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Description:

Recycled polypropylene (rPP) exhibits large property variability due to mixed origins and degrada-
tion histories, complicating nondestructive grading. In this study, we propose an interpretable
Bayesian framework that links X-ray di raction (XRD) peak features to tensile modulus for virgin/
recycled PP blends subjected to xenon-arc weathering. XRD pro les were analyzed by Bayesian
peak deconvolution, extracting physically interpretable descriptors from four low-angle crystalline
peaks (α(110), α(040), α(130), β(300)) and a broad amorphous halo, yielding 21 explanatory variables
per sample. A Bayesian nite mixture of linear regressions with probabilistic feature selection was
©tted, and posterior inference using replica-exchange Monte Carlo was performed to explore
a highly multimodal posterior. The model selected two clusters and achieved an in-sample t
(R2 = 0.81, RMSE = 145 MPa). Replicate-holdout group k-fold cross-validation provided
a conservative generalization estimate at the tensile level (R2 = 0.15, RMSE = 320 MPa, N = 120),
providing a conservative lower-bound estimate due to specimen mismatch and repeated labels at
0 cycles. Clusters di ered in the β(300) descriptor space, and direct comparison of cluster-speci c
posterior coe cient distributions indicated that the β(300) peak position provided the clearest
evidence of cluster-dependent regression behavior, whereas peak broadening was relevant in both
clusters. These results suggest that β(300)-related descriptors– potentially re ecting β-phase lattice
strain or local disorder– may contribute to modulus beyond β fraction alone. This framework
provides interpretable XRD descriptors and uncertainty-aware modulus estimates for grading
heterogeneous rPP.

Rights:

• Creative Commons BY Attribution 4.0 International
  

Keyword: Recycled polypropylene, X-ray diffraction, Bayesian mixture regression, Bayesian peak deconvolution, structure–property relationship

Date published: 2026-12-31

Publisher: Informa UK Limited

Journal:

• Science and Technology of Advanced Materials: Methods (ISSN: 27660400) vol. 6 issue. 1 2662046

Funding:

• JSPS KAKENHI Grant-in-Aid for Scientific Research 23K26724
• JST CREST JPMJCR19J3
• MEXT Program: Data Creation and Utilization-Type Material Research and Development Project JPMXP1122714694
• Cross-ministerial Strategic Innovation Promotion Program JPJ012290

Manuscript type: Publisher's version (Version of record)

MDR DOI:

First published URL: https://doi.org/10.1080/27660400.2026.2662046

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Updated at: 2026-05-18 08:49:04 +0900

Published on MDR: 2026-05-18 10:23:35 +0900