A data-driven sequencer that unveils latent “codons” in synthetic copolymers

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A data-driven sequencer that unveils latent “codons” in synthetic copolymers
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AI-based technique capable of determining the monomeric sequence of a polymer, for plastic recycling

wing to the lack of efficient sequencing methods, hindering the integration of experiments and theories. Herein, we propose a polymer sequencer based on mass spectrometry of pyrolyzed oligomeric fragments. Despite the random fragmentation along copolymer main-chains, the characteristic fragment patterns of the codons are identified and quantifiedunsupervised learning of a spectral dataset of random copolymers.

The codon complexities increase with their length and monomer component number. Our data-driven approach accommodates the increasing complexities by expanding the dataset; the codon compositions of binary triads, binary pentads and ternary triads are quantifiable with small datasets . The sequencer allows describing copolymers with their codon compositions/distributions, facilitating sequence engineering toward innovative polymer materials.Please wait while we load your content...

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