Entropy and defect coupling in high-entropy hexagonal materials mitigates coulombic interaction for superior aluminum storage.

From structure to safety: Material characterization and toxicological evaluation of Vanshlochan (Bambusa bambos (L.) voss [Poaceae]) using AI-assisted and In vivo approaches.

Milk is a complex biochemical mixture in which proteins significantly influence the behaviour of xenobiotics and bioactive compounds. Interactions between milk proteins and substances such as veterinary drugs or natural bioactives can modify molecular stability, binding dynamics, and exposure pathways, affecting food safety and the One Health concept. This study presents a comparative, matrix-focused investigation on how three chemically distinct ligand classes, sulfanilamide antibiotics, naturally occurring phenolic compounds and zinc–polyphenol complexes, interact with major milk proteins, β-lactoglobulin and casein. Protein–ligand interactions were examined using steady-state fluorescence spectroscopy to assess quenching behaviour and comparative interaction trends. Molecular docking was employed as a qualitative tool to provide structural context. Distinct interaction patterns were observed across ligand classes, reflecting differences in molecular structure, hydrophobicity, and coordination chemistry. Importantly, zinc coordination modified interaction profiles relative to the corresponding free ligands, indicating that metal coordination can affect ligand–protein interactions within the milk matrix. These findings support the concept that milk proteins may function as matrix-dependent modulators of ligand behaviour. The study is positioned as a hypothesis-generating framework highlighting the importance of food matrices as active biochemical environments. Herein, we provide a foundation for hypothesising how the milk matrix affects residue behaviour and bioactive interactions, with relevance to veterinary pharmacology and food safety risk assessment.

Introduction. The article is devoted to the theoretical prediction of the spatial structure of proteins of protein three-dimensional structures, with a focus on the specifics of modeling chemical compounds containing a significant number of amino acid residues. The relevance of the study is driven by the need to develop methods capable of producing reliable structural models without resorting to complex and resource-intensive experimental procedures. This is particularly important given the growing volume of genetic data and the need to interpret it at the level of the spatial organization of protein molecules. The aim of the work was to analyze the possibilities of using ring-shaped models of amino acids to determine the spatial structure of proteins, to evaluate their effectiveness and applicability in problems of theoretical modeling, bioinformatics and structural chemistry. Material and Methods. Modeling is performed using ring-polyhedral models of amino acids, which represent polyhedra formed from cyclic structures. These models impose constraints on the variability of bond angles and the possible positions of side chains, significantly reducing the number of permissible configurations and simplifying computational procedures. Algorithms are applied that allow determination of protein three-dimensional structures solely from the nucleotide coding sequence (CDS), without relying on additional experimental data, including X-ray crystallography and spectroscopic methods. Results. The possibility of determining the forms of chemical compounds using ring-shaped models is demonstrated using the example of the amino acids serine (C3H7NO3) and leucine (C6H12NO2), as well as the standard structural element of proteins – the alpha helix. The results show consistency of the proposed approach with fundamental concepts of protein spatial organization and confirm its applicability for analyzing typical structural motifs. Conclusion. The use of ring-shaped models of amino acids allows us to limit the space of possible structural configurations, increase the predictability of modeling, and can be considered as a promising approach to the theoretical study of proteins, especially when experimental data are limited.

Phosphate crystals have attracted significant interest as platforms for deep-ultraviolet (deep-UV) optical materials, attributable to their rich structural chemistry and tunable optical properties. However, simultaneously achieving strong second-harmonic generation (SHG) and enhanced birefringence in phosphate systems remains a formidable challenge. Herein, through a dimensionality-engineering strategy, we report five deep-UV transparent fluorozirconium phosphate compounds, including the three-dimensional (3D) ZrPO4F (I), two-dimensional (2D) Zr(H2PO4)(HPO4)F·3H2O (II) and (NH4)4Zr4(HPO4)2(PO4)4F4 (III), as well as the one-dimensional (1D) Rb2.9(NH4)3.1Zr4(H2PO4)F21 (IV) and Rb4.2(NH4)3.8Zr4(PO4)2F18 (V). These compounds exhibit clear dimension-dependent optical properties, with reduced dimensionality leading to enhanced SHG responses and larger birefringence. Notably, the fluorine-rich 1D compounds display the strongest SHG efficiency (up to 2.1 × KDP) and the largest birefringence (0.053 @ 550 nm) within the series. Structure-property analyses reveal that decreasing the structural dimensionality promotes increased local distortion of Zr-centered polyhedra and more uniform dipole alignment, thereby amplifying both SHG and birefringence. This work establishes dimensionality engineering of fluorozirconium phosphates as an effective strategy for fine-tuning key optical properties while maintaining deep-UV transparency.

Carbon nanomaterials have emerged as promising alternatives to noble metals in perovskite solar cells (PSCs) due to their excellent electrical properties, chemical stability, and scalability. Tailoring hydrogen incorporation during synthesis enables precise modification of the electronic structure and surface chemistry, thereby enhancing charge transport and improving the electrode/perovskite interface quality. Here, we report the synthesis of hydrogenated carbon nanosheets via a solid-gas reaction at various temperatures and times, achieving controllable H content and tunable electrical conductivity as well as sheet resistance. As the heating temperature increases, carbon content increases. In contrast, H content decreases, leading to enhanced electrical conductivity (e.g., a sheet resistance of 452 and 91 ohm sq-1 for 400C-12h and 500C-12h samples, respectively) and decreased disorder bands in Raman spectroscopy. XRD, SEM and BET analysis confirmed an amorphous carbon with a mesoporous architecture and high surface area (245 m2 g-1 for 500C-12h). Using the 500C-12h sample as the electrode, PSCs achieved the highest power conversion efficiency of 16.54%, outperforming devices with commercial carbons. Furthermore, hybrid carbon electrodes (7 : 3 hydrogenated carbon : carbon black) improved both Jsc and Voc values, leading to a PCE of 18.21%, as well as long-term stability up to 1000 h. These findings position hydrogenated carbon, in pure or hybrid form, as a scalable, cost-effective, and durable alternative to noble-metal electrodes for next-generation PSCs.

ConspectusSurface-enhanced Raman scattering (SERS) provides a powerful spectroscopic approach for molecular identification and interfacial analysis by combining chemical specificity with ultrahigh sensitivity. While chemically synthesized nanoparticles have enabled broad use of SERS, increasing attention is being paid to how structural uniformity, aggregation behavior, and surface chemistry influence signal reproducibility, reliability, and mechanistic interpretation. In this context, plasmonic nanoarrays fabricated by template-assisted physical deposition offer a complementary and increasingly important SERS platform.This Account summarizes recent advances in SERS using nanoarrays fabricated by template-assisted evaporation. In these approaches, nanoscale geometry and hotspot distributions are predefined by the template and realized through directional deposition. These template-defined architectures enable reproducible electromagnetic enhancement, polarization-controlled excitation, and stable plasmonic responses. Moreover, physical deposition yields clean, ligand-free metal surfaces, providing a well-defined interface for probing plasmon-molecule interactions and interfacial chemical processes. Using anodic aluminum oxide (AAO) lithography as a representative platform, we illustrate how precise control over template thickness enables angle-resolved deposition and structural programmability, allowing the fabrication of dimers, trimers, and compositionally heterogeneous architectures with nanometer-scale gaps. These capabilities support advanced SERS functionalities, including efficient hotspot activation for enhanced sensitivity, selective molecular trapping, and access to interfacial processes on nonplasmonic or weakly plasmonic materials. Furthermore, integration with transparent substrates and soft supports enables liquid-phase SERS configurations and flexible sensing platforms. These liquid-phase SERS configurations improve signal stability and measurement reliability for real-time, in situ measurements, while mitigating aggregation-related issues commonly encountered in conventional SERS. Beyond molecular detection, nanoarray-based SERS provides a controlled experimental framework for mechanistic studies in plasmonic chemistry. The combination of chemically clean surfaces with nonaggregating and structurally stable architectures enables plasmon-driven interfacial processes to be examined under well-defined and reproducible conditions, and facilitates in situ, real-time tracking of reaction dynamics in liquid-phase SERS measurements. This well-controlled environment serves as a reliable physical model for investigating interfacial reaction mechanisms, allowing direct identification of key reaction intermediates and offering an effective route to resolving long-standing mechanistic debates in plasmonic chemistry.Overall, this Account underscores the value of template-fabricated plasmonic nanoarrays as a versatile SERS platform that connects sensitive detection with mechanistic insight. Looking ahead, continued advances in template engineering and deposition strategies are expected to further expand their role in well-controlled studies of light-matter interactions and interfacial physics and chemistry.

Multipolar scattering models, such as the transferable aspherical atom model, account for atomic chemical interactions and provide a more accurate representation of experimental data. However, the simpler independent atom model (IAM), which assumes non-interacting atoms, is the only model available in the most widely used macromolecular refinement programs. This is primarily because IAM offers a hard-to-beat combination of computational efficiency and modelling power at typical macromolecular resolutions. By contrast, more accurate multipolar modelling has historically been limited due to its computational cost and the absence of an interface between software capable of calculating structure factors and gradients based on multipolar models and software designed for macromolecular refinement. This work introduces pyDiSCaMB, a Python software package designed to integrate between the computational crystallography toolbox (cctbx) and the quantum crystallography library DiSCaMB (Densities in Structural Chemistry and Molecular Biology), thus enabling multipolar scattering models in Phenix's toolkit. The implementation, features and capabilities of pyDiSCaMB are presented, the runtimes for the calculation of structure factor and target gradients with respect to atomic parameters are explored, and Fourier images of electrostatic potential, electron density and deformation maps are computed as illustrative examples. The pyDiSCaMB library will make multipolar modelling widely available to the structural biology community, potentially transforming refinement and model-building for both crystallography and cryogenic electron microscopy (cryoEM).

Crystal chemistry and topology of hybrid structures: A new "intermediate" Ga,Ge–representative of dumortierite – ellenbergerite series

Solubility trapping is a key mechanism in geological carbon sequestration (GCS), yet CO2 solubility in water-filled nanopores often deviates markedly from bulk behavior. We hypothesize that variations in CO2 solubility within silica nanopores originate from differences in interfacial water structure that are controlled by surface chemistry. In particular, specific Si-OH arrangements on Q2, Q3, and Q4 silica surfaces (defined by the number of Si atoms bonded through oxygen to a central Si atom) modulate hydrogen-bonding (HB) environments and adsorptive volumes that regulate CO2-water-solid interactions. We conducted molecular dynamics simulations of water-saturated Q2, Q3, and Q4 silica confinements under representative GCS conditions. Interfacial density profiles, HB distributions, and CO2 spatial probability maps were analyzed to quantify fluid-solid interactions and to evaluate CO2 solubility relative to bulk water. Hydrophilic Q3 surfaces exhibit enhanced CO2 solubility compared to the bulk liquid, arising from CO2-water co-adsorption facilitated by a dense interfacial HB network. Hydrophobic Q4 confinements, by contrast, show pronounced over-solubility dominated by strong direct CO2 adsorption within enlarged low-HB regions. Q2 surfaces display intermediate behavior reflecting mixed hydrophilic-hydrophobic character. We introduce two mechanistic descriptors, adsorptive volume and HB site density. High HB site density promotes hydrophilicity and co-adsorption, whereas large adsorptive volume favors direct CO2 adsorption and over-solubility. Overall, these results demonstrate that CO2 solubility in silica nanopores is jointly governed by interfacial water structure and surface chemistry. The findings provide molecular-scale insights into solubility trapping in silica-rich formations and inform the design of engineered materials for CO2 capture and storage.

ABSTRACT The advancement of semiconductor nanomaterials for optoelectronic applications becomes choice in nanotechnology research. Herein, we report simple facile hydrothermal synthesis route for copper sulfide (CuS) and Fe‐doped CuS (Fe:CuS) nanostructures, aimed to enhance photocatalytic efficiency under solar irradiation. By adjusting Fe dopant, crystallographic strain, band structure and defect chemistry of CuS were modulated. XRD with Rietveld refinement, SEM–EDX, Raman spectroscopy, UV–vis, x‐ray photoelectron spectroscopy (XPS) and photoluminescence study revealed precise structural and electronic transformations. Fe incorporation was found to enhance light‐harvesting capability and suppress electron–hole recombination. Photodegradation of methylene blue dye by as synthesized nanomaterials are analyzed. Notably, 4% Fe:CuS achieved an outstanding degradation efficiency of 99.31% within 280 min under natural sunlight. These results underscore the strategic role of transition‐metal doping in tailoring chalcogenide‐based photocatalysts, offering a scalable and energy‐efficient route for optoelectronic applications.

Deep eutectic solvent-based extraction of bioactive polysaccharides from oilseed pumpkin and valorization of extraction residues

Optimization of pore structure and surface chemistry for enhanced iodine adsorption by fungus-derived porous carbon

Effects of cleavable comonomer structure and crosslinker chemistry on the performance and deconstructability of frontally cured pDCPD composites

This paper considers the current and future use of magnetoencephalography (MEG) for assessing neural activity in children (birth to 18 years old), including the well-established use of SQUID (Superconducting QUantum Interference Device) MEG technology as well as the very rapidly developing Optically Pumped Magnetometry (OPM) technology. A primary conclusion is that the changing landscape of pediatric neurophysiology studies foretells a revolution in electromagnetic neuroimaging. These changes will produce some discontinuity, progressing away from what once worked well enough, namely, examining neural activity at the level of the EEG or MEG sensor, but is not up to current and anticipated challenges. Given features intrinsic to MEG, including simpler mathematical models for source localization and higher-dimensional representation of neural activity, little effect of open fontanelles and sutures on infant neural measures, and reference-free neural measures, MEG will often be the preferred method for assessing neural activity in children. In particular, noninvasive, whole-brain MEG sensor data with source localization provide measures of neural activity in brain space that richly represent the structure and maturation of neural activity spanning both local and regional processes, as well as measures of connectivity within and between brain regions. Assessing neurophysiology in brain space is also essential for associating local neural activity with local brain structure (e.g., gray and white matter) and brain chemistry (e.g., magnetic resonance spectroscopy data). It is also highly likely that MEG data are more future-proof than EEG data (higher dimensionality, ease of source localization), especially for advanced source localization methods as well as advanced analysis methods yet to be developed and applied to previously collected data. The emergence of OPM-based MEG opens a new age of imaging for children and infants, such as translating the source localizing abilities of MEG in adults to wearable systems in young children. Looking forward, greater access to MEG and other advanced imaging technologies, the accessibility of greater computational power, and the rapid development of open-source software will combine to improve our methods and inform our research questions, all leading to a better understanding of how the human brain changes and supports behavioral development from birth to adulthood.

Simultaneous removal of imidacloprid, carbendazim, and bispyribac sodium using zinc and bentonite functionalized rice husk biochar composite.

A sustainable one-step microwave-assisted strategy is reported for the synthesis of nitrogen-doped carbon quantum dots (MPE-NCQDs) using malic acid as a bio-derived carbon precursor in the presence of polyvinylpyrrolidone and ethanolamine. The approach integrates rapid carbonization with in situ surface passivation and heteroatom incorporation, enabling controlled formation of emissive carbon nanostructures under mild processing conditions. The resulting MPE-NCQDs exhibit uniform nanoscale characteristics, good aqueous dispersibility, and stable photoluminescence, arising from the combined influence of carbon core structure and surface functional chemistry. Systematic structural and photophysical analyses reveal that emission behaviour is governed by the interplay between precursor chemistry, nitrogen configuration, and surface passivation, rather than by size effects alone. By correlating sustainable precursor design with microwave processing and structure–property relationships, this work addresses a key gap in the development of environmentally benign CQD systems. The findings highlight the potential of malic-acid-based, microwave-derived N-CQDs as versatile platforms for optoelectronic and sensing-related applications.

Supported metal clusters maximize atom efficiency and expose diverse low-coordination metal motifs, but under reaction conditions, they are inherently fluxional-adsorbed reactants can constantly reform and even break the underlying metal-metal and metal-support bonds, generating an ensemble of metastable structures for catalysis. Identification of the interplay between supported clusters and surface chemistries is vital but a challenge for their complex dynamic evolutions. Here, we uncover three characteristic and universal regimes: (i) a fluxional regime, where fast restructuring erases site individuality; (ii) a kinetically trapped regime, where slow restructuring freezes the catalyst into a single geometry; and (iii) a unique coupled regime, where structural dynamics and chemistry occur on comparable timescales and where multiple metastable motifs actively participate in turnover. Moreover, we identify a single, dimensionless metric, N c=τ struct /τ chem, the ratio between the structural rearrangement timescale (τ struct) and the chemical residence time of the reactant (τ chem), to differentiate these three regimes with distinct activity and stability. It is found that N c should be neither too small (fluxional regime) nor too large (kinetically trapped regime). When the optimal value N c∼1 is approached (coupled regime), structural and chemical 'clocks' match, enabling the multiple active metastable isomers to persist long enough to participate in turnover and maximize reaction rates. Using CO adsorption-desorption on size-selected Cu n /TiO2(110) clusters as a model system, we demonstrate a master kinetic curve versus N c and reveal tunable levers that drive clusters into the optimal coupled regime. Trends generalize across metals: coinage clusters (Ag, Au) prefer fluxionality, Rh/Pd favor trapping, and Cu and Pt/Ru often lie near the coupled boundary. Time-scale matching thus emerges as a design rule for adaptive, fluxional catalysts with high activity and stability at the same time.

Understanding how platinum cocatalysts interact with semiconductor supports remains a central challenge in photocatalysis. Although Pt nanoparticles are often assumed to act as a universal electron sink, this study demonstrates that their catalytic behaviour is governed by the electronic structure, defect chemistry, and interfacial energetics of the support. To isolate support-induced effects, TiO 2 nanorods (S BET = 93.3 m 2 g −1 ) and two texturally distinct forms of g-C 3 N 4 (a low-surface-area CN-L (20 m 2 g −1 ) and a high-surface-area CN-H (85.0 m 2 g −1 )) deliberately matched to TiO 2 were modified with identical 1 wt% Pt nanoparticles using the same impregnation-reduction protocol. Despite comparable surface areas for TiO 2 and CN-H, Pt-modified catalysts exhibit fundamentally different interfacial energetics and charge-transfer behaviour. TiO 2 stabilizes highly metallic Pt nanoparticles (~1.0 nm) and forms a very low Schottky barrier (0.16 eV), enabling rapid electron extraction and pronounced Pt-mediated visible-light ROS generation. In contrast, g-C 3 N 4 induces strong band bending and stabilizes mixed Pt 0 /Pt 2+ states, resulting in higher interfacial barriers (0.26–1.19 eV), suppressed hydroxyl radical formation, and enhanced selective one-electron oxidation, more than doubling ABTS generation compared to pristine CN-L. These electronic effects translate directly into photocatalytic performance. TiO 2 @Pt exhibits efficient visible-light BPA degradation and the lowest NO 2 reduction onset temperature, while CN-H@Pt outperforms CN-L@Pt due to improved charge separation and approximately 40% lower charge-transfer resistance. The persistence of these contrasts between TiO 2 @Pt and CN-H@Pt confirms that Pt nanoparticles functionality is electronically programmed by the support rather than dictated by surface accessibility. This insight provides a rational framework for engineering noble-metal photocatalysts through deliberate control of semiconductor electronic structure and interfacial chemistry. • Platinum reactivity is determined by the electronic structure of the support. • Comparable surface areas isolate support-controlled charge-transfer behaviour. • g-C 3 N 4 induces band bending and directs Pt towards selective one-electron oxidation. • TiO 2 stabilizes metallic Pt and enables Pt-driven visible-light ROS generation. • Support-dependent Pt chemistry governs both photocatalytic and thermocatalytic reactivity.

Ti3C2 MXene-Based composites for photocatalytic antibiotic degradation: Etching strategies, mechanistic insights, and future perspectives.