Lithium batteries are becoming increasingly vital thanks to electric vehicles and large-scale energy storage. Carbon materials have been applied in battery cathode, anode, electrolyte, and separator to enhance the electrochemical performance of rechargeable lithium batteries. Their functions cover lithium storage, electrochemical catalysis, electrode protection, charge conduction, and so on. To rationally implement carbon materials, their properties and interactions with other battery materials have been probed by theoretical models, namely density functional theory and molecular dynamics. This review summarizes the use of theoretical models to guide the employment of carbon materials in advanced lithium batteries, providing critical information difficult or impossible to obtain from experiments, including lithiophilicity, energy barriers, coordination structures, and species distribution at interfaces. Carbon materials under discussion include zero-dimensional fullerenes and capsules, one-dimensional nanotubes and nanoribbons, two-dimensional graphene, and three-dimensional graphite and amorphous carbon, as well as their derivatives. Their electronic conductivities are explored, followed by applications in cathode and anode performance. While the role of theoretical models is emphasized, experimental data are also touched upon to clarify background information and show the effectiveness of strategies. Evidently, carbon materials prove promising in achieving superior energy density, rate performance, and cycle life, especially when informed by theoretical endeavors.

Hydrogel-based sensors are recognized as key players in revolutionizing robotic applications, healthcare monitoring, and the development of artificial skins. However, the primary challenge hindering the commercial adoption of hydrogel-based sensors is their lack of high stability, which arises from the high water content within the hydrogel structure, leading to freezing at subzero temperatures and drying issues if the protective layer is compromised. These factors result in a significant decline in the benefits offered by aqueous gel electrolytes, particularly in terms of mechanical properties and conductivity, which are crucial for flexible wearable electronics. Previous reports have highlighted several disadvantages associated with using cryoprotectant co-solvents and lower mechanical properties for ion-doped anti-freezing hydrogel sensors. In this study, the design and optimization of a photocrosslinkable ionic hydrogel utilizing silk methacrylate as a novel natural crosslinker are presented. This innovative hydrogel demonstrates significantly enhanced mechanical properties, including stretchability (>1825%), tensile strength (2.49 MPa), toughness (9.85 MJ m^–3), and resilience (4% hysteresis), compared to its non-ion-doped counterpart. Additionally, this hydrogel exhibits exceptional nonfreezing behavior down to −85°C, anti-drying properties with functional stability up to 2.5 years, and a signal drift of only 5.35% over 2450 cycles, whereas the control variant, resembling commonly reported hydrogels, exhibits a signal drift of 149.8%. The successful application of the developed hydrogel in advanced robotics, combined with the pioneering demonstration of combinatorial commanding using a single sensor, could potentially revolutionize sensor design, elevating it to the next level and benefiting various fields.

Luminescence in organics that lasts for seconds to a few hours after light excitation has been reported recently, showcasing significant application potentials in flexible electronics and bioimaging. In contrast, long-lasting luminescence that can be electrically excited, whether in organics or inorganics, is much rarer and often less efficient. In this study, we report persistent luminescence (PersL) in organic light-emitting diodes (OLEDs) that lasts over 100 s and an energy storage effect beyond 60 min after charging with a direct-current electric field. Thermoluminescence studies reveal that the PersL in OLEDs is induced by traps formed in a host-guest molecular system serving as an emission layer (EML) with a trap depth of approximately 0.24 eV, consistent with the results from the same EML materials under light irradiation. Integrating results from electronic spin resonance, and density functional theory calculations, we propose a model delineating the charge carrier migration responsible for the trap-induced PersL in OLEDs. This study on trap-induced PersL in OLEDs may deepen our understanding of the luminescence mechanism in organic semiconductors and pave the way for expanding their applications in optoelectronics, energy storage and biological detection technologies.

Organic memristors, integrating chemically designed resistive switching and mechanical flexibility, present promising hardware opportunities for neuromorphic computing, particularly in the development of next-generation wearable artificial intelligence devices. However, challenges persist in achieving high yield, controllable switching, and multi-modal information processing. In this study, we introduce an efficient distribution of conversion bridges (EDCB) strategy by dispersing organic semiconductor (poly[2,5-bis(3-tetradecylthiophen-2-yl)thieno[3,2-b]thiophene], PBTTT) in elastomer (polystyrene-block-poly(ethylene-ran-butylene)-block-polystyrene, SEBS). This innovative approach results in memristors with exceptional yield, high stretchability, and reliable switching performance. By fine-tuning the semiconductor content, we shift the primary charge carriers from ions to electrons, realizing modulable non-volatile, and volatile duo-mode memristors. This advancement enables multi-modal signal processing at distinct operational mechanisms—non-volatile mode for image recognition in convolutional neural networks (CNNs) and volatile mode for dynamic classification and prediction in reservoir computing (RC). A fully analog RC hardware system is further demonstrated by integrating the distinct volatile and non-volatile modes of the EDCB-based memristor into the dynamic neuron network and the linear regression layer of the RC respectively, achieving high accuracy in online arrhythmia detection tasks. Our work paves the way for high-yield organic memristors with mechanical flexibility, advancing efficient multi-mode neuromorphic computing within a unified memristor system integrating volatile and non-volatile functionalities.

As the best-performing materials for thermoelectric cooling, Bi₂Te₃-based alloys have long attracted attention to optimizing the room-temperature performance of Bi₂Te₃ for both power generation and refrigeration applications. This focus leads to less emphasis and fewer reports on the cooling capability below room temperature. Given that the optimal carrier concentration (n_opt) for maximizing the cooling power is highly temperature dependent, roughly following the relationship n_opt∝T^3/2, lowering the carrier concentration is essential to improve the cooling capability at cryogenic temperatures. Taking p-type Bi_0.5Sb_1.5Te₃ as an example, careful control of doping in this work enables a reduction in carrier concentration to 1.7 × 10¹⁹ cm^–3 from its optimum at 300 K of 3.4 × 10¹⁹ cm^–3. This work successfully shifts the temperature at which the thermoelectric figure of merit (zT) peaks down to 315 K, with an average zT as high as 0.8 from 180 to 300 K. Further pairing with commercial n-type Bi₂Te₃-alloys, the cooling device realizes a temperature drop as large as 68 K from 300 K and 24 K from 180 K, demonstrating the extended cooling capability of thermoelectric coolers at cryogenic temperatures.

Reconfigurable memristors featuring neural and synaptic functions hold great potential for neuromorphic circuits by simplifying system architecture, cutting power consumption, and boosting computational efficiency. Building upon these attributes, their additive manufacturing on sustainable substrates further offers unique advantages for future electronics, including low environmental impact. Here, exploiting the structure–property relationship of inkjet-printed MoS₂ nanoflake-based resistive layer, we present paper-based reconfigurable memristors. We demonstrate a sustainable process covering material exfoliation, device fabrication, and device recycling. With >90% yield from a 16 ×  65 device array, our memristors demonstrate robust resistive switching, with >10⁵ ON–OFF ratio and <0.5 V operation in non-volatile state. Through modulation of compliance current, the devices transition into a volatile state, with only 50 pW switching power consumption. These performances rival state-of-the-art metal oxide-based counterparts. We show device recyclability and stable, reconfigurable operation following disassembly, material collection and re-fabrication. We further demonstrate synaptic plasticity and neuronal leaky integrate-and-fire functionality, with disposable applications in smart packaging and simulated medical image diagnostics. Our work shows a sustainable pathway toward printable, reconfigurable neuromorphic devices, with minimal environmental footprints.

Continuously printable electronics have the significant advantage of being efficient for fabricating conductive polymer composites; however, the precise tailoring of the 3D hierarchical morphology of conductive nanocomposites in a simple dripping step remains challenging. Here, we introduce a one-step direct printing technique to construct diverse microdome morphologies influenced by the interfacial Marangoni effect and nanoparticle interactions. Using a jet dispenser for continuous processing, we effectively fabricated a soft epidermis-like e-skin containing 64 densely arrayed pressure sensing pixels with a hierarchical dome array for enhanced linearity and ultrasensitivity. The e-skin has 36 temperature-sensing pixels in the outer layer, with a shield-shaped dome that is insensitive to pressure stimuli. Our prosthetic finger inserted with the printed sensor arrays was capable of ultragentle detection and manipulation, such as stably holding a fragile biscuit, using a soft dropper to elaborately produce water droplets and harvesting soft fruits; these activities are challenging for existing high-sensitivity tactile sensors.

Developing cost-effective and highly efficient oxygen evolution reaction (OER) electrocatalysts that operate in both acidic and alkaline media is crucial for industrial electrocatalytic water splitting. However, achieving high performance under dual pH conditions remains a significant challenge. Herein, we report the synthesis of multi-sized RuO₂ sub-nanoclusters on Co₃O₄ nanoarrays via a facile method, which demonstrates exceptional OER activity in both acidic and alkaline environments. The optimized catalyst exhibits remarkably low overpotentials of 165 mV in 0.5 M H₂SO₄ and 223 mV in 1 M KOH at a current density of 10 mA cm^–2, respectively. Additionally, it exhibits outstanding stability, maintaining performance over a 10-h continuous operation, which is attributed to the robust structural stability of the dispersed RuO₂ sub-nanocluster morphology. Atomic-scale investigations reveal a layer-by-layer growth mechanism of Ru on the Co₃O₄ substrate, transitioning from single atoms to monolayer clusters and ultimately to sub-nanoclusters as Ru loading increases. This growth mechanism provides a rational strategy for the precise design and synthesis of advanced cluster-based catalysts. Density functional theory (DFT) calculations further elucidate the strong oxide-support interactions between RuO₂ clusters and the Co₃O₄ matrix, facilitating electron transfer from RuO₂ to Co₃O₄ and generating an electron-deficient region. This electronic modulation enhances –OH adsorption and accelerates OER kinetics. These findings underscore the potential of metal sub-nanoclusters for designing highly efficient and durable electrocatalysts for water electrolysis.

Since its emergence in 2009, perovskite photovoltaic technology has achieved remarkable progress, with efficiencies soaring from 3.8% to over 26%. Despite these advancements, challenges such as long-term material and device stability remain. Addressing these challenges requires reproducible, user-independent laboratory processes and intelligent experimental preselection. Traditional trial-and-error methods and manual analysis are inefficient and urgently need advanced strategies. Automated acceleration platforms have transformed this field by improving efficiency, minimizing errors, and ensuring consistency. This review summarizes recent developments in machine learning-driven automation for perovskite photovoltaics, with a focus on its application in new transport material discovery, composition screening, and device preparation optimization. Furthermore, the review introduces the concept of the self-driven Autonomous Material and Device Acceleration Platforms (AMADAP) laboratory and discusses potential challenges it may face. This approach streamlines the entire process, from material discovery to device performance improvement, ultimately accelerating the development of emerging photovoltaic technologies.