Proton dynamics within molecular organic solids are crucial for energy-related technologies. Proton conductors for use as solid electrolytes in hydrogen fuel cells have been developed, elucidating the higher proton transport mechanism and establishing design guidelines for higher conduction. Many anhydrous proton conductors for proton transport utilizing molecular motion in solids have been studied; however, low-barrier conduction is challenging. In this study, we addressed proton tautomerism as a new guideline for proton conduction, rather than molecular motion. The key to facilitating low-barrier conduction is proton transport without molecular motion via dynamic interconversion between multiple tautomers. We demonstrated the effectiveness of proton-tautomerism strategy in 1,2,3-triazole dihydrogen phosphate crystal, which exhibited low-barrier, isotropic superprotonic conductivity exceeding 10-3 S cm-1. Both theoretical and experimental results confirmed that superprotonic conduction originates from proton tautomerism, demonstrating for the first time that proton tautomerism can serve as a design guide for highly efficient anhydrous proton conductors.

In this work, we present enhanced representation-based sampling (ERBS), a novel enhanced sampling method designed to generate structurally diverse training data sets for machine-learned interatomic potentials. ERBS automatically identifies collective variables by dimensionality reduction of atomic descriptors and applies a bias potential inspired by the On-the-Fly probability enhanced sampling framework. We highlight the ability of Gaussian moment descriptors to capture collective molecular motions and explore the impact of biasing parameters using alanine dipeptide as a benchmark system. We show that free energy surfaces can be reconstructed with high fidelity using only short biased trajectories as training data. Further, we apply the method to the iterative construction of a liquid water data set and compare the quality of simulated self-diffusion coefficients for models trained with molecular dynamics and ERBS data. Further, we active-learn models for liquid water with and without enhanced sampling and compare the quality of simulated self-diffusion coefficients. The self-diffusion coefficients closely match those simulated with a reference model at a significantly reduced data set size. Finally, we compare the sampling behavior of enhanced sampling methods by benchmarking the mean squared displacements of BMIM+BF4- trajectories simulated with uncertainty-driven dynamics and ERBS and find that the latter significantly increases the exploration of configurational space.

The critical progression of structural disorder, which governs the bulk glass-forming ability (GFA), can be elucidated as an ergodicity-breaking process. Understanding the atomic characteristics involved is imperative for establishing advanced glass design principles. However, conventional glasses present significant challenges due to their inherently complex and ambiguous disorder motifs, such as intricate and random atomic clusters or ring distributions. In this work, we synthesize a family of zero-dimensional hybrid metal halides with tunable short- to medium-range structural arrangements to form glasses with diverse GFAs. Through altering molecular shape and surface electrostatic potential of organic cations, their rotational order is able to be broken in a controlled manner. This leads to distinct phase space partitions of molecular movements, allowing for tunable GFAs. Our findings provide a fundamental design approach for synthesizing property-oriented glasses by controlling molecular rotational order, which can be applicable to a wide range of molecular glasses and amorphous solids.

ABSTRACT Effective hemostasis and pathogenic microorganism clearance are essential primary steps for establishing a stable internal microenvironment during the early stage of wound repair. Therefore, a simultaneous thermal crosslinking‐integration strategy was applied to electrospun polyvinylpyrrolidone (PVP) and graphene oxide nanosheets (GO) composite fibers to achieve early‐stage intervention for acute wounds. During thermal crosslinking, molecular thermal motion facilitates interactions between PVP and GO via various non‐covalent interactions, thereby further stabilizing the macromolecular chains. Specifically, the fabricated PVP‐G5 membranes exhibit robust morphological stability in aqueous environments, enabling them to accommodate excessive wound exudate. As a supporting substrate, the fibrous structure of PVP‐G5 also facilitates platelet accumulation, thereby providing a favorable physical platform for activating the blood coagulation cascade. Furthermore, the embedded GO nanosheets within the fiber matrix not only ensure biocompatibility but also endow the fibrous membranes with NIR‐switched photothermal therapy (PTT), thus achieving broad‐spectrum pathogen eradication. These findings demonstrate that the PVP‐GO composite fiber membranes, fabricated through the in situ thermal integration strategy during crosslinking, offer significant advantages for managing severe bleeding and infection, with promise for early treatment of acute wounds.

We present a Python package, DynoPore, to study the liquids confined in cylindrical and slit-like geometries. Structural analysis functions such as density profiles and radial distribution functions are included to facilitate the understanding of the environment and local structure of liquid molecules within the confined systems. For dynamics, DynoPore includes region-resolved mean-squared displacement and lifetime functions to investigate molecular motion in different regions of the pore. For ionic systems, Dynopore also offer Nernst-Einstein and Einstein-Helfand conductivity analysis functions. By combining these structural and dynamical analysis tools in a single, user-friendly framework, DynoPore delivers a convenient and comprehensive package to analyze confined liquids.

Triggering near-infrared (NIR) room-temperature phosphorescence (RTP) poses a major challenge, because the narrow optical gap promotes nonradiative decay via thermal vibrations. Here, we report a series of high-performance RTP materials based on graphene nanoribbons, namely nHBT (n = 1-4). Unlike the modulation of fluorescence by extending π-conjugation, enhancing molecular conjugation more effectively induces red-shifted phosphorescence, enabling NIR emission. By doping nHBT in polyvinylpyrrolidone, NIR RTP with a maximum emission wavelength of 898 nm is achieved, exhibiting a quantum yield of 2.9% and a lifetime of 1.9 ms. Moreover, the rigid fused-ring framework suppresses molecular motions and nonradiative decay, resulting in a persistent afterglow even at 377 K. Well-dispersed NIR RTP nanoparticles were further obtained using polystyrene-b-poly(ethylene glycol) as the host and surfactant. In vivo studies demonstrate excellent capability to suppress background fluorescence, achieving a signal-to-background ratio as high as 47.3 ± 4.2. These results highlight rigid graphene nanoribbons as a versatile platform for high-performance NIR RTP and biophotonic applications.

Melting and glass transitions are classically viewed as distinct phenomena, the former in crystalline materials and the latter in amorphous systems. Here we show that fully crystalline, low-molecular-weight solids can display heat-capacity changes during melting that closely mimic those seen at the glass transition. Using temperature-modulated differential scanning calorimetry, we detect continuous, reversible steps in heat capacity in crystalline roxithromycin and glucose that mirror those of their amorphous counterparts. Similar behavior is observed during the crystallization of semicrystalline poly(3-hydroxybutyrate). These findings provide direct experimental evidence for a transition exhibiting a Cp signature similar in shape and magnitude to that observed at the glass transition, a transition-like thermodynamic signature in the melting of crystalline materials. This discovery expands the conceptual boundary of glass transitions and suggests that large-amplitude molecular motions underlie both melting and vitrification.

LiFePO4 (LFP) batteries are widely used in power and energy storage applications due to their high safety, but their large-scale applications are still constrained by the thermal runaway problem, and the mechanism of electrolyte thermal stability has yet to be elucidated. To deeply understand the behavior of LFP battery electrolytes during thermal runaway, this study uses a commercial mixed-solvent electrolyte system as the research object and adopts the method of combining molecular dynamics (MD) and density functional theory (DFT) to systematically analyse ionic migration, solvation structures, and degradation pathways. Calculation results show that in the undegraded stage of the electrolyte, temperature increase has a dual effect on the migration behavior of ions, where the molecular thermal motion and the dynamics of the solvation shell synergistically enhance the diffusion rate of ions. In the thermal degradation stage, the degradation rate of solvent molecules generally shows a three-stage characteristic of "rise-fall-rise", in which EC is the first to decompose and dominates the initial degradation due to the concentration of electrostatic potential and the high ring strain. In addition, the thermal degradation behavior of each solvent is significantly different due to the molecular structure, the catalytic effect of PF5, and the coupling of bond dissociation energies.

Fluorescence nanoscopy has opened a new frontier for visualizing and understanding polymeric and fibrous materials with molecular precision. Building on advances in single molecule localization microscopy (SMLM), researchers are now extending beyond structure to probe dynamic and functional properties that govern material behavior. This Focus article highlights recent progress in functional SMLM for mapping polarity, viscosity and molecular motion within polymers and fibers, revealing how these nanoscale parameters influence macroscopic performance. Examples include tracking polymerization and phase evolution, resolving nanofiber organization, and correlating structural heterogeneity with local chemical environments. We further discuss the growing convergence between artificial and biological systems with shared principles of hierarchical organization. By integrating structural, dynamic, and functional imaging, fluorescence nanoscopy provides a unifying framework for studying and engineering complex molecular assemblies across living and synthetic matter.

Concentrated solar as a spectrally matched photonic platform for chlorophenol contaminant abatement.

The simulation of biological systems has undergone a revolutionary transformation, progressing from modeling single proteins to entire cellular environments. This leap forward is driven by the convergence of molecular dynamics (MD) simulations and artificial intelligence (AI)-powered structure prediction. Traditionally, MD simulations provided atomic-level insights into protein function and interactions, yet their accuracy relied on experimentally determined structures. AI-based models, such as AlphaFold, now enable the rapid and accurate prediction of protein structures, expanding the scope of simulations beyond isolated biomolecules to complex assemblies. However, a structure alone is not sufficient to capture biological function. Molecular motion underlies almost all cellular processes, from enzyme catalysis to signal transduction. MD simulations breathe life into static models, revealing dynamic conformational changes and mechanistic pathways. With computational power and AI capabilities, we are now approaching the long-sought goal of simulating entire cellular processes with unprecedented resolution. This chapter explores how AI and MD are bridging the gap between static snapshots and dynamic cellular models, paving the way for whole-cell simulations. The ability to computationally reconstruct cellular behavior at the molecular scale is poised to transform biological research, drug discovery, and synthetic biology, marking an era in which digital cells become a fundamental tool in scientific exploration.

Polymeric films with self-healable room temperature phosphorescence (RTP) and mechanical performance are eagerly anticipated for wearable and electronic devices. However, simultaneously recovering phosphorescence and mechanical properties remains a great challenge due to improper interactions quenching phosphorescence and the inherent conflict between chain rigidity and flexibility in polymeric films. Herein, we propose the use of a chromophore binder between the polymer matrix to fabricate RTP films with simultaneous recovery of phosphorescence and mechanics. A covalent cross-linking network was established, restricting the molecular motion of chromophore binders to achieve bright deep-blue phosphorescence emissions. Additionally, the films exhibited processability, flexibility, stretchability, and self-healing ability. Both the phosphorescent and mechanical properties could be recovered with an efficiency of more than 90% for the films healed in water under room temperature. Theoretical simulation showed that this noteworthy self-healing capacity could be ascribed to the relatively low energy for the formation and re-formation of the covalent cross-linkage between the chromophore binders and polymer matrix. Accordingly, we realized an assembly-reassembly process for multi-emission phosphorescence by healing film fragments using different chromophores with boronic acid groups. It is anticipated that this facile and universal strategy via covalent cross-linkage could provide possibilities for the design of multi-functional optical materials with expanded application fields.

The size, shape, and symmetry of cations and anions in ionic liquids determine the adjustable properties of these industrially relevant liquid salts. In this work, we show how the deliberately introduced asymmetry of cations can change and even control the phase behavior and rotational diffusion in protic ionic liquids. The method of choice here is solid-state NMR spectroscopy, which provides structural and dynamic information about the heterogeneous material. In addition, we show that the chosen trialkyl ammonium-based ionic liquids are excellent model systems for studying Coulomb-enhanced hydrogen bonding and anisotropic molecular motion.

Aggregation-induced emission triggered by external stimuli plays an important role in smart materials. Herein, we report the serendipitous discovery that 3Star-Ph-Ph, a soft tripod, forms pressure-induced assemblies with distinctly amplified fluorescence owing to restricted molecular motions within these assemblies.

Accessing molecular motion activation parameters with 1H low-field time-domain NMR: Examples from glass transition in polymers.

A DNA triangular prism scaffold-guided directionally-controlled derailment-free DNAzyme walker.

This study aims to analyze students’ misconceptions on the topic of temperature and heat using the Certainty of Response Index (CRI) model as a diagnostic tool. The research employed a quantitative descriptive approach involving a multiple-choice test accompanied by CRI scales to identify not only incorrect answers but also the confidence level behind each response. Data analysis included calculating the proportion of correct and incorrect answers, average CRI scores, and categorizing students into groups of scientific conceptions, misconceptions, or lack of knowledge. The results showed that several students selected incorrect answers with high CRI values, indicating strong misconceptions that must be addressed through targeted instructional strategies. Meanwhile, students with incorrect answers but low CRI scores were categorized as having a lack of knowledge rather than misconceptions. The findings highlight that misconceptions occurred most frequently in indicators related to heat transfer, the relationship between temperature and molecular motion, and factors affecting changes in temperature. The study concludes that CRI is an effective diagnostic method for distinguishing levels of student understanding and is useful for informing remedial instruction. These findings provide meaningful insight for ededucators in designing better learning interventions to reduce misconceptions.

INTRODUCTION: Many current scientific theories understand nature as made of complex networks of interaction and causality. Despite that, the everyday way people see the world presents many simplifier biases that can lead to a contradiction with the scientific perspective. As a result, scientific concepts can be challenging to learn, and misconceptions can be produced. Among the biases mentioned, many are related to distorted judgments about randomness and the probability of events, which makes it plausible to suppose that they can also be involved in the production of scientific misconceptions. OBJECTIVES: To increase knowledge about the relationship between difficulties in reasoning with randomness and difficulties in learning scientific concepts, this study aimed to answer the following questions: 1) do students consider randomness when thinking about molecular processes? 2) how do they explain processes related to randomness? MATERIALS AND METHODS: The study was conducted in undergraduate biochemistry courses and investigated the students’ reasoning for the participation of randomness in enzymatic catalysis and membrane transport. Problem tests were used to collect answers and produce epistemological matrices, which made it possible to group the answers by different ways of speaking. Semi-structured interviews were conducted to gain further information about the student's reasoning. DISCUSSION AND RESULTS: After more than 200 answers were analyzed, three ways of talking about enzymatic catalysis and three about membrane transport were identified. Enzymatic catalysis was sometimes regarded as a) obligatory once enzyme and substrate met each other, b) obligatory only when this interaction happened under ‘ideal conditions’, or c) non-obligatory after the interaction. The direction of membrane transport was seen as dictated by a) the cellular/organism needs, b) a law/principle, or c) because of free molecular movement. CONCLUSION: Not all students take randomness into account, instead their reasoning may rely on simple causal chains created by the presence of rules, entities, and goals, which can lead to inadequate understanding of molecular processes from a scientific perspective.

P-glycoprotein (P-gp) is an ATP-driven transporter that effluxes a wide range of xenobiotics from cells, and its overexpression is a primary cause of multidrug resistance (MDR) in cancer. It is well-established that P-gp functions through conformational changes, yet its large-scale structural dynamics at work have been unexplored. Here, we directly visualized single P-gp molecules reconstituted in nanodiscs using high-speed atomic force microscopy (HS-AFM). The HS-AFM movies revealed that P-gp is intrinsically dynamic in its apo state, with its nucleotide-binding domains (NBDs) undergoing large, spontaneous opening and closing motions. However, addition of ATP stabilized a conformation characterized by NBD proximity with a strong tendency toward closure. We then leveraged this dynamic viewpoint to elucidate the relationship between Elacridar’s function and the resulting structural dynamics of P-gp. Elacridar is designed to overcome multidrug resistance (MDR) in cancer and acts as a potent dual inhibitor of both P-gp and the Breast Cancer Resistance Protein (BCRP), effectively blocking the drug efflux function of these transporters. This inhibitor has suggested concentration-dependent function: it is effluxed as a substrate at low concentrations and acts as an inhibitor at high concentrations. Our direct observations revealed that low concentrations induced active dynamics in P-gp, whereas high concentrations severely restricted its motion, leading to a rigid, non-productive state. Our study provides critical insights into how observing molecular motion itself can unravel complex biological mechanisms.

Theoretical simulations of time-resolved transient absorption spectra, combining on-the-fly trajectory surface hopping with doorway-window formalism, provide a powerful approach to unravel the nonadiabatic dynamics of cis-stilbene. The results show that the key molecular motions driving photoisomerization are captured by the evolution of spectral components, including ground-state bleach, stimulated emission, and excited-state absorption. By establishing a critical link between nuclear dynamics and time-resolved spectral responses, this work demonstrates the essential role of theoretical spectroscopy in decoding ultrafast photochemical processes and offers a predictive framework for mapping molecular dynamics from spectral signatures.