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Functional Polymers and Fibers: Sensing Materials and Applications
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Functional Polymers and Fibers: Sensing Materials and Applications

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Materials".

Deadline for manuscript submissions: 31 October 2025 | Viewed by 3711

Special Issue Editor

Dr. Jiashen Li

E-Mail Website
Guest Editor
Department of Materials, University of Manchester, Manchester M139PL, UK
Interests: functional fiber; biomaterial; scaffold; electrospinning; graphene
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue, “Functional Polymers and Fibers: Sensing Materials and Applications”, focuses on the advanced and innovative applications of functional polymers and fiber sensors fabricated through the technology of solvent spinning, melt spinning, gel spinning, and so on. Functional polymers and fibers possess exceptional properties, including high surface area-to-volume ratios, customizable porosity, and superior mechanical strength. The fibrous membranes and fibers can help reduce the sensor size, and the number of areas and channels with the porous fibers and fibrous membranes can enhance the interaction between determinants, making them ideal for sensor development in different fields such as electrochemical, optical, microwave, and mechanical sensing.

This Special Issue aims to showcase the latest innovations in the design, fabrication, and deployment of functional polymers and fibers as sensing materials, fostering discussions on novel sensing mechanisms, integration strategies, and future trends in this technology. By exploring the intricate interplay between material properties, sensor performance, and real-world applications, this collection of articles in this Special Issue will provide insights into the potential of functional polymer and fiber sensors.

Dr. Jiashen Li
Guest Editor

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. Sensors 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 2600 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

  • functional polymers 

  • spinning fibers

  • functional fibers and composites

  • porous fibers

  • ultra-high surface area materials

  • polymers composites

  • micro/nanofibers

 

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Published Papers (3 papers)

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Research

24 pages, 2613 KiB  
Article
Hierarchical Sensing Framework for Polymer Degradation Monitoring: A Physics-Constrained Reinforcement Learning Framework for Programmable Material Discovery
by Xiaoyu Hu, Xiuyuan Zhao and Wenhe Liu
Sensors 2025, 25(14), 4479; https://doi.org/10.3390/s25144479 - 18 Jul 2025
Viewed by 163
Abstract
The design of materials with programmable degradation profiles presents a fundamental challenge in pattern recognition across molecular space, requiring the identification of complex structure–property relationships within an exponentially large chemical domain. This paper introduces a novel physics-informed deep learning framework that integrates multi-scale [...] Read more.
The design of materials with programmable degradation profiles presents a fundamental challenge in pattern recognition across molecular space, requiring the identification of complex structure–property relationships within an exponentially large chemical domain. This paper introduces a novel physics-informed deep learning framework that integrates multi-scale molecular sensing data with reinforcement learning algorithms to enable intelligent characterization and prediction of polymer degradation dynamics. Our method combines three key innovations: (1) a dual-channel sensing architecture that fuses spectroscopic signatures from Graph Isomorphism Networks with temporal degradation patterns captured by transformer-based models, enabling comprehensive molecular state detection across multiple scales; (2) a physics-constrained policy network that ensures sensor measurements adhere to thermodynamic principles while optimizing the exploration of degradation pathways; and (3) a hierarchical signal processing system that balances multiple sensing modalities through adaptive weighting schemes learned from experimental feedback. The framework employs curriculum-based training that progressively increases molecular complexity, enabling robust detection of degradation markers linking polymer architectures to enzymatic breakdown kinetics. Experimental validation through automated synthesis and in situ characterization of 847 novel polymers demonstrates the framework’s sensing capabilities, achieving a 73.2% synthesis success rate and identifying 42 structures with precisely monitored degradation profiles spanning 6 to 24 months. Learned molecular patterns reveal previously undetected correlations between specific spectroscopic signatures and degradation susceptibility, validated through accelerated aging studies with continuous sensor monitoring. Our results establish that physics-informed constraints significantly improve both the validity (94.7%) and diversity (0.82 Tanimoto distance) of generated molecular structures compared with unconstrained baselines. This work advances the convergence of intelligent sensing technologies and materials science, demonstrating how physics-informed machine learning can enhance real-time monitoring capabilities for next-generation sustainable materials. Full article
(This article belongs to the Special Issue Functional Polymers and Fibers: Sensing Materials and Applications)
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13 pages, 5070 KiB  
Article
Pollen-Modified Flat Silk Cocoon Pressure Sensors for Wearable Applications
by Shengnan Wang, Yujia Wang, Yi Wang, Jiaqi Liu, Fan Liu, Fangyin Dai, Jiashen Li and Zhi Li
Sensors 2024, 24(14), 4698; https://doi.org/10.3390/s24144698 - 19 Jul 2024
Cited by 2 | Viewed by 1297
Abstract
Microstructures have been proved as crucial factors for the sensing performance of flexible pressure sensors. In this study, polypyrrole (PPy)/sunflower pollen (SFP) (P/SFP) was prepared via the in situ growth of PPy on the surface of degreased SFP with a sea urchin-like microstructure; [...] Read more.
Microstructures have been proved as crucial factors for the sensing performance of flexible pressure sensors. In this study, polypyrrole (PPy)/sunflower pollen (SFP) (P/SFP) was prepared via the in situ growth of PPy on the surface of degreased SFP with a sea urchin-like microstructure; then, these P/SFP microspheres were sprayed onto a flat silk cocoon (FSC) to prepare a sensing layer P/SFP-FSC. PPy-FSC (P-FSC) was prepared as an electrode layer through the in situ polymerization of PPy on the FSC surface. The sensing layer P/SFP-FSC was placed between two P-FSC electrode layers to assemble a P/SFP-FSC pressure sensor together with a fork finger electrode. With 6 mg/cm2 of optimized sprayed P/SFP microspheres, the prepared flexible pressure sensor has a sensitivity of up to 0.128 KPa−1 in the range of 0–13.18 KPa and up to 0.13 KPa−1 in the range of 13.18–30.65 KPa, a fast response/recovery time (90 ms/80 ms), and a minimum detection limit as low as 40 Pa. This fabricated flexible P/SFP-FSC sensor can monitor human motion and can also be used for the encrypted transmission of important information via Morse code. In conclusion, the developed flexible P/SFP-FSC pressure sensor based on microstructure modification in this study shows good application prospects in the field of human–computer interaction and wearable electronic devices. Full article
(This article belongs to the Special Issue Functional Polymers and Fibers: Sensing Materials and Applications)
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12 pages, 9442 KiB  
Article
Fabrication of a Capacitive 3D Spacer Fabric Pressure Sensor with a Dielectric Constant Change for High Sensitivity
by Ji-Eun Lee, Sang-Un Kim and Joo-Yong Kim
Sensors 2024, 24(11), 3395; https://doi.org/10.3390/s24113395 - 24 May 2024
Viewed by 1747
Abstract
Smart wearable sensors are increasingly integrated into everyday life, interfacing with the human body to enable real-time monitoring of biological signals. This study focuses on creating high-sensitivity capacitive-type sensors by impregnating polyester-based 3D spacer fabric with a Carbon Nanotube (CNT) dispersion. The unique [...] Read more.
Smart wearable sensors are increasingly integrated into everyday life, interfacing with the human body to enable real-time monitoring of biological signals. This study focuses on creating high-sensitivity capacitive-type sensors by impregnating polyester-based 3D spacer fabric with a Carbon Nanotube (CNT) dispersion. The unique properties of conductive particles lead to nonlinear variations in the dielectric constant when pressure is applied, consequently affecting the gauge factor. The results reveal that while the fabric without CNT particles had a gauge factor of 1.967, the inclusion of 0.04 wt% CNT increased it significantly to 5.210. As sensor sensitivity requirements vary according to the application, identifying the necessary CNT wt% is crucial. Artificial intelligence, particularly the Multilayer Perception (MLP) model, enables nonlinear regression analysis for this purpose. The MLP model created and validated in this research showed a high correlation coefficient of 0.99564 between the model predictions and actual target values, indicating its effectiveness and reliability. Full article
(This article belongs to the Special Issue Functional Polymers and Fibers: Sensing Materials and Applications)
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