Polyvinylidene fluoride (PVDF)-based composite electrolytes can effectively avoid the key defects of traditional liquid electrolytes, such as flammability, volatility, and leakage after sealing failure, making them a core candidate material for advanced solid-state batteries. However, these electrolytes still face several significant challenges, such as extremely low ionic conductivity at room temperature and insufficient interface compatibility, which hinders their large-scale commercialization. The adoption of multi-scale interface regulation is an effective strategy for enhancing the comprehensive performance of materials. This is mainly achieved by precisely regulating the interfacial interactions at different scales, ranging from the nanometer level to the micrometer level and up to the macroscopic level. This paper systematically reviews the preparation methods, characterization techniques, and Li+ transport mechanisms of PVDF-based composite solid electrolytes. Meanwhile, the interface engineering at macroscale, microscale, and nanoscale is discussed in depth. At the same time, it also discusses its application in other metal batteries. Moreover, the article points out current challenges and deepens the research on the interface mechanism. In conclusion, PVDF-based composite electrolytes can demonstrate significant potential in enhancing ion conductivity and interface compatibility through multi-scale interface control strategies, providing crucial theoretical basis and technical paths for the practical application of high-safety and high-performance solid-state batteries.
Research on high-performance anodes other than graphite has been accelerated with the increasing demand for advanced lithium-ion batteries. Phosphorus-based anodes, including black phosphorus and red phosphorus, possess a high theoretical capacity, but there are intrinsic limitations, including severe volume changes, low electrical conductivity, and unstable cycling stability. This mini-review focuses on the latest developments in solving the above problems by designing phosphorus-carbon composites with different dimensionalities, from zero-dimensional quantum dots and one-dimensional carbon nanotube hybrids to two-dimensional graphene and three-dimensional sponges. Moreover, the intrinsic mechanisms behind the performance improvement in phosphorus-carbon composites with different dimensionalities are deeply discussed in this review. Furthermore, the unique challenges and recent progress of phosphorus-carbon anodes in all-solid-state batteries are also discussed, comparing their degradation mechanisms with conventional liquid-electrolyte systems. Finally, perspectives on the design and synthesis strategies for phosphorus-carbon composites are provided in this mini-review.
Developing an effective electrocatalyst to regulate the kinetics of the sulfur redox reaction is essential for inhibiting polysulfide shuttling in Li-S batteries. Here, the electronic structure of CoTe, modulated by cation doping, is systematically investigated to elucidate how orbital hybridization optimizes sulfur redox electrocatalysis. Among the cation-doped CoTe compounds, the introduction of Ni dopants results in the most significant shift of the p -band center of CoTe (Ni-CoTe) towards the Fermi energy level, which facilitates the formation of numerous Te vacancies on the CoTe surface. This structural configuration endows CoTe with a multitude of active sites that bind polysulfide intermediates, lowering the activation energy (Ea ) of the sulfur reduction reaction, specifically reducing the Ea for the conversion of lithium polysulfides to Li2S from 0.73 eV to 0.65 eV. As a result, the Li-S battery with Ni-CoTe catalyst delivers exceptional cyclability, with a low-capacity decay of 0.049% per cycle over 1,000 cycles at 2.0 C. Moreover, an initial areal capacity of 13.5 mAh cm-2 at 0.02 C is achieved with a high sulfur loading (10.0 mg cm-2) and a low electrolyte-to-sulfur ratio (5.0 µL mg-1). These findings will provide fundamental insights into orbital-level catalyst design principles for advanced sulfur electrochemistry.
Wearable electronics require compact, reliable, and sustainable power sources. Fabric-based triboelectric nanogenerators (TENGs) offer a promising solution by combining energy harvesting with the inherent softness and breathability of textiles. However, conventional functionalization methods, such as surface coatings or multilayer structures, inevitably diminish these essential properties. To address this issue, we developed a knitted fabric-based direct current triboelectric nanogenerator (KF DC-TENG) using whole-garment knitting technology and the air breakdown effect. This design allows direct application without complex post-processing and eliminates the need for external rectification. A single unit of KF DC-TENG (8 & times; 2.5 cm(2)), after structural optimization, is capable of lighting 744 series-connected Light Emitting Diodes (LEDs) when manually rubbed against polytetrafluoroethylene (PTFE) fabric. Integration with a low-cost power management circuit (PMC, similar to 1.5 CNY) further enhances the output power by 290 times. Asa result, 17 s of friction can power an electronic watch for up to 8 min. Moreover, continuous sliding can sustain a 1.5 W LED without noticeable flickering. This work presents the first demonstration of integrating whole-garment knitting technology with DC-TENGs, offering a scalable and cost-effective strategy for self-powered wearable electronics.
Lithium-sulfur cells are among the most promising candidates for next-generation energy-storage systems because of their high theoretical capacity (1,675 mA & centerdot;h g-1) and energy density (2,600 W & centerdot;h kg-1). The low cost and environmental benignity associated with sulfur's natural abundance further position lithium-sulfur cells as sustainable solutions for large-scale applications such as electric vehicles and grid storage. This review summarizes recent advances and remaining challenges in three critical aspects that govern the performance of practical lithium-sulfur cell: high-loading sulfur cathodes, thin lithium-metal anodes, and lean-electrolyte configurations. High-loading cathodes continue to suffer from limited sulfur utilization, sluggish redox kinetics, and structural instability, all of which restrict the achievable high charge-storage capacity. Thin lithium-metal anodes, while essential for reducing the negative-to-positive capacity (N/P) ratio, are prone to rapid deterioration arising from unstable interfaces, uneven lithium deposition, and dead-lithium accumulation. Lean-electrolyte systems, which are essential for achieving high energy density, face additional challenges associated with polysulfide dissolution, resulting in increased cell impedance and shortened cycle life. Recent progress in multifunctional binders, catalytic sulfur hosts, and engineered separators or interlayers offers promising pathways to mitigate these interrelated challenges. By integrating these material and architectural strategies, lithium-sulfur batteries are steadily progressing from laboratory demonstrations toward industrial scalability, opening viable opportunities for high-energy, low-cost rechargeable storage.
Achieving high activity and stability at high current densities is critical in the practical application of electrocatalytic CO2 reduction (ECO2R). In this study, a fluorine-doped Bi2O3 electrocatalyst with oxygen vacancies (denoted as F-Bi2O3-Ov) has been synthesized using a Bi-based metal-organic framework as the sacrificial template. Catalyst characterizations reveal that the fluorine doping induces results in lattice expansion and generates abundant oxygen vacancies. The F-Bi2O3-Ov catalyst delivers a formate Faradaic efficiency of 94% at a current density of 300 mA cm-2 and with stable performance for over 25 h in a flow cell. Electrochemical kinetics analysis and in situ attenuated total reflectance Fourier transform infrared spectroscopy establish a fluorine doping and oxygen vacancies synergy that facilitates interfacial charge transfer, lowering the energy barrier for the formation of the key *OCHO intermediate. The findings of this study offer an effective strategy for modulating the electronic and geometric characteristics of metal oxide catalysts for high-performance ECO2R.
Polymer dielectrics are promising materials for electrostatic capacitors because of their high dielectric strength and fast charge-discharge capability. However, improving the energy-storage density of fluorinated ferroelectric polymers remains challenging because enhanced polarization is often accompanied by reduced breakdown strength. In this work, methacrylate) [P(VDF-TrFE-CFE)/PMMA] dielectric films are prepared by solution blending and casting. By tuning the blend ratio, the intermolecular interactions and crystallization behavior of the films are systematically regulated, resulting in distinct changes in their electrical properties. The results suggest that dipolar interactions between PMMA carbonyl groups and polar groups in P(VDF-TrFE-CFE) disturb chain packing and suppress crystallization, thereby reducing dielectric loss and improving breakdown stability. At the optimized composition, the P(VDF-TrFE-CFE)/PMMA (50/50 wt.%) film delivers a dischaged energy density of 17.12 J/cm3 with an energy-storage efficiency of 88.11%. Further gamma-irradiation of the optimized film at doses of 10-150 kGy leads to additional improvement in dielectric energy-storage performance. These results demonstrate that regulating intermolecular interactions and crystalline morphology through all-organic blending and irradiation is an effective strategy for developing high-performance polymer dielectrics.
Self-assembled monolayers (SAMs) have emerged as powerful interfacial modifiers for high-performance organic solar cells. Currently reported asymmetric substitution strategies have primarily focused on tuning molecular dipole moments and work functions or enhancing π-π stacking to improve interfacial quality. In contrast, our work reports an asymmetric carbazole-based SAM molecule, P-4PACz, featuring a unilateral phenyl substituent at the 3-position of the carbazole core. This asymmetric design alters the π-π stacking mode to a tightly packed yet slipped configuration, which enables ordered solid-state assembly while suppressing excessive pre-aggregation in solution. Such an approach enables a favorable balance between solution processability and interfacial ordering. Compared with its symmetric analogue 4PACz, P-4PACz exhibits reduced surface energy on indium tin oxide, improved energy-level alignment, and suppressed molecular aggregation, resulting in enhanced active-layer wetting and interfacial contact. This optimized interface promotes efficient hole extraction while mitigating interfacial recombination losses. Consequently, P-4PACz-based devices achieve a champion power conversion efficiency (PCE) of 19.03%, outperforming 4PACz (champion 18.28%) and PEDOT:PSS (champion 18.22%) controls. The superiority of P-4PACz is further validated across multiple representative systems, including PM6:L8-BO (champion 18.16%), PM6:PY-DT (champion 16.35%), PM6:Y6 (champion 16.73%), demonstrating its broad applicability. In addition to enhanced efficiency, P-4PACz-based devices exhibit improved operational stability, retaining 80% of their initial PCE after 782 h of continuous illumination.
Additive engineering has emerged as a powerful strategy for enhancing the efficiency and stability of perovskite solar cells (PSCs), enabling precise control over crystallization kinetics, defect passivation, interfacial energetics, and long-term environmental stability. By controlling nucleation and crystal growth, and thereby optimizing film morphology, additives effectively suppress non-radiative recombination and ion migration, addressing key challenges in the path toward commercialization. However, the conventional discovery process remains largely empirical and time-consuming. The integration of machine learning (ML) offers a promising avenue for data-driven screening, rational molecular design, and accelerated optimization of additive systems. ML models trained on experimental datasets and augmented with density functional theory and molecular dynamics simulations can predict interactions between additives and perovskites, identify performance-determining descriptors, and guide the discovery of novel functional molecules. This review systematically outlines the multifaceted roles of additives in PSCs, from crystallization regulation to interfacial stabilization. We further highlight the synergy between ML and additive engineering, emphasizing its potential to establish a predictive, intelligent framework for next-generation photovoltaic materials.
Photocatalytic ammonia synthesis, which leverages solar energy to convert nitrogen and water into ammonia, presents a sustainable and environmentally friendly alternative to the energy-intensive Haber-Bosch process. However, the effective activation of the particularly strong N≡N bond remains a significant challenge. Bismuth (Bi)-based materials have been identified as promising photocatalysts due to their strong absorption of visible light, high nitrogen adsorption capacity, and low toxicity. To further improve their photocatalytic performance, extensive research has been directed toward the design of Bi-based heterojunctions. This review highlights the essential and often decisive influence of heterojunction interface engineering in enhancing photocatalytic nitrogen fixation performance. Unlike traditional heterojunction construction, precise interfacial engineering - including the development of built-in electric fields, chemical bonds at the interface, atomic-scale charge transfer pathways, and defect-mediated active sites can fundamentally modulate charge separation, promote N2 adsorption and activation, and enhance structural stability. A systematic summary of recent advancements in various Bi-based heterojunctions (e.g., Type II, Z-scheme, and S-scheme) is provided, with particular emphasis on how interface design governs reaction mechanisms and catalytic efficiency. Finally, current challenges and future perspectives are discussed to inform the rational design of high-performance catalysts and to further the development of photocatalytic nitrogen fixation through interface-focused strategies.
Realizing the full potential of solid-state lithium metal batteries requires high-loading cathodes; however, their practical implementation is fundamentally hindered by sluggish mass transport and severe polarization arising from highly tortuous ion-diffusion pathways. Herein, we report a scalable three-dimensional (3D) composite architecture that decouples ion and electron transport through laser-engineered vertical microchannels. By uniformly infiltrating a solid polymer electrolyte into these directional channels within a thick LiNi0.8Co0.1Mn0.1O2 cathode, the tortuous diffusion bottlenecks inherent to conventional electrodes are effectively alleviated. Spatiotemporal multiphysics simulations, together with operando impedance analysis, reveal that this 3D architecture markedly reduces mass-transfer resistance and mitigates localized concentration polarization. As a result, the integrated cathode delivers highly reversible redox chemistry, enabling stable cycling over a high-voltage window of up to 4.5 V and excellent rate capability up to 5.0 C. To further demonstrate its practical relevance, a prototype single-layer solid-state pouch cell based on this architecture achieves a stable capacity of 95 mAh with high Coulombic efficiency. This laser-patterning strategy offers a broadly applicable route to overcoming fundamental mass-transport limitations in thick solid-state cathodes, thereby accelerating the development of high-energy-density solid-state batteries.
Carbon dots (CDs), an emerging class of zero-dimensional carbon nanomaterials, have attracted extensive attention for lithium-based energy storage due to their high specific surface area, tunable surface chemistry, excellent electronic conductivity, and abundant, readily functionalized surface states. Recent advances have demonstrated that CDs can serve as conductive bridges, chemical regulators, and interfacial stabilizers across all key components of lithium batteries, enabling the simultaneous optimization of electronic and ionic transport, as well as interfacial reactions, in cathodes, anodes, and electrolytes. This review systematically summarizes the synthesis strategies and structural classifications of CDs, emphasizing how precursor selection, heteroatom doping, and surface functionalization determine their core-shell structures, defect states, and chemical reactivity. Subsequently, the applications of CDs in cathode modification, anode reinforcement, and electrolyte optimization are discussed in detail, highlighting their roles in enhancing charge-transfer kinetics, modulating ion transport, stabilizing interphases, and suppressing lithium dendrite formation. Special attention is given to interfacial reconstruction mechanisms driven by heteroatom-doped or functionalized CDs, which simultaneously promote ionic conduction and electron blocking at solid-solid interfaces. Finally, current challenges and future directions are outlined, including predictive synthesis design, interfacial chemistry optimization, multiscale composite construction, and scalable green fabrication. Overall, this review aims to deepen the understanding of CD-mediated interfacial engineering and to provide design guidelines for the development of safe, long-life, and high-energy-density lithium-based batteries.
High-temperature proton exchange membrane fuel cells (HT-PEMFCs) have garnered considerable interest owing to their superior tolerance toward CO impurities and the inherent advantages of facile water management. However, severe phosphoric acid poisoning of Pt catalysts necessitates markedly higher Pt loadings than in low-temperature proton exchange membrane fuel cells, thereby constraining their large-scale deployment. Herein, we present an additive-assisted impregnation approach to synthesize ultrafine PtCo alloy nanoparticles encapsulated by defect-rich S-doped carbon encapsulation layers (CELs). The use of short-chain sodium thioglycolate enables the formation of ultrafine PtCo nanoparticles (-2.82 nm) coated with-0.4 nm-thick CELs, effectively suppressing metal sintering during high-temperature annealing and strengthening metal-support interactions. The S-doped CELs provide dual protection against phosphoric acid poisoning by physically isolating the PtCo alloys and introducing negatively charged carbon defects to electrostatically repel phosphate anions. Consequently, the optimized sodium thioglycolate-PtCo alloy electrocatalyst delivers a high mass activity of 0.695 A mgPt-1 at 0.85 V along with enhanced durability in 0.1 M H3PO4 at 80 degrees C. It further maintains excellent phosphate tolerance and oxygen reduction reaction activity, even in concentrated 85 wt% H3PO4 at 120 degrees C. Remarkably, in HT-PEMFCs, it achieves superior peak power densities of 613 and 908 mW cm-2 in H2-air and H2-O2, respectively, with a low Pt loading of 0.3 mgPt cm-2. Even at an ultra-low Pt loading of 0.1 mgPt cm-2, it delivers a peak power density of 355 mW cm-2 and an exceptional Pt-specific power density of 3.53 kW gPt-1 in H2-air cell, while sustaining stable operation over 100 h with minimal voltage decay. This study offers a versatile strategy to develop phosphate-resistant catalysts for high-performance HT-PEMFCs with low-Pt-loading.
The escalating plastic waste crisis has heightened the need for sustainable, scalable valorization strategies. Catalytic conversion of plastic waste into hydrogen offers dual benefits: waste mitigation and clean fuel generation. However, the variability of plastic feedstock and the complexity of reaction conditions pose significant challenges for designing efficient catalysts. Recent advances in artificial intelligence (AI) and machine learning (ML) are increasingly being employed to optimize process conditions for hydrogen production via electrolysis and traditional thermochemical pathways. ML models, such as neural networks and ensemble methods, have demonstrated high accuracy in predicting hydrogen yields and optimizing parameters for the gasification and pyrolysis of plastic waste. ML is also opening new avenues for accelerating catalyst discovery by enabling rapid prediction of catalyst performance, reaction pathways, and surface interactions. Computational tools and data-driven descriptors are being used to interpret complex catalytic systems and guide the design of more effective catalysts. However, their application to plastic-derived intermediates remains limited. Despite progress, significant gaps persist in applying ML to the unique challenges of plastic waste conversion, including catalyst discovery and the handling of heterogeneous feedstocks. Key limitations include the need for larger, high-quality datasets, improved model interpretability and the integration of domain-specific knowledge with advanced simulation techniques. In this review we critically summarized the current landscape of AI-driven catalyst design focusing on hydrogen production from plastic waste. It identified methodological and practical limitations and proposed a roadmap for integrating AI, domain-specific data, and catalysis simulations to unlock new catalysts for sustainable hydrogen production.
Potassium-sulfur (K-S) batteries have emerged as an ideal candidate for large-scale energy storage because of the high theoretical energy density and low material cost. However, the practical deployment of K-S batteries is impeded by challenges such as polysulfide shuttling, sluggish conversion kinetics, and interfacial instability. Despite extensive experimental work in materials development, the underlying mechanisms at the atomic and molecular levels remain poorly understood. Computational methods have proven useful for elucidating K-S electrochemistry from a microscopic perspective. Nevertheless, a comprehensive review that systematically combines these experimental advances with computational insights is still lacking. To address this gap, this review provides a comprehensive overview of advances in K-S battery research, with emphasis on the materials engineering and computational research. We first summarize the fundamental mechanisms of K-S batteries and progress in key battery components including cathode, anode, electrolyte, as well as binder and separator. Complementing these experimental efforts, we introduce the theoretical foundations of density functional theory and molecular dynamics simulations, and review their recent applications in critical aspects such as adsorption energetics, reaction kinetics, electronic properties, and dynamic behaviors. Finally, we outline future directions for K-S battery research, including the integration of advanced characterization with multi-scale simulation, the development of high-throughput experimental and computational platforms, and the application of artificial intelligence to accelerate the development of materials and computational simulations. This review aims to provide guidance for the rational design of high-performance K-S battery systems by integrating experimental and theoretical insights.
Flame-retardant composite phase-change materials (CPCMs) often face low flame-retardant efficiency and performance degradation after temperature aging owing to flame-retardant migration, limiting their use in electric vehicle battery packs and marine power systems. To address these challenges, we propose an innovative flame-retardant microencapsulated CPCM comprising ammonium polyphosphate (APP), dipentaerythritol (DPER), and melamine cyanurate (MCA) (AD@MCA) to improve battery module thermal safety. The microcapsules, prepared via in situ polymerization, enhance the flame-retardant efficiency and cycling stability of APP. This study compares the properties of CPCMs containing microencapsulated flame retardants and traditional physically blended flame retardants before and after thermal aging. Results show that the synergistic effect between MCA and DPER in the microencapsulated structure markedly improves the flame-retardant efficiency and cycling stability of APP. Furthermore, CPCMs containing microencapsulated flame retardants exhibit excellent battery thermal management performance and delay thermal runaway trigger times. This study presents a novel approach for developing multifunctional flame-retardant CPCMs for battery packs, addressing key challenges in battery thermal safety under extreme conditions.
A supercapacitor diode (CAPode), which combines both functions of energy storage and the current rectification, shows strong potential for a wide range of applications. However, it remains a big challenge to achieve flexible CAPodes with high performance because of limitations in electrode and electrolyte materials. In this study, we construct a high-performance flexible CAPode by synergistical optimization of electrodes and polymer electrolyte. The ion–sieving effects of oxygen–deficient intercalated transition metal oxide (MoOx) are revealed by experimental results and theoretical calculation. The resultant quasi-solid-state CAPode not only shows high specific capacitance and rectification ratio, but also exhibits excellent mechanical flexibility. The CAPodes-based “AND” and “OR” logic gates with good processing ability are also built and investigated.
All-solid-state batteries are regarded as promising next-generation energy-storage systems due to their potential to achieve energy densities exceeding 400 Wh kg-1. However, their practical implementation remains limited by interfacial instability, non-uniform metal deposition, and continuous electrolyte decomposition during cycling. In this work, a dual-sided polymer coating was introduced onto Na3SbS4 solid electrolyte pellets using a siloxane-based sodiated polyelectrolyte (AAM950), combined with sodium bis(trifluoromethylsulfonyl)imide, as an artificial interfacial layer. The conformal coating partially filled surface pores and reduced interfacial gaps, leading to improved solid-solid contact between the electrolyte and electrode. The optimized 7 & micro;L polymer-coated Na3SbS4 (NSS) pellet exhibited an ionic conductivity of 0.348 mS cm-1 at 55 degrees C. In addition, the Na2/3Fe1/2Mn1/2O2|Na-AAM950-NSS-Na-AAM950|Na cell delivered an initial discharge capacity of 137.4 mAh g-1, compared with 83.59 mAh g-1 for the bare NSS system at 0.02 A g-1, together with a capacity retention of 89.6% and an average Coulombic efficiency of 99.56% over 50 cycles at 55 degrees C. Although the polymer layer did not substantially increase the intrinsic bulk ionic conductivity of NSS, it effectively stabilized the electrode/electrolyte interface and promoted more homogeneous Na+transport during cycling. These findings demonstrate that dual-sided polymer modification provides an effective strategy for improving the practical cycling stability of Na3SbS4-based sodium all-solid-state batteries.
Coupling triboelectric nanogenerators (TENGs) with memristors offers a direct route to integrating energy harvesting and adaptive learning within a single physical substrate, thereby enabling self-powered neuromorphic systems driven by ubiquitous mechanical stimuli. Unlike conventional electronics that rely on external power rails, triboelectric-memristive hybrids transduce mechanical excitations into programmable resistive states, supporting synaptic functions such as short-term plasticity, long-term plasticity, and spike-timing-dependent plasticity. This review synthesizes the physical mechanisms of triboelectric-memristive coupling and clarifies how charge transfer, interfacial electron-ion interactions, and device-level state dynamics collectively enable energy-to-information transduction for signal processing and learning. In contrast to previous surveys that focus on TENGs or memristors in isolation, we establish a unified transduction framework that links mechanical stimulus statistics to TENG waveform characteristics and further to memristive state-variable evolution, which serves as the organizing principle throughout the paper. We then present (ⅰ) a mechanism-guided taxonomy of representative device architectures and their achievable plasticity modes; and (ⅱ) a system-level perspective on the integration of self - powered sensing, in-memory learning, and multimodal data fusion. Finally, we summarize key challenges - including charge stability, humidity tolerance, device variability, and scalable integration - and discuss emerging directions such as large-area triboelectric materials for improved array uniformity, multiphysics co-learning for enhanced in-sensor intelligence, and physics-informed compact models to support device-circuit-algorithm co-design under stochastic energy inputs.
