Prediction of Acrylonitrile-Butadiene-Styrene Mechanical Properties through Compressed-Sensing Techniques.

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  • Additional Information
    • Source:
      Publisher: American Chemical Society Country of Publication: United States NLM ID: 101230060 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1549-960X (Electronic) Linking ISSN: 15499596 NLM ISO Abbreviation: J Chem Inf Model Subsets: MEDLINE
    • Publication Information:
      Original Publication: Washington, D.C. : American Chemical Society, c2005-
    • Subject Terms:
      Butadienes*/chemistry ; Acrylonitrile*/chemistry ; Styrene*/chemistry; Mechanical Phenomena
    • Abstract:
      One of the challenges in the plastic industry is the cost and time spent on the characterization of different grades of polymers. Compressed sensing is a data reconstruction method that combines linear algebra with optimization schemes to retrieve a signal from a limited set of measurements of that signal. Using a data set of signal examples, a tailored basis can be constructed, allowing for the optimization of the measurements that should be conducted to provide the highest and most robust signal reconstruction accuracy. In this work, compressed sensing was used to predict the values of numerous properties based on measurements for a small subset of those properties. A data set of 21 fully characterized acrylonitrile-butadiene-styrene samples was used to construct a tailored basis to determine the minimal subset of properties to measure to achieve high reconstruction accuracy for the remaining nonmeasured properties. The analysis showed that using only six measured properties, an average reconstruction error of less than 5% can be achieved. In addition, by increasing the number of measured properties to nine, an average error of less than 3% was achieved. Compressed sensing enables experts in academia and industry to substantially reduce the number of properties that must be measured to fully and accurately characterize plastics, ultimately saving both costs and time. In future work, the method should be expanded to optimize not only individual properties but also entire tests used to simultaneously measure multiple properties. Furthermore, this approach can also be applied to recycled materials, of which the properties are more difficult to predict.
    • References:
      ACS Appl Mater Interfaces. 2022 Sep 28;14(38):42771-42790. (PMID: 36102317)
      Polymers (Basel). 2022 Jan 05;14(1):. (PMID: 35012240)
      ACS Omega. 2018 Oct 17;3(10):13374-13386. (PMID: 31458051)
      Sensors (Basel). 2019 Aug 13;19(16):. (PMID: 31412545)
      Materials (Basel). 2019 May 07;12(9):. (PMID: 31067762)
      Environ Sci Technol. 2023 Apr 25;57(16):6573-6588. (PMID: 37040559)
      ACS Polym Au. 2021 Dec 28;2(1):8-26. (PMID: 36855746)
      Polymers (Basel). 2022 Aug 26;14(17):. (PMID: 36080575)
    • Accession Number:
      0 (Butadienes)
      MP1U0D42PE (Acrylonitrile)
      44LJ2U959V (Styrene)
      JSD5FGP5VD (1,3-butadiene)
    • Publication Date:
      Date Created: 20240919 Date Completed: 20241014 Latest Revision: 20241018
    • Publication Date:
      20241018
    • Accession Number:
      PMC11481090
    • Accession Number:
      10.1021/acs.jcim.4c00622
    • Accession Number:
      39297562