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Advances in developing cost-effective carbon fibers by coupling multiscale modeling and experiments: A critical review

Summary

This is a critical review of strategies to lower the cost of carbon fibers (CFs) while meeting property targets, emphasizing how experiments are combined with multiscale computational modeling (from density functional theory and reactive molecular dynamics such as ReaxFF, to coarse-grained MD, finite-element style continuum models, and machine learning). The article surveys precursor families, spinning and conversion routes (stabilization, carbonization, graphitization), and how computation is used to rationalize reaction pathways, structure evolution, and structure–property links.

Methods

The article is a literature synthesis, not a single primary modeling study. It organizes prior work by precursor chemistry (e.g., PAN- and pitch-based routes, cellulosic and lignin-based alternatives, and related biopolymer approaches discussed in the review), processing levers (spinning modality, draw ratio, thermal schedules, tension), and computational methodology categories: DFT for electronic-level insight where applicable; atomistic MD with reactive force fields (ReaxFF) for bond-breaking chemistry during oxidative stabilization and carbonization; multiscale coupling (e.g., feeding atomistic data into continuum or FEA representations); and ML for mapping high-dimensional process–structure–property relationships when large datasets exist.

Findings

  • Final CF mechanical and transport properties (tensile modulus/strength, thermal and electrical conductivity, etc.) depend jointly on precursor architecture, processing history, carbon crystallinity and defect content, as summarized in the review’s conclusions.
  • Multiscale modeling (explicitly including DFT, ReaxFF MD, CGMD, etc., in the review’s framing) is presented as a way to connect atomistic chemistry and microstructure to engineering-scale behavior when paired with experiments.
  • The outlook highlights remaining cost barriers for commodity-scale CF use, and points to integrated experimental–computational workflows—including ReaxFF developments for C/H/O/N/S polymer chemistry and examples such as lignin chemistry and ML-oriented training data—as routes to screen precursors and processes more efficiently.

Limitations

  • As a review, quantitative benchmarks are second-hand from cited primary sources; readers should consult the original studies for parameters and validation.
  • Coverage is corpus- and field-scoped (cost-focused CF research), not an exhaustive global market or manufacturing survey.

Relevance to group

Adri C. T. van Duin is a co-author; the review foregrounds ReaxFF and related reactive MD as tools for carbonization chemistry alongside broader multiscale and ML threads relevant to materials informatics.

Citations and evidence anchors

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