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

Deadline for manuscript submissions: 31 December 2023 | Viewed by 10969

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Dr. Stefan Zechel

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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
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Prof. Dr. Ulrich S. Schubert

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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
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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 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 (5 papers)

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Research

Open AccessArticle

Automated Parallel Dialysis for Purification of Polymers

İpek Terzioğlu

Carolina Ventura-Hunter

Jens Ulbrich

Enrique Saldívar-Guerra

Ulrich S. Schubert

and

Carlos Guerrero-Sánchez

Polymers 2022, 14(22), 4835; https://doi.org/10.3390/polym14224835 - 10 Nov 2022

Cited by 1 | Viewed by 1279

Abstract

The implementation of a dialysis method for the simultaneous purification of different polymer materials in a commercially available automated parallel synthesizer (APS) is discussed. The efficiency of this “unattended” automated parallel dialysis (APD) method was investigated by means of proton nuclear magnetic resonance (¹H-NMR) measurements, which confirmed that the method enables the removal of up to 99% of the unreacted monomer derived from the synthesis of the corresponding polymers in the APS. Size-exclusion chromatography (SEC) revealed that the molar mass and molar mass distribution of the investigated polymers did not undergo significant changes after the application of the APD method. The method discussed herein can be regarded as a good alternative to the “unattended” and reliable purification of polymer libraries prepared in APS. This method may be useful for overcoming current limitations of high-throughput/-output (HT/O) synthesis of polymer libraries, where purification of the generated materials currently represents a significant constraint for establishing fully automated experimental workflows necessary to advance towards a full digitalization of research and development of new polymers for diverse applications. Full article

(This article belongs to the Special Issue Polymers and Digitalization)

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Open AccessCommunication

The 3D-Printing-Accelerated Design for a Biodegradable Respirator from Tree Leaves (TRespirator)

and

Polymers 2022, 14(9), 1681; https://doi.org/10.3390/polym14091681 - 21 Apr 2022

Cited by 1 | Viewed by 1686

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 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)

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Figure 1

Open AccessArticle

Improvement of High-Throughput Experimentation Using Synthesis Robots by the Implementation of Tailor-Made Sensors

and

Polymers 2022, 14(3), 361; https://doi.org/10.3390/polym14030361 - 18 Jan 2022

Cited by 1 | Viewed by 1749

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 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)

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Open AccessArticle

Fully Automated Multi-Step Synthesis of Block Copolymers

and

Polymers 2022, 14(2), 292; https://doi.org/10.3390/polym14020292 - 11 Jan 2022

Cited by 4 | Viewed by 1596

Abstract

An automated synthesis protocol is developed for the synthesis of block copolymers in a multi-step approach in a fully automated manner. For this purpose, an automated dialysis setup is combined with robot-based synthesis protocols. Consequently, several block copolymerizations are executed completely automated and compared to the respective manual synthesis. As a result, this study opens up the field of autonomous multi-step reactions without any human interactions. Full article

(This article belongs to the Special Issue Polymers and Digitalization)

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Open AccessArticle

Atomistic Descriptors for Machine Learning Models of Solubility Parameters for Small Molecules and Polymers

and

Polymers 2022, 14(1), 26; https://doi.org/10.3390/polym14010026 - 22 Dec 2021

Cited by 1 | Viewed by 3307

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.

Δ H_{vap}

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

Δ H_{vap}

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

Δ H_{vap}

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)

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