Asphaltene is the compound with the most complex structure and the most difficult degradation in oily sludge, which is the key to limit the efficiency of supercritical water oxidation treatment of oily sludge. In this paper, the supercritical water oxidation process of asphaltene was investigated in terms of free radical reaction, degradation pathway, and product generation mechanism using ReaxFF molecular dynamics simulation method. The results showed that increasing temperature, increasing O2, and increasing H2O have different effects on HO2·generation. Benzene rings undergo fusion and condensation through hydrogenation abstraction and oxygen addition reactions, subsequently breaking down into long-chain alkanes. Increasing O2 can effectively promote the ring-opening of nitrogen-containing heterocycles. -COOH is the most important intermediate fragment for CO and CO2 generation, and there is a reaction competition with -CHO3 and -CO3. When the number of oxygen molecules increases from 300 to 700, the reaction frequency of -CHO3 and -CO3 to generate CO and CO2 increases by 17.14 % and 12.77 %·H2O determines the production of H2 by controlling the number of H·radicals present. As the amount of H2O increases from 500 to 1500, the product ratio of H2 increases from 12.73 % to 21.31 %. ENVIRONMENTAL IMPLICATION: Asphaltene is the most structurally complex organic matter in oily sludge, and its presence makes it difficult for oily sludge to be completely degraded by conventional treatment methods such as pyrolysis and incineration. Polycyclic aromatic hydrocarbons (PAHs) represented by asphaltene increase the carcinogenicity and mutagenicity of oily sludge, and even irreversibly pollute soil and groundwater. Supercritical water oxidation, as an efficient organic waste treatment technology, can realize harmlessness in a green and efficient way. So the study on the mechanism of supercritical water oxidation of asphaltene is of great significance for environmental protection.

Syn furan nanothreads have all oxygen atoms arranged on one side of the thread backbone; these polar threads present intriguing opportunities in electromechanical response owing to their rigid ladder-like backbone. We retrained a C/H/O reactive force field to simulate their response to external electric field for both end-anchored individual threads and bulk nanothread crystals, contrasting the results to those for poly(vinylidene fluoride) (PVDF) polymer. Whereas the field induces a length-independent torque in PVDF through backbone rotation about σ bonds, furan-derived nanothreads generate a length-dependent torque by progressively twisting their rigid backbone. This mode of response couples the rotational history of the electric field to axial tension in the anchored thread. In simulations of densely packed syn furan nanothread crystals without anchors, the crystals pole in a field (∼3 GV/m at 300 K) similar to that seen in simulations of PVDF, suggesting that crystals of polar nanothreads can be ferroelectric.

Water has been recognized in promoting material removal, traditionally ascribed to friction reduction and thermal dissipation. However, the physicochemical interactions between water and the workpiece have often been overlooked. This work sheds light on how the physicochemical interactions that occur between water (H2O) and copper (Cu) workpiece influence material deformations during the cutting process. ReaxFF molecular dynamics simulations were employed as the primary method to study the atomistic physical and chemical interactions between the applied medium and the workpiece. Upon contact with the Cu surface, H2O dissociated into OH- ions, H+ ions, and traces of O2- ions. The OH- and O2- ions chemically reacted with Cu to form bonds that weakened the Cu-Cu bonds by elongation, while the H+ ions gained electrons and diffused into the Cu lattice as H- ions. The weakening of surface Cu bonds promoted plastic deformation and reduced the difficulty of material removal. Meanwhile, further addition of H2O molecules saw a plateau in hydrolysis and more dominance of H2O physical adsorption on Cu, which weakens the elongation of Cu-Cu bonds. While the ideal case for atomic-scale material removal was found with an optimal number of 240 H2O molecules, the presented Cu material state with more H2O molecules could account for the observations in microcutting. The constricted nature of physical adsorption and hydrogen ion diffusion in the surface layer prevented the propagation of dislocations through the surface, which subsequently caused pinning points to be closer together during chip formation as observed by smaller chip fold widths on the microscale. Theoretical and experimental analysis identified the importance of accounting for physicochemical interactions between surface media and the workpiece when considering material deformations at micronanoscale.

Polyacrylonitrile (PAN) is an important commercial polymer, bearing atactic stereochemistry resulting from nonselective radical polymerization. As such, an accurate, fundamental understanding of governing interactions among PAN molecular units is indispensable for advancing the design principles of final products at reduced processability costs. While ab initio molecular dynamics (AIMD) simulations can provide the necessary accuracy for treating key interactions in polar polymers, such as dipole-dipole interactions and hydrogen bonding, and analyzing their influence on the molecular orientation, their implementation is limited to small molecules only. Herein, we show that the neural network interatomic potentials (NNIPs) that are trained on the small-scale AIMD data (acquired for oligomers) can be efficiently employed to examine the structures and properties at large scales (polymers). NNIP provides critical insight into intra- and interchain hydrogen-bonding and dipolar correlations and accurately predicts the amorphous bulk PAN structure validated by modeling the experimental X-ray structure factor. Furthermore, the NNIP-predicted PAN properties, such as density and elastic modulus, are in good agreement with their experimental values. Overall, the trend in the elastic modulus is found to correlate strongly with the PAN structural orientations encoded in the Hermans orientation factor. This study enables the ability to predict the structure-property relations for PAN and analogues with sustainable ab initio accuracy across scales.

BACKGROUND: Coal water slurry (CWS) is a new type of liquid coal product with low pollution, which is mainly used in the chemical industry to produce syngas (CO + H2). It is of great significance to study the microscopic mechanism of CWS gasification reaction for improving the efficiency of coal gasification. In this paper, the method of molecular dynamics based on reaction force fields (ReaxFF-MD) is used to study the gasification process of CWS/O2 system at different temperatures. The results show that, in the range of 1600-2400 K, the macromolecular network structure of lignite is decomposed into a large number of small molecular structures and a small number of light tar free radical fragments, and the types and quantities of reaction products increased rapidly. At 2400-4000 K, the free radical fragments of light tar are further decomposed and reacted with gasification agents, but the types and quantities of reaction products have little change. At 3600 K, a full gasification reaction occurred in the system, and the content of syngas is the highest.
METHODS: The model was established and optimized by Materials Studio (MS) software. Based on ReaxFF-MD method, Lammps software was used to simulate the gasification process of CWS/O2 system, and the reaction force field files containing C, H, O, N, and S element were used. By calculating the activation energy of gasification reaction, the rationality of the model and calculation method was illustrated. The post-processing of the results was implemented using OVITO, ChemDraw software, and self-programmed Python scripts.

Naturally occurring coatings on aluminum metal, such as its oxide or hydroxide, serve to protect the material from corrosion. Understanding the conditions under which these coatings mechanically fail is therefore expected to be an important aspect of predictive models for aluminum component lifetimes. To this end, we develop and apply a molecular dynamics (MD) modeling framework for conducting tension tests that is capable of isolating factors governing the mechanical strength as a function of coating chemistry, defect morphology, and variables associated with the loading path. We consider two representative materials, including γ-Al2O3 and γ-Al(OH)3 (i.e., oxide and hydroxide), both of which form readily as aluminum surface coatings. Our results indicate that defects have a significant bearing on the strength of aluminum oxide, with grain boundaries serving to reduce the strain at failure from εzz = 0.300 to 0.219, relative to perfect single crystal. Our simulations also predict that porosity lowers the elastic stiffness and yield strength of the oxide. Relative to perfect crystal, we find porosity factors of 5%, 10% and 20% decrease the yield stress by 26%, 36% and 53%, respectively. MD predicts that perfect hydroxide and oxide single crystal have respective strains at failure of 0.08 and 0.31 under tensile uniaxial strain loading, and that the corresponding yield stresses are respectively 1.6 and 11.1 GPa. These data indicate that the hydroxide is substantially more susceptible to mechanical failure than the oxide. Our results, coupled with literature findings that indicate hot and humid conditions favor formation of hydroxide and defective oxide coatings, indicate the potential for a complicated dependence of aluminum corrosion susceptibility and stress corrosion cracking on aging history.

Polypropylene (PP) and polystyrene (PS) underwent a comprehensive investigation into their mechanical and chemical degradation through reactive molecular dynamics simulations. The simulations utilized the ReaxFF force field for CHO (carbon-hydrogen-oxygen) systems in the combustion branch. The study included equilibrium simulations to determine densities and melting temperatures, non-equilibrium simulations for stress-strain and Young moduli determination, mechanical cleaving to identify surface species resulting from material fragmentation, and shock compression simulations to elucidate chemical reactions activated by some external energy sources. The results indicate that material properties such as densities, phase transition temperatures, and Young moduli are accurately reproduced by the ReaxFF-CHO force field. The reactive dynamics analysis yielded crucial insights into the surface composition of fragmented polymers. Both polymers exhibited backbone breakage, leaving -CH2· and -CH·- radicals as terminals. PP demonstrated substantial fragmentation, while PS showed a tendency to develop crosslinks. A detailed analysis of chemical reactions resulting from increasing activation due to increasing value of compression pressure is presented and discussed.

The combination of Al nanoparticles (ANPs) as fuel and H2O2 as oxidizer is a potential green space propellant. In this research, reactive force field molecular dynamics (ReaxFF-MD) simulations were used to study the influence of water addition on the combustion of Al/H2O2. The MD results showed that as the percentage of H2O increased from 0 to 30%, the number of Al-O bonds on the ANPs decreased, the number of Al-H bonds increased, and the adiabatic flame temperature of the system decreased from 4612 K to 4380 K. Since the Al-O bond is more stable, as the simulation proceeds, the number of Al-O bonds will be significantly higher than that of Al-H and Al-OH bonds, and the Al oxides (Al[O]x) will be transformed from low to high coordination. Subsequently, the combustion mechanism of the Al/H2O2/H2O system was elaborated from an atomic perspective. Both H2O2 and H2O were adsorbed and chemically activated on the surface of ANPs, resulting in molecular decomposition into free radicals, which were then captured by ANPs. H2 molecules could be released from the ANPs, while O2 could not be released through this pathway. Finally, it was found that the coverage of the oxide layer reduced the rate of H2O2 consumption and H2 production significantly, simultaneously preventing the deformation of the Al clusters\' morphology.

EPI-X4, an endogenous peptide inhibitor, has exhibited potential as a blocker of CXCR4-a G protein-coupled receptor. This unique inhibitor demonstrates the ability to impede HIV-1 infection and halt CXCR4-dependent processes such as tumor cell migration and invagination. Despite its promising effects, a comprehensive understanding of the interaction between EPI-X4 and CXCR4 under natural conditions remains elusive due to experimental limitations. To bridge this knowledge gap, a simulation approach was undertaken. Approximately 150,000 secondary structures of EPI-X4 were subjected to simulations to identify thermodynamically stable candidates. This simulation process harnessed a self-developed reactive force field operating within the ReaxFF framework. The application of the Two-Phase Thermodynamic methodology to ReaxFF facilitated the derivation of crucial thermodynamic attributes of the EPI-X4 conformers. To deepen insights, an ab initio density functional theory calculation method was employed to assess the electrostatic potentials of the most relevant (i.e., stable) EPI-X4 structures. This analytical endeavor aimed to enhance comprehension of the inhibitor\'s structural characteristics. As a result of these investigations, predictions were made regarding how EPI-X4 interacts with CXCR4. Two pivotal requirements emerged. Firstly, the spatial conformation of EPI-X4 must align effectively with the CXCR4 receptor protein. Secondly, the functional groups present on the surface of the inhibitor\'s structure must complement the corresponding features of CXCR4 to induce attraction between the two entities. These predictive outcomes were based on a meticulous analysis of the conformers, conducted in a gaseous environment. Ultimately, this rigorous exploration yielded a suitable EPI-X4 structure that fulfills the spatial and functional prerequisites for interacting with CXCR4, thus potentially shedding light on new avenues for therapeutic development.

Predicting material properties of 3D printed polymer products is a challenge in additive manufacturing due to the highly localized and complex manufacturing process. The microstructure of such products is fundamentally different from the ones obtained by using conventional manufacturing methods, which makes the task even more difficult. As the first step of a systematic multiscale approach, in this work, we have developed an artificial neural network (ANN) to predict the mechanical properties of the crystalline form of Polyamide12 (PA12) based on data collected from molecular dynamics (MD) simulations. Using the machine learning approach, we are able to predict the stress-strain relations of PA12 once the macroscale deformation gradient is provided as an input to the ANN. We have shown that this is an efficient and accurate approach, which can provide a three-dimensional molecular-level anisotropic stress-strain relation of PA12 for any macroscale mechanics model, such as finite element modeling at arbitrary quadrature points. This work lays the foundation for a multiscale finite element method for simulating semicrystalline polymers, which will be published as a separate study.