DNA molecular subtraction controlled hybridization chain reaction for high contrast detecting and selective killing of cancer cells.

Plant-derived aroma glycosides are important sensory enhancers in food, beverages, cosmetics, and fragrance formulations. They have considerable economic value. However, their direct commercial use is limited by their low natural abundance and the lack of efficient, scalable synthetic routes. Synthetic routes have largely targeted aroma compounds derived from specific sources such as tea or grapes. A general, streamlined approach capable of incorporating diverse aglycones - including sterically hindered alcohols and less nucleophilic phenolic groups while maintaining high yields under mild conditions - is still lacking. l-Arabinose sugar is notable for its sweet taste and is a safe additive for diabetic patients. This study reports the synthesis of 14 l-arabinose-containing disaccharides, including α-l-arabinofuranosyl-β-d-glucopyranosides and β-vicianosides (α-l-arabinopyranosyl-β-d-glucopyranosides), incorporating a diverse range of aroma aglycones, including primary, secondary, sterically hindered tertiary, and less nucleophilic phenolic alcohols. All the reactions proceeded under mild, catalytic conditions, providing high yields and purity. Importantly, the scalable method prevented unwanted orthoester formation regardless of the molecular sieve type or reaction concentration. This is an effective and green approach that offers a sustainable route for further industrial production. © 2026 Society of Chemical Industry.

Molecular-level removal mechanism of manganese dioxide oxidation of sulfamethoxazole mediated by hydrochar-derived dissolved organic matter.

Characteristics and molecular reaction mechanism of gas/coal dust hybrid deflagrations inhibited by NaHCO3 powder via the integration of experiments and ReaxFF-MD study

Dormant microbial spores provide one of the clearest and most extreme examples of how cells can pause life for extended periods and then reliably restart it. Although bacterial and fungal spores are often grouped under "dormancy," the physical strategies by which they suspend and resume life differ fundamentally. Here, I compare two canonical systems-Bacillus subtilis endospores and Saccharomyces cerevisiae ascospores-using a dynamical-systems framework from a physicist's perspective. I propose that dormancy is not simply "low metabolism," but a dynamical reconfiguration that decouples local molecular clocks from a global biological clock while preserving an intrinsic capacity to resume sustained nonequilibrium dynamics, which I refer to as nonequilibrium capacity. Specifically, in B. subtilis spores, "dry dormancy" is enforced by immobilization: dehydration and material constraints suppress appreciable molecular diffusion and reaction fluxes, arresting global biological time by suppressing local molecular clocks. In S. cerevisiae spores, "wet dormancy" appears to be achieved by throttling: spores remain hydrated, retain molecular mobility, and support some slow irreversible processes such as gene expression, yet global biological time remains arrested because, as I propose, local activity fails to propagate into sustained organism-level progression (e.g., growth and division). Together, these comparisons place dry and wet dormancy as distinct regions of a physical design space defined by hydration, molecular mobility, energetic flux, and cross-scale coupling between local activity and global progression, and motivate quantitative models of dormancy and revival dynamics.

Coordination chemistry presents an ideal molecular platform for the development of superior drug-delivery systems that can be effectively released, selectively targeted, and integrated into functional therapeutic systems. Due to their predictable geometries and tunable bonding properties, metal ions can form a variety of structures, including coordination polymer nanoparticles (CPNs), metal-organic frameworks (MOFs), supramolecular coordination complexes (SCCs), and metal-ligand cross-linked hydrogels. These structures possess a high cargo-loading ability and are sensitive to physiologically significant stimuli, including pH gradients, redox imbalances, enzymatic activity, and light. In addition to drug encapsulation, metal centers are intrinsically imaging-contrastive, catalytic, magnetically responsive, and phototherapeutic, enabling synergistic, multimodal therapies. This review critically analyzes the principles of coordination underlying the rational design of these delivery platforms, the key classes of coordination-based carriers, and their applications in cancer therapy, antimicrobial and antiviral treatment, gene and protein delivery, and theranostics. Emerging trends, such as hybrid organic-inorganic-bimolecular systems, hierarchical self-assembly, and AI-directed design, have also been described as definite areas of transformation in next-generation therapeutics. Issues related to physiological stability, metal toxicity, immune response, and scalable manufacturing are discussed, along with means to support clinical translation. Coordination-based architectures are expected to give the next generation of precise therapeutics that facilitate spectacular regulation of molecular assembly, dynamic reactions, and treatment.

Complex organic molecules (COMs) such as aldoses and polyols are essential prebiotic compounds and serve as fundamental molecular building blocks for key biomolecules, including sugars and lipids. Despite their detection in star-forming regions and carbonaceous meteorites, the mechanisms of formation of these COMs in extraterrestrial environments remain largely controversial. Here, we demonstrate the bottom-up synthesis of a diverse set of COMs in the formaldehyde-water (H2CO-H2O) ice analogs subjected to simulated galactic cosmic rays (GCRs), including aldoses and polyols (glycolaldehyde, ethylene glycol, glyceraldehyde, and glycerol), as well as other compounds like glyoxal, methoxymethanol, (methoxymethoxy)methanol, formic acid, and methanetriol. The identification of these oxygen-bearing COMs was achieved using synchrotron vacuum ultraviolet photoionization reflectron time-of-flight mass spectrometry (SVUV-PI-ReToF-MS) during the temperature-programmed desorption (TPD) phase. Their unambiguous assignment was further confirmed by isotopic labeling experiments and by fitting the photoionization efficiency (PIE) curves of the parent product and fragment ions. The efficient synthesis of these COMs reveals constraints of the molecular complexity and reaction pathways available for forming aldoses and polyols in space, expanding our understanding of how such biorelevant precursors form in the extraterrestrial environments and their potential role in the abiotic origin of life on Earth.

ConspectusElectrons and protons are the simplest particles in chemistry, and their transfers are among the most fundamental chemical reactions. It is increasingly recognized that these two particles often transfer in the same elementary kinetic step, resulting in the most common type of proton-coupled electron transfer (PCET). PCET has evolved from a curiosity to a major research field that is central to a broad range of processes in chemistry, biology, and materials science.PCET evolved from electron transfer, in both its experimental and theoretical origins. One wonders how the field would be different if it had been called electron-coupled proton transfer. This equivalent terminology illustrates that the proton is on equal footing to the electron, making PCET perhaps the simplest case where the quantum properties of both an electron and a nucleus need to be considered.The fundamental understanding of PCET in solution builds on the remarkably impactful theory of electron transfer (ET) developed by R. A. Marcus and others. At a basic level, ET theory is marked by a quadratic dependence of the reaction barrier on the reaction free energy (ΔG⧧ on ΔG°), with normal and 'inverted' regions separated by a barrierless region (ΔG⧧ = 0), plus an electronic coupling that determines the electron tunneling probability. The theory for PCET includes additional essential elements: the quantum mechanical treatment of the transferring proton(s) as tunneling particles, multiple channels corresponding to reactant and product electron-proton vibronic states, vibronic coupling rather than electronic coupling, and a distribution of proton donor-acceptor distances.Our recent studies of ultrafast intramolecular PCET in molecular triads were the first to demonstrate the corresponding free-energy dependence for PCET, including the inverted region. Inverted behavior was previously thought to be difficult to observe experimentally for PCET because it connects vibronic states rather than electronic states. Due to the more closely spaced vibronic state energy levels compared to electronic state energy levels, there is usually a nearly barrierless pair of reactant and product vibronic states that obviates the inverted region. For these molecular triads, however, the vibronic coupling is very small for the barrierless pair, allowing observation of the hallmark inverted region.While looking for ultrafast PCET, we discovered a new elementary chemical reaction that we denoted proton-coupled energy transfer (PCEnT). In PCEnT, proton transfer (PT) is coupled to electronic excitation energy transfer. As with PCET, PT is required for the reaction to be thermodynamically accessible. In our molecular triads, PT occurs within the phenol-pyridine acceptor unit, concerted with electron transfer to a photoexcited anthracene (PCET) or electronic excitation energy transfer from a photoexcited anthracene (PCEnT). The dominant reaction depends on the molecular substituents and reaction conditions. A theory for PCEnT with some of the same essential elements as PCET theory, along with some fundamental differences, has been developed and applied to a triad system.

Nanopores have become transformative tools in single-molecule chemical analysis, enabling detailed interrogation of molecular interactions and reaction dynamics. These advancements have revolutionized the characterization of chemical kinetics and stereospecificity, broadening nanopore applications. This review evaluates the principles of nanopore single-molecule chemistry, highlighting breakthroughs in chemically reactive nanopore construction via site-specific mutagenesis, semisynthetic engineering, and orthogonal modifications. Notably, we highlight the innovative strategies enabling precise subunit stoichiometry control to ensure single-molecule reactions, and the integration of machine learning for high-fidelity ionic current analysis. These developments position nanopores as versatile tools for intricate molecular detection in fundamental and applied research. Looking forward, nanopore single-molecule chemistry promises an impact on diagnostics, environmental monitoring, and precision medicine. Integration of molecular dynamics simulations, artificial intelligence-driven protein design frameworks, and microsystems technology may expand detectable species, enhancing robustness and lowering detection limits. Such advancements will deepen our understanding of chemical transformations and support meaningful real-world applications of nanopore technologies.

The integration of large language models (LLMs) into molecular synthesis is rapidly transforming the field, offering a paradigm shift beyond traditional data-driven approaches. LLMs bring a unique combination of massive knowledge absorption, sophisticated contextual representation, and multi-step logical inference, holding the potential to intelligently automate the entire synthesis workflow. This review explores how LLMs are revolutionizing molecular synthesis by functioning across four key roles: (1) as interactive domain-specific knowledge bases for instant expert consultation; (2) as powerful tools for structuring unstructured reaction data from literature; (3) as versatile predictors for molecular properties and reaction outcomes; and (4) as the central "brain" in intelligent agent systems for autonomous synthesis planning and execution. By systematically outlining these advances, we aim to provide a comprehensive roadmap for chemists to leverage the distinctive capabilities of LLMs, thereby accelerating the journey toward a more automated and intelligent future for molecular synthesis.

Molecular dynamics simulations model chemical reactions as continuous changes in molecular structure over time instead of static minima and transition states. This perspective argues that time-dependent structural change is a crucial, but often overlooked, mechanistic feature as many reactions simply do not follow a single, equilibrated minimum-energy path. We highlight examples where traditional transition state theory fails, typically cases involving short-lived intermediates, nonequilibrium solvation, momentum-controlled selectivity, post-transition state bifurcations, and "hidden" dynamic intermediates and show how molecular dynamics can reveal the actual sequence of structural change which governs a reaction outcome. We also discuss emerging machine learning-based molecular dynamics which have found applications in photochemistry and solvent modeling. While molecular dynamics will not replace methods based on transition state theory, it offers organic chemists a time-resolved view of molecular structure which can be crucial to understanding a given reaction. However, a central barrier for organic chemists is to understand when and why to apply an advanced computational technique such as molecular dynamics simulations. In this perspective, we aim to introduce the methodology in sufficient detail to enable organic chemists to make this assessment and gain an appreciation for the importance of time in reaction mechanisms.

Molecular on-surface photochemistry has emerged as a promising alternative to thermal activation for fabricating low-dimensional carbon-based nanomaterials, offering unique advantages such as non-thermal initiation and high chemoselectivity. Controlling the selectivity and efficiency of on-surface photoreactions remains challenging due to the complex interplay among molecular excitation pathways, substrate properties, and reaction conditions. This review briefly summarizes recent advances in light-driven on-surface synthesis under ultra-high-vacuum conditions. We focus on molecular photoexcitation pathways that can be probed by scanning tunneling microscopy and spectroscopy (STM and STS). Studies of light-driven reactions in three categories are overviewed, i.e., dehalogenative C-C coupling, [2+2] and [4+4] cycloadditions, and photoisomerization. Typical strategies for tuning reactivity are exemplified, including molecular pre-organization via self-assembly, surface passivation, and wavelength/polarization control. The summary of successful case studies may not only facilitate the fundamental understanding of on-surface photochemistry but also inspire the design of functional low-dimensional architectures and light-responsive molecular devices.

Mineral-catalyzed Maillard reactions are considered plausible abiotic pathways for humification and play an essential role in organic carbon (OC) transformation and sequestration. Oxygen vacancy defects (OVDs) are common in minerals and significantly influence their reactivity, yet their role in catalyzing the Maillard reaction remains unexplored. In this study, we systematically investigated the Maillard reaction between glucose and glycine, as model low-molecular-weight organic carbon compounds, catalyzed by hematite with varying OVD concentrations at the molecular level. High-resolution mass spectrometry revealed that OVDs enhance the Maillard reaction, facilitating the formation of recalcitrant geopolymers. Specifically, the relative abundance of lignin-like, tannin-like, and condensed aromatic compounds increased by 17.3% as OVD concentration increased by 0.49 mmol/g. Molecular reaction network analysis further indicated that OVDs increase molecular complexity, with dehydrogenation (-H2) and dehydration (-H2O) serving as key steps in molecular evolution. The presence of OVDs on hematite promoted the generation of Lewis acid sites. Spherical aberration-corrected scanning transmission electron microscopy (Cs-STEM) and density functional theory (DFT) calculations revealed that localized electron density at Lewis acid sites enriched reactant substrates, lowering the activation energy for the Schiff base reaction by 1.01 eV and thereby promoting the polymerization of aromatic compounds. Additionally, a portion of aromatic Maillard products were selectively localized at Lewis acid sites, enhancing OC stability. These findings provide mechanistic insights into OVD-catalyzed polymerization and underscore the importance of mineral defect structures in predicting soil OC persistence.

Nuclear quantum effects (e.g., zero-point motion and tunneling) can control hydrogen transfer and diffusion, but fully quantum dynamics is rarely feasible for realistic systems. Ring-polymer molecular dynamics (RPMD) samples quantum statistics with a classical ring polymer, yet ab initio RPMD is costly because electronic-structure energies and forces are required for every bead at every time step. Here, we introduce a Feynman path tube-guided surrogate RPMD framework that propagates all beads on an on-the-fly surrogate ring-polymer Hamiltonian with an uncertainty estimate: ab initio data are acquired only when the uncertainty exceeds a threshold and are shared across beads and trajectories. Combined with well-tempered metadynamics, the method targets quantum free-energy surfaces and activation barriers. On gas-phase OH + H2O and CH4 + Cl benchmarks with full-dimensional reference potentials, surrogate ab initio RPMD reproduces brute-force RPMD profiles within 0.1-0.2 kcal/mol, preserves the symmetry of the OH + H2O identity exchange, and resolves subkcal barriers. We then compute ab initio RPMD free-energy surfaces for proton transfer on TiO2(011), H migration on graphene, and H2 dissociative chemisorption on Cu(111), including a two-dimensional surface and minimum free-energy path on the metal. Across all five systems, the number of ab initio calls grows sublinearly with bead number, delivering speedups of 103-105 ring-polymer bead-steps per QM evaluation and reducing the expensive force workload by three to more than 4 orders of magnitude. Tube-aware surrogate sharing therefore makes quantum-statistical free-energy sampling with ab initio RPMD practical for both molecular and interfacial reactions.

Photoinduced molecular ring-opening reactions play a critical role in many natural chemical processes; however, there are pending questions regarding the fundamental mechanisms that govern these transformations. Furthermore, the understanding of ring-opening reactions has an important impact on optoelectronic and molecular control applications. These chemical reactions are driven by nonadiabatic coupling between electronic states through conical intersections in their potential energy surfaces, and their excited state dynamics are still a matter of debates. Here, we use a combination of on-the-fly ab initio molecular dynamics simulations and a powerful imaging ultrafast UV pump-IR probe spectroscopy technique to examine in detail the dissociation pathways followed by 2- and 3-bromothiophene. Using time-dependent momentum imaging, we identify clearly three dominant fragmentation channels following UV photon absorption: C-Br bond dissociation and cleavage of either C-S bond, which induce structural changes through molecular ring-opening prior to IR-induced ionization. We also obtain their fragmentation times, which span 600 to 1300 fs. Our findings elucidate both the initial excited states that initiate these dynamics and the nature of the electronic states reached by the nonadiabatic population transfer, a process essential to the observed ring-opening dynamics. Investigations of both 2- and 3-bromothiophene highlight the isomer dependence of UV absorption and reveal differences unknown before. Our results unambiguously support the role of ultrafast internal conversion from the dominant electronic states initiating ring-opening dynamics and identify the conical intersections that determine ring opening. Our work provides findings that advance the understanding of excited-state dynamics in ring molecules.

The water gushing into the goaf makes the oxidized coal undergo the dual effects of oxidation and water immersion and faces the risk of reburning. The oxidation reaction of molecular active structures on the surface of oxidized coal under varying oxygen concentrations was investigated by using Fourier transform infrared spectroscopy (FTIR). Using scanning electron microscopy (SEM) and liquid nitrogen adsorption technology, the study investigates the evolution of the pore morphology and the development mechanism of its pore structure. The results indicate that higher oxygen concentrations provide greater reaction energy, promoting the consumption of -CH3 groups and facilitating the formation of -CH2- groups. The inadequate supply of reaction energy results in a lower rate of -CH2- generation compared to its consumption. Sufficient oxygen facilitates the release of additional activation energy, promoting the formation of transition-state free radicals and their subsequent combination with oxygen to form -CH2- groups. The -CO group primarily participates during the intermediate and late stages of coal oxygenation reactions, exhibiting a lower reaction priority. During the low-oxygen phase, the conversion of peroxide radicals to -C-O- is limited, leading to a reduction in the quantity of -C-O- produced. When the oxygen concentration exceeds 9%, it leads to the rapid generation of peroxide radicals. These radicals efficiently extract hydrogen atoms from the fatty chains of coal molecules and other structural components via hydrogen abstraction reactions, generating unstable hydrogen peroxides that are easily decomposed to produce alcohols and ketones containing -C-O-. Oxidation significantly enhances the development of pores compared to raw coal, and the cumulative pore volume and area tend to increase with the degree of oxidation. Although the water soaking resulted in a decrease in the cumulative pore volume compared to oxidized coal, it remained significantly higher than that of raw coal. The distribution of micropores indicates that as oxygen concentration rises, the proportion of micropores in oxidized coal increases progressively. The oxidation process facilitates the transition of coal pores from mesopores (>100 nm) and small pores (10 to 100 nm) to micropores (<10 nm). As the primary site for coal-oxygen adsorption, an increase in the proportion of micropores effectively expands the coal-oxygen reaction interface.

Molecular adsorption, diffusion, reaction, and desorption on interstellar amorphous solid water (ASW) dominate the chemical processes in interstellar molecular clouds. Elucidating the chemical evolution in molecular clouds requires understanding the interactions between molecules and the ASW surface. Investigating the dynamics on ASW surfaces requires highly accurate and efficient molecular dynamics (MD) simulations. This study proposes a computational scheme that combines a rare-event sampling method, parallel cascade selection MD (PaCS-MD), with machine learning potential-based MD (MLP-MD), referred to as PaCS-MLP-MD. Because PaCS-MLP-MD explores rare-event dynamics without applying external forces or increasing the temperature, it provides a suitable framework for investigating astrochemically relevant molecular processes. Using PaCS-MLP-MD simulations, we simulated desorption of aminoacetonitrile (AAN) from an ASW surface model. Two potential desorption processes were identified: one in which the amino group desorbs first, followed by the nitrile group, and another in which the nitrile group desorbs first, followed by the amino group. Additional unbiased MLP-MD simulations, initiated from representative PaCS-MLP-MD snapshots, were performed to reveal desorption and re-adsorption behaviors of AAN on the ASW surface. These new findings from the PaCS-MLP-MD simulations may contribute to a more detailed understanding of processes occurring on ASW surfaces in molecular cloud environments.

The rapid expansion of enzyme reaction literature has created a major bottleneck in database curation, leaving vast amounts of enzyme-substrate-condition relationships unstructured and inaccessible for DL-driven modeling. How to fully utilize the enzymatic reaction data has been an important task for future accurate enzyme activity prediction models. Current deep learning (DL)-based data extraction models heavily rely on large language models (LLMs) without a fidelity check and the ability to continuously evolve. To address these issues, we developed zERExtractor (Zelixir's Enzyme Reaction Data Extractor), an accuracy-oriented and extensible platform for extracting enzyme-catalyzed reaction data from scientific publications. This system offers a unified multimodal information extraction framework (covering molecular reaction diagrams, tables, and texts) to integrate enzymatic reaction descriptors into structured storage. We employ fine-tuned large LLMs together with DL in a human-in-the-loop pipeline that evolves through data fidelity validation by experts and active learning. Also, zERExtractor achieves 89.9% accuracy in table recognition and over 98% accuracy in molecular image recognition on synthetic data sets, outperforming the strongest baseline by more than 2% and consistently maintaining above 95% on realistic benchmarks. zERExtractor bridges the data gap in enzyme reaction data with a scalable framework for accurate multimodal extraction, advancing DL-driven enzyme modeling and enabling future applications in computational enzymology and biotechnology. The platform is publicly accessible online at https://zpaper.zelixir.com/.

ABSTRACT The influence of detailed molecular transport, notably, of thermo-diffusion, in the compressed fuel/air mixture in rapid compression machine experiments is studied by detailed numerical simulations. The evolution of a one-dimensional layer of fuel-air mixture subjected to an RCM process was simulated using detailed treatment of transport and chemical reaction. The simulation includes compression work and wall heat losses as essential RCM processes, and adds a detailed treatment of molecular transport processes and chemical reaction within the gas mixture to the description. The model outcome reveals how detailed transport can create inhomogeneities in the initially homogeneous mixture composition, well before chemical reaction sets in. The effect is pronounced in hydrogen-containing mixtures, where temperature gradients at the near-wall boundary layer can cause notable “un-mixing” of hydrogen by thermo-diffusion. This can effectively lead to inhomogeneous fuel-air ratio fields, which are present already when the RCM compression phase is finished. Under these circumstances, the ignition delay and temperature assigned to a RCM experiment correspond to a physically different auto-ignition event than nominal. Neglecting these effects may lead to a bias in reported experimental ignition delay time curves.

The complexity of the combustion process makes the computational time using a detailed mechanism unacceptable, therefore, it is necessary to simplify the mechanism. The reaction-diffusion manifolds (REDIM) method is a reduction model that takes the coupling of molecular diffusion and chemical reactions into account to reduce computing times, and can be utilized in different types of combustion simulations. In this work, the REDIM method is implemented into a new OpenFOAM-based CFD solver. The use of both generalized and physical coordinates to represent the manifold is analyzed for freely propagating laminar flames. The REDIM-based solver is then used to calculate 2D laminar counterflow flames. Different detailed mechanisms, progress variables and inlet velocities are applied to calculate the 2D counterflow flames and to evaluate the performance of REDIM at steady and extinction conditions. It is shown that the results computed by the REDIM method have good agreement with the results obtained by detailed simulations. Furthermore, the REDIM method offers a significant reduction in computational cost in the newly developed solver.