Polymers and Digitalization

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

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 10762

Special Issue Editors


E-Mail Website
Guest Editor
Laboratory of Organic and Macromolecular Chemistry (IOMC), Friedrich Schiller University Jena, Humboldtstr. 10, D-07743 Jena, Germany
Interests: self-healing materials; metallopolymers; shape-memory polymers; mechanochemistry; RAFT-polymerization; dynamic polymers; bioinspired polymers; ionomers, digitalization, automation, robot-based chemistry
Special Issues, Collections and Topics in MDPI journals

grade E-Mail Website
Guest Editor
Laboratory of Organic and Macromolecular Chemistry (IOMC) Jena Center for Soft Matter (JCSM), Friedrich-Schiller-Universität Jena, Humboldtstr. 10, D-07743 Jena, Germany
Interests: automization; coordination chemistry; drug delivery; functional polymers; inkjet printing; metallo-supramolecular polymers; polymer batteries; polymer nanoparticles; self-healing materials
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Ongoing digitalization has significantly affected research into polymer science during the last few years and is gaining increasingly more attention. Thus, automated synthesis, characterization and purification protocols have been developed, enabling a fast screening of polymeric materials. Furthermore, synthetic robots have allowed the parallel synthesis of polymer libraries, resulting in a fast screening of different compositions and properties. Recently, artificial intelligence and machine-learning methods have been developed in order to predict properties of polymers. Thus, ongoing digitalization is changing the way we perform research into polymer science.

Consequently, the current Special Issue is focusing on all aspects of digitalization in polymer science, ranging from new aspects in the field of the automation of synthesis, characterization or purification over the utilization of robots for the preparation of new materials and polymer libraries to, finally, the application of artificial intelligence and machine-learning (AI/ML) routines for the predication of polymer-relevant aspects. Finally, the generation of data-using theoretical approaches for the application in such AI/ML programs will also be a part of the Special Issue.

Dr. Stefan Zechel
Prof. Dr. Ulrich S. Schubert
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

  • digitalization
  • automation
  • robot-based chemistry
  • artificial intelligence
  • machine learning

Published Papers (3 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

11 pages, 2600 KiB  
Communication
The 3D-Printing-Accelerated Design for a Biodegradable Respirator from Tree Leaves (TRespirator)
by Ziao Wang, Yao Xu, Rulin Liu and Xi Zhu
Polymers 2022, 14(9), 1681; https://doi.org/10.3390/polym14091681 - 21 Apr 2022
Cited by 2 | Viewed by 2153
Abstract
The unpredictable coronavirus pandemic (COVID-19) has led to a sudden and massive demand for face masks, leading to severe plastic pollution. Here, we propose a method for manufacturing biodegradable masks using high-precision 3D printing technology, called “TRespirator”, mainly made of banana leaves and [...] Read more.
The unpredictable coronavirus pandemic (COVID-19) has led to a sudden and massive demand for face masks, leading to severe plastic pollution. Here, we propose a method for manufacturing biodegradable masks using high-precision 3D printing technology, called “TRespirator”, mainly made of banana leaves and dental floss silk fibers. By adding plastic recycling waste appropriately, TRespirator can achieve similar protection and mechanical properties as N95 masks. In addition, microorganisms attracted during the degradation of plant fibers will accelerate the degradation of microplastics. This respirator provides a new idea for solving the global problem of plastic pollution of masks. Full article
(This article belongs to the Special Issue Polymers and Digitalization)
Show Figures

Figure 1

11 pages, 1333 KiB  
Article
Improvement of High-Throughput Experimentation Using Synthesis Robots by the Implementation of Tailor-Made Sensors
by Timo Schuett, Manuel Wejner, Julian Kimmig, Stefan Zechel, Timm Wilke and Ulrich S. Schubert
Polymers 2022, 14(3), 361; https://doi.org/10.3390/polym14030361 - 18 Jan 2022
Cited by 5 | Viewed by 2522
Abstract
A small, low-cost, self-produced photometer is implemented into a synthesis robot and combined with a modified UV chamber to enable automated sampling and online characterization. In order to show the usability of the new approach, two different reversible addition–fragmentation chain transfer (RAFT) polymers [...] Read more.
A small, low-cost, self-produced photometer is implemented into a synthesis robot and combined with a modified UV chamber to enable automated sampling and online characterization. In order to show the usability of the new approach, two different reversible addition–fragmentation chain transfer (RAFT) polymers were irradiated with UV light. Automated sampling and subsequent characterization revealed different reaction kinetics depending on polymer type. Thus, a long initiation time (20 min) is required for the end-group degradation of poly(ethylene glycol) ether methyl methacrylate (poly(PEGMEMA)), whereas poly(methyl methacrylate) (PMMA) is immediately converted. Lastly, all photometric samples are characterized via size-exclusion chromatography using UV and RI detectors to prove the results of the self-produced sensor and to investigate the molar mass shift during the reaction. Full article
(This article belongs to the Special Issue Polymers and Digitalization)
Show Figures

Graphical abstract

11 pages, 14302 KiB  
Article
Atomistic Descriptors for Machine Learning Models of Solubility Parameters for Small Molecules and Polymers
by Mingzhe Chi, Rihab Gargouri, Tim Schrader, Kamel Damak, Ramzi Maâlej and Marek Sierka
Polymers 2022, 14(1), 26; https://doi.org/10.3390/polym14010026 - 22 Dec 2021
Cited by 7 | Viewed by 4357
Abstract
Descriptors derived from atomic structure and quantum chemical calculations for small molecules representing polymer repeat elements were evaluated for machine learning models to predict the Hildebrand solubility parameters of the corresponding polymers. Since reliable cohesive energy density data and solubility parameters for polymers [...] Read more.
Descriptors derived from atomic structure and quantum chemical calculations for small molecules representing polymer repeat elements were evaluated for machine learning models to predict the Hildebrand solubility parameters of the corresponding polymers. Since reliable cohesive energy density data and solubility parameters for polymers are difficult to obtain, the experimental heat of vaporization ΔHvap of a set of small molecules was used as a proxy property to evaluate the descriptors. Using the atomistic descriptors, the multilinear regression model showed good accuracy in predicting ΔHvap of the small-molecule set, with a mean absolute error of 2.63 kJ/mol for training and 3.61 kJ/mol for cross-validation. Kernel ridge regression showed similar performance for the small-molecule training set but slightly worse accuracy for the prediction of ΔHvap of molecules representing repeating polymer elements. The Hildebrand solubility parameters of the polymers derived from the atomistic descriptors of the repeating polymer elements showed good correlation with values from the CROW polymer database. Full article
(This article belongs to the Special Issue Polymers and Digitalization)
Show Figures

Figure 1

Back to TopTop