Molecular Reaction Research Articles

EM-HyChem: Bridging molecular simulations and chemical reaction neural network-enabled approach to modelling energetic material chemistry

EM-HyChem: Bridging molecular simulations and chemical reaction neural network-enabled approach to modelling energetic material chemistry

Revealing global reaction mechanisms of polypropylene pyrolysis by reactive molecular dynamic simulation and reaction class prediction

Revealing global reaction mechanisms of polypropylene pyrolysis by reactive molecular dynamic simulation and reaction class prediction

Simulation of molecular reaction kinetics of gas and coal dust mixture explosion suppression by MPP based on machine learning potential

Simulation of molecular reaction kinetics of gas and coal dust mixture explosion suppression by MPP based on machine learning potential

Recent Advances in the Synthesis of Quinolines: A Focus on Oxidative Annulation Strategies

Quinoline, a heterocyclic scaffold of paramount importance in medicinal and industrial chemistry, has garnered significant attention due to its versatile application. Traditional synthetic methods, dating back over a century, have evolved into innovative strategies leveraging catalytic C–H bond activation, transition-metal-free protocols, and photo-induced oxidative cyclization. Recent advancements highlight the synergistic roles of catalysts, oxidants, and solvents in enhancing molecular reactivity and reaction efficiency. This review systematically summarizes state-of-the-art oxidative annulation techniques for quinoline synthesis, emphasizing mechanistic insights and practical applications.

Tracking nuclear wave packets in excited-state reactions via quantum mechanics/molecular dynamics simulations.

Nuclear wave packets (NWPs) in electronically excited states generated by ultrashort laser pulses can persist through photochemical processes and be detected in the product state. The NWPs that are coupled with the reaction dynamics undergo changes during the process and provide crucial insights into potential energy surfaces and molecular reaction dynamics. We present a computational method to calculate NWPs in the products of ultrafast photochemical processes by projecting nuclear displacements, obtained via Born-Oppenheimer molecular dynamics simulations, onto the normal modes of the reaction product state. Applying this approach to the excited-state intramolecular proton transfer reaction of 10-hydroxybenzo[h]quinoline, we successfully reproduced the experimentally observed NWPs in the reaction product, which were measured by time-resolved fluorescence of the product state with high fidelity. This significant achievement enables the analysis of individual normal mode motions following photoexcitation in chemical and physical processes. By integrating highly time-resolved spectroscopy with computational modeling, this method provides an effective approach to investigate the excited-state potential energy surfaces and the associated nuclear dynamics.

Relating the early‐age reaction kinetics and material property development in a model metakaolin geopolymer

AbstractGeopolymers, a class of alkali‐activated binders, are studied as sustainable alternatives to Ordinary Portland Cement due to their potential for CO2 emission reduction. However, the critical relationship between early‐age reaction kinetics, the development of material properties, and evolving chemical structure remains insufficiently explored, primarily because of the complexity of the underlying chemical reactions and the wide variety of geopolymer chemistries. To address this, we investigate the mechanism of early‐age (<72 h) strength development of a model metakaolin geopolymer by measuring curing kinetics using isothermal calorimetry, material property development via rheology, and chemical coordination at distinct extents of reaction via 29Si and 27Al NMR. A novel approach of collecting solid‐state 29Si and 27Al NMR spectra at low temperature (−17°C) successfully quenches the geopolymer reaction, allowing for spectrum collection at a desired extent of reaction despite long 29Si NMR spectrum collection times. Applying the Avrami kinetic model to deconvoluted calorimetry data enables independent analysis of dissolution and polycondensation/crosslinking reactions. From these data, the gel reaction product mass fraction is estimated, revealing an exponential relationship with the storage modulus in the activated metakaolin slurry. This study provides new insights into the interconnected dynamics of molecular chemistry, reaction kinetics, rheology, and strength development, offering a semi‐empirical framework for understanding property evolution in geopolymers more broadly.

ChemVLM: Exploring the Power of Multimodal Large Language Models in Chemistry Area

Large Language Models (LLMs) have achieved remarkable success and have been applied across various scientific fields, including chemistry. However, many chemical tasks require the processing of visual information, which cannot be successfully handled by existing chemical LLMs. This brings a growing need for models capable of integrating multimodal information in the chemical domain. In this paper, we introduce ChemVLM, an open-source chemical multimodal large language model specifically designed for chemical applications. ChemVLM is trained on a carefully curated bilingual multimodal dataset that enhances its ability to understand both textual and visual chemical information, including molecular structures, reactions, and chemistry examination questions. We develop three datasets for comprehensive evaluation, tailored to Chemical Optical Character Recognition (OCR), Multimodal Chemical Reasoning (MMCR), and Multimodal Molecule Understanding tasks. We benchmark ChemVLM against a range of open-source and proprietary multimodal large language models on various tasks. Experimental results demonstrate that ChemVLM achieves competitive performance across all evaluated tasks.

Purification and Characteristics of Waste Lubricating Oil as Diesel-Like Fuel

Abstract This study aims to utilize used lubricating oil (OLB) in diesel-like fuel. OLB is processed by distillation method, heating 600 watts for 120 minutes and varying stirring speeds of 0, 300, and 500 rpm. The results of the highest distillation temperature test at 500 rpm stirring and a molecular cracking reaction occurs so that the long chain is easily cracked. The characteristics of LLB obtained are flash point 144 (PMcc,0°C), density 858.3 (kg/m3), viscosity 3,107 (cSt), and calorific value 10,967 (kcal/kg).

Klotho improves Der p1-induced bronchial epithelial cell damage by inhibiting endoplasmic reticulum stress to regulate mitochondrial function

Klotho improves Der p1-induced bronchial epithelial cell damage by inhibiting endoplasmic reticulum stress to regulate mitochondrial function

Fine-Tuned Global Neural Network Potentials for Global Potential Energy Surface Exploration at High Accuracy.

Machine learning potential (MLP), by learning global potential energy surfaces (PES), has demonstrated its great value in finding unknown structures and reactions via global PES exploration. Due to the diversity and complexity of the global PES data set, an outstanding challenge emerges in achieving PES high accuracy (e.g., error <1 meV/atom), which is essential to determine the thermodynamics and kinetics properties. Here, we develop a lightweight fine-tuning MLP architecture, namely, AtomFT, that can explore PES globally and simultaneously describe the PES of a target system accurately. The AtomFT potential takes the pretrained many-body function corrected global neural network (MBNN) potential as the basis potential, exploits and iteratively updates the atomic features from the pretrained MBNN model, and finally generates the fine-tuning energy contribution. By implementing the AtomFT architecture on the commonly available CPU platform, we show the high efficiency of AtomFT potential in both training and inference and demonstrate the high performance in challenging PES problems, including the oxides with low defect content, molecular reactions, and molecular crystals─in all systems, the AtomFT potentials enhance significantly the PES prediction accuracy to 1 meV/atom.

DNA calorimetric force spectroscopy at single base pair resolution

DNA hybridization is a fundamental molecular reaction with wide-ranging applications in biotechnology. The knowledge of the temperature dependence of the thermodynamic parameters of duplex formation is crucial for quantitative predictions throughout the DNA stability range. It is commonly assumed that enthalpies and entropies are temperature independent, and heat capacity changes ΔCp equal zero. However, it has been known that this assumption is a poor approximation for a long time. Here, we combine single-DNA mechanical unzipping experiments using a temperature jump optical trap with a tailored statistical analysis to derive the ten heat-capacity change parameters of the nearest-neighbor model. Calorimetric force spectroscopy establishes a groundbreaking approach to studying nucleic acids that can be further extended to chemically modified DNA, RNA, and DNA/RNA hybrid structures.

Unraveling the molecular mechanism of polysaccharide lyases for efficient alginate degradation

Alginate lyases (ALs) catalyze the depolymerization of brown macroalgae alginates, widely used naturally occurring polysaccharides. Their molecular reaction mechanism remains elusive due to the lack of catalytically competent Michaelis-Menten-like complex structures. Here, we provide structural snapshots and dissect the mechanism of mannuronan-specific ALs from family 7 polysaccharide lyases (PL7), employing time-resolved NMR, X-ray, neutron crystallography, and QM/MM simulations. We reveal the protonation state of critical active site residues, enabling atomic-level analysis of the reaction coordinate. Our approach reveals an endolytic and asynchronous syn β-elimination reaction, with Tyr serving as both Brønsted base and acid, involving a carbanion-type transition state. This study not only reconciles previous structural and kinetic discrepancies, but also establishes a comprehensive PL reaction mechanism which is most likely applicable across all enzymes of the PL7 family as well as other PL families.

Comparison of Common Bacteria That Cause Meningitis in Cerebrospinal Fluid Samples by Culture and Polymerase Chain Reaction

Background: The etiology of meningitis, an infection and inflammation of the meningeal membranes surrounding the brain and spinal cord, is multifactorial. Among the infectious etiologies are viruses, bacteria, parasites, and fungi. Objectives: The objective of this study is to utilize the molecular polymerase chain reaction (PCR) method to detect bacterial infections causing meningitis in cerebrospinal fluid (CSF) samples from hospitalized patients suspected of having meningitis. Subsequently, a comparison is made between the PCR results and the results of sample cultures obtained in the laboratories of Hamadan educational hospitals. Methods: This study was conducted on 104 CSF samples collected from hospitalized patients suspected of having meningitis at the educational hospitals of Hamadan University of Medical Sciences from February 2022 to August 2023. The most common etiological agents of bacterial meningitis were identified using culture and PCR methods. These included Escherichia coli K1, Listeria monocytogenes, Streptococcus agalactiae, Neisseria meningitidis, S. pneumoniae, and Haemophilus influenzae. Results: The mean age of the patients in this study was 31.57 ± 25.85 years. Of the participants, 53.85% were male and 46.15% were female. No bacteria were isolated from the studied samples by culture in hospital laboratories. However, the PCR method yielded the identification of five (4.81%) bacterial cases, including L. monocytogenes (0.96%), S. pneumoniae (1.92%), and S. agalactiae (1.92%), in the CSF samples under investigation. Conclusions: The findings of this study indicated a low prevalence of common bacterial infections in CSF samples. Furthermore, the study demonstrated that the molecular method is more precise and sensitive in detecting these bacteria compared to traditional culture techniques.

In Situ Polymerization of Barium Hexaferrite Ferrofluids for Poly(Ethylene) Succinate Magnetic Nanoparticle Composites

ABSTRACTThe integration of hard magnetic barium hexaferrite (BHF) nanoplatelets into a dense poly(ethylene succinate) (PES) polyester matrix produces an exciting biodegradable thermoplastic magnetic polymer nanocomposite. In this work, scandium‐substituted BHF nanoplatelets are grown and stabilized in hexadecyltrimethylammonium bromide (CTAB) surfactant and subsequently dispersed in ethylene glycol, producing a stable ferrofluid. The ferrofluid is used for an in situ step‐growth condensation polymerization reaction between the ethylene glycol‐based ferrofluid and succinic acid. The polymerized ferrofluid forms a hard magnetic nanocomposite with filler content of up to 4.5 wt% of BHF nanoplatelets, which are homogeneously dispersed within a solid polymer matrix. With a filler content 16 times higher than in previous studies, the nanocomposite was chemically analyzed using Fourier Transform Infrared (FTIR) spectroscopy, nuclear magnetic resonance (NMR) spectroscopy, and gel‐permeation chromatography (GPC) and optimized for chain length and molecular weight, reaction time and temperature, magnetic moment, and surface hardness. The polymer molecular weight was found to be 1359 g/mol with a monomer‐to‐polymer conversion of 89%. Highly dense polymer composites were characterized using thermogravimetric analysis (TGA), while magnetic properties were determined by vibrating sample magnetometry (vsm). The synthesized magnetic thermoplastic polymer composite shows excellent magnetic properties, opening the way to advanced 3D magnetic printing and biomedical applications.

Synergistic Acceleration of CO2 Electroreduction Kinetics by Oxygen Vacancy and Heterogeneous Interface for Efficient HCOOH Production

AbstractConstructing highly efficient bismuth (Bi)‐based catalysts to accelerate the sluggish kinetic process of CO2 electroreduction to HCOOH is crucial for promoting its practical application but also highly challenging. Herein, the bismuth cerium oxide catalyst integrated with dual active centers of oxygen vacancy and the heterogeneous interface is fabricated to facilitate the reduction process and enhance the CO2 electroreduction performance. It is revealed that the introduction of dual active centers endows the catalyst with a remarkably enhanced CO2 adsorption capacity and facilitates the transfer of more electrons to *CO2. Furthermore, it even steers the reaction pathway favorably toward HCOOH production. The optimization of CO2 adsorption, activation, and reaction energy barriers expedited the process of CO2 electroreduction to HCOOH. As expected, this catalyst exhibits enhanced catalytic performance with a Faradaic efficiency of 97% for HCOOH even at the current density of 300 mA cm−2. This work highlights the significant synergistic advantages of oxygen vacancies and heterogeneous interfaces in optimizing molecular adsorption, activation, and reaction energy barriers to accelerate the kinetic process.

Surface Organic Nanostructures Mediated by Extrinsic Components: from Assembly to Reaction.

Surface organic nanostructures demonstrate great potential across multidisciplinary fields ranging from molecular electronics to energy conversion and storage. In the regime of surface chemistry, their preparation generally relies on intrinsic components originally involved in the molecule-substrate systems to drive molecular assembly and reaction. The recent paradigm shift employs extrinsic components as functional mediators to precisely regulate nanostructure formation pathways and the resulting molecular nanostructures. This review highlights three categories of extrinsic modulators, including small gas molecules, metals, and their combinations, that allow the construction and modification of surface organic nanostructures through tailored assembly and reaction. In addition, their detailed roles and regulatory mechanisms at the single-molecule level are also discussed. This emerging extrinsic-components-mediated assembly and reaction methodology displayed herein enriches the preparation toolbox for surface organic nanostructures and further allows the modification of their physicochemical properties, opening new frontiers in supramolecular engineering, nanomaterials development, etc.

Molecular responses of Mytilus coruscus hemocytes to lipopolysaccharide and peptidoglycan as revealed by 4D-DIA based quantitative proteomics analysis.

Molecular responses of Mytilus coruscus hemocytes to lipopolysaccharide and peptidoglycan as revealed by 4D-DIA based quantitative proteomics analysis.

Product-feedback in the molecular interaction-based reaction–diffusion coupling system

Product-feedback in the molecular interaction-based reaction–diffusion coupling system

Microscopic and stochastic simulations of chemically active droplets.

Biomolecular condensates play a central role in the spatial organization of living matter. Their formation is now well understood as a form of liquid-liquid phase separation that occurs very far from equilibrium. For instance, they can be modeled as active droplets, where the combination of molecular interactions and chemical reactions result in microphase separation. However, so far, models of chemically active droplets are spatially continuous and deterministic. Therefore, the relationship between the microscopic parameters of the models and some crucial properties of active droplets (such as their polydispersity, their shape anisotropy, or their typical lifetime) is yet to be established. In this work, we address this question computationally, using Brownian dynamics simulations of chemically active droplets: the building blocks are represented explicitly as particles that interact with attractive or repulsive interactions, depending on whether they are in a droplet-forming state or not. Thanks to this microscopic and stochastic view of the problem, we reveal how driving the system away from equilibrium in a controlled way determines the fluctuations and dynamics of active emulsions.

Diastereomeric Configuration Drives an On-Surface Specific Rearrangement into Low Bandgap Non-Benzenoid Graphene Nanoribbons.

Stereochemistry, usually associated with the three-dimensional arrangement of atoms in molecules, is crucial in processes like life functions, drug action, or molecular reactions. This three-dimensionality typically originates from sp3 hybridization in organic molecules, but it is also present in out-of-plane sp2-based molecules as a consequence of helical structures, twisting processes, and/or the presence of nonbenzenoid rings, the latter significantly influencing their global stereochemistry and leading to the emergence of new exotic properties. In this sense, on-surface synthesis methodologies provide the perfect framework for the precise synthesis and characterization of organic systems at the atomic scale, allowing for the accurate assessment of the associated stereochemical effects. In this work, we demonstrate the importance of the initial diastereomeric configuration in the surface-induced skeletal rearrangement of a substituted cyclooctatetraene (COT) moiety-a historical landmark in the understanding of aromaticity-into a cyclopenta[c,d]azulene (CPA) one in a chevron-like graphene nanoribbon (GNR). These findings are evidenced by combining bond-resolved scanning tunneling microscopy with theoretical ab initio calculations. Interestingly, the major well-defined product, a CPA chevron-like GNR, exhibits the lowest bandgap reported to date for an all-carbon chevron-like GNR, as evidenced by scanning tunneling spectroscopy measurements. This work paves the way for the rational application of stereochemistry in the on-surface synthesis of novel graphene-based nanostructures.