Molecular Reaction Research Articles

Hot Mineral Water as a Medium for Molecular Hydrogen Reactions in the Primordial Hydrosphere for the Origin of Life

Studies have been conducted on the potential development of Hydrogenobacter thermophilus and Pseudomonas aeruginosa in an anaerobic environment, both in the presence and absence of molecular hydrogen (H2). H. thermophilus developed better at 70 °C and pH 7.0 in the presence of molecular hydrogen. It also multiplied in its absence, but to a lesser extent. Dissolved hydrogen in an amount of 1 ppm is biologically active for this thermophilic chemolithotrophic species. The tested strains of P. aeruginosa also showed growth under anaerobic conditions in the presence of H2 concentrations of 1 ppm and 2 ppm, which was ensured by adding Mg. The results indicate that not only the oldest microorganisms on our planet, archaebacteria, but also current species such as H. thermophilus and P. aeruginosa are capable of development under conditions characteristic of the ancient hydrosphere. DFT analyses showed that hydrogen water forms stable water clusters, whose hydrogen bond network retains and stabilizes reducing agents such as molecular hydrogen and magnesium (Mg0). This creates a microenvironment in which key redox processes associated with autotrophic growth and chemical evolution can occur. This is a realistic model of the Earth’s primordial hydrosphere’s conditions.

A scalable machine learning multi-local regression framework for potential energy surface fitting across diverse chemical landscapes.

The accurate characterization of the potential energy surface (PES) is fundamental to understanding molecular structures and chemical reaction mechanisms. Traditional approaches, such as abinitio calculations and empirical force fields, struggle to balance computational efficiency and accuracy, particularly for high-dimensional systems. Machine learning methods have demonstrated remarkable efficacy in addressing chemical problems, yet two critical challenges remain: (1) Inadequate representation of high-energy structures (e.g., transition states) in training data due to their scarcity and inherent complexity, leading to biased PES predictions; and (2) insufficient adaptability of data-driven models to dynamic chemical scenarios, as they rely on static benchmark datasets and lack explicit integration with mechanistic knowledge. This study proposes a Clustering and Local Regression Network (CLRNet), a chemical-principle-guided hierarchical framework for PES construction, which integrates data-driven modeling with quantum mechanical insights. CLRNet employs graph neural networks to extract molecular features, integrating unsupervised clustering of features with local potential energy surface regression. CLRNet has outstanding advantages in accommodating high-energy structures and has achieved a balance between model capacity and computing power. This work not only offers a new approach to PES analysis but also bridges the gap between data-driven modeling and chemical intuition. It has great application prospects in downstream tasks such as transition state energy calculation and PES fitting of catalysis and chemical kinetics in physical chemistry.

Dynamic, multiphase magnetic resonance imaging of in vivo physiological processes with long-lived hyperpolarized 15N,d9-betaine.

Hyperpolarized magnetic resonance imaging (HypMRI) offers valuable insights into dynamic physiological processes in vivo. However, the short signal lifetime of hyperpolarized 13C-labeled compounds commonly used in HypMRI studies restricts investigations to fast molecular reactions and rapid distributions. Here, we introduce hyperpolarized 15N,d9-betaine (trimethyl-2H9-15N-glycine) as an endogenous MRI contrast agent with a long-lived signal suited for comprehensive molecular tracking. With in vivo detectability exceeding 14 minutes and high polarization efficiency, 15N,d9-betaine supports both real-time and delayed-phase MRI from a single dose, enabling flexible, multistage imaging. In preclinical models, renal 15N,d9-betaine images were acquired with strong signal-to-noise ratios across resolutions. This extended imaging window facilitates tracking molecular distribution, assessing tissue perfusion, and monitoring cellular uptake relevant to betaine's roles in cellular protection. By extending MRI capabilities across timescales, hyperpolarized 15N,d9-betaine holds promise for applications like organ function assessment, disease monitoring, and real-time evaluation of therapeutic interventions, advancing noninvasive molecular imaging.

Enhancing amplification efficiency and reducing molecular diagnostic reaction time through rational design of T4 gp32 Variants in recombinase polymerase amplification.

Enhancing amplification efficiency and reducing molecular diagnostic reaction time through rational design of T4 gp32 Variants in recombinase polymerase amplification.

Origin of homochirality in peptides: The first milestone at the origin of life.

Origin of homochirality in peptides: The first milestone at the origin of life.

A Postprocessing Tool for Efficient Molecular Reaction Path Analysis in Kinetic Simulations.

This study presents a Python-based tool for extracting molecular reaction pathways from kinetic simulation trajectory files. Compared to traditional depth first search (DFS) and breadth first search (BFS) algorithms, a more efficient chain analysis algorithm is introduced. The tool utilizes a full-time domain response analysis approach, enabling the identification of reactions across nonadjacent frames, thereby enhancing the comprehensiveness of the analysis. The responses are stored in a directed graph structure, and full integration of parallel computing significantly improves processing efficiency. The tool supports molecular dynamics, ab initio molecular dynamics, and coarse-grained simulations. As an open-source Python project, it offers both portability and wide applicability. The reaction processes in a propyne-ethylene blending system and the cross-linking reaction in an epoxy resin coarse-grained system are demonstrated, highlighting the tool's potential for analyzing various molecular systems.

Programming Directional Strand Polymerization on DNA Origami for Logic Computing

The nanoscale addressability of DNA origami structures enables precise control of molecular reaction pathways and events within confined spaces, allowing for the construction of localized DNA logic gates, which are critical for achieving molecular‐level information processing. However, operations of these localized DNA logic gates are mainly performed in solution, and the obtained results are collective averages, making in situ observation of the computational process of individual DNA logic gates and accurate evaluation of their computational performance challenging. Here, a directional strand polymerization‐based approach for constructing logic gates on DNA origami is developed. DNA switches assembled on the surface of DNA origami serve as monomers in a hybridization chain reaction after activation by input strands, enabling directional transmission of DNA signals, thereby executing defined logic functions. By assembling a reporter to terminate strand polymerization, the results of DNA logic operations can be recorded in situ through single‐molecule fluorescence signals. This approach lays the foundation for the construction of high‐density integrated DNA computational circuits and provides new insights for biosensing applications.

Smart Reaction Templating: A Graph-Based Method forAutomated Molecular Dynamics Input Generation

Accurately modelingchemical reactions in molecular dynamics simulationsrequires detailed pre- and postreaction templates, often created throughlabor-intensive manual workflows. This work introduces a Python-basedalgorithm that automates the generation of reaction templates forthe LAMMPS REACTION package, leveraging graph-theoreticalprinciples and subgraph isomorphism techniques. By representing molecularsystems as mathematical graphs, the method enables the automated identificationof conserved molecular domains, reaction sites, and atom mappings,significantly reducing manual effort. The algorithm was validatedon three case studies: poly addition, poly condensation, and chainpolymerization, demonstrating its ability to map conserved domains,identify reaction-initiating atoms, and resolve challenges such assymmetric reactants and indistinguishable atoms. Additionally, thegenerated templates were optimized for computational efficiency byretaining only essential reactive domains, ensuring scalability andconsistency in high-throughput workflows for computational chemistry,materials science, and machine learning applications. Future workwill focus on extending the method to mixed organic–inorganicsystems, incorporating adaptive scoring mechanisms, and integratingquantum mechanical calculations to enhance its applicability.

Proteomic Approaches to Unravel Complex Interactions Between Ganoderma boninense and Oil Palm in Basal Stem Rot Disease: A Scoping Review

Ganoderma boninense poses the biggest threat to the oil palm industry by inducing basal stem rot (BSR) in Malaysia, a leading palm oil-producing nation. Protein analysis is one of the potential methods in early detection of infected plants by understanding the changes in protein molecules which are useful as biomarkers. However, proteomic methods are not widely used due to their complex protein analysis in nature and the diversity of protein characteristics, including variations in abundance, structure, and post-translational modifications. Thus, a scoping review was analysed based on related literatures by identifying gaps and limitations in protein analysis for managing G. boninense. This scoping review was conducted following PRISMA guidelines. The results were searched using different databases: Scopus, Asean Citation Index (ACI), ScienceDirect, PubMed, and Web of Science (WoS). The keywords used were "Ganoderma boninense" OR "basal stem rot" AND "proteomics" OR "protein analysis" OR "proteomic profiling" OR "mass spectrometry" OR "protein expression." 116 articles were collected from the various databases, and only 13 significant articles were included after reviewing the abstracts and removing the duplicates. The relevant articles showed that proteomic methods, particularly LC-MS, 2-DE, and MALDI TOF/TOF were effectively used to study the molecular reactions of oil palms to G. boninense infection. There was still a lack of proteomic data with other research on certain protein classes, and other post-translationally modified proteins which may contribute to an incomplete understanding of G. boninense pathogenicity. It is recommended that these gaps be addressed by employing advanced proteomic approaches in future studies to facilitate a deeper comprehension of complex protein interactions. The analysis of proteomic methods employed for studying G. boninense is essential for the oil palm sector, as this strategy can contribute to valuable insights that can advance strategies for early detection of the pathogen infection.

Using Partition Information Entropy to Computationally Rank Order Critical Subreactions in a Petri Net Model of a Biochemical Signaling Network.

Improved computational methods to analyze the mathematical structure and function of biochemical networks are needed when the biomolecular connectivity is known but when a complete set of the equilibrium and rate constants may not be available. We use Petri nets, which are equivalently bipartite digraphs, to analyze the rule-based flow of information through the network. We present several computational improvements to Petri net modeling as an aid to improve this approach, previously limited by the combinatorics of network size and complexity. The generation of Petri nets using equations for three elemental stencils (molecular reaction, synthesis complex formation, and decomposition complex formation) has been automated. A set of finite probability measures is defined in terms of a partition information entropy, where the complete listing of unique minimal cycles (UMCs) of the Petri net provides the natural partitioning. This enables the ranking of the UMC listing that covers all possible information flows in the reaction network; the information entropy measure enables the identification of which UMCs are more significant than others. In terms of the information entropy, forward cycles are less surprising and carry less information entropy, whereas backward cycles carry more information entropy and serve as regulators by providing feedback to control the network. As the systems analyzed increase in size and complexity, the automatic rank ordering of the UMCs provides a mechanism to highlight the globally most important information without the need to make local simplifying modeling choices. The information entropy metric is also used to compute source-to-sink information costs and is related to knockout analyses. The hybrid Petri net approach shows the most important species and where it is easiest to disrupt or otherwise affect the network. As exemplar, the enhanced methodology is applied to a model of the initial subnetwork in the EGFR network.

Biomimetic membrane in a microfluidic chip for the electrical and optical monitoring of biological reactions.

Biological membranes separate distinct inner and outer compartments through the organization of fluid lipids into two-dimensional bilayers. The specific lipid composition varies across different membrane types. Model membranes play a crucial role in replicating certain features of biological membranes. They provide invaluable insights to decipher reactions at biological membranes in physicochemical cues. In this Protocol, we present a comprehensive procedure for creating a biomimetic membrane that encompasses key characteristics of biological membranes. Each leaflet of this horizontal and large (~10,000 µm2) membrane is obtained from a separate set of liposomes, allowing control of the lipid distribution between the two bilayer leaflets. Suspended in a vertical conduit separating two controllable horizontal microfluidic channels, this membrane can be used for the reconstitution of chemical or molecular reactions in close proximity to the membrane on the desired leaflet. The microfluidic chip containing the two channels separated by the vertical conduit is made of poly(dimethylsiloxane) and is fabricated from resin molds. Initially, oil is trapped in the conduit. Liposome solutions are pushed in each channel and spread on the trapped oil-buffer interface, forming a separate leaflet facing each channel. As oil is absorbed by poly(dimethylsiloxane), the two leaflets assemble and form a bilayer. We outline four applications of this biomimetic membrane microfluidic setup, incorporating optical microscopy and/or electrical readouts (patch-clamp amplifiers): single-particle and global diffusion, membrane fusion and channel formation. The entire protocol, covering chip fabrication, membrane formation and various measurements, can be completed within 2-3 d.

Impact Assessment and Evaluation of Micro(nano)plastics Exposure in the Human Health System: A Review

Abstract The formation of micro and nano plastics (MNPs) and their exposure to the environment and human health system are new global problems currently being studied by scientific communities. MNPs can enter the body of a person in several ways, such as through the skin, ingestion, respiration, seafood, packed food materials, water, air, and cosmetics. Organ and tissue damage may result from the reactive oxygen species imbalance. The prolonged exposure of human health to plastics and polymer additives leads to asthma, bronchitis, pneumonia, and other respiratory problems. This review analyzes the problems associated with humans and animals due to the exposure of MNPs and focuses on their impacts due to biomagnification through various biological and chemical functions. A deeper comprehension of cellular and molecular level reactions of MNP contamination to the associated environment is also discussed in this review.

The Beauty of Serendipity in Organic Synthesis: Unveiling Unexpected Pathways to Novel Molecules

Abstract Serendipity has long been a fundamental component of scientific research, particularly in the area of chemical synthesis. This idea, which is frequently connected to chance encounters and unexpected results, has led to the creation of new molecular structures, routes, and reactions. Serendipitous events have influenced the development of organic chemistry, from the unintentional discovery of important intermediates to the fortunate improvement of synthetic techniques. This review sheds in‐depth light on the often overlooked yet transformative role of chance observations and unexpected outcomes in shaping the landscape of modern synthetic chemistry. We have gathered intriguing accidental discoveries of organic processes and compounds from 2009 to the present. We believe that this article will undoubtedly inspire young researchers to examine their unexpected findings rather than dismiss them as useless.

Identification of Porrocaecum moraveci in red kites in England and Wales, a species of conservation concern

The population of free-living red kites (Milvus milvus [Linnaeus 1758]) in England and Wales has increased since 1989 as a consequence of species reintroduction. The red kite, however, remains of conservation concern, with populations in Europe considered to be in decline. Plans to translocate birds from England to Spain have been initiated, prompting consideration of the disease risks associated with the translocation of parasites which may be present within the source population. This study utilized published morphological markers and molecular polymerase chain reaction techniques to identify archived adult helminth parasites extracted from the gastrointestinal tract of red kites found dead and examined post-mortem in England and Wales between 2014 and 2021. Helminths of the genus Porrocaecum (Railliet and Harry 1912) were identified in 22 out of the 23 helminth-infected red kites from a wide geographical distribution, suggesting that this parasite is common in the red kite population in England and Wales. Molecular characterization using internal transcribed spacer 2 (ITS-2) and 28S rDNA sequences identified Porrocaecum moraveci (Gu et al. 2023), the first report of this recently described species in the UK. Ascaridia (Dujardi 1845) sp., Capillaria (Zeder 1800) sp., and Syngamus trachea (Montagu 1811) ova were also detected during the post-mortem examinations (PMEs) and are known to be present within European red kite populations, suggesting that these parasites do not represent a novel disease risk to the destination population in Spain. Previous reports of Porrocaecum angusticolle (Molin, 1860) in British and other European red kite populations should now be revisited to confirm identity and assess the risk of parasite translocation.

Biosensors and lateral flow immunoassays: Current state and future prospects.

Biosensors and lateral flow immunoassays: Current state and future prospects.

Anisotropic atom motion on a row-wise antiferromagnetic surface

Diffusion on surfaces is a fundamental process in surface science, governing nanostructure and film growth, as well as molecular self-assembly, chemical reactions and catalysis. Atom motion on non-magnetic surfaces has been studied extensively both theoretically and by real-space techniques such as field ion microscopy and scanning tunneling microscopy. For magnetic surfaces ab-initio calculations have predicted strong effects of the magnetic state onto adatom diffusion, but to date no corresponding experimental data exists. Here, we investigate different atoms on the hexagonal Mn monolayer on Re(0001) using scanning tunneling microscopy at T = 4.2 K and density functional theory. Experimentally, we observe one-dimensional motion of Co, Rh, and Ir atoms on the hexagonal Mn layer, dictated by the row-wise antiferromagnetic state. Co atoms move up to 10 nm when their motion is initiated by local voltage pulses. Our calculations reveal anisotropic potential landscapes, which favor one-dimensional motion for both Rh and Co atoms, avoiding induced Rh spin moments and conserving the Co spin direction during movement, respectively. These findings demonstrate that the magnetic properties of a system can be a means to control adatom mobility, even in the case of non-magnetic adatoms.

Salinity Tolerance in Wheat: Mechanisms and Breeding Approaches.

High salinity and other abiotic stressors severely limit the productivity of wheat (Triticum aestivum L.). Wheat is a moderately salt-tolerant crop, and its salinity tolerance has been extensively studied due to the fact that it is one of the most essential food crops. It is essential to comprehend the mechanisms underlying salinity tolerance and create adaptable wheat types. In this paper, the morphological adaptations in wheat were first introduced under salinity stress, then the main physiological, biochemical and molecular reactions of wheat to salinity stress were summarized in detail. In addition, the advances in breeding approaches to salinity tolerance in wheat through germplasm evaluation, screening and gene editing were generally reviewed. Finally, proposals for further research or possible challenges in this process were also discussed. Our review will provide references for improving salt tolerance of wheat and for breeding salt-tolerant varieties.

When theory came first: a review of theoretical chemical predictions ahead of experiments

Abstract For decades, computational theoretical chemistry has provided critical insights into molecular behavior, often anticipating experimental discoveries. This review surveys twenty notable examples from the past fifteen years in which computational chemistry successfully predicted molecular structures, reaction mechanisms, and material properties before experimental confirmation. By spanning fields such as bioinorganic chemistry, materials science, catalysis, and quantum transport, these case studies illustrate how quantum chemical methods have become essential for multidisciplinary molecular sciences. The impact of theoretical predictions across disciplines shows the indispensable role of computational chemistry in guiding experiments and driving scientific discovery.

CRISPR-Cas12a/Aurora Deoxyribozyme Cascade: A Label-Free Ultrasensitive Platform for Rapid Salmonella Detection.

The rapid and ultrasensitive detection of Salmonella holds strategic significance for food safety surveillance and public health protection systems. This study innovatively developed a label-free biosensing platform based on the synergistic integration of Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-Cas12a and the fluorescent deoxyribozyme Aurora for the efficient detection of foodborne Salmonella. The detection mechanism operates through a molecular cascade reaction: target-activated Cas12a protein specifically degrades Aurora deoxyribozyme via its trans-cleavage activity, thereby abolishing the enzyme's catalytic capability to convert 4-methylumbelliferyl phosphate (4-MUP) into the highly fluorescent product 4-methylumbelliferone (4-MU). This cascade ultimately enables quantitative target analysis through fluorescence signal attenuation. Following systematic optimization of critical reaction parameters, the biosensing system demonstrated exceptional analytical performance: a detection limit of 1.29 CFU/mL with excellent linearity (R2 = 0.992) spanning six orders of magnitude (1.65 × 101-106 CFU/mL), along with high specificity against multiple interfering bacterial strains. Spike-and-recovery tests in complex food matrices (milk, chicken, and lettuce) yielded recoveries of 90.91-99.40% (RSD = 3.55-4.72%), confirming robust practical applicability. Notably, the platform design allows flexible detection of other pathogens through simple replacement of CRISPR guide sequences.

Rational Approaches toward the Design and Synthesis of Carbon Nanothreads.

ConspectusCarbon-based materials─often with superlative electronic, mechanical, chemical, and thermal properties─are often categorized by dimensionality and hybridization. Most of these categories are produced in high-temperature conditions that afford equilibrium-dictated structures, but limit their diversity. In contrast, an emerging class of one-dimensional (1D) carbon materials, coined nanothreads, are accessible through kinetically controlled solid-state reactions of small multiply unsaturated molecules. While abundant in molecular organic synthesis, exerting kinetic control over reactivity is a revolutionary approach to access dense carbon networks. Owing to their internal diamond-like core, these materials are calculated to span a wide range of mechanical and optical properties, with the introduction of functional groups and/or heteroatoms leading to tailorable band gaps and the potential to access electronic states that are not featured in traditional polymers or nanomaterials. Accessing these properties requires the ability to precisely control solid-state molecular reaction pathways, chemical connectivity, and heteroatom/functional group density. Carbon nanothreads are often synthesized through the pressure-induced polymerization of aromatic molecules (e.g., benzene, pyridine, and thiophene) upon compression to 23-40 GPa. While the high pressures required to achieve these crystalline materials often preclude making synthetically viable quantities of product, the use of lessened aromatic reactants, along with light and/or heat, enables more mild reaction pressures. Success to date in forming nanothreads from diverse reactants suggests that physical organic principles govern the reaction, along with topochemical relationships, enabling the emergence of a new field of carbon chemistry that combines the control of organic chemistry with the range of physical properties only possible in extended periodic solids.In this Account, we describe our efforts to rationally synthesize carbon nanothreads with desired structures and present our approaches to dictate reactivity in the organic solid state that enable the formation of crystalline 1D carbon materials. In particular, we focus on the principles being pursued by our group to expand the chemical diversity of materials being accessed, while highlighting efforts that both enhance selectivity over reaction pathways and reduce pressure requirements for polymerization. We begin by leveraging starting materials with lessened or no aromaticity (relative to benzene) to design new backbones while enabling lower pressures such as 15-20 GPa to achieve nanothread formation. Next, we discuss efforts to utilize photochemical activation as a means to dictate the reaction pathway and/or affect the mechanism while also achieving ordered crystalline solids at reduced pressures. Lastly, we highlight efforts to demonstrate kinetic control in solid-state reactions by leveraging supramolecular chemistry (e.g., aryl/perfluoroaryl interactions, hydrogen bonds, π-π stacking) to preorganize starting materials into polymerizable molecular stacks. The resultant design principles provide multiple opportunities to attain previously inaccessible sp3-rich 1D polymeric carbon nanomaterials with unique structures and properties from widely available small molecules. Moreover, the kinetic control provided in the organic solid state enables a priori functionalization and the design of a rich diversity of materials with emergent properties in stark contrast to many well-developed carbon materials.