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The rapid development of Internet of Things and artificial intelligence brings increasing attention on the harvesting of distributed energy by using triboelectric nanogenerator (TENG), especially the direct current TENG (DC-TENG). It is essential to select appropriate triboelectric materials for obtaining a high performance TENG. In this work, we provide a set of rules for selecting the triboelectric materials for DC-TENG based on several basic parameters, including surface charge density, friction coefficient, polarization, utilization rate of charges, and stability. On the basis of the selection rules, polyvinyl chloride, used widely in industry rather than in TENG, is selected as the triboelectric layer. Its effective charge density can reach up to ~8.80 mC m −2 in a microstructure-designed DC-TENG, which is a new record for all kinds of TENGs. This work can offer a basic guideline for the triboelectric materials selection and promote the practical applications of DC-TENG. Appropriate triboelectric material selection is vital to for high performance direct current triboelectric nanogenerator (DC-TENG). The authors here provide effective selection rules as guideline to select triboelectric materials for DC-TENG to reduce the trial-and-error cost for DC-TENG’s research.

As a new-era of energy harvesting technology, the enhancement of triboelectric charge density of triboelectric nanogenerator (TENG) is always crucial for its large-scale application on Internet of Things (IoTs) and artificial intelligence (AI). Here, a microstructure-designed direct-current TENG (MDC-TENG) with rationally patterned electrode structure is presented to enhance its effective surface charge density by increasing the efficiency of contact electrification. Thus, the MDC-TENG achieves a record high charge density of ~5.4 mC m −2 , which is over 2-fold the state-of-art of AC-TENGs and over 10-fold compared to previous DC-TENGs. The MDC-TENG realizes both the miniaturized device and high output performance. Meanwhile, its effective charge density can be further improved as the device size increases. Our work not only provides a miniaturization strategy of TENG for the application in IoTs and AI as energy supply or self-powered sensor, but also presents a paradigm shift for large-scale energy harvesting by TENGs. Low charge density is the bottleneck for the applications of triboelectric nanogenerator (TENG). Here, the authors demonstrate a microstructure-designed direct-current TENG with rationally patterned electrode structure to enhance its effective charge density to a new milestone.

The nanoscale electrical double layer plays a crucial role in macroscopic ion adsorption and reaction kinetics. In this study, we achieve controllable ion migration by dynamically regulating asymmetric electrical double layer formation. This tailors the ionic-electronic coupling interface, leading to the development of triboiontronics. Controlling the charge-collecting layer coverage on dielectric substrates allows for charge collection and adjustment of the substrate-liquid contact electrification property. By dynamically managing the asymmetric electrical double layer formation between the dielectric substrate and liquids, we develop a direct-current triboiontronic nanogenerator. This nanogenerator produces a transferred charge density of 412.54 mC/m 2 , significantly exceeding that of current hydrovoltaic technology and conventional triboelectric nanogenerators. Additionally, incorporating redox reactions to the process enhances the peak power and transferred charge density to 38.64 W/m 2 and 540.70 mC/m 2 , respectively. This research develops a direct-current triboiontronic nanogenerator by dynamically controlling asymmetric electrical double layer formation, achieving a transferred charge density of 412.54 mC/m 2 . Incorporating redox reactions enhances the peak power and charge density to 38.64 W/m 2 and 540.70 mC/m 2 .

Triboelectric charge density and energy density are two crucial factors to assess the output capability of dielectric materials in a triboelectric nanogenerator (TENG). However, they are commonly limited by the breakdown effect, structural parameters, and environmental factors, failing to reflect the intrinsic triboelectric behavior of these materials. Moreover, a standardized strategy for quantifying their maximum values is needed. Here, by circumventing these limitations, we propose a standardized strategy employing a contact-separation TENG for assessing a dielectric material’s maximum triboelectric charge and energy densities based on both theoretical analyses and experimental results. We find that a material’s vacuum triboelectric charge density can be far higher than previously reported values, reaching a record-high of 1250 µC m −2 between polyvinyl chloride and copper. More importantly, the obtained values for a dielectric material through this method represent its intrinsic properties and correlates with its work function. This study provides a fundamental methodology for quantifying the triboelectric capability of dielectric materials and further highlights TENG’s promising applications for energy harvesting. Determining the triboelectric charge and energy density of dielectric materials is generally limited by many factors, failing to reflect their intrinsic behaviour. Here, a standardized strategy is proposed employing contact-separation TENG and supressing air-breakdown to assess max triboelectric charge and energy densities leading to an updated triboelectric series.

Contact electrification (CE) spans from atomic to macroscopic scales, facilitating charge transfer between materials upon contact. This interfacial charge exchange, occurring in solid–solid (S–S) or solid–liquid (S–L) systems, initiates radical generation and chemical reactions, collectively termed contact-electro-chemistry (CE-Chemistry). As an emerging platform for green chemistry, CE-Chemistry facilitates redox, luminescent, synthetic, and catalytic reactions without the need for external power sources as in traditional electrochemistry with noble metal catalysts, significantly reducing energy consumption and environmental impact. Despite its broad applicability, the mechanistic understanding of CE-Chemistry remains incomplete. In S–S systems, CE-Chemistry is primarily driven by surface charges, whether electrons, ions, or radicals, on charged solid interfaces. However, a comprehensive theoretical framework is yet to be established. While S–S CE offers a promising platform for exploring the interplay between chemical reactions and triboelectric charge via surface charge modulation, it faces significant challenges in achieving scalability and optimizing chemical efficiency. In contrast, S–L CE-Chemistry focuses on interfacial electron transfer as a critical step in radical generation and subsequent reactions. This approach is notably versatile, enabling bulk-phase reactions in solutions and offering the flexibility to choose various solvents and/or dielectrics to optimize reaction pathways, such as the degradation of organic pollutants and polymerization, etc. The formation of an interfacial electrical double layer (EDL), driven by surface ion adsorption following electron transfer, plays a pivotal role in CE-Chemical processes within aqueous S–L systems. However, the EDL can exert a screening effect on further electron transfer, thereby inhibiting reaction progress. A comprehensive understanding and optimization of charge transfer mechanisms are pivotal for elucidating reaction pathways and enabling precise control over CE-Chemical processes. As the foundation of CE-Chemistry, charge transfer underpins the development of energy-efficient and environmentally sustainable methodologies, holding transformative potential for advancing green innovation. This review consolidates recent advancements, systematically classifying progress based on interfacial configurations in S–S and S–L systems and the underlying charge transfer dynamics. To unlock the full potential of CE-Chemistry, future research should prioritize the strategic tuning of material electronegativity, the engineering of sophisticated surface architectures, and the enhancement of charge transport mechanisms, paving the way for sustainable chemical innovations.

Traditional chemical processes often generate substantial waste, leading to significant pollution of water, air, and soil. Developing eco-friendly chemical methods is crucial for economic and environmental sustainability. Mechano-driven chemistry, with its potential for material recyclability and minimal byproducts, is well-aligned with green chemistry principles. Despite its origins over 2000 years ago and nearly 200 years of scientific investigation, mechano-driven chemistry has not been widely implemented in practice. This is likely due to a lack of comprehensive understanding and the complex physical effects of mechanical forces, which challenge reaction efficiency and scalability. This review summarizes the historical development of mechano-driven chemistry and discusses its progress across various physical mechanisms, including mechanochemistry, tribochemistry, piezochemistry, and contact electrification (CE) chemistry. CE-induced chemical reactions, involving ion transfer, electron transfer, and radical generation, are detailed, emphasizing the dominant role of radicals initiated by electron transfer and the influence of ion transfer through electrical double layer (EDL) formation. Advancing efficient, eco-friendly, and controllable green chemical technologies can reduce reliance on traditional energy sources (such as electricity and heat) and toxic chemical reagents, fostering innovation in material synthesis, catalytic technologies, and establishing a new paradigm for broader chemical applications. Mechano-driven chemical reactions across various physical mechanisms, including mechanochemistry, tribochemistry, piezochemistry, and contact electrification (CE) chemistry. [Display omitted] •A significant understanding and application of mechano-driven chemistry focusing on various physical mechanisms involved.•Mechano-driven chemistry reduces dependence on traditional electricity or thermal energy, promoting sustainable applications.•CE-chemistry represents a paradigm shift in the field, offering potential for recyclability and environmental sustainability.

The resistance of urinary tract pathogenic bacteria to various antibiotics is increasing, which requires the rapid detection of infectious pathogens for accurate and timely antibiotic treatment. Here, we propose a rapid diagnosis strategy for the antibiotic resistance of bacteria in urinary tract infections (UTIs) based on surface-enhanced Raman scattering (SERS) using a positively charged gold nanoparticle planar solid SERS substrate. Then, an intelligent identification model for SERS spectra based on the deep learning technique is constructed to realize the rapid, ultrasensitive, and non-labeled detection of pathogenic bacteria. A total of 54,000 SERS spectra were collected from 18 isolates belonging to 6 species of common UTI bacteria in this work to realize identification of bacterial species, antibiotic sensitivity, and multidrug resistance (MDR) via convolutional neural networks (CNN). This method significantly simplify the Raman data processing processes without background removing and smoothing, however, achieving 96% above classification accuracy, which was significantly greater than the 85% accuracy of the traditional multivariate statistical analysis algorithm principal component analysis combined with the K-nearest neighbor (PCA-KNN). This work clearly elucidated the potential of combining SERS and deep learning technique to realize culture-free identification of pathogenic bacteria and their associated antibiotic sensitivity.

The growth of aquaculture poses significant challenges for disease management, impacting economic sustainability and global food security. Traditional diagnostics are slow and require expertise, while current deep learning models, including CNNs and Transformers, face a trade-off between capturing global symptom context and maintaining computational efficiency. This paper introduces FishMambaNet, a novel framework that integrates selective state space models (SSMs) with convolutional networks for accurate and efficient fish disease diagnosis. FishMambaNet features two core components: the Fish Disease Detection State Space block (FSBlock), which models long-range symptom dependencies via SSMs while preserving local details with gated convolutions, and the Multi-Scale Convolutional Attention (MSCA) mechanism, which enriches multi-scale feature representation with low computational cost. Experiments demonstrate state-of-the-art performance, with FishMambaNet achieving a mean Average Precision at 50% Intersection over Union (mAP@50) of 86.7% using only 4.3 M parameters and 10.7 GFLOPs, significantly surpassing models like YOLOv8-m and RT-DETR. This work establishes a new paradigm for lightweight, powerful disease detection in aquaculture, offering a practical solution for real-time deployment in resource-constrained environments.

The sustainable cultivation of agarwood, a high-value tree species, is significantly threatened by foliar pests, requiring efficient and accurate monitoring solutions. While deep learning is widely used, mainstream models face inherent limitations: Convolutional Neural Networks have restricted receptive fields and Transformers incur high computational complexity, complicating the balance of accuracy and efficiency for tiny pest detection in complex environments. To address these challenges, a novel Adaptive State-space Convolutional Fusion Network (ASCNet) is proposed. Its core component, the Adaptive State-space Convolutional Fusion Block (ASBlock), integrates the global context modeling of state-space models—which have linear complexity—with the local feature extraction of convolutional networks through a dual-path adaptive fusion mechanism. A Grouped Spatial Shuffle Downsampling (GSD) module replaces standard strided convolutions to preserve fine-grained spatial details during downsampling. For small object detection, a Normalized Wasserstein Distance (NWD)-based loss function mitigates the sensitivity of traditional IoU to minor localization errors. Evaluations on a new agarwood pest dataset show that ASCNet outperforms state-of-the-art detectors (including the YOLO series, RT-DETR, and Gold-YOLO), achieving a maximum mAP@50 of 93.0 ± 0.2% and mAP@50:95 of 71.2 ± 0.3% with high computational efficiency. The results confirm ASCNet as a robust and effective solution for intelligent pest monitoring in high-value crops like agarwood.

Mechanoluminescence (ML) involves light emission induced by mechanical stress, categorized into triboluminescence (TL), piezoluminescence (PL), sonoluminescence (SL), and triboelectrification-induced electroluminescence (TIEL). The most common is TL, in which crystal fracture generates opposing charges that excite surrounding molecules. In PL, applied pressure induces light emission via charge recombination. SL occurs in gas-saturated liquids under sudden pressure changes. TIEL has gained increasing attention as it operates without the need for asymmetric crystal structures or strain fields. However, conventional ML faces practical limitations due to its dependence on complex structures or strain fields. In contrast, contact-electro-luminescence (CEL) has emerged as a promising alternative, enabling luminol luminescence via charge transfer and reactive oxygen species generation through contact electrification (CE) between inert dielectrics and water. CEL provides a simpler and more versatile approach than traditional ML techniques, underscoring the pivotal role of charge-transfer processes. This perspective highlights the potential of CEL in expanding ML applications across sensing, energy conversion, and environmental monitoring.