilitation, and the transition from short-term to long-term potentiation. Moreover, the movement of oxygen vacancies by an electric field is responsible for resistance switching. This work provides different insights into the design of an artificial synapse based on memristors.

Electronic synaptic devices with photoelectric sensing function are becoming increasingly important in the development of neuromorphic computing system. Here, we present a photoelectrical synaptic system based on high-quality epitaxial Ba0.6Sr0.4TiO3 (BST) films in which the resistance ramp characteristic of the device provides the possibility to simulate synaptic behavior. The memristor with the Pt/BST/Nb:SrTiO3 structure exhibits reliable I–V characteristics and adjustable resistance modulation characteristics. The device can faithfully demonstrate synaptic functions, such as potentiation and depression, spike time-dependent plasticity, and paired pulse facilitation, and the recognition accuracy of handwritten digits was as high as 92.2%. Interestingly, the functions of visual perception, visual memory, and color recognition of the human eyes have also been realized based on the device. This work will provide a strong candidate for the neuromorphic computing hardware system of photoelectric synaptic devices.

Small water bodies are extensively distributed and play critical roles in nitrogen (N) removal, primarily through sediment denitrification. However, our comprehension understanding of the N removal rate constant in these systems, particularly within the first-order kinetics model, remains limited. To address this gap, a one-year field study was conducted to investigate the N removal rate and N removal rate constant in various small water bodies within a typical intensive agricultural area. We observed a decrease in N removal rates in the downstream direction, from ditches to downstream ponds and streams, potentially due to upstream water bodies receiving higher nutrient inputs. Moreover, our findings revealed that the N removal process in small water bodies generally follows a first-order kinetics reaction model, with the N removal rate constant varying from 0.22 d1 in streams and 0.48 d1 in vegetated ditches. Both water dissolved organic carbon (DOC) and dissolved oxygen (DO) concentrations collectively influenced the N removal rate constants. By leveraging the relationship between the N removal rate constant and these environmental factors, we further estimated that, on average, small water bodies remove 68% of the N loading in the Dongting Lake Basin. We recommend implementing artificial management measures, such as vegetation, to enhance the N removal capacity of water bodies. However, the caution must be exercised in measures like concrete linings in ditches, as they can hinder N removal. These findings not only offer methods for estimating N removal in small water bodies, but also provide an insight into enhancing the N removal capacity of these systems to effectively mitigate non-point N pollution.

Abstract Lake nitrogen (N) removal, mainly resulting from bacterial denitrification that converts nitrate (NO 3 − ) to gaseous N (N 2 ), is important for lake water quality and eutrophication control. However, quantifying lake N removal is challenging due to the high background atmosphere N 2 concentration and the heavy burden of field surveys, leading to a decoupling of watershed N management and water quality improvement. Here, we developed and validated an innovative nonlinear model for lake N removal rate estimation by linking the N removal rates with remote sensing-derived variables (chlorophyll- a , chromophoric dissolved organic matter, and lake surface water temperature). The model was validated in shallow eutrophic Lake Taihu in the Yangtze River basin and at the global scale. Based on the new N removal model, we estimated that an annual average of 3.21 × 10 4 t N yr ‒1 was removed in Lake Taihu from 2011 to 2020, accounting for 53%–66% of the total lake N loading. The remaining N loading after denitrification removal in Lake Taihu would be approximately 2.37 mg N l ‒1 , and 0.79 × 10 4 t N y ‒1 of lake N loading still needs to be removed to meet the target of class IV water quality (1.5 mg N l ‒1 ). This is the first study linking lake N removal in sediment microcosm incubations with reach-scale remote sensing derived variables, providing timely-much insights into lake N removal. This approach can be easily applied in other lakes with satellite derived data, to better understand lake N budget, drivers of eutrophication control, and watershed N management.

Epitaxial LaNiO3(LNO)/PbZr0.4Ti0.6O3(PZT)/LaNiO3 heterostructures have been deposited on LaAlO3 (LAO) substrates, in which LNO films were prepared by magnetron sputtering and PZT films with thicknesses ranging from 40 nm to 160 nm were deposited via sol-gel method. It is found that the structural and physical properties of PZT films are closely related to the tetragonality (c/a-1). With increasing PZT thickness, both tetragonality and remanent polarization increase, and the maximum tetragonality and remanent polarization correspond to a 120-nm-thick PZT film with a remanent polarization of 61.6 μC/cm2 measured at 425 kV/cm. However, as the thickness of PZT film is further increased, the ferroelectric polarization decreases due to relaxation of the PZT film. The leakage current mechanism of the capacitor remains unchanged regardless of the PZT thickness. It is Space Charge Limited Current (SCLC) conduction mechanism at high electric fields and Ohmic conduction mechanism at low electric fields.

Abstract Perovskite‐type rare earth nickelates based memristor have recently attracted extensive attention in the field of novel storage computing due to their special electronic structure and exotic physical properties. However, there is still a shortage of memristors with ultra‐high stability performance, which will provide a solid foundation for future neural network computing with high accuracy recognition rates. Here, a GdNiO 3 ‐based interfacial memristor is presented, which possesses ultra‐high stable performance, such as electroforming‐free, low device‐to‐device variation, reliable cyclic switching, high on/off ratio (≈10 4 ) and stable pulse modulation of conduction. Combined with the comprehensive microstructure results, this behavior is ascribed to the interface Schottky barrier variation caused by the 1D oxygen vacancy channel conduction according to the transmission electron microscopy results. In particular, based on the device's stable pulse modulation plasticity performance, the study also succeeds in achieving highly accurate neural firing pattern recognition up to ≈99.75% accuracy and monitoring of pattern transitions by implementing a reservoir computing system based on the device. This research advances the progress of nickelates in novel storage computing and paves the way for future efficient memristor‐based reservoir computing systems to handle more complex temporal tasks.

With the development of artificial intelligence technology, it remains a challenge to improve the resistive switching performance of next-generation nonvolatile ferroelectric memristor device (FMD). Here, we report an epitaxial Na0.5Bi0.5TiO3 ferroelectric memristor device (NBT-FMD) with temperature sensing. The NBT epitaxial films with strong polarization strength and suitable oxygen vacancy concentration were obtained by temperature adjustment (700 °C). In addition, the function of the spiking-time-dependent plasticity and paired-pulse facilitation is simulated in ferroelectric memristor devices of Pt/NBT/SrRuO3 (SRO)/SrTiO3 (STO). More importantly, we have designed a neuronal circuit to confirm that NBT-FMD can serve as temperature receptors on the human skin, paving the way for bio-inspired application.

Carbon-based nanomaterials (CBNM)have been widely used in various fields due to their excellent physicochemical properties. In particular, in the area of tumor diagnosis and treatment, researchers have frequently reported them for their potential fluorescence, photoacoustic (PA), and ultrasound imaging performance, as well as their photothermal, photodynamic, sonodynamic, and other therapeutic properties. As the functions of CBNM are increasingly developed, their excellent imaging properties and superior tumor treatment effects make them extremely promising theranostic agents. This review aims to integrate the considered and researched information in a specific field of this research topic and systematically present, summarize, and comment on the efforts made by authoritative scholars. In this review, we summarized the work exploring carbon-based materials in the field of tumor imaging and therapy, focusing on PA imaging-guided photothermal therapy (PTT) and discussing their imaging and therapeutic mechanisms and developments. Finally, the current challenges and potential opportunities of carbon-based materials for PA imaging-guided PTT are presented, and issues that researchers should be aware of when studying CBNM are provided.