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Record W4402838772 · doi:10.1021/acs.macromol.4c01445

Molecular Simulations of Crazes in Glassy Polymers under Cyclic Loading

2024· article· en· W4402838772 on OpenAlexaff
Tobias Laschuetza, Ting Ge, Thomas Seelig, Jörg Rottler

Bibliographic record

VenueMacromolecules · 2024
Typearticle
Languageen
FieldMaterials Science
TopicMaterial Dynamics and Properties
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPolymerMaterials scienceMolecular dynamicsComposite materialPolymer chemistryPolymer scienceChemistryComputational chemistry

Abstract

fetched live from OpenAlex

We study with molecular dynamics simulations of a generic bead–spring model the cyclic crazing behavior of glassy polymers. The aim is to elucidate the mechanical response of sole fibrillated craze matter as well as its interaction with bulk material. The macroscopic stress response exhibits a hysteresis, which is quasi stationary after the first cycle and largely independent of deformation rate and temperature. It results from a complex interplay between constraints imposed by the entanglement network, pore space and pore space closure. Once the craze fibrils are oriented, stretching of the covalent backbone bonds leads to a rapid stress increase. In the initial stages of unloading, a loss in entanglement contact yields a quick stress relaxation in the backbone. During unloading, the craze fibrils undergo a rigid body (i.e., stress-free) folding motion due to the surrounding pore space, so that the structural behavior of craze fibrils during unloading is most accurately described as string-like. The reloading response depends significantly on the degree of pore space closure and the enforced intermolecular interaction during unloading. It ranges from a linear stress increase to a recavitation with a redrawing response. Compared to the bulk stiffness, the craze stiffness is 2 orders of magnitude lower and as a result, the macro response of coexisting craze and bulk matter is governed by the sole fibrillated craze matter.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.867

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.012
GPT teacher head0.253
Teacher spread0.241 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2024
Admission routes1
Has abstractyes

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