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Lifetime and Degradation Study of Poly(Methyl Methacrylate) via a Data-Driven Study Protocol Approach

  • Thematic Section: 7th World Congress on Integrated Computational Materials Engineering
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Abstract

To optimize and extend the service life of polymeric materials in outdoor environments, a domain knowledge-based and data-driven approach was utilized to quantitatively investigate the temporal evolution of degradation modes, mechanisms, and rates under various stepwise accelerated exposure conditions. Six formulations of poly(methyl methacrylate) (PMMA) with different combinations of stabilizing additives, including one unstabilized formulation, were exposed in three accelerated weathering conditions. Degradation was dependent on wavelength as samples in UV light at 340 nm (UVA) exposure showed the most yellowing. The unstabilized PMMA formulation showed much higher yellowness index values (59.5) than stabilized PMMA formulations (2–12). Urbach edge analysis shows a shift toward longer wavelength from 285 to 500 nm with increasing exposure time and an increased absorbance around 400 nm of visible region as the unstabilized samples increase in yellowing. The degradation mechanisms of PMMA were tracked using induced absorbance to dose at specific wavelengths that correspond to known degradation mechanisms. The degradation pathway of PMMA was modeled in a <Stressor | Mechanism | Response> framework using network structural equation modeling (netSEM). netSEM showed changes in degradation pathway as PMMA transition stages of degradation.

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Acknowledgements

This research was performed at the SDLE Research Center, which was established through funding by the Ohio Third Frontier, Wright Project Program Award tech 12-004. This work made use of the Rider High Performance Computing Resource in the Core Facility for Advanced Research Computing at Case Western Reserve University. Micro-indentation measurements were performed at the CWRU Materials for Opto/electronics Research and Education (MORE) Center, a CWRU core facility est. 2011 via Ohio Third Frontier grant TECH 09-021. This material is based upon work supported by the U.S. National Science Foundation Award EEC-2052776 and EEC-2052662 in the MDS-Rely IUCRC Center, under the NSF Solicitation: NSF 20-570 Industry-University Cooperative Research Centers Program (H.H.A, L.S.B). This material is based upon research in the Materials Data Science for Stockpile Stewardship Center of Excellence (MDS3-COE), and supported by the Department of Energy’s National Nuclear Security Administration under Award Number(s) DE-NA0004104 (R.H.F and J.C.J). This work was performed, in part, under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344, LLNL-JRNL-853607-DRAFT (J.C.J). The views expressed herein do not necessarily represent the views of the U.S. Department of Energy or the United States Government.

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  1. Hein Htet Aung and Donghui Li contributed equally to this work.

Authors and Affiliations

  1. Department of Materials Science and Engineering, Case Western Reserve University, Cleveland, OH, 44106, USA

    Hein Htet Aung, Donghui Li, Jiqi Liu, Yea Jin Jo, Erika I. Barcelos, Roger H. French & Laura S. Bruckman

  2. Center for Materials Data Science for Reliability and Degradation: MDS-RELY, Case Western Reserve University, Cleveland, OH, 44106, USA

    Hein Htet Aung & Laura S. Bruckman

  3. Pacific Northwest National Laboratory, Richland, WA, 99354, USA

    Donghui Li

  4. Polymer Competence Center Leoben GmbH, Roseggerstrasse 12, Leoben, 8700, Austria

    Chiara Barretta & Gernot Oreski

  5. Department of Mathematics, Applied Mathematics and Statistics, Case Western Reserve University, Cleveland, OH, 44106, USA

    Yiyang Sheng

  6. Lawrence Livermore National Laboratory, Livermore, CA, 94550, USA

    Jayvic C. Jimenez

  7. Materials Data Science for Stockpile Stewardship: Center of Excellence, Case Western Reserve University, Cleveland, OH, 44106, USA

    Jayvic C. Jimenez, Erika I. Barcelos & Roger H. French

  8. Department of Computer and Data Sciences, Case Western Reserve University, Cleveland, OH, 44106, USA

    Roger H. French

  9. Department of Macromolecular Science and Engineering, Case Western Reserve University, Cleveland, OH, 44106, USA

    Roger H. French

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  1. Hein Htet Aung
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Aung, H.H., Li, D., Liu, J. et al. Lifetime and Degradation Study of Poly(Methyl Methacrylate) via a Data-Driven Study Protocol Approach. Integr Mater Manuf Innov 12, 349–370 (2023). https://doi.org/10.1007/s40192-023-00322-6

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