Molecular dynamics simulation study of the effect of a strong electric field on the structure of a poly(oxyethylene) chain in explicit solvents

A study of forecasting the Nephila clavipes silk fiber's ultimate tensile strength using machine learning strategies

Recent advancements in biomaterial research conduct artificial intelligence for predicting diverse material properties. However, research predicting the mechanical properties of biomaterial based on amino acid sequences have been notably absent. This research pioneers the use of classification models to predict ultimate tensile strength from silk fiber amino acid sequences, employing logistic regression, support vector machines with various kernels, and a deep neural network (DNN). Remarkably, the model demonstrates a high accuracy of 0.83 during the generalization test. The study introduces an innovative approach to predicting biomaterial mechanical properties beyond traditional experimental methods. Recognizing the limitations of conventional linear prediction models, the research emphasizes the future trajectory toward DNNs that can adeptly capture non-linear relationships with high precision. Moreover, through comprehensive performance comparisons among diverse prediction models, the study offers insights into the effectiveness of specific models for predicting the mechanical properties of certain materials. In conclusion, this study serves as a pioneering contribution, laying the groundwork for future endeavors and advocating for the seamless integration of AI methodologies into materials research.

Dynamics and Internal Structure Evolution during the Glass Transition of the Ethylene-Cyclic Olefin Copolymers: A Molecular Dynamics Simulation

Amorphous ethylene-cyclic olefin copolymers (COCs) which can be used in cell phone lenses and prefilled syringes have attracted increasing attention due to their excellent and tunable thermal properties. In order to better explain the influence of COC microstructure (cyclic olefin types and content) on the glass transition mechanism, we used molecular dynamics (MD) simulations to track the evolution of free volume, diffusion coefficients, atomic mobility, trans conformation probabilities, and characteristic parameters of α-relaxation kinetics during the quenching process. MD results show that for the classic COC E-co-NB (ethylene-norbornene copolymer), an increase in cyclic olefin content from 25 to 50 mol % reduces atomic mobility, limiting the molecular chain movement at higher temperatures and improving Tg. Compared to NB, the more rigid rings in tricyclopentadiene (TCPD) and exo-1,4,4a,9,9a,10-hexahydro-9,10(1',2')-bridged phenylidene-1,4-bridged methylideneanthracene (HBM) have the following effects: (1) reducing the thermal expansion coefficient and overall chain mobility; (2) enhancing the diffusion energy barrier; (3) promoting the formation of local ordered structures; (4) accelerating α-relaxation dynamics at high temperatures and improving the dynamic fragility m. These lead to an upward shift in the temperature region where chain movement is limited and thus improve Tg and high-temperature dimensional stability. In this simulation, the correlation equation between Tg, m, and the microstructural parameters of COCs is established, which is of great significance for the development of COCs with high performance.

Unraveling the Mechanical Property Decrease of Electrospun Spider Silk: A Molecular Dynamics Simulation Study

This study investigated the impact of electric fields on Nephila clavipes spider silk using molecular dynamics modeling. Electric fields with varying amplitudes and directions were observed to disrupt the β sheet structure of spider silk and reduce its mechanical properties. However, a notable exception was observed when a 0.1 V/nm electric field was applied in the antiparallel direction, resulting in improvements in Young's modulus and ultimate tensile strength. The antiparallel direction was observed to be particularly sensitive to electric fields, causing disruptions in beta sheets and hydrogen bonds, which significantly influence the mechanical properties. This study demonstrates that spider silk maintains its structural integrity at 0.1 V/nm. Possibly, lowering the power levels of typical electrospinning machines can prevent secondary structural disruption. These findings provide valuable insights for enhancing silk fiber production and applications using natural silk proteins while shedding light on the impact of electric fields on other silk proteins. Finally, this study opens up possibilities for optimizing electrospinning processes to enhance performance in various silk electrospinning applications.

On the Behavior of the Ethylene Glycol Components of Polydisperse Polyethylene Glycol PEG200

Molecular dynamics (MD) simulations are reported for [polyethylene glycol (PEG)200], a polydisperse mixture of ethylene glycol oligomers with an average molar weight of 200 g·mol^-1. As a first step, available force fields for describing ethylene glycol oligomers were tested on how accurately they reproduced experimental properties. They were found to all fall short on either reproducing density, a static property, or the self-diffusion coefficient, a dynamic property. Discrepancies with the experimental data increased with the increasing size of the tested ethylene glycol oligomer. From the available force fields, the optimized potential for liquid simulation (OPLS) force field was used to further investigate which adjustments to the force field would improve the agreement of simulated physical properties with experimental ones. Two parameters were identified and adjusted, the (HO)-C-C-O proper dihedral potential and the polarity of the hydroxy group. The parameter adjustments depended on the size of the ethylene glycol oligomer. Next, PEG200 was simulated with the OPLS force field with and without modifications to inspect their effects on the simulation results. The modifications to the OPLS force field significantly decreased hydrogen bonding overall and increased the propensity of intramolecular hydrogen bond formation at the cost of intermolecular hydrogen bond formation. Moreover, some of the tri- and more so tetraethylene glycol formed intramolecular hydrogen bonds between the hydroxy end groups while still maintaining strong intramolecular interactions with the ether oxygen atoms. These observations allowed the interpretation of the obtained RDFs as well as structural properties such as the average end-to-end distances and the average radii of gyration. The MD simulations with and without the modifications showed no evidence of preferential association of like-oligomers to form clusters nor any evidence of long-range ordering such as a side-by-side stacking of ethylene glycol oligomers. Instead, the simulation results support the picture of PEG200 being a random mixture of its ethylene glycol oligomer components. Finally, additional MD simulations of a binary mixture of tri-and hexaethylene glycol with the same average molar weight as PEG200 revealed very similar structural and physical properties as for PEG200.

Molecular dynamics of preferential adsorption in mixed alkali–halide electrolytes at graphene electrodes

Understanding the microscopic behavior of aqueous electrolyte solutions in contact with graphene and related carbon surfaces is important in electrochemical technologies, such as capacitive deionization or supercapacitors. In this work, we focus on preferential adsorption of ions in mixed alkali–halide electrolytes containing different fractions of Li+/Na+ or Li+/K+ and/or Na+/K+ cations with Cl− anions dissolved in water. We performed molecular dynamics simulations of the solutions in contact with both neutral and positively and negatively charged graphene surfaces under ambient conditions, using the effectively polarizable force field. The simulations show that large ions are often intuitively attracted to oppositely charged electrodes. In contrast, the adsorption behavior of small ions tends to be counterintuitive. In mixed-cation solutions, one of the cations always supports the adsorption of the other cation, while the other cation weakens the adsorption of the first cation. In mixed-cation solutions containing large and small cations simultaneously, adsorption of the larger cations varies dramatically with the electrode charge in an intuitive way, while adsorption of the smaller cations changes oppositely, i.e., in a counterintuitive way. For (Li/K)Cl mixed-cation solutions, these effects allow the control of Li+ adsorption by varying the electrode charge, whereas, for LiCl single-salt solutions, Li+ adsorption is nearly independent of the electrode charge. We rationalize this cation–cation lever effect as a result of a competition between three driving forces: (i) direct graphene–ion interactions, (ii) the strong tendency of the solutions to saturate the network of non-covalent intermolecular bonds, and (iii) the tendency to suppress local charge accumulation in any region larger than typical interparticle distances. We analyze the driving forces in detail using a general method for intermolecular bonding based on spatial distribution functions and different contributions to the total charge density profiles. The analysis helps to predict whether an ion is more affected by each of the three driving forces, depending on the strength of the ion solvation shells and the compatibility between the contributions of the charge density profiles due to the ion and water molecules. This approach is general and can also be applied to other solutions under different thermodynamic conditions.