Search Results (270)

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

Direct Coal Liquefaction (DCL) is a promising route for converting abundant coal resources into liquid fuels, yet its efficiency remains strongly dependent on catalyst performance. In this work, we present an integrated computational framework combining density functional theory (DFT) calculations with machine learning (ML) to investigate iron–sulfur (FeS) cluster catalysts for DCL. DFT calculations were employed to examine hydrogen-donor dissociation and coal-derived radical hydrogenation on representative FeS clusters. The results indicate that the most favorable catalytic pathways arise from the cooperation between metallic Fe sites (Fe_2) and interfacial Fe sites adjacent to sulfur (Fe_1), while sulfur atoms mainly play an indirect structural and electronic modulation role. Based on these mechanistic insights, a database containing thermodynamic and kinetic data for 636 reactions across 50 FeS cluster models was constructed. This dataset was then used to train three ML classifiers, among which the Random Forest model showed the best performance, reaching accuracies of 80% for H-donor cleavage and 93% for radical hydrogenation on the held-out test sets. SHapley Additive exPlanations (SHAP) analysis further showed that descriptors associated with Fe active-site identity were among the most influential variables in both tasks. Overall, this work provides a mechanistically informed and interpretable computational framework for understanding FeS-catalyzed DCL chemistry and for the preliminary screening of catalyst motifs within the chemical space covered by the present FeS cluster library. Full article

The transition to renewable carbon resources has positioned lignocellulosic biomass as a key feedstock for sustainable fuel and chemical production; however, its intrinsic recalcitrance limits efficient conversion. Dilute acid pretreatment functions as a homogeneous Brønsted acid catalytic system that selectively depolymerizes hemicellulose and disrupts lignin–carbohydrate complexes, while competing with consecutive sugar dehydration reactions, thereby enhancing downstream processing. This review presents a feedstock-specific analysis of acid catalyzed biomass deconstruction across agricultural residues, woody biomass, and energy crops, with xylose yield employed as a kinetically and mechanistically relevant descriptor of catalytic performance. By correlating proton activity, reaction severity, diffusion constraints, lignin chemistry, and mineral interference with observed conversion behavior, the work establishes a structure–reactivity–performance framework for biomass dependent hydrolysis. Particular attention is given to competing dehydration and condensation pathways that reduce pentose selectivity and generate fermentation inhibitors. The analysis identifies optimal severity windows for maximizing catalytic efficiency while suppressing degradation reactions and provides guidance for feedstock-tailored pretreatment and next-generation acid catalytic systems and reactor configurations in integrated biorefineries. Full article

Electrocatalytic nitrate reduction to ammonia (NH₃) offers a sustainable alternative to the Haber–Bosch process while enabling remediation of nitrate-contaminated water. However, the mechanistic origin of performance differences among bimetallic catalysts remains poorly understood, particularly regarding the metal-dependent evolution of reaction intermediates. Here, we construct a series of phase-pure tandem Cu–M catalysts (M = Co, Ni, Fe, Sn) by physically integrating commercial nanoparticles to examine the role of the secondary metal. In this architecture, Cu governs nitrate adsorption and its initial reduction to nitrite, whereas M dictates downstream hydrogenation toward NH₃. Operando ATR–FTIR spectroscopy reveals that NH₃ FE is determined by the hydrogenation kinetics of nitrite-derived intermediates rather than nitrate activation itself. Among the examined systems, Cu–Co achieves optimal kinetic matching, enabling rapid nitrite consumption and continuous hydrogenation, delivering an ammonia Faradaic efficiency of 91.2% with minimal nitrite accumulation (~1.0%) and a yield rate of 0.86 mmol h⁻¹ cm⁻² at −0.5 V vs. RHE. In contrast, Ni and Fe exhibit sluggish hydrogenation, while Sn induces pronounced intermediate buildup. These findings identify nitrite hydrogenation as the selectivity-determining step in tandem nitrate reduction and establish the chemical nature of the secondary metal as a decisive descriptor for rational catalyst design. Full article

Background: Identifying potential targets for bioactive compounds is crucial for elucidating the mechanisms of action and drug development. Methods: This study presents ComTarget, a computational tool that integrates 3D molecular shape similarity analysis (based on combined 3D descriptors, C3DD) with reverse docking to predict protein targets for small molecules. ComTarget screens against a library of 4429 unique protein targets derived from 26,272 PDB complexes. Results: Validation on benchmark datasets (DEKOIS 2.0 and DUDE-Z) demonstrated that the C3DD molecular similarity calculation method effectively enriches active ligands by capturing critical 3D shape information not evident from chemical topology alone. It outperformed conventional 2D fingerprint methods and offered a favorable balance between shape sensitivity and computational efficiency, serving as a rapid pre-screening filter within the integrated workflow. For FDA-approved drugs (e.g., Imatinib, Aspirin) and natural products (e.g., Berberine). ComTarget identified targets consistent with reported therapeutic targets or putative off-targets in the literature, while also revealing potential targets aligned with the compounds’ pharmacological mechanisms. Conclusions: As a local program, ComTarget offers flexibility in computational resources customization and is freely available for polypharmacology studies, drug repurposing, and adverse reaction prediction. Full article

Short-chain fatty acids (SCFAs) are key biomarkers of gut microbiota activity; however, routine quantification in fecal samples relies largely on chromatography, which is instrument-intensive and throughput-limited chromatography techniques. Herein, we present a rapid machine-learning-assisted electroanalysis platform for SCFAs profiling that integrates a disposable three-electrode planar gold chip with voltammetric fingerprinting and artificial neural network (ANN)-based signal decoupling. To generate orthogonal chemical information and improve the discrimination of structurally similar species, a dual pretreatment strategy combining acid-catalyzed esterification and alkaline dissociation was employed prior to electrochemical analyses. Differential pulse voltammetry (DPV) and cyclic voltammetry (CV) were employed to acquire high-dimensional fingerprints, from which current-, potential-, and area-based descriptors were extracted using a cross-information feature strategy. A hierarchical modeling framework improved total SCFAs prediction by incorporating ANN-predicted propionate and butyrate concentrations as auxiliary inputs. While linear calibration was achievable in standard mixtures, direct linear models performed poorly in real fecal matrices due to strong sample-dependent matrix interference. In contrast, the ANN captured nonlinear relationships among multifeature inputs and suppressed matrix effects. Validation against gas chromatography–mass spectrometry in an independent fecal test cohort (n = 30) demonstrated excellent agreement and low prediction errors, with mean absolute error/root mean square error values of 0.063/0.072 mM (propionic acid), 0.029/0.034 mM (butyric acid), and 0.135/0.202 mM (total SCFAs). The DPV/CV acquisition requires only minutes per sample, whereas pretreatment takes 1~3 h depending on the target route but can be performed in parallel for batch processing; thus, overall throughput is determined mainly by batch pretreatment rather than per-sample instrument time. This electrochemical–ANN workflow provides a portable, high-throughput alternative to chromatography for fecal SCFAs profiling in clinical screening and microbiome research. Full article

A structural analysis of phenylpropanoids demonstrates that the benzene ring and the propenoic fragment act as two largely independent π-electron systems. This distinctive feature provides a theoretical basis for the rational design of novel compounds obtained through the structural integration of phenylpropanoids with polyene aldehydes and acids. These classes may be combined by elongating the carbon backbone via iterative vinyl group extension, thereby generating an expanded conjugated double-bond system. Alternatively, the structure of polyene aldehydes may be modified by replacing the unreactive methyl group with a benzene ring bearing suitable functional substituents. DFT computational studies performed at the B3LYP/QZVP level of theory indicate that the designed analogs predominantly scavenge radicals through the sequential proton loss electron transfer (SPLET) mechanism in aqueous environments. This pathway involves the initial deprotonation of carboxyl, aldehyde, or phenolic groups, with the hydroxyl moiety exhibiting the greatest propensity for proton dissociation. Carbon chain extension exerts only a minor influence on proton affinity (PA) values but significantly affects electron transfer enthalpy (ETE) parameters. Consequently, increasing the number of conjugated double bonds enhances activation of the second step of the SPLET mechanism, thereby improving overall radical-scavenging activity. Comparison of the calculated chemical reactivity parameters substantiates the conclusions drawn from the thermodynamic analysis. A pronounced enhancement in the reactivity of the modeled compounds, relative to the parent constituents, is observed. Time-dependent density functional theory (TD-DFT) calculations further predict absorption in the visible region, indicating potential applications of the modeled compounds as radical-scavenging dyes in food, pharmaceutical, cosmetic, and dietary supplement formulations. Full article

Mineral scale deposition remains a major flow-assurance constraint in oil and gas operations, especially in water-flooding and produced-water reinjection, where mixing between incompatible brines promotes super-saturation and precipitation of poorly soluble salts. This work introduces a novel extension of traditional methods used for modeling chemical inhibition and the predictive evaluation of oilfield scale-inhibitor molecules. A systematically optimized Two-Dimensional Quantitative Structure–Activity Relationship Model based on the k-Nearest Neighbors algorithm 2D-QSAR-KNN model was developed to quantitatively link molecular constitution of phosphonate inhibitors, brine chemistry, and operating factors with inhibition efficiency IE %. The optimized model achieved strong accuracy and generalization R²_train = 0.9182, R²_test = 0.9306, and R²_global = 0.9208 with low prediction errors RMSE_train = 4.7888%, RMSE_test = 4.5485%, and RMSE_global = 4.7421%. Median absolute errors remained minimal for the train set = 0.80%, and test set = 1.63%, and model stability was confirmed by high correlation with experimental IE % r = 0.94 and R²_train/R²_test ≈ 0.99, showing no sign of overfitting. Additionally, an inverse-2D-QSAR framework was applied to identify the optimal molecular descriptor profile expected to maximize inhibitory performance within normalized bounds, providing rational rules for next-generation inhibitor design. The findings highlight the practical value of QSAR-inspired AI modeling to accelerate molecule screening and dosage exploration prior to laboratory validation, supporting more cost-effective, interpretable, and environmentally aware sulfate-scale inhibition strategies under high-salinity reservoir conditions. Full article

The growing demand for lightweight, high-performance, and fire-safe polymer materials has accelerated research into advanced flame-retardant composites. Traditional experimental approaches to designing sustainable flame-retardant biodegradable polymer composites still rely heavily on empirical formulation and iterative testing, which are time-consuming and costly, and they often struggle to capture the coupled effects of chemical composition, processing conditions, and material performance. Recent advances in artificial intelligence (AI) provide opportunities to address these challenges by learning formulation–structure–performance relationships from curated datasets and by translating materials chemistry and flame-retardant mechanisms into data-ready descriptors and targets. This review summarizes recent progress of AI-assisted approaches to design sustainable flame-retardant biodegradable polymer composites, emphasizing machine learning, deep learning, and active learning methods for predicting and optimizing key fire performance metrics, including limiting oxygen index and heat release-related parameters. Biodegradable-specific limitations, including narrow processing window, thermal degradation, and moisture sensitivity, are discussed in the content of descriptor selection and constraint-aware optimization, together with the role of interpretable/explainable models in supporting experimentally actionable guidance. Current challenges such as limited data availability, protocol variability, model transferability, and interpretability are highlighted, and emerging solutions, including data harmonization, standardized fire testing, and physics-informed models are outlined. AI-assisted strategies are expected to play a central role in accelerating efficient, sustainable, halogen-free, and performance-driven development of next-generation flame-retardant biodegradable polymer composites. Full article

The search for new antibacterial agents is an important task due to the emergence of resistance to widely used drugs. Bromine-, chlorine-, and nitro-substituted phenyl ring azomethines with long alkyl chains (C₁₂, C₁₄, C₁₆, and C₁₈) were synthesized and characterized using several experimental methods (NMR and IR spectroscopy, elemental analysis, mass spectrometry). Antibacterial and antifungal activity was tested on several cultures; the synthesized compounds show activity at the level of some commercial antiseptics. Lipophilicity (an important descriptor for predicting biological properties) of the experimentally synthesized and isomeric molecules was determined by three different approaches: quantum chemistry, machine learning (GraphormerLogP model), and an atom contribution model (RDKit library). The quantum-chemical method can account for any spatial arrangements and can be considered the most accurate of the approaches used, but it requires significant computational time. The atom contribution model is the fastest of the methods used, but it gives underestimated results, and different isomers have exactly the same values, in contrast to the quantum chemistry results. Machine learning-based methods (GraphormerLogP) demonstrate acceptable accuracy, sensitivity to isomerism, and orders-of-magnitude higher throughput, making them an optimal tool for high-throughput screening. Full article

Heteroatom functionalization of graphene is an effective strategy for designing more sustainable lithium-ion battery electrodes, as it can tune both interfacial adhesion and the electronic features of the carbon lattice. In this work, we investigated the interfacial compatibility between three graphene sheets—pristine graphene, graphene doped with one nitrogen atom (Graphene–N), and graphene doped with one sulfur atom (Graphene–S)—and three lignocellulosic binders (carboxymethylcellulose (CMC); coniferyl alcohol (LcnA); and sinapyl alcohol (LsiA)) using density functional theory (DFT). Geometries were optimized using CAM-B3LYP and M06-2X in combination with the LANL2DZ basis set, while ωB97X-D/LANL2DZ was employed for dispersion-consistent single-point refinements. The computed adsorption energies indicate that all binder–surface combinations are thermodynamically favorable within the present finite-model framework (ΔE_int ≈ −22.6 to −31.1 kcal·mol⁻¹), with LSiA consistently showing the strongest stabilization across surfaces. Nitrogen doping produces a modest but systematic strengthening of adsorption relative to pristine graphene for all binders and is accompanied by electronic signatures consistent with localized donor/basic sites while preserving the delocalized π framework. In contrast, sulfur doping yields a more binder-dependent response: it maintains strong stabilization for LSiA but weakens LCnA relative to pristine/N-doped sheets, consistent with an S-induced local distortion/polarizability pattern that can alter optimal π–π registry depending on the adsorption geometry. A combined interpretation of adsorption energies, electronic descriptors (including ΔE_gap as a model-dependent HOMO–LUMO separation), and topological analyses (AIM, ELF, LOL, and MEP) supports that Graphene–N provides the best overall balance between electronic continuity and chemically active interfacial sites, whereas Graphene–S can enhance localized anchoring but introduces more heterogeneous, lone-pair–dominated domains that may partially perturb electronic connectivity. Full article

Copper(II) coordination complexes with coumarin-derived heterocyclic ligands are promising in inorganic therapeutics for anticancer and antimicrobial applications. To establish quantitative structure–activity relationships for lead design, we studied six copper(II) complexes (Cu1–Cu6)with four- and five-coordinate geometries using Density Functional Theory, Conceptual Density Functional Theory, and visualization analyses. Geometry optimization at M06/6-31G(d)+DZVP revealed distorted coordination environments from $d^9$ Jahn–Teller effects. Tridentate $N_{2}O$-chelatedcomplexes (Cu4–Cu6) showed greater aqueous stability ($\Delta G_{solv}=-43$ to $-50$ kcal·mol⁻¹) than four-coordinate analogs ($-29$ to $-31$ kcal·mol⁻¹). CDFT global descriptors contrasted reactivity: four-coordinate Cu1–Cu2 had higher electron affinity (>4.2 eV) and electrophilicity (>5.7 eV), suggesting propensity for redox cycling and for undergoing nucleophilic attack by DNA bases, whereas Cu4–Cu6 displayed increased chemical hardness (3.43–3.54 eV) and lower electrophilicity (≈3.8 eV), implying enhanced kinetic stability and bioavailability. Frontier orbital analysis indicated ligand-to-metal charge transfer via a LUMO delocalized over the $\pi$-conjugated coumarin, facilitating intercalation by $\pi$-$\pi$ stacking. The visualization showed strong covalent bonds (blue isosurfaces) stabilizing the metal and dispersive $\pi$ interactions (green surfaces) on the ligand, enabling solvent interactions and biomolecular recognition. Tridentate $N_{2}O$ coordination thus balances electronic stability and biological reactivity, making Cu4–Cu6 promising for further study. Full article

The electronic stability and global reactivity of Cu_xSc_γ (x + y = 4) bimetallic nanoclusters were investigated within the framework of rational design of functional materials and active catalytic phases. M06-2X/def2-TZVP DFT was used for geometric optimization and electronic characterization, and global descriptors were calculated, including ΔE_gap, chemical toughness, chemical potential, and electrophilicity. The orbital contribution was analyzed using DOS/PDOS with Multiwfn; PCA and ANOVA were applied to quantify descriptor–structure relationships. The results show that adding Sc changes the cluster’s electron density and stiffness in a consistent manner, enabling distinction between more stable and more reactive configurations. In particular, Cu₃Sc is the most electronically stable, exhibiting the highest ΔE_gap, while Cu₂Sc₂ shows a more tunable electronic response, consistent with scenarios requiring greater reactivity. Multivariate analysis shows that ΔE_gap accounts for most of the electronic variability in the dataset, making it the primary descriptor for selection and design. Taken together, these results open a descriptor-guided path to designing active Cu–Sc phases for supported catalysis and to their assembly into tunable metal nanocomposites. Full article

Additive engineering plays a crucial role in enhancing the performance of perovskite solar cells (PSCs), yet identifying suitable additives within the vast chemical space remains a significant challenge. This paper describes a paradigm shift in additive discovery from trial-and-error methods to AI-driven approaches. We first establish the physicochemical foundations of additive engineering and the descriptors commonly employed in machine learning algorithms. Next, we discuss intelligent process optimization, highlighting how active learning algorithms effectively tune complex precursor formulations with minimal experimental iterations. Additionally, we explore the role of AI in mechanism elucidation and the potential prospects of generative models in the field of additives. Finally, we emphasize the emerging trend of integrating large language models with autonomous laboratories for closed-loop autonomous discovery, offering a promising pathway to accelerate the commercialization of PSCs. Full article

This paper discusses the total replacement of egg white (EW) with faba bean protein concentrate (FPC) in a marshmallow formulation. The physico-chemical and techno-functional characterizations of the ingredients revealed that FPC, with a protein content of 68%, exhibited an interesting foaming capacity (200%) compared to EW, which had comparable foaming stability. The physico-chemical properties of the final products indicated that the FPC marshmallow (FPCM) had a higher density (0.519 g/mL), lower moisture (17.337%), and a water activity within the recommended range for this type of product. The FPCM had the highest hardness and elasticity values but the lowest cohesiveness and adhesiveness. Scanning electron microscopy showed that the FPCM structure is similar to that of the EW marshmallow (EWM). In front-face fluorescence spectroscopy measurements, the FPCM exhibited higher emission intensity for tryptophan with a maximum at 382 nm and vitamin A with a maximum located around 338 nm. FTIR analysis presented higher peaks at 850, 918, and 1034 cm⁻¹ for the EWM compared to the FPCM. In a hedonic evaluation, the majority of descriptors (hardness, odor, and general acceptability) showed similar scores for both formulations. All results demonstrated the success of the total substitution of egg white by FPC in the marshmallow formulation. Full article