Search Results (488)

Self-assembled monolayers (SAMs) on indium tin oxide (ITO) surfaces tend to undergo molecular aggregation, resulting in non-uniform interfacial coverage and thus limiting the charge extraction efficiency and device performance in organic solar cells (OSCs). To address this issue, a novel dispersant molecule, 3,6-Diiodo-9-[2-(2-methoxyethoxy) ethyl]-9H-carbazole (2ICzMPE), featuring dipolar characteristics and flexible side chains, is designed and synthesized to regulate the interfacial distribution of 2PACz molecules on ITO electrodes. With a large dipole moment and steric hindrance, 2ICzMPE suppresses 2PACz aggregation, while the oxygen atoms in the side chains interact with the ITO surface, enabling a more uniform interfacial structure. Upon introducing 2ICzMPE, the interfacial energy level alignment is optimized, leading to more efficient charge extraction. Device physics analysis reveals suppressed carrier recombination and enhanced charge transport. As a result, the power conversion efficiency (PCE) of OSCs based on the PM6:L8-BO system is improved from 17.51% to 18.67%. This work provides a simple and effective molecular design strategy for constructing high-quality SAM interfaces and promoting the scalable application of OSCs. Full article

This study aims to investigate the performance and mechanism of N′-[(E)-phenylmethylidene] furan-2-carbohydrazide (FNH), a hydrazone Schiff base, as a corrosion inhibitor for carbon steel in 1.0 M HCl. The research was conducted by coupling electrochemical testing (Tafel analysis and Impedance spectroscopy) with surface characterization (SEM and AFM) and advanced computational tools, including quantum-chemical modeling and classical molecular dynamics (MD) simulations. Tafel analysis revealed that FNH acts as a mixed-type inhibitor, concurrently slowing iron oxidation and hydrogen reduction. Impedance data showed that the Faradaic resistance grew monotonically with FNH dosage, reaching 95% protection at 1 × 10⁻⁴ M. Fitting the results to the Langmuir model indicated a joint physical–chemical anchoring pathway, further confirmed by SEM/AFM inspection which disclosed a uniform organic deposit. Quantum-chemical modeling revealed that protonated species broaden the molecule’s capacity for bidirectional electron exchange, while MD simulations on the Fe (110) slab confirmed a flat-lying geometry that maximizes heteroatom–metal contact. The consistency between laboratory observables and atomic-scale predictions provides a detailed, mechanism-oriented picture of how this organic protective layer curtails acid corrosion. Full article

Molecular trajectory prediction is fundamental to computational chemistry, drug discovery, and materials simulation, enabling insights into dynamics, reaction pathways, and conformational stability. Its natural alignment with graph-structured spatiotemporal data has made it a key frontier in GNN research. However, current mainstream spatiotemporal GNNs, while enforcing E(3)-equivariance, treat atoms as unconstrained point masses and lack explicit rigid geometric constraints, often yielding unphysical deformations that compromise predictive interpretability. To address this challenge, we propose STEGMN—the first spatiotemporal graph architecture for molecular trajectory prediction that explicitly encodes rigid constraints. Inspired by Graph Mechanics Networks, we design a constraint-preserving equivariant spatiotemporal attention mechanism that captures temporal dependencies while rigorously maintaining both E(3)-equivariance and rigid-body constraints. Additionally, we introduce a constraint-preserving equivariant pooling module that generates future states by performing a learnable weighted aggregation of historical angular velocities, followed by forward kinematics mapping. This ensures that all outputs simultaneously satisfy E(3)-equivariance and strict bond-length conservation. Evaluated on real-world molecular dynamics datasets, STEGMN consistently outperforms strong baselines. On the rMD17 benchmark, it achieves an average ∼40% reduction in prediction MSE relative to representative spatiotemporal graph models (ST-GNN, ST-GCN, and ST-EGNN) across eight small-molecule systems, highlighting the critical value of explicit constraint modeling for physically stable trajectory prediction. Full article

Rare earth element (REE) detection sensitivity with minimal sample damage is exciting. Laser-induced breakdown spectroscopy (LIBS) with a typical methodology is a useful diagnostic tool, but often shows poor REE sensitivity. This study presents the qualitative, quantitative, and classification analysis of REE-bearing ore samples that contain multiple elements from the lanthanoid (Ln) group (e.g., La, Ce, Nd, Sm, and Gd) using the LIBS technique, and the results are compared with those obtained using a magnetic-field-assisted LIBS (MFA-LIBS) system. The LIBS spectrum was recorded using a Nd:YAG Laser with a 532 nm emission wavelength, a 5 ns pulse duration, and a 10 Hz repetition rate. Optical regions exhibiting the strongest emission lines of REEs were identified, followed by MFA-LIBS to improve the qualitative signatures of the elements of interest. MFA-LIBS also assists in confirming signal enhancement for Sm and Gd, which were unidentified with a conventional LIBS setup. Quantitative analysis was performed using a calibration-free and magnetic-field-assisted LIBS (CF-MF-LIBS) method. La, Ce, and Nd concentrations were estimated to be from 1 to 3 wt.%, whereas Sm and Gd were detected within 0.5 wt.%. The results obtained using CF-MF-LIBS were compared with those obtained using the X-ray fluorescence spectroscopy (XRF) technique, showing good agreement between the LIBS/XRF techniques. Further, the limit of detection (LOD) of the REEs using in-house prepared samples was estimated, and the results were compared with those previously reported in the literature. Furthermore, classification analysis of REE ores based on compositional variations was achieved using principal component analysis (PCA). The first two principal components (PCs) with maximum spectral variance, such as PC1~74.5% and PC2~14.5%, were considered for the clustering, and ellipses with 95% confidence using major (x) and minor (y) axes were created to explore outliers. Therefore, the CF-MF-LIBS method in combination with PCA demonstrates a rapid, robust, and effective methodology for the detection, quantification, and classification investigation of REE-bearing ores. Full article

Mean atomic bond strain (MABS), based on the globally averaged bond length, has recently emerged as a new strain metric that retains clear physical meaning even as severe atomic neighborhood reconstruction occurs. It has been shown to exhibit a nearly perfect linear relationship with global stress throughout the elastic and plastic deformation in single-crystal face-centered cubic (FCC) metals, contradicting conventional expectations based on nonlinear dislocation activity. Whether this near-linear relationship holds in other materials stands out as an important and intriguing question. In this study, we examine the MABS–stress relationship in representative unary, binary, and ternary metallic glasses (MGs), where neither a crystal structure nor dislocations are present. Large-scale molecular dynamics simulations of uniaxial tensile tests and statistical analysis of millions of atomic bonds are performed. Irrespective of their differing compositions, all the MGs exhibit a persistent near-linear relationship between total MABS (all bonds included) and global stress up to fracture, even in the presence of significant local nonaffine displacements (shear transformation zones and shear bands), with the Pearson correlation coefficient consistently exceeding 0.99. Unlike the nonaffine displacements, the spatial distribution of individual atomic bond strain does not localize under the uniaxial loading. In the MGs containing more than one element, MABS computed for a single bond type may not correlate as linearly with global stress as total MABS. The results demonstrate that the persistent near-linear total MABS–stress relationship over the entire deformation process, recently discovered in single-crystal FCC metals, also applies to MGs despite their vastly different atomic structures. This strengthens the candidacy of total MABS as a universal stress descriptor across materials classes and deformation regimes. With further development and implementation in atomistic simulations and constitutive modeling, the MABS concept has the potential to reshape our understanding of materials mechanics and generate new insights into the design of stronger, tougher, and more thermally and chemically stable materials. Full article

Full-waveform hyperspectral LiDAR offers a new approach for precise forest ecological monitoring by simultaneously acquiring the three-dimensional structure and continuous spectral information of targets. However, uncertainty in the backscattering cross-section and the inseparability of the reflectance coefficient lead to systematic underestimation of multi-echo reflectance retrieved using traditional methods. This limitation significantly hinders quantitative applications. The existing multi-echo reflectance correction using neighborhood single-echo reflectance (MCNS) method provides an effective solution by establishing proportional models between similar targets, laying an important foundation for the extraction of multi-echo reflectance. However, its applicability in complex forest scenes is limited due to its dependence on specific vegetation single-echo samples. To address this, an iterative correction method based on ground reflectance baseline, namely Bare-Ground-Echo-Based Iterative Correction of Multi-Echo Reflectance for Hyperspectral LiDAR (BGE-ICMER), is proposed. Using ground single-echo reflectance as a stable baseline, a multi-target energy distribution model is constructed based on energy conservation, and backscattering cross-section proportions for each echo are iteratively solved to recover true reflectance. Validation using a high-fidelity dataset generated by the Large-Scale remote sensing data and image Simulation framework (LESS) confirmed the effectiveness of the proposed method. This dataset encompasses three typical tree species with vegetation layers ranging from two to four, incorporates micro-topographic ground surfaces and ten spectral channels from 500 to 1000 nm, thereby capturing the structural and spectral complexity of real forests. The results showed that coefficients of determination (R²) between the corrected and true reflectance exceeded 0.9560, with an RMSE below 0.0418 and MAE below 0.0360. The average relative error was reduced from 26.66% to 10.07%, representing a 62.22% improvement in accuracy. Even in the most challenging scenarios with four-layer vegetation occlusion within this dataset, no significant error accumulation occurred. These results demonstrate the robustness and effectiveness of the proposed method for multi-echo reflectance extraction. This study lays a foundation for more accurate forest biochemical attribute assessment and enables the vertical characterization of multiple targets using high-resolution spectral reflectance. Full article

Since the first successful synthesis of borophene in 2015, this atomically thin boron allotrope has attracted extensive attention due to its polymorphic structures, metallic conductivity, and outstanding mechanical flexibility. As a new member of the two-dimensional (2D) materials family, borophene exhibits a unique triangular lattice with tunable hexagonal vacancies, leading to rich structural diversity and anisotropic physical properties. Recent breakthroughs in synthesis—particularly molecular beam epitaxy (MBE), chemical vapor deposition (CVD), and solvothermal-assisted liquid-phase exfoliation (S-LPE)—have significantly expanded the accessible structural phases and improved control over film quality and stability. Meanwhile, borophene’s distinctive combination of structural and electronic characteristics has enabled its rapid development in both energy and biomedical applications. In energy storage, borophene serves as a promising anode material for lithium/sodium-ion batteries and a lightweight medium for hydrogen storage and supercapacitors, owing to its metallic conductivity, high surface charge density, and large adsorption capacity. In biomedicine, borophene-based nanoplatforms exhibit excellent photothermal conversion efficiency, enabling multifunctional roles in cancer diagnosis and therapy. Despite these advances, several challenges—such as environmental instability, oxidation susceptibility, and limited scalable synthesis—continue to restrict practical implementation. Future progress will depend on chemical functionalization, surface passivation, and machine-learning-assisted materials design to achieve oxidation-resistant, large-area, and biocompatible borophene derivatives. This review summarizes recent advances in borophene synthesis, structural engineering, and multifunctional applications, while outlining key scientific challenges and future opportunities for the realization of borophene-based materials in next-generation energy and biomedical systems. Full article

Gold nanoparticles offer a versatile platform for cancer theranostics because their high atomic number can enhance X-ray energy deposition, their plasmonic properties support photothermal and photoacoustic applications, and their surfaces allow drug loading and molecular targeting. However, therapeutic benefit remains heterogeneous because tumor uptake, intratumoral coverage, and subcellular localization determine whether deposited gold can be converted into biologically effective damage. Redox context further shapes this conversion by determining whether AuNP-triggered physical or catalytic events can overcome local buffering and propagate into durable injury. During radiotherapy, AuNPs increase local secondary electron release and ROS formation, which can intensify DNA damage when GSH-dependent peroxide detoxification, thioredoxin-related buffering, and KEAP1-NRF2-regulated antioxidant responses are insufficient to contain the redox burden. In catalytic systems, Au-containing nanozymes can convert endogenous H₂O₂ into highly reactive radicals and may simultaneously deplete glutathione, thereby amplifying mitochondrial dysfunction and lipid peroxidation. During photoactivation, plasmonic heating and photosensitizer coupling further reshape ROS generation in a time-dependent and location-dependent manner. On the diagnostic side, CT or spectral CT can quantify tumor gold burden and coverage, whereas ROS-responsive photoacoustic, SERS, or fluorescence probes can report treatment-related oxidants and verify whether redox activation has occurred within the tumor. Clinical translation will therefore depend on quantification-guided dosing, definition of spatial coverage and activation timing, standardized redox-response readouts, and long-term safety evaluation. Full article

Numerical simulation of metal microstructure evolution is essential for material design and performance optimization. This paper reviews major simulation methods for key evolution mechanisms, including recrystallization, grain growth, slip, twinning, and phase transformation. The reviewed methods are classified into atomistic models, discrete-field models, and continuous-field models. Molecular dynamics (MD) is discussed as an independent atomistic approach, with emphasis on its role in revealing atomic-scale mechanisms, calibrating mesoscale parameters, and bridging atomistic, mesoscale, and continuum simulations. Discrete-field methods, including Monte Carlo, cellular automata, and vertex models, are compared with continuous-field methods, including artificial neural networks, phase field models, finite element methods, and level-set methods. Furthermore, a semi-quantitative evaluation matrix based on accuracy, computational cost, scalability, and applicability is established to clarify the practical trade-offs among different methods. The results show that no single method is universally optimal; instead, method selection should depend on the dominant physical mechanism, target length scale, required accuracy, and available computational resources. This review provides methodological guidance for multiscale microstructure simulation and supports future applications in precision machining, additive manufacturing, and process parameter optimization. Full article

Antimicrobial resistance (AMR) is emerging as one of the most serious global health problems, necessitating the urgent development of alternative approaches to pathogen control. The present study describes the synthesis and characterization of a novel iodinated BODIPY derivative (BODIPY5), designed as a highly efficient photosensitizer for antimicrobial photodynamic inactivation (aPDI). The molecular design of the compound involves the introduction of two iodine atoms into the BODIPY5 core, which induces a “heavy atom effect”, accelerates the intersystem transition from the singlet to the triplet state, and leads to increased generation of singlet oxygen upon irradiation with visible light. Photophysical measurements show a significant fluorescence quenching of BODIPY5 compared to its unsubstituted counterpart, which is a direct indicator of increased photodynamic activity. The compound’s antimicrobial efficacy was tested in a homogeneous medium and after immobilization on cotton textiles via physical adsorption. In solution, BODIPY5 nearly eliminated the model bacterial strains B. cereus and P. aeruginosa at a low concentration of 10 µg/mL under light, with cell viability below 1%. The functionalized cotton fabric exhibits pronounced self-disinfection properties, retaining high photodynamic activity against the Gram-negative pathogen P. aeruginosa. Scanning electron microscopy results confirm extensive morphological damage and loss of structural integrity in bacterial cells on the treated textile following irradiation. The non-specific mechanism of action, which generates reactive oxygen species (¹O₂) in situ, prevents the development of bacterial resistance and makes the developed material a promising candidate for use in hospital environments, including antibacterial clothing and protective equipment. Full article

Silica aerogels are ideal candidates for gas adsorption due to their exceptional porosity and high specific surface area; however, the inherent mechanical fragility of their skeletal framework significantly compromises their operational stability in engineering applications. While the incorporation of carbon nanomaterials effectively enhances the mechanical robustness of aerogels, the specific microscopic mechanisms by which filler microstructure and surface chemistry dictate gas adsorption behavior remain insufficiently understood. In this study, we employed all-atom molecular dynamics (MD) simulations to develop a model of silicon-based porous composites synergistically doped with carbon nanotubes (CNTs) and graphene. The adsorption and diffusion characteristics of nitrogen (N₂) were systematically investigated across a CNT doping concentration range of 5% to 20%, and the influence of surface hydrophilicity/hydrophobicity on adsorption performance was quantitatively analyzed by modulating potential energy parameters. Our results demonstrate that the introduction of CNTs reconfigures the porous architecture, leading to an approximately 18.25% increase in the normalized specific surface area, which subsequently drives a 15% enhancement in the overall adsorption capacity of the composite. Nevertheless, analysis reveals that the weight-specific adsorption efficiency of the CNT component itself exhibits a declining trend as the doping concentration increases. This phenomenon is primarily attributed to the convex curvature of the CNTs, which restricts the effective contact area and weakens the adsorption potential, alongside the steric hindrance effects arising from local filler agglomeration at higher concentrations. Furthermore, surface chemical properties exert a significant regulatory influence on adsorption; a strongly hydrophilic modified surface (λ = 1.5) achieved an adsorption capacity approximately 98% higher than the baseline condition—an improvement that exceeds the gains provided by purely physical volume expansion. This research elucidates the synergistic mechanism between physical architecture and surface chemical modification in the adsorption process, suggesting that while the physical architecture determines the abundance of potential adsorption sites, the surface chemistry governs the actual efficiency of site utilization. These findings provide critical theoretical insights for the future design of composite aerogel materials that balance structural stability with superior adsorption performance. Full article

Multilingual learning is key in natural language processing, but is challenged by the transfer–interference trade-off, where positive transfer benefits certain languages, while negative interference affects others. Prior methods, including linguistic-based and embedding-based language clustering, have attempted to address this; yet, they remain constrained by their static design and lack of task-specific feedback. In this study, we propose a novel computational strategy inspired by molecular design that constructs molecules with targeted properties. Languages are modeled as nodes in an undirected graph, with edges representing the transfer strength. This language molecule is optimized via Reinforcement Learning to adjust edge connections and weights to enhance positive transfer and minimize interference, where graph clustering is applied, and clusters are then evaluated on the Named Entity Recognition and POS tagging tasks using 25 languages from the WikiANN dataset and 12 typologically diverse languages from the UDPOS dataset. Compared to linguistic and embedding-based language clustering baselines, our method yields substantial improvements, especially for low-resource languages, with some showing over 35% increase in F1 score, while high-resource languages benefit moderately, confirming reduced transfer–interference trade-off. Our atom–language model offers a novel path for multilingual learning, inspired by molecular principles from physical sciences. Full article

With the shortage of natural aggregates and the massive accumulation of iron tailings (ITs) solid waste restricting the sustainable development of asphalt pavement engineering, replacing natural aggregates with ITs has become a promising low-carbon solution with prominent economic and social benefits. However, the poor interfacial adhesion between ITs and asphalt severely restricts the engineering application of tailings, and the micro-interaction mechanism at their interface still lacks systematic clarification, which is the key research gap addressed in this work. Different from conventional macro road performance tests, this study innovatively combined molecular dynamics (MD) simulation with microscopic characterization, including Fourier transform infrared spectroscopy (FT-IR) and atomic force microscopy (AFM), to comprehensively reveal the interfacial interaction mechanism between ITs and asphalt at the molecular and microscales. The results indicate that asphalt molecules exhibit higher aggregation concentration and diffusivity on Al₂O₃ and Fe₂O₃ surfaces than on SiO₂ surfaces, proving stronger interfacial interaction between asphalt and iron-rich oxide minerals. Moderate temperature optimizes the adhesion performance of asphalt with Al₂O₃ and Fe₂O₃, while the interfacial bonding of asphalt on CaCO₃ and SiO₂ weakens as temperature rises. The silane coupling agent KH-550 can effectively react with acidic minerals, SiO₂ minerals in ITs, which significantly increases the concentration, diffusion coefficient, and distribution uniformity of asphalt molecules at the interface. FT-IR results verify that the combination of ITs and asphalt mainly relies on physical adsorption without generating new chemical bonds. AFM tests further confirm that alkaline minerals improve the surface roughness of asphalt mastic, and KH-550 greatly enhances the micro-adhesion force of the interface. The novelty of this work lies in clarifying the mechanism of typical mineral components in ITs and revealing the modification enhancement law of silane coupling agent and alkali minerals at the micro level. This study provides a scientific theoretical support for the high-value engineering utilization of ITs in asphalt pavement, and offers a reference for optimizing the interfacial modification design of solid waste aggregate. Full article

Achieving selective cleavage of specific chemical bonds using ultrafast laser pulses remains a central challenge in ultrafast strong-field molecular physics. Here, we theoretically investigate the coherent control of strong-field dissociation of the heteronuclear molecular ion HD⁺ initially prepared in vibrationally excited states driven by an ultrashort pulse with a quadratic spectral phase. Our results reveal a pronounced sensitivity of both the total dissociation probability and the branching ratio (H⁺ + D vs. H + D⁺) to the chirp rate of the laser pulse. To uncover the underlying physical mechanism, we analyze the population dynamics in the coupled $1s\sigma$ and $2p\sigma$ electronic states and identify pronounced Rabi oscillations arising from the coherent interplay between multiphoton excitation and field-induced stimulated emission. By tuning the laser chirp rate, these oscillations can be suppressed via quantum interference, thereby reshaping the dissociation dynamics and significantly enhancing the dissociation probability of the H + D⁺ channel. These findings demonstrate that spectral-phase engineering provides a robust and versatile strategy for selective control of branching ratios in strong-field molecular dissociation. Full article