Photocatalytic Reduction of Graphene Oxide-TiO2 Nanocomposites for Improving Resistive-Switching Memory Behaviors

Organic iontronic memristors for artificial synapses and bionic neuromorphic computing

To tackle the current crisis of Moore's law, a sophisticated strategy entails the development of multistable memristors, bionic artificial synapses, logic circuits and brain-inspired neuromorphic computing. In comparison with conventional electronic systems, iontronic memristors offer greater potential for the manifestation of artificial intelligence and brain-machine interaction. Organic iontronic memristive materials (OIMs), which possess an organic backbone and exhibit stoichiometric ionic states, have emerged as pivotal contenders for the realization of high-performance bionic iontronic memristors. In this review, a comprehensive analysis of the progress and prospects of OIMs is presented, encompassing their inherent advantages, diverse types, synthesis methodologies, and wide-ranging applications in memristive devices. Predictably, the field of OIMs, as a rapidly developing research subject, presents an exciting opportunity for the development of highly efficient neuro-iontronic systems in areas such as in-sensor computing devices, artificial synapses, and human perception.

Triphenylamine-Based Helical Polymer for Flexible Memristors

Flexible nonvolatile memristors have potential applications in wearable devices. In this work, a helical polymer, poly (N, N-diphenylanline isocyanide) (PPIC), was synthesized as the active layer, and flexible electronic devices with an Al/PPIC/ITO architecture were prepared on a polyethylene terephthalate (PET) substrate. The device showed typical nonvolatile rewritable memristor characteristics. The high-molecular-weight helical structure stabilized the active layer under different bending degrees, bending times, and number of bending cycles. The memristor was further employed to simulate the information transmission capability of neural fibers, providing new perspectives for the development of flexible wearable memristors and biomimetic neural synapses. This demonstration highlights the promising possibilities for the advancement of artificial intelligence skin and intelligent flexible robots in the future.

Multi-Level Resistive Al/Ga2O3/ITO Switching Devices with Interlayers of Graphene Oxide for Neuromorphic Computing

Recently, resistive random access memory (RRAM) has been an outstanding candidate among various emerging nonvolatile memories for high-density storage and in-memory computing applications. However, traditional RRAM, which accommodates two states depending on applied voltage, cannot meet the high density requirement in the era of big data. Many research groups have demonstrated that RRAM possesses the potential for multi-level cells, which would overcome demands related to mass storage. Among numerous semiconductor materials, gallium oxide (a fourth-generation semiconductor material) is applied in the fields of optoelectronics, high-power resistive switching devices, and so on, due to its excellent transparent material properties and wide bandgap. In this study, we successfully demonstrate that Al/graphene oxide (GO)/Ga₂O₃/ITO RRAM has the potential to achieve two-bit storage. Compared to its single-layer counterpart, the bilayer structure has excellent electrical properties and stable reliability. The endurance characteristics could be enhanced above 100 switching cycles with an ON/OFF ratio of over 10³. Moreover, the filament models are also described in this thesis to clarify the transport mechanisms.

Tin Oxide Nanorod Array-Based Photonic Memristors with Multilevel Resistance States Driven by Optoelectronic Stimuli

One-dimensional (1D) metal oxide-based photonic memristors, combining information storage and optical response, have shown great potential for the design and development of high-density and high-efficient computing systems beyond the era of von-Neumann architecture and Moore's law. Here, the functional memristive devices based on SnO_x slanted nanorod arrays are demonstrated; wherein both the optical and electrical stimuli have been used to modulate the switching characteristics to achieve multilevel cell operations. The switching characteristics of Al/SnO_x/FTO devices include low operating voltages (0.7 V/-0.6 V), moderate ON/OFF ratio (>10), and longer endurance (>10² cycles) and retention (>10³ s) with a self-compliance effect in the dark. Under illumination, ranging from ultraviolet (254 and 365 nm) to visible light (405 and 533 nm), an unusual negative photo response with an enlarged ON/OFF ratio of >10⁷ and a faster response time of <8 ms is observed. Additionally, multiple low and high resistance states have been achieved by modulating the programming current and the optical stimulus, respectively. The optoelectronic resistive memory behavior is attributed to the electric field-induced formation and light-stimulated dissolution of oxygen vacancies. Comprehensively, the results suggest that the optical illumination reduces the oxygen ion migration barrier, leading to the dissolution of conductive filaments and thereby locally increasing the OFF state resistance. The fabricated photonic memristors demonstrate the potential applications of metal oxide-based 1D nanostructures for artificial visual memory and optoelectronic applications.

Nanoscale and ultra-high extinction ratio optical memristive switch based on plasmonic waveguide with square cavity

A resistive switch effect-based optical memristive switch with an ultra-high extinction ratio and ultra-compact size working at 1550 nm is proposed. The device is composed of a metal-insulator-metal waveguide and a square resonator with active electrodes. The formation and rupture of conductive filaments in the resonant cavity can alter the resonant wavelength, which triggers the state of the optical switch ON or OFF. The numerical results demonstrate that the structure has an ultra-compact size (less than 1 µm) and ultra-high extinction ratio (37 dB). The proposed device is expected to address the problems of high-power consumption and large-scale optical switches and can be adopted in optical switches, optical modulation, optical storage and computing, and large-scale photonic integrated devices.

Advances in Emerging Photonic Memristive and Memristive-Like Devices

Possessing the merits of high efficiency, low consumption, and versatility, emerging photonic memristive and memristive-like devices exhibit an attractive future in constructing novel neuromorphic computing and miniaturized bionic electronic system. Recently, the potential of various emerging materials and structures for photonic memristive and memristive-like devices has attracted tremendous research efforts, generating various novel theories, mechanisms, and applications. Limited by the ambiguity of the mechanism and the reliability of the material, the development and commercialization of such devices are still rare and in their infancy. Therefore, a detailed and systematic review of photonic memristive and memristive-like devices is needed to further promote its development. In this review, the resistive switching mechanisms of photonic memristive and memristive-like devices are first elaborated. Then, a systematic investigation of the active materials, which induce a pivotal influence in the overall performance of photonic memristive and memristive-like devices, is highlighted and evaluated in various indicators. Finally, the recent advanced applications are summarized and discussed. In a word, it is believed that this review provides an extensive impact on many fields of photonic memristive and memristive-like devices, and lay a foundation for academic research and commercial applications.

Synaptic transistors and neuromorphic systems based on carbon nano-materials

Carbon-based materials possessing a nanometer size and unique electrical properties perfectly address the two critical issues of transistors, the low power consumption and scalability, and are considered as a promising material in next-generation synaptic devices. In this review, carbon-based synaptic transistors were systematically summarized. In the carbon nanotube section, the synthesis of carbon nanotubes, purification of carbon nanotubes, the effect of architecture on the device performance and related carbon nanotube-based devices for neuromorphic computing were discussed. In the graphene section, the synthesis of graphene and its derivative, as well as graphene-based devices for neuromorphic computing, was systematically studied. Finally, the current challenges for carbon-based synaptic transistors were discussed.

Recent advances in optical and optoelectronic data storage based on luminescent nanomaterials

The substantial amount of data generated every second in the big data age creates a pressing requirement for new and advanced data storage techniques. Luminescent nanomaterials (LNMs) not only possess the same optical properties as their bulk materials but also have unique electronic and mechanical characteristics due to the strong constraints of photons and electrons at the nanoscale, enabling the development of revolutionary methods for data storage with superhigh storage capacity, ultra-long working lifetime, and ultra-low power consumption. In this review, we investigate the latest achievements in LNMs for constructing next-generation data storage systems, with a focus on optical data storage and optoelectronic data storage. We summarize the LNMs used in data storage, namely upconversion nanomaterials, long persistence luminescent nanomaterials, and downconversion nanomaterials, and their applications in optical data storage and optoelectronic data storage. We conclude by discussing the superiority of the two types of data storage and survey the prospects for the field.

Bacterial Cellulose-Graphene Based Nanocomposites

Bacterial cellulose (BC) and graphene are materials that have attracted the attention of researchers due to their outstanding properties. BC is a nanostructured 3D network of pure and highly crystalline cellulose nanofibres that can act as a host matrix for the incorporation of other nano-sized materials. Graphene features high mechanical properties, thermal and electric conductivity and specific surface area. In this paper we review the most recent studies regarding the development of novel BC-graphene nanocomposites that take advantage of the exceptional properties of BC and graphene. The most important applications of these novel BC-graphene nanocomposites include the development of novel electric conductive materials and energy storage devices, the preparation of aerogels and membranes with very high specific area as sorbent materials for the removal of oil and metal ions from water and a variety of biomedical applications, such as tissue engineering and drug delivery. The main properties of these BC-graphene nanocomposites associated with these applications, such as electric conductivity, biocompatibility and specific surface area, are systematically presented together with the processing routes used to fabricate such nanocomposites.

High-performance position-sensitive detector based on the lateral photoelectrical effect of two-dimensional materials

Two-dimensional (2D) materials such as graphene and transition-metal chalcogenides have been extensively studied because of their superior electronic and optical properties. Recently, 2D materials have shown great practical application in position-sensitive detectors (PSDs), originating from the lateral photoelectrical effect of the materials or junctions. The high position sensitivity and ultrafast photoresponse of PSDs based on 2D materials, especially compatibility with Si technology, may enable diverse optoelectronic applications. In this review, recent studies of PSDs based on 2D materials are summarized, providing a promising route for high-performance PSDs.

Resistive switching and synaptic learning performance of a TiO2 thin film based device prepared by sol-gel and spin coating techniques

A resistance random access memory device based on TiO₂ thin films was fabricated using a sol-gel spin and coating techniques. The composition, surface morphology, and microstructure of the TiO₂ films were characterized using x-ray diffraction, Raman spectroscopy, scanning electronic microscopy, and transmission electron microscopy, respectively. The fabricated Al/TiO₂ film/fluorine-doped tin oxide device exhibited electroforming-free bipolar resistive switching characteristics with a stable ON/OFF ratio higher than 300. The performance of the endurance cycling was still good after 100 direct sweeping cycles. A retention time of no less than 10⁴ s was confirmed. A switching mechanism is systematically discussed based on the test results, and space-charge-limited current was found to be responsible for the switching behavior. Multilevel memory performance was realized in the as-fabricated devices. The synaptic performance was investigated by applying consecutive positive (0-2 V) and negative (0 to -1.6 V) voltage sweeps. The fabricated devices were found to exhibit 'learning-experience' behavior.

Toward a generalized Bienenstock-Cooper-Munro rule for spatiotemporal learning via triplet-STDP in memristive devices

The close replication of synaptic functions is an important objective for achieving a highly realistic memristor-based cognitive computation. The emulation of neurobiological learning rules may allow the development of neuromorphic systems that continuously learn without supervision. In this work, the Bienenstock-Cooper-Munro learning rule, as a typical case of spike-rate-dependent plasticity, is mimicked using a generalized triplet-spike-timing-dependent plasticity scheme in a WO_3-x memristive synapse. It demonstrates both presynaptic and postsynaptic activities and remedies the absence of the enhanced depression effect in the depression region, allowing a better description of the biological counterpart. The threshold sliding effect of Bienenstock-Cooper-Munro rule is realized using a history-dependent property of the second-order memristor. Rate-based orientation selectivity is demonstrated in a simulated feedforward memristive network with this generalized Bienenstock-Cooper-Munro framework. These findings provide a feasible approach for mimicking Bienenstock-Cooper-Munro learning rules in memristors, and support the applications of spatiotemporal coding and learning using memristive networks.

Enhanced detoxification of p-bromophenol by novel Zr/Ag-TiO2@rGO ternary composite: Degradation kinetics and phytotoxicity evolution studies

The toxicity and persistence of the halogenated aromatics, particularly brominated phenolic compounds have drawn serious concerns to the environment, emphasizing the potential effects on human health and ecosystems balance. Advanced oxidation process (AOP) has received much attention as an alternative for the conventional wastewater treatment methods to treat water contaminated with toxic pollutants. This study investigated the degradation and detoxification of p-bromophenol (p-BP) by a novel Zr/Ag-TiO₂@rGO photocatalyst under visible light. Upon 3 h of visible light irradiation over Zr/Ag-TiO₂@rGO, more than 95% of p-BP (15 mg/L) degradation was achieved at a rate of 0.23 min^-1. The degradation products were identified by GC-MS and possible degradation pathway was proposed. The phytotoxicity evolution of the degraded products was assessed on Vigna radiata (V. radiata), in which seeds treated with pure p-BP showed less germination (40%) compared to degradation products (100%). Furthermore, the germination index (GI) of p-BP was found to be 11.1% before degradation while it increased to 80.5% after 3 h of degradation indicated that this photodegradation process achieved detoxification of p-BP. Thus, this study demonstrated that p-BP elimination and detoxification could be simply achieved with Zr/Ag-TiO₂@rGO nanocomposite under visible light irradiation, which provides new solution for wastewater treatment and water reuse in crop irrigation.