An AI-based mass spectrometric method that can identify a polymer’s monomeric sequence was developed by the National Institute for Materials Science (NIMS). The development of new materials, the solution of plastic recycling issues, and a deeper comprehension of basic polymeric structures may all benefit from this approach. Chemical Science is where the work was published.
A polymer is a very large molecule that is made up of a chain of many small molecules called monomers, which are bonded together and can be anywhere from hundreds to hundreds of thousands. Numerous normal polymers (e.g., plastics and saps) are copolymers, comprising a few unique kinds of monomers. The sequence distribution is produced as a result of the monomers’ stochastic arrangement along the main chain during the copolymerization process.
The development of a polymer sequencer—an analytical method for quantitatively evaluating the distribution—had been anticipated as a potentially effective tool for innovative polymer materials because it is believed that this sequence distribution has a significant impact on the material performance of copolymeric materials.
This examination group as of late fostered the world’s most memorable pragmatic polymer sequencer, evaluating the short monomer portions in unambiguous arrangements, thus called “codon,” utilizing a relationship from nucleotide trios through pyrolysis mass spectra. An analyte copolymer is slowly heated from room temperature to 600°C in this method, resulting in successive oligomeric fragments that are recorded by mass spectrometry in order of their heat susceptibility.
AI analysis is used to reconstruct virtual copolymers that repeat a single codon species based on fragment patterns. This makes it possible to quantify the analyte’s codon composition. Variable monomer combinations can be used with this polymer sequencer. Other materials whose fragments vaporize during thermal treatment can also be analyzed with the polymer sequencer. Sample materials that are either insoluble or unable to be liquefied, as well as inorganic composites, fall under this category.
Using the newly developed polymer sequencer as the primary technology, the research team intends to investigate the relationship between sequence distribution and copolymer properties and develop sequence-controlled polymerization methods. This endeavor has the potential to address environmental issues brought on by plastics and enhance the performance of various polymer materials.
More information: Yusuke Hibi et al, A data-driven sequencer that unveils latent “codons” in synthetic copolymers, Chemical Science (2023). DOI: 10.1039/D2SC06974A
