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Record W4295128213 · doi:10.1002/mame.202200402

Effect of Martensitic Transformation of NiTi Particles on Temperature Sensitivity of Flexible VGCF/PDMS Films

2022· article· en· W4295128213 on OpenAlexaff
Teng Li, Jiaqi Li, Dongyu Liu, Shen Gong, Chanyuan Wu, Zheng Zhu

Bibliographic record

VenueMacromolecular Materials and Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsYork University
FundersNational Defense Pre-Research Foundation of ChinaChina Postdoctoral Science FoundationNational Natural Science Foundation of China
KeywordsMaterials scienceComposite materialNickel titaniumElectrical resistivity and conductivityComposite numberPercolation (cognitive psychology)Thermal expansionShape-memory alloy

Abstract

fetched live from OpenAlex

Abstract A novel NiTi/vapor grown carbon fiber (VGCF)/polydimethylsiloxane flexible film is successfully prepared, of which the resistivity can keep stable under various strain deformation. By introducing NiTi particles with appropriate phase transition temperature, the resistivity change value of the composite in the temperature range can be doubled without affecting the flexibility of the composite. A computer simulation based on percolation network (PN) theory and Monte Carlo (MC) calculation is developed to understand the mechanisms of resistivity temperature curves. The simulation results agree with experiments quantitatively, which reveal that the inhomogeneous martensitic transformation of NiTi particles is a dominant mechanism for the enlarged and nonlinearity resistivity temperature variation of the composite. Parametric studies have been conducted and the calculation results show that selecting NiTi particles with smaller particle diameter, VGCF with higher conductivity, and polymer with larger coefficient of thermal expansion is conducive to further obtain flexible composite films with better temperature sensitivity.

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 score0.304
Threshold uncertainty score0.646

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)0.0000.000

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 head0.004
GPT teacher head0.186
Teacher spread0.182 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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