Search Results (486)

Hydrogen is widely considered a promising clean energy carrier because of its high energy density and environmental benignity, yet the development of safe and reversible hydrogen storage materials remains a major challenge. Two-dimensional materials are particularly attractive for this purpose owing to their large specific surface area, fully exposed active sites, and highly tunable electronic structures. Here, using crystal structure prediction combined with first-principles calculations, we predict a stable metallic Li₃B₂N₂ monolayer as a potential hydrogen storage material. This monolayer can adsorb up to six H₂ molecules per unit cell with an average adsorption energy of ∼0.23 eV/H₂, yielding a high hydrogen storage capacity of ∼7.8 wt.%. Further analysis reveals that hydrogen adsorption is governed by the synergistic effects of electrostatic polarization and orbital hybridization. Moreover, calculations on the temperature- and pressure-dependent hydrogen storage behavior show that all hydrogen-adsorbed structures remain stable at room temperature under a pressure of 3.7 MPa. The van’t Hoff analysis indicates that the maximum desorption temperature at atmospheric pressure is 316 K, suggesting favorable reversibility under near-ambient conditions. These results establish Li₃B₂N₂ as a promising intrinsic two-dimensional material for high-density and reversible hydrogen storage. Full article

High-performance flexible electronics require soft materials that combine mechanical robustness with efficient ionic conduction. In conventional ionogels, however, these requirements often conflict: dense networks improve strength but reduce the free volume and mobility needed for ion transport. This review provides a critical overview of recent progress in cellulose-based ionogels, with emphasis on design principles for decoupling mechanical and conductive properties. We discuss how cellulose precursors, crosslinking architectures (hydrogen bonding, covalent networks, and metal-ion coordination), and processing histories determine gel structure and mechanical integrity. We then highlight strategies that mitigate the trade-off, including precursor engineering, phase-separated networks, double-network architectures, crystallization-induced reorganization, and anisotropic assembly. Representative applications in flexible sensors, flexible energy-storage devices, and soft actuators are also summarized. This review offers a practical framework for designing cellulose-based soft functional materials with robust mechanics and sustained ionic conductivity. Full article

Artificial intelligence has advanced from merely predicting static protein structures to modeling equilibrium conformational ensembles. It now concurrently forecasts structure and binding affinity and actively participates in candidate selection during the initial stages of drug discovery. Foundation models introduced between 2024 and 2026, including BioEmu, AlphaFlow, DiG, Boltz-2, Chai-1, NeuralPLexer, and explicit-solvent prediction systems such as SuperWater, have begun to address issues previously identified as fundamental concerns in earlier critiques of AI in drug discovery. Nevertheless, many of these models are presently accessible only as preprints and require validation through independent peer review. Evidence indicates a shift in the primary bottleneck from representation challenges to validation difficulties. However, this transition remains incomplete and heavily dependent on context. The risks associated with AI-enabled drug discovery are increasingly not solely about the models’ capacity to accurately represent ensembles, but also about whether the evidentiary standards used to validate AI-derived predictions keep pace with the rapidity with which these predictions are generated and employed. This article introduces a four-tier validation framework designed to align the extent of computational and experimental evidence with the translational and regulatory risks associated with various artificial intelligence (AI) applications within the molecular sciences. These applications include machine learning (ML) models that analyze sequences, structures, conformational ensembles, protein–ligand complexes, and molecular dynamics trajectories. Tier 1 addresses the internal reproducibility of ML inference when applied to molecular inputs; Tier 2 pertains to the robustness of molecular-science benchmarks such as CASP, CASF-2016, PoseBusters, and OpenFE; Tier 3 involves prospective experimental validation against biophysical and biochemical measurements; and Tier 4 encompasses clinical and translational calibration within physiologically based pharmacokinetic (PBPK) and quantitative systems pharmacology (QSP) frameworks. This validation hierarchy functions as an explicit conceptual guide, serving as a framework rather than a regulatory requirement. It is firmly grounded in established principles derived from ICH Q8/Q9/Q10, the FDA model-informed drug development (MIDD) approach, the EMA reflection paper on AI in the medicinal product lifecycle, and the EU AI Act. The manuscript further incorporates recent evidence from ensemble-aware AI, prospective docking, free-energy campaigns, and clinical-stage AI-derived candidates. It concludes with specific recommendations pertaining to lifecycle governance, uncertainty reporting, and the adoption of harmonized evidentiary templates for AI/ML applications in the molecular sciences. Full article

Quartz crystal microbalance (QCM) technology provides a powerful, label-free platform for monitoring molecular interactions in real time with nanogram sensitivity. Recent advances in compact instrumentation have enhanced analytical performance while reducing energy consumption, aligning with the principles of Green Analytical Chemistry. In parallel, the European Union has recommended the replacement of animal-derived antibodies with non-animal alternatives, creating an urgent need for sustainable affinity receptors. In this study, we present an innovative application of polynorepinephrine (PNE)-based molecularly imprinted polymers (MIPs) with a compact QCM sensing. PNE, a bioinspired polymer formed under mild aqueous conditions, offers strong adhesive properties and biocompatibility, enabling robust immobilization of imprinted receptors on gold-coated quartz disks. The resulting PNE-MIP/QCM platform combines the ultrasensitivity of quartz microbalances with the selectivity of molecular imprinting, delivering a reproducible and environmentally responsible affinity sensor. The sensor showed a limit of detection of 11.2 nM and enabled accurate IgG quantification in diluted human serum samples. As a proof of concept, the system was applied to Human Immunoglobulin G (IgG1) detection, demonstrating its potential for sustainable clinical diagnostics. Full article

This work proposes a polarization-insensitive scalable wide-angle metasurface array for highly efficient ambient energy harvesting in the 5.8 GHz Wi-Fi band. Inspired by dipole antenna principles, we design an asymmetric planar orthogonal dipole-based metasurface featuring monolithic integration of Schottky diodes (HSMS-2860) at unit cell feed gaps. This novel direct-impedance-matching strategy eliminates conventional matching networks, reducing energy conversion losses while enabling 99% radiation-to-AC efficiency across all polarization angles at 5.8 GHz. The coplanar architecture interconnects metasurface unit cells via inductors, simultaneously establishing low-loss DC channels and suppressing RF leakage. Fabricated as a 5 × 5 array, the prototype achieves 77.9% peak RF-to-DC efficiency with polarization insensitivity at an incident power of 25 dBm. Furthermore, with incident powers of 15 dBm and 20 dBm, the proposed metasurface array attained RF-to-DC conversion efficiencies exceeding 40% and 60%, respectively. This result indicates that the array is capable of achieving high energy harvesting efficiency across a broad power range. This scalable, drill-free, and polarization-insensitive design demonstrates strong potential for harvesting ambient RF energy in real-world multipath environments. Full article

Computational chemistry has played a central role in early-stage drug discovery by accelerating target selection, hit identification, and lead optimization. This review summarizes recent developments in molecular docking, pharmacophore modeling, molecular dynamics (MD), and virtual screening (VS), with a focus on their application in practical drug discovery workflows. Advances in docking protocols, including consensus scoring, physics-based rescoring, and ensemble approaches, addressed the challenges of receptor flexibility. Both ligand-based and structure-based pharmacophore models facilitated scaffold hopping and guided library prioritization. MD simulations were used to assess binding pose stability, identify cryptic binding pockets, and characterize solvent interactions. These simulations also supported free-energy calculations using endpoint and alchemical methods. Large-scale VS campaigns employed curated compound libraries, often composed of make-on-demand molecules, and relied on high-performance computing or cloud infrastructure to screen up to 10⁹ compounds. Hits were validated using orthogonal biophysical assays and filtered by absorption, distribution, metabolism, excretion, and toxicity (ADMET) predictions. Integrated pipelines combining pharmacophore modeling, docking, MD, and free-energy calculations improved enrichment rates and reduced the number of compounds requiring synthesis. Several case studies demonstrated the identification of nanomolar-affinity leads from ultra-large screening campaigns. The review also addressed ongoing challenges, such as inconsistent scoring of binding affinity, protonation, and tautomeric errors, dataset bias, and reproducibility issues. Strategies to mitigate these limitations included standardized library preparation, adherence to FAIR (Findable, Accessible, Interoperable, and Reusable) data principles, and the use of prospective benchmarking protocols. The review discussed emerging trends, including the use of quantum chemistry for electronic structure refinement, ensemble docking guided by cryo-electron microscopy (cryo-EM) data, and the integration of computational tools with automated synthesis and high-throughput screening in closed-loop discovery systems. These approaches have the potential to accelerate the design–make–test cycle, increase hit novelty, and improve decision-making in early drug development programs. Full article

The cantilever pick-and-place arm of the high-speed placement machine is susceptible to micro-vibration and elastic deformation under high-acceleration motion, thereby degrading chip placement accuracy. To address this issue, this paper presents an analytical study on the natural frequency characteristics and structural optimization of slender variable-cross-section rods. First, based on the thin-walled shell theory, a displacement field model of the thin-walled cantilever rod is established. Second, combining the energy method and Hamilton’s principle, the undamped free vibration equation is derived. Using the Rayleigh–Ritz method with Chebyshev polynomials as the basis functions, an analytical calculation model for the natural frequency of the variable-section thin-walled rod is constructed. The model is validated against finite element simulations, and the relative errors of the low-order natural frequencies are controlled within 5%, confirming its favorable accuracy and robustness. Furthermore, the four-factor three-level orthogonal experiment is designed with the objective of maximizing natural frequency to conduct parameters sensitivity analysis. Accordingly, the optimal structural parameter combination (${\varphi }_3$ = 8 mm, L₁ = 10 mm, L₂ = 50 mm, and L₃ = 5 mm) is determined. Finally, the maximum dynamic deformation under high-acceleration motion decreases from 0.066 mm to 0.021 mm, a reduction of 68.2%, which effectively suppresses residual vibration and end displacement deviation. The analytical method proposed in this study provides a theoretical basis for the rapid dynamic performance evaluation of flexible components in high-speed precision equipment, and the optimization strategy can offer engineering references for the high-stiffness design of key components in chip placement machines. Full article

Artificial intelligence is typically formulated as an information-processing system composed of artificial neurons, where computation is understood as recursive operations connecting inputs and outputs. However, real neural systems are materially embodied and continuously reconfigured by metabolic and physical processes, suggesting that computation cannot be reduced to fixed causal structures. In this paper, we propose a theoretical framework that captures the interplay between informational and material processes as the interaction between two computational schemes: a vertical scheme, representing fixed cause–effect relations, and a horizontal scheme, representing transformations between such relations. We show that the vertical scheme corresponds to Bayesian inference, which updates probability distributions over a fixed hypothesis space, and is consistent with the free-energy minimization principle. In contrast, the horizontal scheme is formalized as inverse Bayesian inference, which modifies the hypothesis space itself by updating likelihood structures based on experienced data. We further demonstrate that the interplay between these schemes can be expressed algebraically as a process of continuously gluing Boolean algebras. This construction yields a non-distributive orthomodular lattice, i.e., quantum logic, without invoking Hilbert space formalism. In this view, quantum logic emerges not as a static logical system but as a structural consequence of dynamically reconfiguring causal contexts. This framework provides a unified perspective in which inference is understood not only as optimization within a fixed model but also as a process that generates and transforms the model itself. It offers a formal basis for describing open-ended computation and suggests a connection to approaches such as unconventional computing and Natural Born Intelligence, where computational structures evolve through interaction with material processes. Unlike existing approaches, this framework derives quantum-logic-like structure from the continual reconfiguration of causal contexts rather than from Hilbert-space assumptions or optimization within a fixed hypothesis space. Full article

With the strategic shift toward reducing reliance on critical raw materials, Cobalt-free eutectic high-entropy alloys (EHEAs) have emerged as a pivotal frontier for high-performance structural applications. This review systematically elucidates the synergistic relationship between Co-free alloy design and the non-equilibrium solidification mechanisms of Selective Laser Melting (SLM). The ultra-high cooling rates (10⁵–10⁸ K/s) inherent in SLM are shown to refine eutectic lamellae to the sub-micron scale (typically <300 nm), effectively suppressing the macro-segregation common in conventional casting. We evaluate the design principles of Al-Cr-Fe-Ni and related systems, noting that SLM-processed Co-free EHEAs frequently achieve yield strengths exceeding 1000 MPa and ultimate tensile strengths (UTSs) surpassing 1300 MPa, while maintaining tensile elongations above 10%—a significant improvement over the coarse-grained structures produced by traditional methods. Furthermore, the study identifies critical processing windows, such as laser energy densities (60–120 J/mm³), required to mitigate micro-cracking and achieve near-full density (>99.5%). By synthesizing recent experimental breakthroughs and AI-driven modeling, this review provides a quantitative roadmap for the precision manufacturing of cost-effective, high-performance EHEAs, bridging the gap between theoretical alloy design and industrial additive manufacturing. Full article

Growing municipal solid wastes, environmental deterioration, and the world’s increasing energy demand highlight the urgent need for effective, sustainable energy recovery solutions. Uncontrolled municipal solid wastes contribute explicitly to the global crises of climate change, pollution, and biodiversity loss. Food and organic waste are converted into value-added products using biochemical and thermochemical techniques. Anaerobic digestion (AD) is a versatile, multi-phase waste-to-energy technology that transforms organic waste into renewable energy in an oxygen-free environment. AD uses microorganisms to break down waste, yielding biogas (mostly methane and carbon dioxide) and digestate, a nutrient-fortified by-product. Compared with traditional Single-Stage Anaerobic Digesters (SSAD), Two-Stage Anaerobic Digesters (TSAD) offer notable benefits by separating hydrolysis–acidogenesis from acetogenesis–methanogenesis. These include increased methane yield, improved process control, increased microbial stability, and resistance to inhibitory substances. According to the literature, TSAD systems have been shown to increase methane yield by about 10–30% compared to SSAD. This article covers the dynamics of the microbial population at various stages, the impact of operational factors (HRT, OLR, pH, and temperature), and novel reactor designs with modular and multi-state functions. In line with Oman’s Vision 2040, this study discusses the continuous operation of a two-phase AD co-digestion process and the in-depth techno-economic feasibility of decentralized waste management through optimized biogas production. Optimizing the carbon-to-nitrogen (C/N) ratio within the range of 20–30 in co-digestion systems significantly enhances microbial activity and methane production. The potential of recent developments, such as microbial immobilization, biogas generation techniques, and hybrid integration with photobioreactors or electrochemical systems, to enhance the scalability and efficiency of bioconversion is addressed in a TSAD system. In addition to encouraging circular economy principles through efficient organic waste valorization, this review identifies TSAD as a promising approach to achieving the SDGs related to sustainable cities, clean energy, and responsible consumption. Full article

Chaotic itinerancy—irregular switching among metastable collective states—provides a dynamical substrate for flexible social coordination, yet its mechanistic origin in multi-agent systems remains unclear. We present a multi-agent Active Inference model in which chaotic itinerancy emerges from Expected Free Energy minimisation without outcome-level social priors. Agents select actions to minimise Expected Free Energy while updating preferences through a precision-gated learning mechanism modulated by interpersonal trust. Hill-function nonlinearity in state transitions creates bistable “affordance landscapes” that gate behavioural mode switching. Simulations with small number of agents on an Erdős–Rényi trust network reveal spontaneous alternation among multiple metastable behavioural clusters, heavy-tailed dwell-time distributions, and sign-changing finite-time Lyapunov exponents—three hallmarks of chaotic itinerancy. Crucially, replacing Hill-function dynamics with linear transitions reduces the chaotic-itinerancy detection rate from 80% to 20%, demonstrating that nonlinear affordance structure is necessary for generating metastable switching. We further show that agents with simplified internal models of the world sustain richer itinerant dynamics as a group than “perfect-foresight” agents, suggesting that bounded rationality may be functionally advantageous for maintaining behavioural flexibility. These results establish active inference as a principled framework for modelling chaotic itinerancy in social systems and offer a computational account of trust-mediated collective transitions observed in theatre workshops and group dynamics. Full article

Nano–bio hybrid catalysts have emerged as a promising platform for sustainable energy conversion by integrating the high selectivity of enzymes with the structural robustness and conductivity of nanomaterials. In recent years, the growing demand for clean energy technologies has driven the development of biohybrid systems capable of efficient electron transfer, enhanced catalytic activity, and improved operational stability. This review comprehensively discusses the design principles, mechanistic foundations, and performance metrics of enzyme–nanomaterial interfaces for energy-related applications. We first outline the fundamentals of enzymatic redox catalysis and the limitations of free enzymes in practical systems. Subsequently, we examine the functional roles of nanomaterials including carbon-based materials, metal and metal oxide nanoparticles, and two-dimensional platforms such as MXenes in facilitating enzyme immobilization and promoting direct or mediated electron transfer. Special emphasis is placed on engineering strategies at the bio–nano interface, including immobilization techniques, surface functionalization, and structural tuning to optimize catalytic efficiency. The review further highlights representative hybrid systems based on laccase, glucose oxidase, peroxidase, and hydrogenase enzymes, and evaluates their applications in biofuel cells, solar–bio hybrid systems, green oxidation reactions, and self-powered biosystems. Stability challenges, deactivation mechanisms, and enhancement strategies such as polymer coatings, cross-linking, and nanoconfinement are critically analyzed. Finally, emerging directions including artificial enzymes, AI-guided catalyst design, and self-healing bioelectrodes are discussed to provide a forward-looking perspective on next-generation sustainable bioelectrocatalytic systems. Full article

To tackle the drawbacks of small effective area and limited incident angle in conventional Fabry–Pérot (F-P) etalons, this paper presents a high-utilization laser frequency-selective ultra-narrow band F-P filter module, along with systematic investigations into its operating principle, simulation, design, and verification. A simulation model with a central wavelength of 532.20 nm (±0.1 nm) and a free spectral range of 300 pm is developed, and a prototype filter is fabricated accordingly. The prototype exhibits a transmission peak linewidth better than 0.037 nm and a peak transmittance over 68%, matching well with the simulation. Simulations also reveal symmetric high-transmittance peaks at ±2.9° with stable frequency selection performance. On this basis, an integrated module is proposed using field-of-view angle conversion and optical path multiplexing. Under combined incidence at 0° and ±2.9°, the module achieves 1.67 times the energy of single-angle incidence while satisfying wavelength and bandwidth requirements. The proposed structure breaks through the conventional normal-incidence restriction and offers a novel approach for high-efficiency, multi-angle multiplexing applications of F-P etalons in precision optical systems. Full article

Accurate estimation of the mean-reversion speed $\alpha$ in the AR(1) process $X_{t+1}=(1-\alpha )X_t+{\epsilon }_t$ is central to energy-commodity modelling. Classical estimators such as GARCH, jump-diffusion, and regime-switching produce model-conditioned estimates by embedding $\alpha$ within distributional assumptions, so that different model choices yield different $\widehat{\alpha }$ values from the same series without a principled criterion to adjudicate. We propose a distribution-free neural estimator based on a Temporal Convolutional Network (TCN) trained on synthetic AR(1) series with Sinh-ArcSinh (SAS) innovations. Distribution-free here means that no parametric family is assumed for the innovation distribution at inference time: the estimator imposes no distributional hypothesis when processing a new series. The SAS family serves as a training vehicle—not a model for the real data—chosen for its ability to span a broad range of tail weights and asymmetry profiles. The theoretical foundation is spectral invariance: the Yule–Walker equations establish that the autocorrelation structure ${\rho }_k={(1-\alpha )}^k$ depends on $\alpha$ alone, provided innovations are uncorrelated across lags—a condition satisfied not only by i.i.d. innovations but also by conditionally heteroscedastic processes such as GARCH. The TCN therefore generalises to volatility-clustering environments without modification, learning to extract $\alpha$ from temporal dependence alone, independently of the marginal innovation distribution and of the temporal variance structure. On held-out test series the estimator outperforms all classical competitors, with the advantage growing monotonically with non-Gaussianity. A robustness analysis on three out-of-distribution innovation families and on AR(1)-GARCH(1,1) processes empirically validates the spectral invariance guarantee across both marginal and temporal variance structure, including near-integrated GARCH processes where innovation kurtosis far exceeds the training range. The distribution-free $\widehat{\alpha }$ enables a two-stage pipeline in which $\alpha$ and the innovation distribution are characterised independently—a decoupling structurally impossible in classical likelihood-based approaches. Once trained, the TCN acts as a universal mean-reversion estimator applicable to any price series without re-fitting. Applied to four energy markets—Italian natural gas (PSV price), Italian electricity (PUN price), US Henry Hub, and US PJM West Hub—spanning log-return kurtosis from near-Gaussian to strongly heavy-tailed, the TCN yields robust, distribution-free estimates of mean-reversion speed. Full article

The adsorption of rare-earth Y-based inclusions (Y, Y₂O₃, Y₂O₂S) on Al₂O₃, YAlO₃, and Y₂O₃ refractory surfaces is a primary cause of nozzle clogging during the continuous casting of rare-earth steels. Conventional anti-clogging strategies, being passive and offline, lack real-time adjustability. This study aims to elucidate the mechanism by which external positive charge modulates interfacial adsorption. Using first-principles calculations combined with partial density of states, charge density difference, and thermodynamic analyses, we investigated the adsorption behavior of Y, Y₂O₃, and Y₂O₂S on Al₂O₃ (001), YAlO₃ (001), and Y₂O₃ (001) surfaces under neutral and positively charged states (+2, +4). A triple inhibition mechanism is revealed: electronically, external charge disrupts O-p and Y-d orbital hybridization, attenuating interfacial covalent bonding; electrostatically, the net positive charge shifts the interfacial interaction from attraction to repulsion, creating a physical barrier; and thermodynamically, the Gibbs free energy change ΔG increases under charged conditions, indicating a quantifiable reduction in adsorption spontaneity. These findings provide a theoretical basis for the development of active anti-clogging strategies in rare-earth steel production. Full article