Soft electronic devices require durability to endure their inherent exposure to diverse mechanical deformations, including scratches, punctures, and repeated bending. Without intrinsic damage recovery mechanisms, such deformations inevitably compromise mechanical integrity and limit device lifetime. To address this issue, the strategic incorporation of reversible dynamic bonds enables autonomous self-healing while simultaneously achieving high mechanical toughness through energy dissipation during bond rupture. To this end, optimizing the glass transition temperature and bond exchange kinetics is essential to ensure sufficient chain mobility for rapid interfacial diffusion and autonomous mechanical recovery. Building on the reversible bond nature, this review presents emerging self-healable and tough soft electronics applications in three major areas: (1) Multimodal electronic skins capable of comprehensive physiological signal sensing; (2) modularly reconfigurable systems with adhesive-free interlayer bonding that enable user-on-demand device assembly; (3) optoelectronic devices that seamlessly integrate light-emitting and pressure-sensing capabilities. These applications demonstrate that dynamic bond engineering enables elastomeric devices to simultaneously achieve mechanical robustness, functional adaptability, and autonomous self-healing. Such advancements position them as durable platforms with extended operational lifetimes, paving the way for next-generation wearable and implantable bioelectronics in real-world applications.

Statistical mechanics of far-from-equilibrium systems requires trajectory-based ensembles rather than static configurations. Biasing fields conjugate to dynamical activity (s-field) and time-integrated trajectory energy (g-field) provide powerful tools for probing rare dynamical states. While s-ensemble studies have demonstrated first-order dynamical phase transitions in glass-forming models, it remains unclear whether energy-only biasing can induce transitions in kinetic observables to which it is not directly coupled. Here, we investigate this question in the Kob–Andersen binary Lennard–Jones model by constructing the two-dimensional (T, g) phase diagram using transition path sampling. We identify a first-order dynamical phase transition line separating active and inactive trajectory phases, confirmed by diverging dynamical susceptibilities and bimodal order parameter distributions. Binder cumulant analysis, enabled by Gaussian process regression and large-deviation relations, locates the upper critical point (Tuc, guc) ≃ (0.675, 1.9 × 10−3). We further demonstrate that g-ensemble glasses are structurally indistinguishable from conventionally quenched glasses, while intermediate scattering functions confirm that the active–inactive transition is purely dynamical in nature. Spatial analysis further reveals that mobile particles form a system-spanning cluster in the active phase but remain fragmented in the inactive phase, consistent with the dynamical facilitation picture. These results demonstrate that energy-landscape biasing alone is sufficient to drive first-order dynamical phase transitions in an atomistic glass-forming model, establishing the g-ensemble as a controlled framework that connects the thermodynamic potential energy landscape with dynamical arrest phenomena central to kinetic theories of the glass transition.

Orbital-relaxed bath theory (ORBT) provides a practical theoretical framework for describing charge-transfer processes in transition-metal complexes. Insights from the Schmidt decomposition of the full configuration interaction wave function underscore the importance of bath orbital relaxation for achieving a balanced description of electronic states with different charge distributions. Directions and implications for the systematic development of these approaches are discussed.

This review inclusively focuses on studies addressing the product selectivity of the electrochemical ammonia oxidation reaction (eAOR). We first introduce the various methods to assay the products of the eAOR. Sequentially, previous research efforts for mechanism exploration are categorized into three distinct aspects according to their focus of research: (1) reactivity of intermediates, (2) additional mediating species, and (3) catalytic phase. In situ analysis techniques to investigate each aspect of the mechanisms are also summarized. Based on knowledge of the mechanism, researchers scrutinized how strategies that modulate three important determinants for electrochemical reaction systems—operating potential, electrolyte conditions, and catalyst surface—can tune the product selectivity of the eAOR and revealed the link between product selectivity and the mechanism of the eAOR. Despite these remarkable efforts, some impediments to the commercialization of the eAOR remain. Therefore, this review introduces room for improvement in the current eAOR fields.

Precise modeling of the energetic landscape is a prerequisite for predicting the charge transport properties of organic light-emitting diodes (OLEDs). However, a significant gap remains between highly accurate but computationally prohibitive self-consistent field (SCF) calculations and efficient but often oversimplified models. In this work, we propose an accurate and effective electrostatic framework with high computational efficiency that encompasses these complex polarization effects through an anisotropically screened dielectric function augmented by a position-dependent background potential. Optimized for the archetypal host material 4,4′-Bis(N-carbazolyl)-1,1′-biphenyl (CBP), our model accurately reproduces the microscopic details, including the polarization-induced stabilization and the surface-reduced energetic disorder, while maintaining high transferability across independent morphological realizations and film thicknesses down to D ≈ 4 nm. Kinetic Monte Carlo (KMC) simulations further confirm that the model faithfully replicates the reference mean squared displacement (MSD) and current–voltage (JV) characteristics, whereas simple image charge models significantly underestimate the current density by failing to describe the downhill gradient at the interface. This framework offers a practical pathway for generating realistic energy distributions for large-scale device simulations, effectively bridging the tradeoff between physical accuracy and computational efficiency.

Endotrophin (ETP) is a cleavage fragment of collagen VI alpha 3 (COL6A3) that functions as a potent fibrotic and protumorigenic factor. ETP is a diagnostic and prognostic biomarker in hepatocellular carcinoma (HCC), continuously increasing throughout tumor development and promoting HCC progression. Elucidation of the underlying molecular mechanisms by which ETP exerts protumorigenic effects in the liver could uncover potential therapeutic strategies. Using peroxidase-catalyzed proximity labeling, we identified CD44 as an ETP receptor. ETP binding to CD44 activated STAT3 signaling, promoting epithelial-mesenchymal transition (EMT), proliferation, and sorafenib resistance. Hepatic stellate cell-derived ETP targeted pericentral CD44(+) tumor cells, inducing COL6A3 expression and sustaining ETP production via a STAT3-dependent feedback loop. Disruption of this axis by CD44 knockout, STAT3 inhibition, or CD44 binding-deficient ETP mutants suppressed malignant phenotypes in vitro. In metabolic dysfunction-associated HCC induced by diethylnitrosamine plus high-fat diet, dual knockout of Col6a3 and Cd44 in mice markedly reduced tumor burden, restored sorafenib sensitivity, and attenuated EMT, fibrosis, and steatotic-fibrotic niche formation. These findings establish the ETP-CD44-STAT3 axis as a driver of tumor-stroma cross-talk linking fibroinflammation to malignancy, highlighting it as a therapeutic target in obesity-associated liver cancer.

With the growing concerns about multidrug-resistant (MDR) gram-negative bacteria, many efforts have been made to develop alternative antimicrobial agents. Exploiting outer membrane (OM)-perturbing peptides is one strategy, but their low stability and specificity have hindered clinical application. Here, two histidine-modified peptides (KLH3 and KLH4) were developed by substituting lysine residues in a novel membrane-perturbing peptide, KL-L9P, with histidine. These peptides show pH-dependent selective binding to the bacterial membrane and permeabilize the OM of gram-negative bacteria without completely disrupting it. Notably, they specifically increase the influx of non-permeable antibiotics under acidic pH. Moreover, stability studies show that KLH3 and KLH4 peptides were more stable than KL-L9P peptides, primarily due to reduced recognition by the mononuclear phagocyte system (MPS). Consequently, KLH3 and KLH4 demonstrate improved therapeutic efficacy compared to KL-L9P in mouse model of both MDR A. baumannii skin infection and E. coli NDM-1 bacteremia, while showing reduced host toxicity. These results suggest that substituting cationic residues, such as lysine or arginine, with histidine residues is a simple yet effective strategy to enhance in vivo stability and infection site specificity of OM-perturbing peptides.

Humanity has developed a wide range of physical machines engineered to perform work for specific purposes while emphasizing controllability and upscaling. Recently, a growing aspiration has emerged to construct minuscule mechanical systems, often enabled by a microscopic, bottom-up understanding of nature. As biological systems inherently function at sub-micrometer scales, artificial nanoscale machines have attracted increasing attention as a route to achieve nature-like synthetic and functional precision. Among the possible strategies to activate and drive such systems, light stands out as a highly efficient and versatile energy source that can interact strongly with plasmonic nanomaterials. Owing to strong optical responses, efficient photothermal conversion, and catalytic activity, plasmonic nanostructures can transduce light energy into spatially confined optical, thermal, and chemical gradients, which in turn generate nanoscale mechanical motions. This Perspective highlights the designing principles for these plasmonic nanomachines and fundamental physics behind plasmonically driven force generation and motion, while focusing on approaches that localize energy inputs via material integration. We further explore how energetic and geometric asymmetries provide directional forces that enable translational and rotational motions, guiding movement along targeted trajectories. This framework lays a foundation for advancing autonomous, optically addressable plasmonic nanomachines, overcoming key challenges and opening avenues for nanomachinery and nanorobotics.

lycerol, a low-cost and abundant byproduct of biodiesel production, has attracted attention as a feedstock for conversion into value-added chemicals. To maximize the economic value of products, maintaining three carbons (C3) as the dominant product is important yet difficult to achieve at high potentials due to the favorable C–C bond scission. We demonstrate that an atomically dispersed Pt catalyst anchored on defect-rich ceria-carbon selectively controls the glycerol electrooxidation reaction (GEOR), favoring C3 products. The isolated Pt sites favored single-carbon adsorption, preventing multi-carbon binding and subsequent cleavage up until high potential of 1.2 VRHE. The catalyst maintained nearly 70% of C3 selectivity across various potentials with high glycerate productivity and selectivity. In contrast, catalysts with Pt nanoparticles rapidly shifted towards C2 and C1 products, especially glycolate and formate as potential increases. Moreover, Pt single atoms on the catalyst maintained high glycerate productivity without much Pt agglomeration under 48 h operation. Beyond batch operation, the Pt single atom catalyst was validated in a continuous flow-cell reactor. Glycerate remained as the major product, reaching a selectivity of 51.6% as potential increases and exhibited a productivity of 37.0 mmol L−1 mgPt−1 h−1 at 1.2 VRHE. This work highlights atomic dispersion on defect-engineered supports as a powerful strategy to control electrocatalytic pathways in the GEOR via suppressing C–C cleavage.

The electrochemical CO2 reduction reaction (CO2RR) relies heavily on the surrounding microenvironment to promote formation of desirable multicarbon (C2+) products. However, microenvironment control to achieve high C2+ yields at industrially relevant current densities remains a crucial challenge. We report that chitosan, cellulose and chitin biopolymer coatings on CO2RR electrocatalysts enhance the microenvironment by increasing local CO2/CO concentration, reducing local water activity and providing suitable ion conductivity and local pH. This facile approach achieves C2+ Faradaic efficiencies of 90 +/- 1.7% at 1.6 A cm-2 and C2+ Faradaic efficiency = 83 +/- 3.2% at 2.2 A cm-2 with a formation rate of 5,926 mu mol h-1 cm-2. Importantly, within the cathode, these ion-conductive hydrophilic biopolymers can fully substitute traditional hydrophobic ionomers/binders, such as Nafion, challenging previous assumptions about the non-viability of hydrophilic materials for selective CO2RR due to excess interfacial H2O. These findings unveil key insights into microenvironment design to enhance C-C coupling through a simple method.