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Polymers
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Multiscale Modeling of Polymeric Systems for Time-Dependent Nonlinear Properties
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Multiscale Modeling of Polymeric Systems for Time-Dependent Nonlinear Properties

A special issue of Polymers (ISSN 2073-4360). This special issue belongs to the section "Artificial Intelligence in Polymer Science".

Deadline for manuscript submissions: 10 December 2025 | Viewed by 737

Special Issue Editors

Dr. Hyeonu Heo

E-Mail Website
Guest Editor
College of Engineering and Polymer Science, The University of Akron, Akron, OH 44325, USA
Interests: tire mechanics and materials; acoustic/mechanical metamaterials; noise/vibration control; ultrasound
Dr. Md Salah Uddin

E-Mail Website
Guest Editor
Department of Mechanical Engineering, University of Texas Permian Basin, Midland, TX 79707, USA
Interests: smart polymer; energy harvesting; drug delivery; composites; molecular dynamics; constitutive modeling

Special Issue Information

Dear Colleagues,

Polymeric systems exhibit macroscopic time-dependent properties, which are important in many applications such as biomedical engineering (drug delivery, tissue culture), rubber industries (automobile tires, seals/gaskets in the oil and gas industry), and soft electronics. Modeling of strain-rate dependent elasticity, hyperelasticity, hysteresis, stress relaxation, and creep behavior at the materials level is required for finite element analysis at the coupon level for designing and optimization. Different models are fitted with experimental data to determine material constants for constitutive modeling and finite element analysis. Discovering new materials through this trial-and-error method is time-consuming. A multiscale modeling technique such as bridging molecular dynamic (MD) simulations with constitutive models facilitates the discovery of new materials. However, as time and length scales are different between molecular and continuum levels, a bridging technique is required between these two levels. This Special Issue welcomes any bridging techniques, not limited to MD simulations; however, the prediction of nonlinear time-dependent properties is expected. 

Dr. Hyeonu Heo
Dr. Md Salah Uddin
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Polymers is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • multiscale modeling
  • molecular dynamics
  • bridging techniques: time gap
  • hysteresis
  • viscoelastic
  • hyperelastic
  • strain-rate dependency
  • constitutive modeling

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Published Papers (1 paper)

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Research

23 pages, 3869 KiB  
Article
Thermal Degradation of Palm Fronds/Polypropylene Bio-Composites: Thermo-Kinetics and Convolutional-Deep Neural Networks Techniques
by Abdulrazak Jinadu Otaru and Zaid Abdulhamid Alhulaybi Albin Zaid
Polymers 2025, 17(9), 1244; https://doi.org/10.3390/polym17091244 - 2 May 2025
Viewed by 268
Abstract
Identifying sustainable and efficient methods for the degradation of plastic waste in landfills is critical for the implementation of the Saudi Green Initiative, the European Union’s Strategic Plan, and the 2030 United Nations Action Plan, all of which are aimed at achieving a [...] Read more.
Identifying sustainable and efficient methods for the degradation of plastic waste in landfills is critical for the implementation of the Saudi Green Initiative, the European Union’s Strategic Plan, and the 2030 United Nations Action Plan, all of which are aimed at achieving a sustainable environment. This study assesses the influence of palm fronds (PFR) on the thermal degradation of polypropylene plastic (PP) using TGA/FTIR experimental measurements, thermo-kinetics, and machine learning convolutional deep learning neural networks (CDNN). Thermal degradation operations were conducted on pure materials (PFR and PP) as well as mixed (blended) materials containing 25% and 50% PFR, across degradation temperatures ranging from 25 to 600 °C and heating rates of 10, 20, and 40 °C·min−1. The TGA data indicated a synergistic interaction between the agricultural waste (PFR) and PP plastic, with decreased thermal stability at temperatures below 500 °C, attributed to the hemicellulose and cellulose present in the PFR biomass. In contrast, at temperatures exceeding 500 °C, the presence of lignin retards the degradation of the PFR biomass and blends. Activation energy values between 81.92 and 299.34 kJ·mol−1 were obtained through the application of the Flynn–Wall–Ozawa (FWO) and Kissinger–Akahira–Sunose (KAS) model-free methods. The application of CDNN facilitated the extraction of significant features and labels, which were crucial for enhancing modeling accuracy and convergence. This modeling and simulation approach reduced the overall cost function from 41.68 to 0.27, utilizing seven hidden neurons, and 673,910 epochs in 13.28 h. This method effectively bridged the gap between modeling and experimental data, achieving an R2 value of approximately 0.992, and identified sample composition as the most critical parameter influencing the thermolysis process. It is hoped that such findings may facilitate an energy-efficient pathway necessary for the thermal decomposition of plastic materials in landfills. Full article
(This article belongs to the Special Issue Multiscale Modeling of Polymeric Systems for Time-Dependent Nonlinear Properties)
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