Unraveling the influence of storage condition on off-flavor generation in fried pepper (Zanthoxylum bungeanum Maxim.) oil during storage through molecular sensory science and simulation experiment.

pH-induced structural switch of a parallel duplex to triplex-DNA at a BOLF1 gene segment of the human herpes virus 4 (HH4) genome.

Water confined in low-dimensional materials exhibits structural and dynamical behaviors that diverge fundamentally from bulk liquid water. Nanoscale confinement reshapes the hydrogen-bond network, induces molecular ordering, and alters dielectric, vibrational, and transport properties through the interplay between geometry, surface chemistry, and electrostatics. This review presents a comparative synthesis of confined water in 0D–2D environments, from single-molecule encapsulation in molecular cages and single-file flow in carbon nanotubes to layered phases trapped between atomically flat van der Waals crystals. We outline how dimensionality and surface polarity dictate hydrogen-bond rearrangement, layering, and crystallization into low-dimensional ice polymorphs. Spectroscopically, Raman, infrared, terahertz, and nonlinear optical probes reveal distinct vibrational fingerprints reflecting modified hydrogen-bond strength, dipole alignment, and collective dynamics. In the transport regime, continuum hydrodynamics breaks down, giving rise to superlubric flow, anisotropic diffusion, and quantized single-file motion. Across these systems, confinement transforms water from a fluctuating three-dimensional liquid into a tunable, ordered medium bridging molecular and solid-state physics. By unifying results across structural, spectroscopic, and transport studies, this review provides a coherent physical framework for understanding confined water in low-dimensional materials and highlights its implications for nanofluidics, energy storage, and bio-inspired systems.

Understanding how vibrational energy is generated, redistributed, and dissipated at the nanoscale is central to contemporary molecular and chemical physics. Plasmonic nanostructures offer highly efficient channels for both driving and probing molecular vibrations, enabling access to regimes where steady-state populations markedly depart from thermal equilibrium. This perspective examines how anti-Stokes surface-enhanced Raman scattering (SERS) has become a quantitative tool for resolving such thermal and non-thermal vibrational populations within nanoscale hotspots. We first outline the general framework linking Stokes and anti-Stokes Raman/SERS intensities to vibrational occupation, followed by experimental approaches that realize and probe thermal excitation (nanoscale thermometry) and non-thermal excitation pathways. We conclude by highlighting key methodological challenges-especially plasmonic bias correction and quantitative population analysis-and discuss future opportunities for employing anti-Stokes SERS as a molecular-level probe of energy flow in next-generation nanophotonic and catalytic systems.

ConspectusFor centuries, the reductionist view that "the whole equals the sum of its parts" has guided scientific study, particularly materials design. Nature, however, often defies this logic: an aggregate (whole) can display emergent properties that are totally absent in its individual parts. Aggregation-induced emission (AIE) exemplifies this "anomaly": nonluminescent molecules become emissive upon aggregation, achieving a qualitative "0-to-1" leap that challenges the reductionist tenet and provides a unique lens through which to view the emergence of new properties.Since it was proposed as a concept in 2001, AIE has been mechanistically understood as arising from the restriction of molecular motion (RMM) in the excited state. In dilute solutions, molecular rotors and vibrators dissipate exciton energy through active motions, leading to nonradiative decay. Upon aggregation, these motions are physically restricted by molecular packing and noncovalent interactions, impeding nonradiative channels and opening radiative pathways. This mechanistic understanding has motivated extensive research into AIE and expanded the field into a diverse platform of aggregation-enabled luminescent systems, including clusteroluminescence (CL), room-temperature phosphorescence (RTP), and circularly polarized luminescence (CPL)─all absent in the isolated molecular constituents and emerging through aggregation.With accumulated knowledge in AIE, the attention has broadened toward the exploration of aggregation-generated function (AGF). From this perspective, molecular motions─previously viewed as energy "wasted" that reduced emission─can be harnessed to convert excited-state energy into heat through rotations and vibrations. By channeling the same exciton energy that underlies luminescence into nonradiative decay pathways, we can engineer aggregates to exhibit emergent photothermal (PT), photoacoustic (PA), and photocatalytic (PC) activities. These functions open new application avenues, including solar energy conversion, high-resolution deep-tissue imaging, and "intelligent" actuation.From the serendipitous encounter with AIE to the systematic study of AGF, advances in the field have shifted scientific attention from isolated molecules to complex aggregates. With the elucidation of principles governing emergent properties, it is becoming clear that a paradigm shift is needed─from molecularism to aggregatism or from molecular science to aggregate science (AS). Guided by emergentism, AS studies how molecules, through noncovalent interactions and hierarchical organization, give rise to macroscopic functions absent in their individual constituents. Notably, the materials we use and the life we see around us are all aggregates. This aggregate-level perspective enables the development of new systems with complex functionalities (e.g., advanced multimodal theranostics) and deepens our understanding of life─an archetypal multiary system in which the aggregation of nonliving biomolecular constituents yields a living organism.In this Account, we detail the intellectual trajectory from AIE to AGF and finally to AS. We distill the guiding principles and outline future directions, including transitions from unary to multiary systems, static structures to dynamic processes, and descriptive aggregate science to prescriptive aggregate engineering. A deeper understanding of AS will enable new scientific discoveries and technological innovations, inviting us to imagine a future designed not merely with matter but with the sophisticated organizational logic that endows it with "life-like" functions.

AlphaFold, a groundbreaking artificial intelligence model developed by DeepMind, has transformed the field of structural biology by predicting protein structures with unprecedented accuracy. Despite its widespread recognition and application across academia and industry, comprehensive reviews detailing AlphaFold's unexpected applications within the molecular sciences remain scarce. In this review, we critically examine AlphaFold's emerging roles across diverse molecular scientific disciplines. Specifically, we highlight its applications in enzyme engineering and drug development, nucleic acid modeling and vaccine design, the development of protein-based materials and targeted drug delivery systems, and modeling of complex systems and biological networks. To conclude, the review outlines potential future developments and enduring challenges within the application of AlphaFold to molecular sciences. Overall, this review aims to systematically analyze the most recent advances; explore novel interdisciplinary applications of AlphaFold within the realms of biology, chemistry, and materials science; and offer insights into future directions for research and application.

Recent molecular-level simulations suggest that slip at solid–liquid interfaces can depend on shear. This work integrates molecular dynamics (MD) and direct numerical simulations (DNS) to quantify how shear-dependent slip modifies near-wall turbulence in wall-bounded flows. The MD is used to characterise how the slip length depends on wall shear stress across a range of solid–liquid affinities, revealing a threshold-like, bimodal response: the slip length is approximately constant at low and high stresses, with a sharp transition near a slip-activation threshold. This MD-derived relation is then implemented as a wall boundary condition in DNS of turbulent channel flow at friction Reynolds numbers 180, 400 and 1000, using five threshold values to represent different interfacial affinities. The DNS show that the logarithmic region is largely preserved, aside from an approximately constant upward shift, while the near-wall turbulence is modified through changes in the streamwise Reynolds stress. In particular, the streamwise turbulence intensity in the viscous sublayer is strongest when the mean wall stress is close to the slip-activation threshold, and it weakens as the mean stress moves away from that threshold. Analysis further indicates that shear-dependent slip reduces near-wall dissipation and promotes elongated near-wall coherent structures. Finally, a mean flow model that incorporates shear-dependent slip shows good agreement with the DNS mean velocity profiles. Overall, this work provides a multiscale framework that links molecular interfacial physics to continuum-scale turbulence.

Characterization of key aroma compounds contributing to tropical fruity and Citrus notes and the mechanism of characteristic aroma formation in Dali-variety black tea.

Precise and experimentally accessible determination of the electronic structure of transition metal complexes remains a challenge in the development of molecular qubits, particularly for leading candidates with integer spin. Existing techniques often require large-scale facilities and substantial sample quantities or offer limited spectral access and sensitivity to subtle anisotropies. Here, we demonstrate that cantilever torque magnetometry (CTM) overcomes these limitations by combining high sensitivity to magnetic anisotropy with wide sample compatibility, minimal sample demands, and true laboratory-scale accessibility. By exploiting the distinct temperature dependences of g-tensor anisotropy and zero-field splitting (ZFS), CTM enables their experimental decoupling, yielding exceptionally precise bulk-mean value determination of spin Hamiltonian parameters from microgram-scale single crystals. The parameters extracted by CTM were found to be qualitatively consistent but quantitatively different from those determined using high-frequency electron paramagnetic resonance spectroscopy (∼1% for g and ∼5-15% for ZFS), highlighting that perfect agreement between magnetometric and resonance techniques is not guaranteed. Our study establishes CTM as a powerful and broadly accessible complement to magnetic resonance methods, opening new routes for high-precision characterization of low-anisotropy spin systems in molecular quantum information science.

Chiral photoswitchable architectures represent a frontier in molecular science, offering platforms where light can precisely control both molecular conformation and stereochemical environment. While promising, integrating photoswitches into robust 3D scaffolds to achieve superior thermal stability and functional responsiveness remains challenging. Here, we report the rational design and synthesis of an azobenzene-containing BINOL-derived chiral dialdehyde that undergoes Dynamic Covalent Chemistry to form a discrete macrocycle with a bisamine and a novel cage with a trisamine. We conducted a systematic, comparative study across the three systems to isolate the effect of increasing structural confinement on photo-switching characteristics. All three architectures-the aldehyde, macrocycle, and cage-exhibit reversible E↔Z photoisomerization of the azobenzene units under alternate irradiation with 370 and 456nm light. In both the macrocycle and cage, the azobenzene units isomerize sequentially in both directions. Notably, both macrocyclization and cage formation significantly enhance photoswitching efficiency and substantially improve the thermal stability of the metastable Z-isomer, a crucial requirement for practical applications. Furthermore, the cage exhibits pronounced light-induced cavity modulation, resulting in significant shifts in its Circular Dichroism spectrum. These results establish molecular architecture as a crucial design principle for constructing highly responsive chiral hosts with tunable optical and structural features.

Accurate prediction of drug metabolism and pharmacokinetics (ADMET) properties is crucial in drug discovery. Here, we present a novel approach to enhance ADMET property predictions using Cross-Aligned Multimodal Attention (CMA) mechanisms, pretrained models, and multimodal techniques. ADMET data is collected and processed using image processing, graph neural networks, and chemical fingerprinting. Pretrained models like GROVER and ResNet generate a multi-channel data format, and the CMA mechanism aligns and correlates the data modalities. Grad-CAM technology interprets the model's predictions, visually demonstrating the relationship between compound properties and fragments. Our ADMET property prediction server ( http://guolab.mpu.edu.mo/CMA ) implements the CMA-based model and a substantial language model for ADMET property prediction. The innovation lies in the integration of multimodal data, the application of pretrained models, and the development of cross-modal alignment. This approach improves the efficiency and accuracy of ADMET property predictions and opens new avenues for research in molecular science, particularly in drug design and evaluation.

Aroma engineering in paddy fields: Field-experimental evidence of nitrogen-dependent aroma compound accumulation patterns in rice.

Pu-erh tea, a geographically indicated tea product from Yunnan, China, is renowned for its unique flavor profile shaped by intricate chemical compositions and complex processing techniques. This review systematically summarizes the material basis of Pu-erh tea flavor, focusing on key taste, sensation and aroma compounds such as tea polyphenols, alkaloids, amino acids, and volatiles. A major highlight of this review is its integration of molecular sensory science, detailing taste, sensation, and odor perception mechanisms mediated by key receptors. The interplay between flavor compounds is explored to decode the complexity of Pu-erh tea's flavor profile. The transformation pathways of these components during critical processing steps are elucidated, highlighting microbial succession and enzymatic reactions that drive the evolution of sensory attributes like bitterness, sweetness, and aged aroma. Advanced analytical methods, including quantitative descriptive analysis, electronic nose/tongue, and computational approaches like molecular docking, are discussed for their roles in bridging chemical data with flavor perception. Despite advancements, challenges remain, such as clarifying the molecular basis of sweet aftertaste and optimizing numerically controlled fermentation. This review provides a multidisciplinary framework for future research, emphasizing the convergence of metabolomics, sensory neuroscience, and artificial intelligence to advance Pu-erh tea flavor science.

Abstract In quantitative structural property relationship (QSPR) and quantitative structural activity relationship (QSAR) analyses, topological indices are widely recognized as powerful mathematical tools for modeling and predicting the thermodynamic and physicochemical properties of chemical components. This study investigates the thermodynamic properties of gallium arsenide (GaAs$(m,n)$) networks by utilizing Connection Number (CN) - based topological indices. Various connection-number-based topological indices and their corresponding entropy measures for the GaAs$(m,n)$ network are examined, including Randi\v{c}, geometric--arithmetic, and general sum connectivity indices. Through this evaluation, we analyze the capabilities of specific networks within the electro-optical field. The prediction process assesses the third redefined Zagreb and the Randi'{c} connection-based topological indices as highly correlated, through entropy measures, with total electron energy, band gap values, and the number of atoms of the GaAs$(m,n)$ network. This analysis achieves a correlation coefficient of $R^{2} = 0.9998$. To evaluate the properties of the GaAs$(m,n)$ network, QSPR analysis is conducted using a polynomial regression model. Numerical and graphical analyses are performed using MATLAB 24b and Origin software to provide a comprehensive visualization of the topological indices and entropy values. Unlike earlier topological-index-based studies on GaAs networks, this work emphasizes connection-number-driven entropy correlations and demonstrates near-perfect predictive performance for key electronic properties. These findings provide valuable insights into the molecular structure, electron distribution, and reactivity of the gallium arsenide network, thereby enabling future advances in molecular design and materials science.

Aroma compounds in fermented foods are from raw material or produced by microorganisms, contributing to their diverse aroma profiles. The key odor-active compounds (OACs) present in Shanxi aged vinegar (SAV), one of China's famous vinegars, were systematically analyzed by molecular sensory science. A total of 152 OACs were identified in this study. Moreover, 87 OACs were quantitatively determined, and 37 OACs exhibited their odor activity values (OAVs) larger than 1. The aroma recombination and omission experiments confirmed that ketones, pyrazines, lactones, and acids are the most significant contributors to the aroma profiles of SAV. The correlation analysis confirmed that the absence of specific aroma compounds not only directly impacts the sensory characteristics associated with the missing compounds but also influences other sensory attributes. Therefore, 47 OACs, classified as key OACs through the verification of recombination and omission test, based on AEDA (FD ≥ 400) or OAV (≥1). Lastly, 6 aging-related markers (methional, 1-dodecanol, acetoin, benzyl acetate, propanoic acid, and trimethyl-pyrazine) were determined by machine learning based on key OACs. These results provide a standard for screening the key OACs that determine aroma characteristics of SAV, as well as for product quality control.

Highly emissive organic molecular crystals find applications in several areas, such as organic electronics, solar cells, and sensors. Understanding the excited-state mechanisms underlying these applications is essential for optimizing and controlling them effectively. Exciton models coupled with nonadiabatic dynamics, particularly quantum dynamics, provide crucial insights into photochemical and photophysical processes in molecular crystals. Nevertheless, there remains a lack of general tools and automated workflows to facilitate such simulations. In this paper, we present a computational strategy to investigate the photoactivated dynamics of organic molecular crystals, bridging methodologies traditionally used for molecular systems and materials science, with a particular focus on the interplay between local excitations and charge transfer (CT) processes. We have implemented an interface between the fromage and Overdia programs, enabling the construction of vibronic Hamiltonians for molecular crystals within an excited-state ONIOM(QM:QM') framework, incorporating long-range electrostatics through a RESP-based Ewald summation. Fragment-based diabatization provides a route to quantum dynamics simulations in weak-to-intermediate coupling regimes. The method was applied to the photophysics of dibenzo[g,p]chrysene (DBC) crystals using time-dependent DFT. The fromage/Overdia interface was employed to compute the couplings of local excitations and CT states for 18 unique DBC dimers in the crystal and to quantify the influence of electrostatic embedding, which was found to be modest (10-20%). Simulations on π-stacked dimers reproduced the small red shift observed experimentally from solution to crystal, attributed to electronic interactions among fixed monomers rather than crystal electrostatics. Quantum dynamics simulations revealed ultrafast population transfer from bright local excitations to CT states. This approach establishes a robust framework linking molecular and solid-state excited-state dynamics, with potential applications for studying excitations, defects, and impurities in molecular crystals.

ABSTRACT Transdermal drug delivery presents an innovative alternative to conventional methods of drug administration by avoiding the gastrointestinal system and enhancing patient adherence. This study systematically reviews and conducts a meta‐analysis of the efficacy, safety, and potential clinical applications of polymeric nanocarriers in transdermal drug delivery systems (TDDS). A literature review was conducted to collect data from the PubMed database covering the period from 2013 to 2023. Tools like VOSviewer, Bibliometrix, Jamovi, and RStudio revealed a steady growth in TDDS research and global publications, highlighting increasing contributions from diverse authors worldwide. A total of 8799 articles were analyzed using RStudio for quantitative synthesis, identifying 42 382 authors and 22 999 keywords. Among these, 176 were single‐authored, with an average of 7.21 co‐authors per document. The international co‐authorship rate was 17.44%. The meta‐analysis of ten studies showed a pooled prevalence of 10.97% (95% CI: 5.38%–16.55%; Z = 3.85; p < 0.001), indicating a moderately significant therapeutic effect of polymeric nanocarriers in TDDS. Moderate heterogeneity was observed ( I 2 = 52.37%, p = 0.034), suggesting real differences across studies. The funnel plot showed most data points within the non‐shaded region, indicating minimal publication bias. Key journals include Pharmaceutics , PLOS ONE , Scientific Reports , and International Journal of Molecular Sciences . The United States, China, Japan, Italy, and South Korea led research output, with institutions like Harvard Medical School and Zhejiang University playing central roles. This bibliometric and quantitative synthesis provides a comprehensive overview of research trends, collaborative networks, and the promising future of polymeric nanocarrier‐based TDDS.

Accurate models for electrostatic and induction interactions are fundamental for computational molecular science, including drug discovery, studies of biomolecular systems and materials design. Given a precise model of the entire charge distributions, the electrostatic interaction between molecules can be calculated accurately using Coulomb's law. Here, we evaluate partitioning methods for deriving charges from electron density as well as the popular method of fitting point charges for use in force field calculations to the electrostatic potential (ESP). For the data set used in this work, which consists of charged amino-acid side chain analogs, inorganic ions and water, the best of these methods yield a root-mean-square deviation (RMSD) of 17 kJ/mol. By combining positive point charges (PC) with Gaussian or Slater distributed negative charges, ESP-fitted models predict electrostatic interactions approximately 30% better than just point charges (RMSD 12 kJ/mol), similar to the Minimal Basis Iterative Stockholder (MBIS-S) method [Verstraelen, T. . J. Chem. Theory Comput. 2016, 12, 3894-3912.]that employs a PC and a Slater charge as well. Since interaction energies are perhaps the most important deliverable of force field calculations, it may be advantageous to train models directly to reproduce energy components from symmetry-adapted perturbation theory (SAPT) calculations, rather than taking a detour through monomer-based charge models. To this end, we employ machine learning using the Alexandria Chemistry Toolkit [van der Spoel, D. . Digit. Discovery 2025, 4, 1925-1935.] to generate parameters for multiple physics-based models that reproduce electrostatic and, optionally, induction interaction energies from SAPT calculations of compound dimers. For a nonpolarizable model combining a PC and a Gaussian distributed charge on the core, the RMSD drops to 3 kJ/mol thanks to direct training on dimer energy components. The approach outlined in this work consists of applying force field science to make apples-to-apples comparisons between models and machine learning to design physics-based force fields that yield interaction energies consistent with SAPT calculations. Together, these tools will enable rapid progress in force field development and enhance the predictive power of molecular simulations for applications in many fields of science.

Extracellular vesicles (EVs) have emerged as promising cell-free therapeutic agents in regenerative medicine due to their ability to deliver bioactive molecules with enhanced stability and low immunogenicity. Their potential to replicate stem cell functions without the risks of live-cell transplantation has catalyzed a surge in global research. This study aims to perform a scientometric analysis of EV-based regenerative medicine research from 2014 to 2024, identifying publication trends, influential contributors, thematic clusters, and translational challenges. Data were retrieved from the Web of Science Core Collection and analyzed using CiteSpace software. The analysis included journal impact mapping, co-authorship networks, co-citation analysis, and thematic cluster identification. Metrics such as citation bursts, total link strength, and silhouette values were used to assess influence and thematic coherence. The most prolific journals were Stem Cell Research & Therapy and International Journal of Molecular Sciences. China led in publication volume, while the USA dominated citation impact. Foundational works by Théry and Lai, including the MISEV guidelines, shaped methodological standards. Nine thematic clusters were identified, including oxidative stress, small EVs, mesenchymal stromal cells, muscle regeneration, and chronic kidney disease. A strategic shift toward engineered EVs and novel sources such as iPSCs and macrophages was evident. Key translational barriers include lack of standardization, scalability issues, and regulatory ambiguity. EV-based therapies are transitioning from foundational research to clinical application. Overcoming methodological and regulatory challenges will be critical to realizing their full therapeutic potential in regenerative medicine.

Graph Neural Networks (GNNs) are routinely used in molecular physics, social sciences, and economics to model many-body interactions in graph-like systems. However, GNNs are inherently local and can suffer from information flow bottlenecks. This is particularly problematic when modeling large molecular systems, where dispersion forces and local electric field variations drive collective structural changes. We introduce RANGE, a model-agnostic framework that employs an attention-based aggregation-broadcast mechanism that significantly reduces oversquashing effects, and achieves remarkable accuracy in capturing long-range interactions with linear scaling. Notably, RANGE integrates attention with positional encodings and regularization to dynamically expand virtual representations in virtual-node message-passing implementations. Across multiple state-of-the-art baselines, RANGE consistently restores long-range information, enabling the models to correctly predict electrostatic and dispersion-driven behavior even in out-of-distribution extrapolation tasks, where other unmodified baselines fail. Compared with other long-range paradigms, RANGE achieves the highest accuracy while requiring significantly less computational overhead, and it enables stable and scalable molecular dynamic simulations. RANGE offers accurate and efficient modeling of long-range interactions for simulating large molecular systems.