Highly robust molecular information carriers for traceability of plastic materials

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Authors: Thibault Schutz, Nezha Badi, Filip Du Prez
Cited by: 0
References: 51

Abstract

Sequence-defined oligomers offer programmable molecular architectures with potential in data storage, authentication, and anticounterfeiting. However, their deployment in real-world materials has been constrained by their low scale, limited thermal resilience and the need for specialized analytical methods. Here we introduce a molecular tagging strategy based on abiotic sequence-defined oligoamides incorporated at ultralow concentrations ( < 0.05 wt%) that endure the thermal and chemical conditions of industrial polymer processing. A four-monomer alphabet derived from unnatural amino acids enables the construction of digital molecular sequences that permit a first optical identification through ultraviolet absorption and/or fluorescence from chromophoric terminal groups. Sequence authentication, implemented via an automated Python-scripted readout strategy from tandem mass spectra, enables rapid and reproducible molecular code identification. Comparative studies of secondary and tertiary amide backbones reveal structure-stability correlations that govern the design of thermally robust taggants. The oligoamides retain structural integrity within both commodity thermoplastics and thermosets, surviving extrusion (up to 270 °C) and curing processes while remaining fully decodable. This approach establishes a versatile platform for secure molecular identification and traceability across several types of industrial plastic materials. Sequence-defined oligomers offer programmable molecular architectures but their deployment in real-world materials has been constrained by their low scale, limited thermal resilience and the need for specialized analytical methods. Here the authors introduce a molecular tagging strategy based on abiotic sequence-defined oligoamides incorporated at ultralow concentrations that endure the thermal and chemical conditions of industrial polymer processing

Fields of Study & Subjects

Machine Learning in Materials Science

Publication details

Journal / source
Nature Communications
Publisher
Springer Science and Business Media LLC
Volume
17
Issue
1
Publication date
26 June 2026
Language
EN