With the emergence of triboelectric nanogenerators (TENGs), the monitoring technology based on the triboelectric effect is becoming more and more popular due to the advantages of the wide selection of materials and flexible working modes. Traditional condition monitoring technologies for machines, infrastructure, and environment (MIE) are usually based on piezoelectric effects, thermal effects, and acoustic effects, which need external power to drive. The advancement of TENGs provides more possibilities to enable condition monitoring technologies with self-driving ability in the society of artificial intelligence of things (AIoT) systems. The flexible structure design and materials selection facilitate the condition monitoring of modern MIE in a more economical and effective way. An increasing number of related works are emerging. In these regards, this paper reviews the state of the art in condition monitoring based on TENGs for the applications of MIE and related interdisciplinary research, such as materials science, information, engineering, and so forth. The introduction of condition monitoring for MIE is illustrated and the basic mechanism of TENG is introduced first. Subsequently, the condition monitoring based on TENG technologies for machines, infrastructure, and environment is elucidated respectively. The most popular and hot research trends are pointed out and the current challenges are also discussed and illustrated, thus giving hints and guidance for future research trends.

The persistent pursuit of miniaturization and energy efficiency in semiconductor technology has driven the scaling of complementary metal-oxide-semiconductor field-effect transistors (CMOS FETs, i.e., the MOSFETs) to their physical limits. Conventional MOSFETs face intrinsic challenges, especially the Boltzmann limit that imposes a fundamental lower bound on the subthreshold swing (SS ≥ 60 mV dec⁻¹ at room temperature). This limitation severely restricts voltage scaling and exacerbates static power dissipation. To overcome these bottlenecks, tunnel field-effect transistors (TFETs) have emerged as a promising post-CMOS alternative. The advantages of ultra-small SS well below the Boltzmann limit, as well as ultralow leakage currents, make TFETs ideal for low-power electronics and energy-efficient computing in the future information industry. However, its current development has encountered significant resistance to further performance improvement requirements; new breakthroughs have evolved to be based on interdisciplinary research that covers materials science, device technology, theoretical physics, and so on. Here, we provide a review on the design and development of TFET, which mainly describes the device physics model of tunnel junctions, and discusses the optimization direction of key parameters, the design direction of potential structures, and the development direction of the innovation system based on the device physics. Also, we visualize the framework for the figures of merit of TFET performance and further forecast the future applications of TFET.

Multimodal perception, pivotal for artificial intelligence (AI) systems demanding real-time decision-making and environmental adaptability, might be significantly improved through two-dimensional (2D) piezo-ferro-opto-electronic (PFOE) semiconductors, like, NbOX₂ (X = Cl, Br, I). Such improvement may enable in-sensor fusion of sense organ signals (e.g., vision, audition, gustation, and olfaction) within a single functional component, overcoming limitations of conventional discrete sensor architectures. Such function cohesion, combined with their recently uncovered properties, not only provides a robust foundation for expanding sensory modalities and developing novel mechanisms to establish an all-in-one multimodal perception platform, but also paves the way for multisensory-integrated artificial systems beyond human sensory systems. This single-component system employing such PFOE semiconductors substantially mitigates intermodule communication latency while boosting integration density of information, thereby circumventing persistent inefficiencies in AI hardware architectures for real-time applications, such as embodied robotics and immersive human-machine interfaces. This fusion of multimodal perception and computation, enabled by multiphysics coupling of 2D NbOX₂, drives AI systems toward biological-grade efficiency while maintaining environmental adaptability, representing a critical leap toward autonomous intelligence operating in dynamic real-world settings.

Graphite's resilience to high temperatures and neutron damage makes it vital for nuclear reactors, yet irradiation alters its microstructure, degrading key properties. We used small- and wide-angle X-ray scattering to study neutron-irradiated fine-grain nuclear graphite (Grade G347A) across varied temperatures and fluences. Results show significant shifts in internal strain and porosity, correlating with radiation-induced volume changes. Notably, porosity volume distribution (fractal dimensions) follows non-monotonic volume changes, suggesting a link to the Weibull distribution of fracture stress.

α-MgAgSb is one of the few high-performance thermoelectric materials near room temperature, thanks to its inherently suppressed lattice thermal conductivity. However, conventional approaches to optimizing electrical properties often inadvertently degrade carrier mobility, adversely impacting thermoelectric performance at lower temperatures. In this study, we discovered in an experiment that Mg-Ag anti-site defects exist in the lattice and create staggered nanoscale anti-site zones in the matrix. This unique structure significantly scatters phonons while having a negligible influence on carrier transport due to the preservation of carrier transport channels. By fine-tuning the formation energy of Mg-Ag anti-sites through Zn doping, both carrier transport and phonon scattering were successfully bolstered. Consequently, a high figure of merit (zT) of ~0.45 at 200 K and an average zT of ~0.75 within the low-temperature range of 200-400 K can be achieved. Furthermore, a single-pair device constructed using the obtained α-MgAgSb and commercial Bi₂Te₃ legs exhibited a temperature difference of ~56 K at 325 K, showcasing promise for thermoelectric cooling applications. This demonstration underscores the efficiency of anti-site manipulation as a means to enhance the thermoelectric cooling performance of α-MgAgSb.

Hypertrophic scar (HS) is a common pathological fibrous hyperplasia with high incidence and recurrence rates. The limited understanding of the pathological characteristics of HS restricts the therapeutic efficacy of current strategies. In this study, we first identified the elevated mitophagy and suppressed apoptosis in hypertrophic scar fibroblasts (HSFs), which combined with excess inflammation to constitute the pathological microenvironment of HS, driving us to develop a functionalized microneedle (MN) patch for inhibiting mitophagy, promoting apoptosis and modulating inflammation to adapt HS treatment. The MNs integrate curcumin-loaded HSFs-derived extracellular vesicles (Cur@EV) and a decellularized extracellular matrix from umbilical cord-derived mesenchymal stem cells (UC-MSCs-dECM, UdECM). The homologous Cur@EV with enhanced cellular uptake significantly induced HSFs apoptosis via mitophagy inhibition, meanwhile reducing collagen deposition. Meanwhile, the UdECM exerted immunomodulation capacity by facilitating the M2 polarization of macrophages, aiding in the suppression of HSFs. Notably, the Cur@EV/UdECM-functionalized MN patches exhibited regenerative therapeutic outcomes on a rabbit HS model, with HS inhibition and new hair follicle formation. Overall, this study presents a synergistic strategy based on the regulation of “mitophagy-apoptosis-inflammation,” offering a novel, minimally invasive approach for HS management. The integration of homologous Cur@EV and UdECM into an MN patch represents an innovative, multifunctional approach that combines mitophagy inhibition, apoptosis induction, and immunomodulation. The regenerative outcomes observed in the rabbit HS model, including hair follicle formation, further underscore the translational potential of this strategy. Future research will focus on optimizing patch design for scalable production, assessing long-term safety and efficacy, and exploring its broader applicability.

Given the confluence of dysregulated inflammation, vasculopathy, and neuropathy, diabetic wounds pose a significant clinical challenge. Commercially available wound dressings often lack sufficient bioactivity, failing to meet clinical demands. Herein, we developed a PCL-PLLA-MgSiO₃ (PP-MgSi) patch with promising therapeutic effects. The PP-MgSi composite patch was manufactured via electrospinning and characterized by controllable degradation and local release of Mg²⁺ and SiO₃²⁻. The patch showed favorable in vitro biocompatibility and bioactivity, notably increased angiogenesis, myelination, and neurite outgrowth. In type 2 diabetic mice, the PP-MgSi patch exhibited MgSi dose-dependent effects on enhancing diabetic wound healing by modulating the expression of TNF-α, iNOS, and CD206 to balance inflammation, while boosting CD31 and β3-tubulin levels to promote neurovascularization. With the significant suppression of pro-inflammatory-related TNF and IL-17 pathways, while activating the peripheral nerve-associated axon guidance pathway, blood vessel-associated HIF-1α and VEGF pathways, the PP-MgSi patch ultimately achieved accelerated healing compared to the control group. Ultimately, the PP-MgSi patch exhibited an accelerated repair rate, with comparable neovascularization and superior peripheral nerve regeneration capacity compared to three representative commercially available products. This proof-of-concept work presents a promising bioactive PP-MgSi patch for future clinical diabetic wound management, particularly in terms of its neurovascular network recovery properties.

Composite quasi-solid-state electrolytes are pivotal for enabling high-energy-density lithium metal batteries (LMBs), yet their practical application is hindered by discontinuous ion transport, poor interfacial stability, and limited high-voltage endurance. Here, a universal in situ growth strategy is developed to construct a metal-organic framework (MOF)/polymer composite electrolyte (ZCPSE) with hierarchically ordered ion-conducting networks. The ultra-uniform MOF nanoparticles (e.g., ZIF-8) are anchored onto polymer nanofibers, creating abundant nanopores and Lewis acid sites that synergistically enhance Li⁺ transport and oxidative stability. The resulting ZCPSE exhibits unprecedented ionic conductivity (0.46 mS cm⁻¹ at 25°C), a wide electrochemical window (5.15 V vs. Li/Li⁺), and exceptional mechanical strength (151.2 MPa, 4× higher than pristine polymer membrane). Theoretical simulations reveal that the 3D continuous MOF/polymer interface facilitates rapid Li⁺ dissociation and uniform flux distribution, endowing ZCPSE with a high Li⁺ transference number (0.74) and dendrite-free Li plating/stripping (2000 h in Li|Li symmetric cells). Practical applicability is demonstrated in Li|LiFePO₄ cells (stable cycling at 25°C-100°C) and high-voltage Li|LiNi_0.8Co_0.1Mn_0.1O₂ full cells (4.5 V, 100 cycles with 99.2% capacity retention). This study provides a paradigm for designing MOF-based hybrid electrolytes with simultaneous ionic, mechanical, and interfacial optimization, paving the way for safe and high-energy LMBs.

All solid-state lithium batteries (ASSLBs) are identified as the next-generation energy storage technology due to their prospects of nonflammability and improved energy density. Elevating the charging cutoff voltage of cathode materials is an effective strategy to improve the energy density of ASSLBs. However, the limited oxidative stability of solid-state electrolytes (SEs) and structural and chemically irreversible changes in the cathode active material result in inferior electrochemical performance. Here, we synthesized nano-Li_1.2Al_0.1Ta_1.9PO₈ (LATPO) coatings on the surface of lithium cobalt oxide (LCO) by a facile ball-milling method combined with heat treatments. This artificial intermediate phase effectively enhances the structural stability and interfacial transport kinetics of the cathode and mitigates continuous side reactions at the cathode/solid electrolyte interface. As a result, the ASSLBs with modified LCO cathode exhibit a reversible capacity of 203.5 mAh g⁻¹ at 0.1 C and 4.0 V (corresponding to the potential of 4.6 V vs. Li⁺/Li), superior cycling stability (85.4% capacity retention after 500 cycles), a high areal capacity (4.6 mAh cm⁻²), and a good rate capability (62 mAh g⁻¹ at 3 C). This study emphasizes the importance of cathode surface modification in achieving stable cycling of halide-based ASSLBs at high voltages.

This article is a response to the comment “Reassessing Machine Learning Techniques for Electrocatalyst Design: A Call for Robust Methodologies”. First, we clarify that the artificial neural network-SHapley Additive exPlanation (ANN-SHAP) method mentioned in the comment originates from the original work of Ding et al., which we only briefly summarized. In that study, nine different machine learning models were employed to predict the performance of proton exchange membrane fuel cells, among which the ANN model performed best. SHAP, together with multiple interpretability techniques (PDP, Tree-based Rule, EIX, etc.), was used to cross-validate feature importance, which was further compared with the results from manual feature selection, PCA, and t-distributed stochastic neighbor embedding, and complemented by experimental validation to reduce the risk of bias amplification. We agree with the commenter that model interpretability should be approached with caution, as the absence of a definitive “ground truth” for feature importance remains a current challenge. However, benchmarking SHAP explanations against domain knowledge or validating them using synthetic datasets can help reduce the risk of misinterpretation. Regarding the unsupervised methods suggested in the comment (FA and HVGS), we consider them to have exploratory value for certain data structures, but caution is needed when applying them to experimental systems involving nonlinearity or high noise.