Effect of water molecules at different temperatures on properties of cellulose based on molecular dynamics simulation

2023

Preprint

Self Cite

The impact of combined oil and thermal modification on the properties of bamboo was explored through macroscopic tests, but the internal mechanism remaine4d challenging to comprehend. To gain further insights, this research employed molecular dynamics simulation to estimate the mechanical properties, diffusion coefficient, cohesive energy density, and chain flexibility of bamboo fibers following oil heat treatment. A model of oil-cellulose composite was established and simulated at varying temperatures. Results showed that oil heat treatment led to higher mechanical strength and modulus of elasticity in bamboo fibers compared to untreated ones. Additionally, the increase in diffusion coefficient and cohesive energy density, as well as the optimization of cellulose chain flexibility, indicated an improvement in the fiber characteristics. Of note, the most significant enhancement in the mechanical properties of cellulose and the utilization rate of bamboo was observed after oil heat treatment at 180℃.

Section: Dynamic Simulationmentioning

confidence: 99%

Effect of Oil Heat Treatment on Mechanical Properties of Bamboo Fibers in Molecular Dynamics Simulation

Ouyang

2023

Preprint

Self Cite

“…All six sets of simulations were performed at atmospheric pressure (0.1 MPa) and 1 ns under isothermal isobaric system synthesis (NPT). The pressure of the simulated experiments was controlled using the Berendsen method, chosen to be suitable for calculating the PCFF force field of natural polymer materials [30]. The electron summation was controlled by the Ewald method, and the van der Waals force calculation was controlled by the Atom-based method [37][38][39][40].…”

Section: Dynamic Simulationmentioning

confidence: 99%

“…Molecular dynamics is a powerful tool that enables the study of macroscopic properties of materials at the microscopic molecular level [29,30]. This allows for the determination of physical parameters at a theoretical level with good generalizability of conclusions.…”

Section: Introductionmentioning

confidence: 99%

Molecular Dynamics Simulation of the Effect of Low Temperature on the Properties of Lignocellulosic Amorphous Region

Jiang

Guo

et al. 2023

Forests

Self Cite

In this paper, a molecular model of cellulose amorphous region-water molecule was developed using Materials Studio software by applying the molecular dynamics method. The effect of low temperature on the properties of the lignocellulosic amorphous region, the main component of wood, was investigated in an attempt to explain the macroscopic property changes from a microscopic perspective and to provide a theoretical basis for the safe use of wood and wood products in low-temperature environments and other related areas of research. Dynamic simulations were carried out at 20 °C, 0 °C, −30 °C, −70 °C, −110 °C and −150 °C for the NPT combinations to obtain the energy, volume, density, and hydrogen bonding change trends of their models, respectively. The changes in the microstructure of the water molecule–cellulose amorphous region model were analyzed, and the mechanical properties were calculated. The results showed that the interaction between the amorphous cellulose region and water molecules was enhanced as the temperature decreased, the density of the models increased, and the volume decreased. The number of total hydrogen bonds and the number of hydrogen bonds between water molecule–cellulose chains increased for each model, and the decrease in temperature made the cellulose molecular activities weaker. The values of G, E, and K increased with the decrease in temperature, and K/G decreased with the decrease in temperature. It shows that the decrease in temperature is beneficial to enhance the mechanical properties of the amorphous region of cellulose and increases the stiffness of the material. However, the toughness and plasticity decrease when the temperature is too low.

“…The number of hidden layer nodes is generally selected based on the empirical Equation (23). The range of the hidden layer nodes is calculated to be [3,12]. After several error calculations, the number of nodes in the hidden layer is confirmed to be 9.…”

Section: Model Parameter Settingmentioning

confidence: 99%

“…Water vapor, nitrogen, and other gases are used as protective media during this period. Heat treatment changes the wood's chemical composition and structure, improving its dimensional stability and mechanical properties [3]. Heat-treated wood is widely preferred in the market due to its environmentally friendly properties, excellent dimensional stability, and durability [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Mechanical Properties of Wood Prediction Based on the NAGGWO-BP Neural Network

Cao

2022

Forests

Self Cite

The existing original BP neural network models for wood performance prediction have low fitting accuracy and imprecise prediction results. We propose a nonlinear, adaptive grouping gray wolf optimization (NAGGWO)-BP neural network model for wood performance prediction. Firstly, the original gray wolf optimization (GWO) algorithm is optimized. We propose CPM mapping (the Chebyshev mapping method combined with piecewise mapping followed by mod operation) to generate the initial populations and improve population diversity, and an ‘S’-type nonlinear control parameter is proposed to balance the exploitation and exploration capabilities of the algorithm; an adaptive grouping strategy is also proposed, based on which the wolves are divided into the predator, wanderer, and searcher groups. The improved differential evolution strategy, the stochastic opposition-based learning strategy, and the oscillation perturbation operator are used to update the positions of the wolves in the different groups to improve the convergence speed and accuracy of the GWO. Then, the BP neural network weights and thresholds are optimized using the NAGGWO algorithm. Finally, we separately predicted heat-treated wood’s five main mechanical property parameters using different models. The experimental results show that the proposed NAGGWO-BP model significantly improved the mean absolute error (MAE), the mean square error (MSE), and the mean absolute percentage error (MAPE) of the specimens, compared with the BP, GWO-BP, and TSSA-BP algorithms. Therefore, this model has strong generalization ability and good prediction accuracy and reliability, which can fully meet practical engineering needs.