Reaction ensemble Monte Carlo simulations in the isothermal isobaric ensemble have been performed to study the thermodynamics of gas phase hydrodeoxygenation of phenol to benzene (C6H5OH + H-2 (sic) C6H6+H2O ) using continuous fractional component Monte Carlo (CFCMC) approach via Brick-CFCMC open source software package. We have explored the use of single-site and two-site models for H2, with TraPPE-EH force field to describe the interactions present in phenol and benzene, and TIP4P water model. Simulations have been performed at elevated temperatures from 973.15-1273.15 K, and at 1 bar. Equilibrium mole fraction of benzene (and consequently, equilibrium conversion of phenol) reduces with increase in temperature. From our simulations, ln K values are greater than unity in the temperature range 973.15 K to 1073.15 K, between zero and unity at 1123.15 K and 1173.15 K, and negative at the two highest temperatures (i.e. 1223.15 K and 1273.15 K), showing qualitative agreement with literature reported data. The slope of ln K vs 1/T estimated from our simulations result in negative standard heat of reaction (Delta H-0), further confirming exothermic nature of the reaction. Our Rx/CFCMC simulations with both H-2 models yield similar values of equilibrium mole fractions of benzene, conversion of phenol and ln K over entire elevated temperature range.
Polymer conformations in solution are central to self-assembly and related applications. However, the mechanisms by which cooperative associations influence conformational transitions remain incompletely understood. Cooperative association refers to correlations between the association states of neighbouring monomers. In this study, a coarse-grained Monte Carlo simulation protocol was developed to model reversible polymer-solvent association with an explicit nearest-neighbour cooperative term. Polymer conformational transitions in non-cooperative association systems (NCS) and cooperative association systems (CS) were compared, showing that local association cooperativity can substantially alter transition behaviour within this generic model. The underlying mechanisms were investigated by analyzing association-state and chain-conformation distributions at systematically estimated transition points. NCS displayed a single-mode, binomial-like association distribution, whereas CS exhibited bimodal association distributions coupled to coexistence-like collapsed and extended conformational states. This coexistence was further examined by free-energy calculations using umbrella sampling. Finally, a reduced analytical model based on consecutive association sequences was developed, showing good agreement with shorter-chain simulations and supporting the interpretation that sequence-level cooperativity contributes to the observed transition behaviour. This study establishes a framework for simulating cooperative association and provides qualitative insight into how local cooperative interactions can shape polymer conformational transitions.
New abstract: This study explores the impact of structural modifications in wireframe DNA origami nanostructures on their mechanical properties using coarse-grained oxDNA2 simulations. A large wireframe DNA origami nanostructure was selected to enable a detailed examination of how alterations in its nanocomponents affect its stiffness. The structural components were modified using the updated version of the DAEDALUS software. Following the design process and initial relaxation stages, oxDNA2-based force applications were performed to evaluate their mechanical behaviour. The results demonstrate a significant relationship between the number of staple crossovers and the mechanical stiffness of the nanostructures. Specifically, an inverse correlation was observed, where an increase in staple crossovers leads to a reduction in stiffness. Conversely, reducing these crossovers enhances the structural stiffness. The removal of poly-T bulges was also found to decrease the stiffness, highlighting the influence of specific nanocomponent features on the overall mechanical behaviour. These findings suggest that optimising the design of individual nanocomponents can effectively improve the mechanical properties of DNA origami structures, offering insights for applications requiring enhanced mechanical stability. The study emphasises the importance of tailoring the nanocomponent arrangement to achieve desired structural rigidity in DNA-based nanostructures.
Polymers are highly versatile materials that can be enhanced further by adding nano-sized filler particles to meet the requirements of high-performance applications. To this end, coarse-grained molecular dynamics (CGMD) simulations are used to unravel the complex structure-property relation of polymer nanocomposites (PNC). While a wide range of established software is available for CGMD, creating PNC samples with the necessary flexibility, i.e. nanofiller shape and positioning, poses a challenge. To address this, we introduce a novel self-avoiding random walk (SARW) algorithm. This algorithm offers a wide range of functionalities that allow users to adjust the geometry of the simulation box and the polymer chains, including bond lengths, angle constraints, and dispersity. Additionally, the SARW provides various options for customising the shape, size, orientation, number, and positioning of nanoparticles. It effectively incorporates colloids, fibres, and platelets into the polymer matrix. The SARW is highly efficient, capable of generating systems with over 50 million beads in minutes, and is designed to be user-friendly and easily extensible. We showcase the SARW's features through practical examples and publish the associated code as open-source. Hence, this work paves the way for large-scale molecular dynamics studies on polymer melts and polymer nanocomposites, helping to unlock their full potential.
In this study, the interactions between the amino acids that constitute Cytochrome C (Cyt C) and IR-MOF-74-VI metal-organic frameworks (MOF) have been investigated using density functional theory (DFT). The main objective of this work is to exploit the potential applicability of using MOF as enzyme stabilisation. For this purpose, the interactions between 19 amino acids and IR-MOF-74-VI have been investigated. We found that the MOF is not reactive to LYS amino acid with the interaction energy (EInt) about +0.29 eV. While the MOF shows high binding affinity and low electronic perturbation to ASP, CYS, GLY, ILE, and THR amino acids. Furthermore, our DFT calculations revealed that the considered IR-MOF-74-VI represents both binding affinity toward ARG and HIS amino acids with EInt value and variation in bandgap energy (%Delta Egap) about -3.5 eV and 70%, respectively. Furthermore, the ultraviolet-visible results show that the electronic spectra of the amino acid/MOF complexes show a red shift toward higher wavelengths compared to the pristine MOF. Our Quantum Theory of Atoms in Molecule (QTAIM) calculations reveal the electrostatic nature of the amino acid/MOF bonding. Our results revealed that the IR-MOF-74-VI could be used as a potential platform for enzyme stabilisation.
The structure of a cluster plays a decisive role in determining its physical and chemical properties. However, as cluster size increases, the number of possible isomers grows exponentially, and first-principles calculations become computationally demanding, posing significant challenges for structure prediction. To address this challenge, we examined an efficient method for searching low-energy structures of atomic clusters integrating machine learning interatomic potentials (MLIPs) and a Comprehensive Genetic Algorithm (CGA). We constructed training datasets for Cu, Ag, and Au clusters and trained the Orb-v2 potential using two strategies: fine-tuning a pre-trained model and training from scratch. Our benchmarking results demonstrate that the fine-tuned model achieves significantly lower energy prediction mean absolute errors (5 similar to 7 meV/atom) compared to the pre-trained model (36 similar to 368 meV/atom). Compared to training from scratch, fine tuning a pre-trained model requires 50% less epochs to train and demonstrates better accuracy. Integrating the fine-tuned MLIP with CGA enables efficient and effective searching for low-energy structures of atomic clusters, successfully reproducing known low-energy configurations of small clusters with less than 55 atoms.
Chronic pain is a global burden, driving urgent demand for novel, effective, and non-addictive therapeutic strategies. The vanilloid receptor TRPV1 is a key nociceptive mediator in mammals, yet structural understanding of related channels in invertebrates remains unclear, limiting translational research efforts. Caenorhabditis elegans relies on TRPV-like proteins (OSM-9 and OCR-2) for chemical and thermal nociception, offering an opportunity to probe the conservation of interaction principles governing channel receptors across species. Using a combination of structural modelling, flexible docking, and molecular dynamics, we mapped capsaicin binding across homo- and heterotetrameric assemblies of TRPV1, OSM-9, and OCR-2. All receptors established capsaicin-binding complexes within conserved inter-subunit architectures. However, OSM-9 and OCR-2 exhibited distinct interaction chemistries, shifting away from the TRPV1-dominating polar and electrostatic contacts toward enriched aromatic, hydrogen bond, and sulfur-mediated contacts. Importantly, the OCR-2 binding site displayed the most favourable interaction profile, including conserved hydrogen-bonding networks at positions analogous to TRPV1's validated ligand-contact residues. These results reveal that TRPV-like channels preserve common structural features for vanilloid recognition even if different interaction chemistries drive ligand engagement. Our findings support OCR-2 binding as the primary capsaicin response in C. elegans and uncover architectural determinants that signal novel, unexplored regulatory elements in pain mechanisms.
Materials containing organic and inorganic mercury compounds are hazardous once diffused into air, water, or soil. Current methodologies for the simultaneous detection and detoxification of Hg(II) from animal tissues, aqueous samples, and plant cells are limited and require further research. Recently, an exceptionally selective fluorescent sensor (PyDMSA) has been reported for this purpose. It is prepared from a cationic dye, Pyronin Y (Py), and meso-2,3-dimercaptosuccinic acid (DMSA). This article presents the chemistry of this fluorescent sensor from a theoretical standpoint, using Density Functional Theory (DFT) with the level of theory, PBE1PBE/def2tzvp. The thermochemistry of reactions leading to the dissociation of PyDMSA and complex formation with Hg(II) in tissues are analysed computationally. The Gibbs free energies of reaction indicate spontaneity. The stability constants of the complexes [Hg-(II)(DMSA)] and [Hg-(II)(DMSA)(2)](2-) indicate their stability in aqueous medium. The bond dissociation energies of the -C-S- bond in PyDMSA and the -S-H bond in DMSA support the experimental observation of weaker bonds and dissociation. Given the need for further research on protocols for simultaneous detection and detoxification of Hg(II) and similar toxic metals from biological samples, both experimental and computational studies are beneficial for researchers designing molecular sensors for hazardous metals. [GRAPHICS] .
In recent years, depleted carbonate gas reservoirs in the Sichuan Basin have been repurposed as underground gas storage facilities for urban natural gas peak shaving, yet high concentrations of H2S remain. To ensure safe and efficient operation, understanding the adsorption and diffusion behaviour of sulfur-containing mixed gases is critical. Current studies largely focus on single gas interactions on carbonate surfaces. This paper employs Monte Carlo and molecular dynamics simulations to construct a dolomite crystal model and predict the adsorption and diffusion behaviour of multi-component gases in nanoscale dolomite. Results indicate that as dolomite slit pore size increases, the isothermal adsorption heat of H2S decreases while its adsorption amount increases up to a critical pore size, beyond which it rapidly stabilises. The diffusion coefficient of H2S exhibits a linear relationship with pore size. Elevated temperatures lower the isothermal adsorption heat of CH4 and H2S but boost their diffusion coefficients, whereas increased pressure raises CH4 adsorption and reduces H2S adsorption. In multi-component systems with cushion gas injection, CO2 dominates adsorption, and the gas diffusion order is N-2 > CH4 > H2S > CO2, suggesting CO2 as cushion gas to mitigate mixing. These findings offer theoretical insights for optimising sulfur-containing gas storage operations.
To establish a comparative molecular-level understanding of how structurally distinct active components in Xanthii extract contribute to corrosion inhibition on iron, seven major active molecules were investigated using quantum chemical calculations and molecular dynamics simulations. Caffeic acid and ferulic acid exhibited the highest electron-donating ability, with E-HOMO values of -5.389 and -5.383 eV, whereas aloe emodin showed the lowest E-LUMO (-4.028 eV) and the smallest energy gap (2.003 eV). Xanthiazone combined a relatively low E-LUMO (-3.143 eV), a small energy gap (2.371 eV), and pronounced multipolarity. All molecules spontaneously adsorbed on the Fe surface in water but showed distinct configurations, ranging from flat-lying coverage to localised anchoring. RDF results showed first O-Fe peak distances of 2.11-3.13 & Aring;, all shorter than that of water O atoms (3.29 & Aring;), while the corresponding peak intensities ranged from 9.2-23.7, all exceeding that of water O atoms (6.50). MSD analysis showed that caffeic acid had the lowest diffusion coefficient (5.17 & times; 10(-9) cm(2) s(-1)), whereas tomentosin and xanthatin had the highest values (1.52 & times; 10(-7) and 1.46 & times; 10(-7) cm(2) s(-1)). These results clarify structure-dependent adsorption mechanisms and help identify key corrosion-inhibiting components in Xanthii extract.
The mechanical integrity of composite solid propellants is critically governed by the interfacial mechanics of binder-filler interactions. However, the atomistic mechanisms of functional groups in neutral polymeric bonding agents (NPBAs) remain insufficiently understood, especially in the typical multilayer cross-linked interfaces of propellant systems. Here, we develop a multiscale simulation framework integrating molecular dynamics and density functional theory to elucidate how representative functional groups (OH, CH3, and CN) regulate the mechanical behaviour of a typical propellant interface system composed of cyclotetramethylene tetranitramine (HMX), NPBAs, glycidyl azide polymer (GAP), and the N-100 polyisocyanate curing agent. Hydroxyl groups form dynamic hydrogen-bond networks that elevate the glass transition temperature to 260.5 K and exhibit the strongest binding with HMX (728.2 kJ/mol), leading to similar to 19% higher interfacial yield stress. In the crosslinked three-layer architecture, a functional specialisation emerges: CN group primarily enhances bulk rigidity through dipole-induced interchain constraints, whereas densely packed CH3 groups unexpectedly dominate interfacial adhesion via van der Waals accumulation under sterically confined conditions. These findings provide mechanistic insight into the cooperative yet differentiated roles of NPBA functional groups and offer guidance for molecular-level design of high-reliability propellant interfaces.
