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PreprintCC BYChemRxiv

Atomistic Probability Kinetic Modeling of Polymer Functionalization: A Framework for Post-Functionalization and Upcycling of Polymers

  • Brandon D. Howard1,2
  • Christian J. Howard3,4
  • Aspen Tapley1,2
  • John R. Dorgan1,2
  • Scott Calabrese Barton1,2
4 affiliations
  1. 1Chemical Engineering and Materials Science
  2. 2Michigan State University
  3. 3Computer Science
  4. 4University Illinois
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Abstract

Post-functionalization and the upcycling of post-consumer waste are valuable strategies for enhancing the material properties and value of polymers, but predictive modeling of these reactions is limited because of their inherent randomness. We present a mathematical framework to describe the effect of polymer functionalization reactions on downstream material properties or products using a combinatoric atomistic probability distribution (APD). The APD is derived generally to predict the spacing of subchains, Δ, between substituents or at chain ends when S substituents are randomly distributed along a polymer chain of length L. This approach is applied to a model upcycling system: the functionalization of polyethylene (PE) via sulfonation and subsequent H 2 O 2 -driven oxidative depolymerization. By coupling the APD with models for downstream depolymerization of sulfonated PE and hydroxyl radical-mediated decomposition kinetics, we develop a kinetic probability distribution (KPD) that explicitly describes the resulting degradation products. This approach results in dimensional reduction by requiring 14 total parameters for an N-species system (N = 6 here), while traditional modeling approaches require 4N + 7 (31 in our case). Probability parameters obtained from the KPD enabled the calculation of product and CO 2 carbon yields within ~10% of experimental values. This framework presents a novel approach to incorporating the underlying randomness governing polymer functionalization reactions, vastly reducing the parameter space and improving targeting of more economically relevant products during upcycling.

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Cite

BibTeXRIS
BibTeX
@misc{howard2026atomistic,
  title = {{Atomistic Probability Kinetic Modeling of Polymer Functionalization: A Framework for Post-Functionalization and Upcycling of Polymers}},
  author = {Brandon D. Howard and Christian J. Howard and Aspen Tapley and John R. Dorgan and Scott Calabrese Barton},
  howpublished = {ChemRxiv},
  year = {2026},
  month = sep,
  doi = {10.26434/chemrxiv.15009226/v1},
  url = {https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009226/v1},
  note = {CC BY},
}
RIS
TY  - UNPB
TI  - Atomistic Probability Kinetic Modeling of Polymer Functionalization: A Framework for Post-Functionalization and Upcycling of Polymers
AU  - Brandon D. Howard
AU  - Christian J. Howard
AU  - Aspen Tapley
AU  - John R. Dorgan
AU  - Scott Calabrese Barton
PY  - 2026
DA  - 2026/09/22
PB  - ChemRxiv
DO  - 10.26434/chemrxiv.15009226/v1
UR  - https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009226/v1
AB  - Post-functionalization and the upcycling of post-consumer waste are valuable strategies for enhancing the material properties and value of polymers, but predictive modeling of these reactions is limited because of their inherent randomness. We present a mathematical framework to describe the effect of polymer functionalization reactions on downstream material properties or products using a combinatoric atomistic probability distribution (APD). The APD is derived generally to predict the spacing of subchains, Δ, between substituents or at chain ends when S substituents are randomly distributed along a polymer chain of length L. This approach is applied to a model upcycling system: the functionalization of polyethylene (PE) via sulfonation and subsequent H 2 O 2 -driven oxidative depolymerization. By coupling the APD with models for downstream depolymerization of sulfonated PE and hydroxyl radical-mediated decomposition kinetics, we develop a kinetic probability distribution (KPD) that explicitly describes the resulting degradation products. This approach results in dimensional reduction by requiring 14 total parameters for an N-species system (N = 6 here), while traditional modeling approaches require 4N + 7 (31 in our case). Probability parameters obtained from the KPD enabled the calculation of product and CO 2 carbon yields within ~10% of experimental values. This framework presents a novel approach to incorporating the underlying randomness governing polymer functionalization reactions, vastly reducing the parameter space and improving targeting of more economically relevant products during upcycling.
ER  -