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Polymers
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Smart Polymeric Systems for Bioengineering: AI-Driven Design, Characterization and Manufacturing
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Smart Polymeric Systems for Bioengineering: AI-Driven Design, Characterization and Manufacturing

A special issue of Polymers (ISSN 2073-4360). This special issue belongs to the section "Smart and Functional Polymers".

Deadline for manuscript submissions: closed (30 November 2025) | Viewed by 2398

Special Issue Editors

Dr. Claudio Tosto

E-Mail Website
Guest Editor
Department of Civil Engineering and Architecture, University of Catania, Viale Andrea Doria 6, 95125 Catania, Italy
Interests: polymers; composites; additive manufacturing; sustainability
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Gianluca Cicala

E-Mail Website
Guest Editor
Department of Civil Engineering and Architecture, University of Catania, Viale Andrea Doria 6, 95125 Catania, Italy
Interests: polymers; composites; additive manufacturing; sustainability
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to highlight the innovative integration of artificial intelligence (AI) and bioengineering in the development and processing of smart polymeric systems. The convergence of AI-driven methodologies with bioengineering is opening up new avenues for designing, characterizing, and manufacturing advanced polymer-based materials tailored for biomedical applications. We invite contributions that showcase how AI can enhance the design of polymeric biomaterials, optimize additive manufacturing processes, and predict the performance of biofunctional structures. Topics of interest include, but are not limited to, AI-based modeling and simulation, smart polymer synthesis, data-driven approaches for property prediction, and the use of machine learning to improve the precision and reproducibility of biofabrication techniques.

We welcome the submission of papers that present both innovative theoretical approaches and practical implementations, showcasing the significant impact of AI-powered smart polymeric systems.

We look forward to receiving your valuable contributions.

Dr. Claudio Tosto
Prof. Dr. Gianluca Cicala
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

  • smart polymeric systems
  • artificial intelligence
  • bioengineering
  • additive manufacturing
  • machine learning
  • biomaterial design
  • biofabrication
  • polymer processing optimization
  • data-driven material design

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Published Papers (2 papers)

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18 pages, 3231 KB  
Article
Effect of Artificial Neural Network Design Parameters for Prediction of PS/TiO2 Nanofiber Diameter
by R. Seda Tığlı Aydın, Fevziye Eğilmez and Ceren Kaya
Polymers 2026, 18(3), 328; https://doi.org/10.3390/polym18030328 - 26 Jan 2026
Viewed by 654
Abstract
In this study, polystyrene (PS) and PS/TiO2 nanofibers were fabricated through electrospinning and quantitatively characterized to analyze and predict fiber diameters. To advance predictive methodologies for materials design, artificial neural network (ANN) models based on multilayer perceptron (MLP) and radial basis function [...] Read more.
In this study, polystyrene (PS) and PS/TiO2 nanofibers were fabricated through electrospinning and quantitatively characterized to analyze and predict fiber diameters. To advance predictive methodologies for materials design, artificial neural network (ANN) models based on multilayer perceptron (MLP) and radial basis function (RBF) architectures were developed using system- and process-level parameters as inputs and the fiber diameter as the output. Two data classes were constructed: Class 1, consisting of PS/TiO2 nanofibers, and Class 2, containing both PS and PS/TiO2 nanofibers. The architectural optimization of the ANN models, particularly the number of neurons in hidden layers, had a critical influence on the correlation between predicted and experimentally measured fiber diameters. The optimal MLP configuration employed 40 and 20 neurons in the hidden layers, achieving mean square errors (MSEs) of 4.03 × 10−3 (Class 1) and 7.01 × 10−3 (Class 2). The RBF model reached its highest accuracy with 30 and 250 neurons, yielding substantially lower MSE values of 1.42 × 10−32 and 2.75 × 10−32 for Class 1 and Class 2, respectively. These findings underline the importance of methodological rigor in data-driven modeling and demonstrate that carefully optimized ANN frameworks can serve as powerful tools for predicting structural features in nanostructured materials, thereby supporting rational materials design and synthesis. Full article
(This article belongs to the Special Issue Smart Polymeric Systems for Bioengineering: AI-Driven Design, Characterization and Manufacturing)
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16 pages, 2026 KB  
Article
Artificial Neural Network Prediction of Mechanical Properties in Mycelium-Based Biocomposites
by Štěpán Hýsek, Miroslav Jozífek, Benjamín Petržela and Miroslav Němec
Polymers 2025, 17(18), 2506; https://doi.org/10.3390/polym17182506 - 17 Sep 2025
Cited by 1 | Viewed by 1290
Abstract
Mycelium-based biocomposites (MBBs) represent a sustainable alternative to synthetic composites, as they are produced from lignocellulosic substrates bonded by fungal mycelium. Their mechanical performance depends on multiple interacting factors, including the substrate composition, fungal species, and processing conditions, which makes property optimisation challenging. [...] Read more.
Mycelium-based biocomposites (MBBs) represent a sustainable alternative to synthetic composites, as they are produced from lignocellulosic substrates bonded by fungal mycelium. Their mechanical performance depends on multiple interacting factors, including the substrate composition, fungal species, and processing conditions, which makes property optimisation challenging. In this study, an artificial neural network (ANN) model was developed to predict two mechanical properties of MBBs, namely internal bonding (IB) and compressive strength (CS). An ANN model was trained on experimental data, using the substrate composition, fungal species, and physical properties of MBBs. The ANN predictions were compared with measured values, and the model accuracy was evaluated. The results showed that the ANN achieved a high predictive accuracy, with coefficients of determination of 0.992 for IB and 0.979 for CS. IB values were predicted more precisely than CS, likely due to microstructural heterogeneities. The heterogeneities were visualised using scanning electron microscopy. Composites produced with Ganoderma sessile and Trametes versicolor exhibited the highest IB. Interestingly, Trametes versicolor achieved the highest CS on virgin wood particles but the lowest values on recycled wood, underlining the strong influence of the substrate quality. The study demonstrates that ANNs can effectively predict the mechanical properties, reducing the number of experimental tests needed for material characterisation. Full article
(This article belongs to the Special Issue Smart Polymeric Systems for Bioengineering: AI-Driven Design, Characterization and Manufacturing)
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