Molecular Dynamics Simulation of Poly(ethylene terephthalate) Oligomers

Wang, Qifei; Keffer, David J.; Petrovan, S.; Thomas, J. Brock

doi:10.1021/jp909762j

Cited by 50 publications

(47 citation statements)

References 52 publications

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“…Karayiannis et al (2004) used detailed atomistic simulations to study the structural, conformational, dynamic and barrier properties of the amorphous phases of two poly-isomers, poly(ethylene terephthalate) and poly(ethylene isophthalate). Wang et al (2010a) studied the thermodynamic, transport and structural properties of PET with oligomer size ranges from 1 to 10 by MD simulation. There are also some reports about the diffusion of small non-polar gases (oxygen, carbon dioxide) and water in PET (Pavel & Shanks 2005;Eslami & Müeller-Plathe 2009b;Liao et al 2013).…”

Section: Introductionmentioning

confidence: 99%

Molecular dynamics simulation of three plastic additives’ diffusion in polyethylene terephthalate

Wang

Lin

et al. 2017

Food Additives & Contaminants: Part A

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Accurate diffusion coefficient data of additives in a polymer are of paramount importance for estimating the migration of the additives over time. This paper shows how this diffusion coefficient can be estimated for three plastic additives [2-(2'-hydroxy-5'-methylphenyl) (UV-P), 2,6-di-tert-butyl-4-methylphenol (BHT) and di-(2-ethylhexyl) phthalate (DEHP)] in polyethylene terephthalate (PET) using the molecular dynamics (MD) simulation method. MD simulations were performed at temperatures of 293-433 K. The diffusion coefficient was calculated through the Einstein relationship connecting the data of mean-square displacement at different times. Comparison of the diffusion coefficients simulated by the MD simulation technique, predicted by the Piringer model and experiments, showed that, except for a few samples, the MD-simulated values were in agreement with the experimental values within one order of magnitude. Furthermore, the diffusion process for additives is discussed in detail, and four factors - the interaction energy between additive molecules and PET, fractional free volume, molecular shape and size, and self-diffusion of the polymer - are proposed to illustrate the microscopic diffusion mechanism. The movement trajectories of additives in PET cell models suggested that the additive molecules oscillate slowly rather than hopping for a long time. Occasionally, when a sufficiently large hole was created adjacently, the molecule could undergo spatial motion by jumping into the free-volume hole and consequently start a continuous oscillation and hop. The results indicate that MD simulation is a useful approach for predicting the microstructure and diffusion coefficient of plastic additives, and help to estimate the migration level of additives from PET packaging.

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Section: Introductionmentioning

confidence: 99%

Molecular dynamics simulation of three plastic additives’ diffusion in polyethylene terephthalate

Wang

Lin

et al. 2017

Food Additives & Contaminants: Part A

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show abstract

“…Time-averaged data from the smaller simulations at 280 K, 300 K, and 320 K were used to calculate the heat capacity at constant pressure and T = 300 K, c p = (∂H/∂T) p , via the linear approximation method [30]. The same basic approach was used to determine the thermal expansivities, α p = v −1 (∂v/∂T) p , at T = 300 K.…”

Section: Methodsmentioning

confidence: 99%

Thermodynamic and Transport Properties of Tetrabutylphosphonium Hydroxide and Tetrabutylphosphonium Chloride–Water Mixtures via Molecular Dynamics Simulation

Crawford

Ismail

2020

Polymers

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Thermodynamic, structural, and transport properties of tetrabutylphosphonium hydroxide (TBPH) and tetrabutylphosphonium chloride (TBPCl)–water mixtures have been investigated using all-atom molecular dynamics simulations in response to recent experimental work showing the TBPH–water mixtures capability as a cellulose solvent. Multiple transitional states exist for the water—ionic liquid (IL) mixture between 70 and 100 mol% water, which corresponds to a significant increase in water hydrogen bonds. The key transitional region, from 85 to 92.5 mol% water, which coincides with the mixture’s maximum cellulose solubility, reveals small and distinct water veins with cage structures formed by the TBP+ ions, while the hydroxide and chloride ions have moved away from the P atom of TBP+ and are strongly hydrogen bonded to the water. The maximum cellulose solubility of the TBPH–water solution at approximately 91.1 mol% water, appears correlated with the destruction of the TBP’s interlocking structure in the simulations, allowing the formation of water veins and channeling structures throughout the system, as well as changing from a subdiffusive to a near-normal diffusive regime, increasing the probability of the IL’s interaction with the cellulose polymer. A comparison is made between the solution properties of TBPH and TBPCl with those of alkylimidazolium-based ILs, for which water appears to act as anti-solvent rather than a co-solvent.

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“…The obtained α L and c p at each temperature was calculated based on the averaged data over the second 50 ps period. To calculate the properties of α L and c p , the centered finite difference method was employed

α_{L} = \frac{1}{L (P, T)} \frac{L (P, T + ε_{T}) - L (P, T - ε_{T})}{2 ε_{T}}

c_{p} = \frac{H (P, T + ε_{T}) - H (P, T - ε_{T})}{2 ε_{T}}

where L is the length of the simulation cell, H is the enthalpy, P is pressure, T is temperature, and ɛ T is the temperature difference.…”

Section: Methodsmentioning

confidence: 99%

Interatomic Potential Model Development: Finite‐Temperature Dynamics Machine Learning

Wang

Shin

Lee

2019

Advcd Theory and Sims

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Developing an accurate interatomic potential model is a prerequisite for achieving reliable results from classical molecular dynamics (CMD) simulations; however, most of the potentials are biased as specific simulation purposes or conditions are considered in the parameterization. For developing an unbiased potential, a finite‐temperature dynamics machine learning (FTD‐ML) approach is proposed, and its processes and feasibility are demonstrated using the Buckingham potential model and aluminum (Al) as an example. Compared with conventional machine learning approaches, FTD‐ML exhibits three distinguished features: 1) FTD‐ML intrinsically incorporates more extensive configurational and conditional space for enhancing the transferability of developed potentials; 2) FTD‐ML employs various properties calculated directly from CMD, for ML model training and prediction validation against experimental data instead of first‐principles data; 3) FTD‐ML is much more computationally cost effective than first‐principles simulations, especially when the system size increases over 103 atoms as employed in this research for ensuring reliable training data. The Al Buckingham potential developed by the FTD‐ML approach exhibits good performance for general simulation purposes. Thus, the FTD‐ML approach is expected to contribute to a fast development of interatomic potential model suitable for various simulation purposes and conditions, without limitation of model type, while maintaining experimental‐level accuracy.

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Molecular Dynamics Simulation of Poly(ethylene terephthalate) Oligomers

Cited by 50 publications

References 52 publications

Molecular dynamics simulation of three plastic additives’ diffusion in polyethylene terephthalate

Molecular dynamics simulation of three plastic additives’ diffusion in polyethylene terephthalate

Thermodynamic and Transport Properties of Tetrabutylphosphonium Hydroxide and Tetrabutylphosphonium Chloride–Water Mixtures via Molecular Dynamics Simulation

Interatomic Potential Model Development: Finite‐Temperature Dynamics Machine Learning

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