Contact interfaces fundamentally govern the friction behavior and wear mechanisms of tribological systems through their inherent physical and chemical characteristics. This work presents an interfacial engineering strategy that constructs an ultrasmooth and adaptive sliding contact interface by synergistically combining vanadium-doped tungsten disulfide (WS2-V) films with a multialkylated cyclopentanes (MACs) gel. We demonstrate that vanadium doping acts not merely as a microstructural modifier but as a crucial driver for creating a chemically active and mechanically robust interface. Optimal V doping (∼10 at. %) transforms the WS2 film from a porous, columnar architecture into a dense nanocomposite, while simultaneously introducing V-S species that enhance interfacial compatibility. This V-modified WS2 surface exhibits exceptional adsorption and retention of the MACs oil molecules and gelator. Under vacuum conditions, this dynamic interface exhibits self-smoothing capabilities, effectively healing surface asperities and maintaining an ultralow shear plane with an areal arithmetic mean roughness (Sa) as low as 2.0 nm. The optimal hybrid system (WS2-10%V/MACs gel) achieves a stable friction coefficient of 0.051 and reduces the wear rate to 1.1 × 10-8 mm3/(N·m). This work establishes a dopant-mediated paradigm for actively engineering solid-gel contact interfaces, providing a foundational design principle for next-generation lubrication in space mechanisms.

Liquid organic hydrogen carriers (LOHCs) based on cycloalkane/aromatic pairs are promising for safe hydrogen storage and transportation, but their practical implementation is limited by sluggish dehydrogenation kinetics and catalyst deactivation under working conditions. Herein, we report a rationally designed platinum catalyst supported on curved graphene-coated nanodiamond (ND@G), in which fully exposed subnanometric Pt clusters (Ptn) and three-dimensional Pt clusters (Ptc) are integrated in close proximity on the support (Ptn+c/ND@G), which delivers remarkably enhanced hydrogen production in the solvent-free dehydrogenation of bicyclohexyl and other representative cycloalkanes, outperforming either single-counterpart catalysts or their physical mixtures while maintaining good recyclability. Mechanistic investigations combined with density functional theory calculations demonstrate that this synergistic effect originates from the cooperation between these respective Pt ensembles: Ptc sites possess higher intrinsic activity for C-H bond activation than Ptn clusters, yet are more susceptible to poisoning. By contrast, Ptn clusters, although less active for substrate activation, remain operative in the presence of aromatic adsorbates. Hydrogen generated on Ptn clusters can weaken product binding on adjacent Ptc sites via support-mediated spillover. As a result, poisoning is mitigated, active Ptc sites are continuously regenerated, and overall dehydrogenation performance is enhanced. These findings provide a general design principle for developing high-performance LOHC dehydrogenation catalysts through Pt ensemble engineering and support-mediated hydrogen management.

Lithium-sulfur (Li-S) batteries are currently hindered by the notorious polysulfide shuttling and lithium dendrite formation, despite their high theoretical energy density and low cost. These challenges are fundamentally rooted in the electrolyte chemistry, as the electrolyte environment critically influences both sulfur redox reactions and lithium plating/stripping behavior. Here, we report a synergistic electrolyte design that integrates solvation structure engineering and redox mediation to simultaneously address these bottlenecks. The well-designed siloxane-based electrolyte features a weakly solvating nature that enables an anion-rich Li+ solvation structure. This electrolyte design not only suppresses polysulfide dissolution through a quasi-solid-state sulfur reaction mechanism but also promotes stable lithium deposition to mitigate dendrite growth. Furthermore, isopropylxanthic disulfide (DIP) is introduced as a reaction mediator to chemically modulate sulfur conversion via targeted interactions with polysulfides. The resulting Li-S cells achieve superior electrochemical performance, exhibiting a specific capacity of 576.1 mAh g-1 at 4 C and 85% capacity retention after 100 cycles at 2 C. The 3 Ah and 6 Ah pouch cells fabricated under an ultralow electrolyte loading of 2 μL mg-1 deliver gravimetric energy densities of 391.7 Wh kg-1 and 453.8 Wh kg-1, respectively. This study presents a dual-functional electrolyte design that integrates solubility control with kinetic regulation, providing critical insights toward the development of practical Li-S batteries.

Objective: This study aimed to measure the effect of a Social Cognitive Theory (SCT)-based, urban gardening, cooking, and nutrition education intervention on health behavior mediators and fruit and vegetable intake in food-insecure college students. Methods: In a 6-week, randomized controlled trial, 89 food-insecure students at a southeastern university (86% female, 59% Hispanic), randomized into a controlled 107 (n = 42) or intervention group (n = 47), attended once a week, cooking/gardening sessions, that incorporated nutrition education, recipes, and at home garden materials. Outcome measures were assessed at baseline and post intervention and included anthropometrics, health behavior mediators such as nutrition knowledge (Dickson-Spillmann Consumer Oriented Nutrition Knowledge Survey), Dewar's SCT construct subscales: self-efficacy, outcome expectations, reciprocal determinism, and fruit and vegetable intake using the National Cancer Institute's Fruit and Vegetable Screener. Linear regressions and ANOVA's were conducted. Results: Intervention participants increased fruit and vegetable intake (F = 19.19, p < .001) and health behavior mediators: self-efficacy (F = 17.65, p < .001), reciprocal determinism (F = 4.16, p = .045), An increase in reciprocal determinism was significantly associated with an increase in self-efficacy (β = .392, t = 2.66, p = .011). Intervention mediators did not have a significant effect on change in fruit and vegetable intake (F = . 580, p = .679, R2 = . 061). Conclusions: An SCT-based cooking and gardening nutrition program tailored to food-insecure college students improved health behavior mediators and increased fruit and vegetable intake. A nutrition education program that utilizes a hands-on, interactive approach may be an effective intervention to promote fruit and vegetable intake and health behaviors among food insecure college students.

AI systems are increasingly governed by natural language rules, yet a key challenge arising from reliance on language remains underexplored: interpretive ambiguity. As in legal systems, ambiguity arises both from how these rules are written and how they are applied. But while legal systems use institutional safeguards to manage such ambiguity, such as transparent appellate review policing interpretive constraints, rule-based AI alignment pipelines lack comparable protections. Different interpretations of the same rule can lead to inconsistent model behavior. Drawing on U.S. legal theory, we identify key gaps in current rule-based alignment pipelines by examining how legal systems constrain ambiguity at both the rule creation and rule application steps. We then propose a computational framework that formalizes interpretive ambiguity as constrained entropy minimization and introduces two law-inspired mechanisms: 1) a rule refinement pipeline that minimizes interpretive disagreement by revising ambiguous rules (analogous to agency rulemaking or iterative legislative action), and 2) prompt-based interpretive constraints that reduce disagreement during rule application (analogous to legal canons that guide judicial discretion). We evaluate our framework on a 5,000-scenario subset of the WildChat dataset and show that both interventions significantly improve judgment consistency across a panel of reasonable interpreters. Our approach offers a step toward systematically managing interpretive ambiguity, an essential step for building more robust, law-following AI systems.

Recent policy proposals aim to improve the safety of general-purpose AI, but there is little understanding of the efficacy of different regulatory approaches. We present a strategic model that explores interactions between safety regulation, general-purpose AI technology creators, and domain specialists-those who adapt the technology for specific applications. Our analysis examines how regulatory measures targeting different parts of the AI development chain affect the outcome of this game. Our model assumes AI technology is characterized by two key attributes: safety and performance. The regulator first sets a minimum safety requirement that applies to one or both players. The general-purpose creator then invests in the technology, establishing its initial safety and performance levels. Next, domain specialists refine the AI for their use cases, updating the safety and performance levels and taking the product to market. Resulting revenue is shared between the specialist and generalist. Our analysis reveals two insights: first, weak safety regulation imposed predominantly on domain specialists can backfire. While it might seem logical to regulate AI use cases, our analysis shows that weak regulations targeting domain specialists alone can reduce safety in a large class of parameterizations. Second, in contrast to the previous finding, we observe that stronger, well-placed regulation can mutually benefit all players. When regulators impose appropriate safety standards on both general-purpose AI creators and domain specialists, the regulation can function as a commitment device, leading to safety and performance gains, surpassing what is achievable under no regulation or regulating only one player.

Research indicates that the human visual system is highly efficient at detecting snakes, yet less is known about how ecological visual contexts modulate this advantage. We conducted two experiments with university students (N = 58 each) to test whether background complexity influences snake-detection efficiency using a dual-task paradigm. In Experiment 1, stimuli were presented on a uniform grey background. Here, snakes were detected and visually fixated significantly faster than non-snake control animals, consistent with previous findings. Crucially, this advantage was not influenced by participants' self-reported fear of snakes or anxiety levels. In Experiment 2, the same stimuli were embedded in a complex leaf-litter background. Under these conditions, non-snake stimuli were detected earlier than snakes, and overall accuracy declined. This indicates that the snake detection advantage is sensitive to changes in perceptual and contextual conditions, which may attenuate or reverse the pattern observed under simplified settings. Subjective fear scores again showed no moderating effect. Overall, these findings are consistent with an evolved sensitivity to snake-related visual features, but show that this advantage is context-dependent and can be attenuated or even reversed under ecologically complex visual conditions, independently of self-reported fear. They underscore the necessity of incorporating ecological variables into threat-detection research.

The electrocarboxylation of butadiene (BD) offers a sustainable route to hexenedioate synthesis through CO2 utilization. Hexenedioate can be converted into adipic acid, the main precursor to Nylon, which is currently produced from fossil-based feedstocks. However, key aspects of BD electrocarboxylation remain unclear, particularly the involvement of surface-bound intermediates and competing interfacial reactions, such as the CO2 reduction reaction (CO2RR). Herein, we employ in situ Attenuated Total Reflectance Surface Enhanced Infrared Absorption Spectroscopy (ATR-SEIRAS), electrolysis experiments with product analysis and density functional theory (DFT) to investigate BD electrocarboxylation on Au, Cu, and Pt. ATR-SEIRAS tracks the evolution of interfacial species and shows that BD suppresses the CO adsorption band on Cu but remains unchanged on Pt, consistent with DFT predictions that result from its preferential binding and the destabilization of BD chemisorption at increasingly cathodic potentials. Electrolysis experiments reveal higher hexenedioate Faradaic efficiency on Au, followed by Cu and Pt, while CO selectivity shows the opposite trend (Pt > Cu > Au), providing complementary evidence for metal-dependent trends observed in the spectroscopic measurements. Although CO still forms in the presence of BD, part of the CO2•- is consumed by BD to generate electrocarboxylation products, and the CO that is produced does not adsorb on Cu because BD or BD-derived species outcompete and displace it. These findings highlight the effect of BD adsorption and potential-dependent radical competition in shaping electrocarboxylation reactions, emphasizing the importance of considering interfacial contributions in the electrochemical process.

Decision theories commonly model human behavior as maximizing the expected value of a utility function. This function may vary from one person to another but is assumed to be stable over time. Recent theoretical developments demonstrate that these assumptions are generally incompatible with growing wealth at the fastest rate. Growth optimality requires utility functions to mirror ergodicity transformations and adapt to the dynamic environment. We exposed human participants to different wealth dynamics in a consequential risky decision-making experiment. Via Bayesian modelling, we estimated utility functions separately for each dynamic. Pre-registered analyses revealed strong evidence supporting the quantitative predictions of the ergodicity model. Our study provides evidence that human risk-taking can adapt quickly to the dynamical context, in ways that align closely to the theoretical optimum for maximizing wealth over time.

Cracks on the surface of concrete structures are an obvious indication of their condition. However, traditional visual inspection methods are significantly influenced by human factors. They are accurate for larger cracks. However, they introduce subjective judgment when detecting smaller cracks. They also pose safety risks. Automated detection methods face challenges in achieving lightweight implementation due to limited annotated data and difficulties in complex environments. This paper proposes a method for detecting and measuring concrete cracks. The method uses a generative adversarial network (GAN) to augment data and a U-Net segmentation network. Conditional GAN (cGAN) is proposed to generate high-fidelity crack images using training-set data only, avoiding data leakage and effectively solving insufficient sample issues. A U-Net segmentation network that has been enhanced performs crack segmentation. This network incorporates channel attention modules and dilated residual blocks. A hybrid loss function that combines the Dice coefficient and binary cross-entropy resolves class imbalance. Fractal theory quantifies the geometric features of the detected cracks. The crack segmentation model that was trained on cGAN-augmented datasets achieved an average precision (AP) of 82.03% and an F1 score of 83.24%, as demonstrated by experimental results. The fractal dimension-based crack measurement method achieves an accuracy of ≤4% error for complex crack networks (fractal dimension >1.6). The proposed method provides a reproducible, automated, high-precision solution for concrete crack detection and quantification, with verified stability and engineering applicability.

Online lending marketplaces have emerged as potentially disruptive players in the financial industry by digitally connecting borrowers and lenders. However, little is known about the emergence of dominant business model patterns in this regulated, platform-based service industry. Drawing on dominant design theory, this study examines the international convergence of business model components in online lending since 2005. Using a comparative, longitudinal case study of the market leaders Funding Circle (the UK), LendingClub (the USA), and auxmoney (Germany), we analyze the Business Model Canvas components "customer segments" and "value propositions." Our findings reveal a convergence toward peer-to-institution models, shaped by regulatory pressures, technological advances, and institutional capital. All marketplaces now address underserved borrower segments such as SMEs, the self-employed, and founders, while excluding retail investors. This indicates the emergence of internationally dominant business model components in potentially disruptive two-sided platform markets. We synthesize these findings into a generic, process-oriented model explaining how platform convergence unfolds across national contexts. This study extends dominant design theory to business models in regulated service industries and highlights external forces as key drivers of standardization. It offers practical insights for policymakers, institutional investors, and marketplace operators navigating scalability, regulation, and financial integration.