# Replica-exchange Bayesian mixture regression reveals cluster-dependent XRD descriptors of tensile modulus in recycled polypropylene

https://mdr.nims.go.jp/datasets/70570c39-5556-4740-9efc-a3f567b3a7f8

## File

- [Replica-exchange Bayesian mixture regression reveals cluster-dependent XRD descriptors of tensile modulus in recycled polypropylene-2.pdf](https://mdr.nims.go.jp/filesets/eb4128a6-120e-45e1-939c-64a0c6ee4311/download) ([Detail](https://mdr.nims.go.jp/filesets/eb4128a6-120e-45e1-939c-64a0c6ee4311.md))

## Id

70570c39-5556-4740-9efc-a3f567b3a7f8

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2026-05-16T02:24:37.009971Z

## Updated at

2026-05-17T23:49:04.422556Z

## Published at

2026-05-18T01:23:35.934673Z

## Doi



## First published url

https://doi.org/10.1080/27660400.2026.2662046

## Date published

2026-12-31

## Recorded date published

2026-12-31

## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: Replica-exchange Bayesian mixture regression reveals cluster-dependent XRD
    descriptors of tensile modulus in recycled polypropylene
  title_type: original
  lang: en

## Description

- description: "Recycled polypropylene (rPP) exhibits large property variability due
    to mixed origins and degrada-\r\ntion histories, complicating nondestructive grading.
    In this study, we propose an interpretable\r\nBayesian framework that links X-ray
    di raction (XRD) peak features to tensile modulus for virgin/\r\nrecycled PP blends
    subjected to xenon-arc weathering. XRD pro les were analyzed by Bayesian\r\npeak
    deconvolution, extracting physically interpretable descriptors from four low-angle
    crystalline\r\npeaks (α(110), α(040), α(130), β(300)) and a broad amorphous halo,
    yielding 21 explanatory variables\r\nper sample. A Bayesian nite mixture of linear
    regressions with probabilistic feature selection was\r\n©tted, and posterior inference
    using replica-exchange Monte Carlo was performed to explore\r\na highly multimodal
    posterior. The model selected two clusters and achieved an in-sample t\r\n(R2
    = 0.81, RMSE = 145 MPa). Replicate-holdout group k-fold cross-validation provided\r\na
    conservative generalization estimate at the tensile level (R2 = 0.15, RMSE = 320
    MPa, N = 120),\r\nproviding a conservative lower-bound estimate due to specimen
    mismatch and repeated labels at\r\n0 cycles. Clusters di ered in the β(300) descriptor
    space, and direct comparison of cluster-speci c\r\nposterior coe cient distributions
    indicated that the β(300) peak position provided the clearest\r\nevidence of cluster-dependent
    regression behavior, whereas peak broadening was relevant in both\r\nclusters.
    These results suggest that β(300)-related descriptors– potentially re ecting β-phase
    lattice\r\nstrain or local disorder– may contribute to modulus beyond β fraction
    alone. This framework\r\nprovides interpretable XRD descriptors and uncertainty-aware
    modulus estimates for grading\r\nheterogeneous rPP."
  description_type: abstract
  lang: und

## Creator

- name: Kazuki Hammura
  role: author
- name: Kiyotaka Hitomi
  role: author
  orcid: https://orcid.org/0009-0005-0736-0214
  organization: National Institute for Materials Science
- name: Kenji Nagata
  role: author
  orcid: https://orcid.org/0000-0001-9894-4461
  organization: National Institute for Materials Science
- name: Masanobu Naito
  role: author
  orcid: https://orcid.org/0000-0001-7198-819X
  organization: National Institute for Materials Science

## Contact agent



## Publisher

organization: Informa UK Limited

## Managing organization



## Keyword

- subject: Recycled polypropylene
  schema: not_defined
- subject: X-ray diffraction
  schema: not_defined
- subject: Bayesian mixture regression
  schema: not_defined
- subject: Bayesian peak deconvolution
  schema: not_defined
- subject: structure–property relationship
  schema: not_defined

## Rights

- identifier: https://creativecommons.org/licenses/by/4.0/
  date_licensed: 2026-05-11

## Other identifier(s)



## Data origin

- data_origin_type: other

## Embargo



## Journal

- title: 'Science and Technology of Advanced Materials: Methods'
  issn: '27660400'
  volume: '6'
  issue: '1'
  article_number: '2662046'

## Conference



## Related item



## Funding

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

## Instrument



## Instrument operator



## Instrument managing organization



## Measurement method



## Specimen



## Chemical composition



## Structure for specimen



## Structural feature for specimen



## Specific property for specimen



## Process for specimen treatment



## Computational method



## Energy level/transition state



## Software



## Custom property



## Fileset

- id: eb4128a6-120e-45e1-939c-64a0c6ee4311
  filename: Replica-exchange Bayesian mixture regression reveals cluster-dependent
    XRD descriptors of tensile modulus in recycled polypropylene-2.pdf
  content_type: application/pdf
  size: 6062118
  md5: 828d9a507b4c8bea8e9fb27734257587

## Thumbnail

fileset_id: eb4128a6-120e-45e1-939c-64a0c6ee4311
filename: Replica-exchange Bayesian mixture regression reveals cluster-dependent XRD
  descriptors of tensile modulus in recycled polypropylene-2.pdf