Hydrogen peroxide (H₂O₂), an environmentally benign oxidizer, finds extensive applications in pulp bleaching, wastewater treatment, and medical sterilization. Photocatalytic H₂O₂ synthesis via water and oxygen activation on semiconductor surfaces presents a sustainable production strategy. Notably, structurally tunable organic photocatalysts have emerged as promising candidates, in which targeted molecular engineering can boost the photocatalytic performance by enlarging specific surface areas, extending light absorption ranges, and facilitating charge carrier transport-separation dynamics. Given the growing significance of organic photocatalysts in H₂O₂ synthesis, a comprehensive review of this field has become imperative. This paper offers a systematic examination of visible-light-driven H₂O₂ synthesis using various organic photocatalysts, including graphitic carbon nitride (g-C₃N₄), resorcinol-formaldehyde (RF) resin, covalent organic frameworks (COFs), and linear conjugated polymers (LCPs). The focus lies on fundamental mechanistic elucidation, design of reaction pathways and active sites, modification strategies, and establishment of efficient photocatalytic systems. Extensive studies have correlated photocatalytic efficiency with interfacial electron transfer kinetics and spatial charge separation. Therefore, we methodically analyze key determinants governing photogenerated carrier dynamics and present engineering strategies for performance enhancement. Furthermore, we discuss emerging application scenarios enabled by photocatalytic H₂O₂ generation. Importantly, this review critically evaluates persistent challenges and cutting-edge solutions in visible-light-mediated H₂O₂ synthesis, ultimately providing design principles for developing high-efficiency organic photocatalysts.

Achieving high selectivity, high sensitivity, and superior conductivity simultaneously for chemiresistive gas sensors (CGS) remains a significant challenge due to inherent trade-offs among the material's properties. Inspired by biological olfaction, we design and fabricate a Ni-MOF/SnO₂ heterojunction as a perception-transduction-transmission separation membrane for bionic CGS, where Ni-MOF acts as a selective perception layer for H₂S, SnO₂ serves as the electron transmission and proton blocking layer, while the heterojunction enables efficient signal transduction from perception to transmission. The sensor demonstrates exceptional performance, including fingerprint-level selectivity, high moisture stability, and an ultra-low detection limit of 10 ppb. Notably, the sensor operates based on the heterojunction rectification enhancement model. The sensitivity is solely determined by the Ni-MOF perception layer, while the baseline current is governed by the SnO₂ transmission layer. By reducing the resistance of SnO₂, the net response is geometrically amplified. Additionally, applying a bias voltage enables geometric acceleration of the response/recovery rates. This study proposes a novel strategy for designing high-performance CGS.

Surface passivation and reconstruction in quantum dot (QD) materials are crucial for enhancing the performance of optoelectronic devices, particularly in high-sensitivity, low-noise short-wave infrared (SWIR) photodetectors (PDs). This study presents an optimized approach for PbS/CdS core-shell QDs through optimized surface engineering through controlled CdS shell modulation and solution-phase ligand exchange with concentrated lead halides. The refined surface reconstruction significantly reduces QD aggregation and reduces trap states, resulting in ordered QD stackings with narrower energy distributions. Consequently, the QD PDs achieve a significantly reduced dark current density of 192 nA cm⁻² and an enhanced detectivity of 5.06 × 10¹² Jones, resulting in a 29.6% reduction in dark current and a 7.4-fold improvement in detectivity compared to pristine QD PDs. Electrochemical impedance spectroscopy confirms the reduction in trap-assisted recombination, supported by extended photoluminescence lifetimes and higher quantum efficiencies. These findings underscore the potential of surface reconstructed QDs for advanced SWIR PD applications, particularly in achieving high sensitivity in imaging systems.

With the rapid advancement of information technology and artificial intelligence, the energy efficiency bottleneck of the von Neumann architecture and the scaling limits of silicon-based semiconductors are increasingly constraining improvements in computing performance. Neuromorphic computing, by emulating the cooperative behavior of neurons and synapses in the brain, enables the integration of computation and memory, offering a promising route toward next-generation low-power and high-performance computing. As a key strategy for artificial synapse implementation, optoelectronic synapses exhibit ultrafast response, broad bandwidth, and contactless optical signal transmission, conferring significant advantages in information sensing, transmission, and storage. In particular, two-dimensional (2D) transition metal dichalcogenides (TMDs) with their atomic-scale thickness, tunable bandgaps, and outstanding optoelectronic properties provide an ideal material platform for highly integrated optoelectronic synaptic devices. This review presents a comprehensive overview of the fundamental concepts of artificial synapses and synaptic plasticity, examines the device architectures and operating mechanisms of optoelectronic synapses based on 2D TMDs along with their applications in neuromorphic visual systems and neuromorphic computing, and further discusses the key challenges and future research directions in this area.

Memristive materials (MMs) with adjustable conductance states are becoming the fundamental building blocks of artificial neural networks. However, challenges such as nonlinear weight conductance updates and the trade-off between increasing melting uniformity and reducing solid-to-liquid transition time have impeded the demonstration of in situ learning on a large-scale multiple-layer memristive network. Here, we modulate the dynamic interactions between MMs and multiple-pulse excitations in the melting-based voltage-pulse length diagram and temperature dependent mean-square displacement. We utilize all four melting states (FMSs) to create an integrated framework for attaining rapid in-memory computing and in situ deep neural network applications. We achieved near-ideal R² value uniformity and a partial melting time below random-access memory (RAM) device's switching times. Reversible switching using below-RAM switching time pulses was also demonstrated. The FMS network discloses in situ learning capability and competitive classification accuracy on a conventional machine learning dataset. Simulations suggest that increasing the number of hidden neurons would further improve classification accuracy. Ab initio molecular-dynamics simulations provide a clear insight into the melting kinetics in MMs and the structural origins of the prior complete melting-facilitated decrease in output conductance. The memristive neural network represents a potential hardware platform for artificial intelligence, offering high speed and energy efficiency.

Magnetic van der Waals (vdW) semiconductors offer unique opportunities to integrate spin and charge degrees of freedom in atomically thin devices. Here, we fabricate dual-gated heterostructures consisting of exfoliated CrSBr, stacked beneath monolayer graphene and encapsulated by hexagonal boron nitride (h-BN). Magnetotransport measurements performed at temperatures down to 2 K and magnetic fields up to 13.5 T demonstrate that CrSBr acts as an exceptionally strong p-type doping source to graphene in this vdW heterostructure, driving the graphene Fermi level deep into the valence band with hole density reaching ~10¹³ cm⁻². High-frequency Shubnikov-de Haas oscillations and well-quantized quantum Hall plateaus were observed, confirming the high quality of the doped graphene channel. Furthermore, nearly field-independent resistance ridges emerged at a top gate bias of ≈ −3 V, which we attribute to graphene/h-BN moiré minibands with a twist angle of >3°. These miniband features become experimentally accessible solely due to the extreme p-doping by CrSBr, a regime that is inaccessible via conventional gating techniques.