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Record W2804652057 · doi:10.1021/acs.macromol.8b00736

Atomistic Modeling of the Formation of a Thermoset/Thermoplastic Interphase during Co-Curing

2018· article· en· W2804652057 on OpenAlexaff
Magdalena Laurien, Barış Demir, Holger Büttemeyer, Axel S. Herrmann, Tiffany R. Walsh, Lucio Colombi Ciacchi

Bibliographic record

VenueMacromolecules · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicForce Microscopy Techniques and Applications
Canadian institutionsMcMaster University
FundersAustralian Research CouncilUniversität BremenBundesministerium für Bildung und Forschung
KeywordsThermosetting polymerInterphaseEpoxyMaterials scienceThermoplasticCuring (chemistry)Composite materialPolymerUltimate tensile strength

Abstract

fetched live from OpenAlex

Co-curing of a thermoset epoxy matrix in contact with thermoplastic foils is an essential step in damage-free joining of polymers or polymer-based composites. We present results of all-atom molecular dynamics simulations that shed light onto the resulting hybrid interface. Using poly(vinylidene difluoride) (PVDF) and a multicomponent epoxy resin as model systems, we have developed a computational co-curing protocol that ensures both adequate structural representation and mobility of the PVDF chains and a realistic cross-linking conversion and topology of the epoxy resin. As a result, we reveal that mutually entangled loops of thermoplastic chains and resin strands form across the interface within the extended interphase region separating the two polymers. In tensile stress simulations we find that these loops contribute to a surprisingly large interfacial strength. In the absence of extrinsic defects, failures nucleate at the PVDF side of the interphase and propagate via a chain-pullout mechanism characteristic of semi-interpenetrating polymer networks involving thermoplastic materials.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.008
GPT teacher head0.265
Teacher spread0.257 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations49
Published2018
Admission routes1
Has abstractyes

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