Glassy Polymers – Diffusion, Sorption, Aging, and Applications

Arya, Raj Kumar; Thapliyal, Devyani; Sharma, Jyoti; Verros, George D.

doi:10.20944/preprints202107.0576.v1

2021

DOI: 10.20944/preprints202107.0576.v1

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Glassy Polymers – Diffusion, Sorption, Aging, and Applications

Raj Kumar Arya¹,

Devyani Thapliyal²,

Jyoti Sharma³

et al.

Abstract: For the past few decades, researchers have been intrigued with glassy polymers, which have applications ranging from gas separations to corrosion protection to drug delivery systems. The techniques employed to examine the sorption and diffusion of small molecules in glassy polymers are the subject of this review. Diffusion models in glassy polymers are regulated by Fickian and non-Fickian diffusion, with non-Fickian diffusion being more prevalent. The characteristics of glassy polymers are determined by sorpti… Show more

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Cited by 7 publications

References 80 publications

(85 reference statements)

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Modeling of Surfactant-Enhanced Drying of Poly(styrene)-p-xylene Polymeric Coatings Using Machine Learning Technique

et al. 2021

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In this work, a machine learning technique based on a regression tree model was used to model the surfactant enhanced drying of poly(styrene)-p-xylene coatings. The predictions of the developed model based on regression trees are in excellent agreement with the experimental data. A total of 16,258 samples were obtained through experimentation. These samples were separated into two parts: 12,960 samples were used for the training of the regression tree, and the remaining 3298 samples were used to test the tree’s prediction accuracy. MATLAB software was used to grow the regression tree. The mean squared error between the model-predicted values and actual outputs was calculated to be 8.8415 × 10−6. This model has good generalizing ability; predicts weight loss for given values of time, thickness, and triphenyl phosphate; and has a maximum error of 1%. It is robust and for this system, can be used for any composition and thickness for this system, which will drastically reduce the need for further experimentations to explain diffusion and drying.

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Modeling of Surfactant-Enhanced Drying of Poly(styrene)-p-xylene Polymeric Coatings Using Machine Learning Technique

et al. 2021

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On the Validity of a Linearity Axiom in Diffusion and Heat Transfer

et al. 2022

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In this work, the linearity axiom of irreversible thermodynamics for diffusion and heat transfer has been re-examined. It is shown that this axiom is compatible with the entropy production invariance principle with respect to a reference quantity for diffusion and heat transfer in the Euclidean space. Moreover, the underlying relations of the other principles of irreversible thermodynamics for multi-component diffusion and heat transfer, such as the quasi-equilibrium and the Onsager reciprocal relations (ORR) with the entropy production invariance, are re-examined. It was shown that the linearity principle postulates for diffusion and heat transfer and could be directly derived from the entropy production invariance axiom. It is believed that this work could not only be used for the drying of polymer coatings but also for pedagogical purposes. It may also be generalized; thus, leading to a generalized framework for irreversible thermodynamics.

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Universal Evolution of Fickian Non-Gaussian Diffusion in Two- and Three-Dimensional Glass-Forming Liquids

Rusciano

Pastore

Greco

2023

IJMS

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Recent works show that glass-forming liquids display Fickian non-Gaussian Diffusion, with non-Gaussian displacement distributions persisting even at very long times, when linearity in the mean square displacement (Fickianity) has already been attained. Such non-Gaussian deviations temporarily exhibit distinctive exponential tails, with a decay length λ growing in time as a power-law. We herein carefully examine data from four different glass-forming systems with isotropic interactions, both in two and three dimensions, namely, three numerical models of molecular liquids and one experimentally investigated colloidal suspension. Drawing on the identification of a proper time range for reliable exponential fits, we find that a scaling law λ(t)∝tα, with α≃1/3, holds for all considered systems, independently from dimensionality. We further show that, for each system, data at different temperatures/concentration can be collapsed onto a master-curve, identifying a characteristic time for the disappearance of exponential tails and the recovery of Gaussianity. We find that such characteristic time is always related through a power-law to the onset time of Fickianity. The present findings suggest that FnGD in glass-formers may be characterized by a “universal” evolution of the distribution tails, independent from system dimensionality, at least for liquids with isotropic potential.

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Glassy Polymers – Diffusion, Sorption, Aging, and Applications

Cited by 7 publications

References 80 publications

Modeling of Surfactant-Enhanced Drying of Poly(styrene)-p-xylene Polymeric Coatings Using Machine Learning Technique

Modeling of Surfactant-Enhanced Drying of Poly(styrene)-p-xylene Polymeric Coatings Using Machine Learning Technique

On the Validity of a Linearity Axiom in Diffusion and Heat Transfer

Universal Evolution of Fickian Non-Gaussian Diffusion in Two- and Three-Dimensional Glass-Forming Liquids

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