Submit to Special Issue Submit Abstract to Special Issue Review for Polymers Propose a Special Issue

Journal Menu

Journal Browser

Material-Process-Structure Integrated Design for Advanced Polymeric Composites, 2nd Edition

Special Issue Editors
Special Issue Information
Keywords
Benefits of Publishing in a Special Issue
Published Papers

A special issue of Polymers (ISSN 2073-4360). This special issue belongs to the section "Polymer Composites and Nanocomposites".

Deadline for manuscript submissions: 30 June 2026 | Viewed by 1102

Share This Special Issue

Special Issue Editors

Dr. Zhen Wang

E-Mail Website
Guest Editor

School of Automobile, Chang'an University, Middle Section of Nan Erhuan Road, Xi'an 710064, China
Interests: composite materials; fiber-reinforced polymer; crashworthiness; optimization design; numerical simulation
Special Issues, Collections and Topics in MDPI journals

Dr. Guohua Zhu

E-Mail Website
Guest Editor

School of Automobile, Chang'an University, Middle Section of Nan Erhuan Road, Xi'an 710064, China
Interests: composite structures; polymer composite; mechanics of lattice materials; multi-scale modeling; crashworthiness; lghtweight design
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the widespread application of advanced polymer composites in aerospace, new energy vehicles, and biomedicine, the collaborative design of materials, processes, and structures has become a key pathway to improve material performance and functionality. This Special Issue aims to explore in depth the integrated research methods from microscopic material design to macroscopic structural performance, focusing on core issues, such as multi-scale modeling, intelligent fabrication processes, performance prediction, and optimization. The scope of submissions covers (but is not limited to) the following areas: constitutive models and multi-field coupling behavior of novel polymer composites; advanced molding processes such as additive manufacturing and automated fabrication; integrated structure-function design and performance verification; material design and process optimization driven by machine learning and data; and sustainability and circular design strategies for composite materials. We sincerely invite scholars, engineers, and industry experts from home and abroad to contribute original research papers, reviews, and case studies to jointly promote the innovative development of polymer composite design theory and engineering applications.

Dr. Zhen Wang
Dr. Guohua Zhu
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 250 words) can be sent to the Editorial Office for assessment.

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

fiber-reinforced polymers
multi-scale
integrated model
optimization algorithm
collaborative design
lightweight

Benefits of Publishing in a Special Issue

Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

14 pages, 3785 KB

Open AccessArticle

Topology-Induced Reduction in the Order–Disorder Transition in AB Block Copolymer: A Unit-Matched Comparison of Diblock, Multiblock, Comb, and Star Architectures

by June Huh

Polymers 2026, 18(7), 869; https://doi.org/10.3390/polym18070869 - 1 Apr 2026

Viewed by 443

Abstract

Chain topology offers a chemistry-preserving route to tune block copolymer (BCP) self-assembly by modifying intrachain correlations and relaxation pathways without changing monomer interactions. Here, we perform a unit-matched comparison of four lamella-forming AB architectures reconstructed from an identical constitutive diblock unit ( [...] Read more.

N_{0}

): a linear diblock (DB), a linear multiblock (MB), a comb-like architecture (CB), and a star-like architecture (SB). Using dynamical density functional theory (DDFT), we quantify topology-dependent bulk ordering thresholds and show that architectural reconfiguration systematically stabilizes the ordered phase, reducing the order–disorder transition relative to DB (MB/CB/SB

\approx 0.793 / 0.762 / 0.752

of the diblock value), in semi-quantitative agreement with random phase approximation (RPA) spinodal trends. We also compare topology-dependent directed self-assembly in a common trench geometry under matched reduced quench depth

Δ (χ N_{0}) = χ N_{0} - {(χ N_{0})}_{ODT}

, thereby isolating kinetic differences at comparable thermodynamic distance from bulk ordering. A Fourier-based alignment order parameter

α (t)

reveals sigmoidal alignment kinetics over decades in time and is well captured by a logistic form in

ln t

, enabling compact descriptors (

t_{50}

t_{90}

, and a steepness parameter k) that separate alignment onset from late-stage defect annihilation, while selective sidewalls robustly template sidewall-parallel lamellae across all topologies, the late-stage kinetics remain strongly connectivity dependent and can exhibit long-tailed completion associated with slow late-stage defect annihilation. These results demonstrate a dual role of topology in DSA: lowering the segregation strength required for bulk ordering while reshaping defect-mediated alignment pathways under confinement. Full article

(This article belongs to the Special Issue Material-Process-Structure Integrated Design for Advanced Polymeric Composites, 2nd Edition)

► Show Figures

Figure 1

24 pages, 8627 KB

Open AccessArticle

Machine-Learning-Assisted Viscoelastic Characterization of PC/ABS Blends via Multi-Frequency Dynamic Mechanical Analysis

by Yancai Sun, Wenzhong Deng, Haoran Wang, Ranran Jian, Wenjuan Bai, Dianming Chu, Peiwu Hou and Yan He

Polymers 2026, 18(5), 599; https://doi.org/10.3390/polym18050599 - 28 Feb 2026

Viewed by 366

Abstract

This study combines multi-frequency dynamic mechanical analysis (DMA) with machine learning (ML) to characterize and predict the viscoelastic properties of a commercial polycarbonate/acrylonitrile–butadiene–styrene (PC/ABS) blend. DMA temperature sweeps at four frequencies (1–10 Hz) in single cantilever mode yielded a glass transition range of [...] Read more.

123.2

°C (

E^{″}

peak), frequency sensitivity of

7.18

°C/

decade

, and an apparent activation energy of

335 \pm 85 k J {mol}^{- 1}

. Time–temperature superposition master curves were parameterized with a six-term Prony series (

R^{2} = 0.998

). Four data-driven models (RF, XGB, SVR, MLP) and a physics-informed NeuralWLF model were evaluated through a hierarchical validation framework. Temperature-blocked CV ranked MLP (

\bar{R^{2}} = 0.989

) above RF (0.950) for interpolation; LOFO validation revealed that NeuralWLF achieved the best cross-frequency generalization (

R^{2} > 0.92

for all targets) with interpretable WLF parameters (

C_{1} \approx 12.2

C_{2} \approx 51.7 ° C

). A systematic block size sweep (5–30

° C

) revealed a validation inflation effect in which MLP

tan δ

R^{2}

dropped from 0.986 to 0.592 as the gap-to-FWHM ratio increased from 0.5 to 3.1, establishing the gap/FWHM ratio as a quantitative validation stringency criterion. A physics–data crossover was identified at gap/FWHM

\approx 2

: beyond this threshold, NeuralWLF outperformed all data-driven models in

tan δ

prediction by up to

+ 0.300

R^{2}

, while curriculum learning (freezing the WLF layer for 300 epochs) further improved the most stringent 30

° C

validation from

R^{2} = 0.660

to 0.731. The integrated framework demonstrates that honest evaluation of DMA–ML models requires validation gaps exceeding the characteristic feature width and introduces a quantifiable physics-data crossover criterion for selecting between data-driven and physics-informed architectures. Full article