Cheminformatics Approaches to the Analysis of Additives for Sustainable Polymeric Materials

Additives are compounds used for material to increase specific properties. When used for polymers, they extend their life and contribute to environmental sustainability. This article presents the study findings related to 24 additives-antioxidants, UV stabilizers, and quenchers-using cheminformatics methods. The compounds' characteristics (e.g., number of atoms, functional groups) were emphasized, followed by some descriptors. The Tanimoto coefficient, computed based on the maximum common structure algorithm, and the overlap coefficient indicated the degree of similarity between the molecules. The molecules were grouped by binning and hierarchical clustering (HC) based on the extracted results. In the last case, two scenarios were considered-with four (CL1-CL4) and six clusters (CL1.1, CL1.2, CL2, CL3, CL4.1, CL4.2) being built. Considering the mechanical properties of the compounds and the standard deviation and amplitude of their values, the most homogenous class was CL2 (respectively CL4.2). Considering the toxicity of additives, the highest possible negative impact on the environment is that of the compounds in CL1 and CL3. The clustering results guide the selection of additives with reduced environmental impact, thereby supporting the development of sustainable polymer formulations aligned with circular economy principles.

Authors

Institutions

Publication Details

Journal
Polymers
Published
2025-05-29
DOI
https://doi.org/10.3390/polym17111522
Citations
1
Primary Topic
Microplastics and Plastic Pollution
Type
article
Field-Weighted Citation Impact
0.50
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Cheminformatics Approaches to the Analysis of Additives for Sustainable Polymeric Materials

Alina Bărbulescu, Lucica Barbeș
1 citations
Polymers
Microplastics and Plastic Pollution
0.50
article

Cheminformatics Approaches to the Analysis of Additives for Sustainable Polymeric Materials

Alina Bărbulescu, Lucica Barbeș
article en
1 citations

Abstract

Additives are compounds used for material to increase specific properties. When used for polymers, they extend their life and contribute to environmental sustainability. This article presents the study findings related to 24 additives-antioxidants, UV stabilizers, and quenchers-using cheminformatics methods. The compounds' characteristics (e.g., number of atoms, functional groups) were emphasized, followed by some descriptors. The Tanimoto coefficient, computed based on the maximum common structure algorithm, and the overlap coefficient indicated the degree of similarity between the molecules. The molecules were grouped by binning and hierarchical clustering (HC) based on the extracted results. In the last case, two scenarios were considered-with four (CL1-CL4) and six clusters (CL1.1, CL1.2, CL2, CL3, CL4.1, CL4.2) being built. Considering the mechanical properties of the compounds and the standard deviation and amplitude of their values, the most homogenous class was CL2 (respectively CL4.2). Considering the toxicity of additives, the highest possible negative impact on the environment is that of the compounds in CL1 and CL3. The clustering results guide the selection of additives with reduced environmental impact, thereby supporting the development of sustainable polymer formulations aligned with circular economy principles.

PolymersVol. 17(11)
Ovidius University (RO), Transylvania University of Brașov (RO), Universitatea Națională de Știință și Tehnologie Politehnica București (RO)
Openalex Percentile: Top 40%
Microplastics and Plastic Pollution
0.50
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.