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Record W2782775094 · doi:10.1088/1361-6463/aaa696

Wrinkling of flexoelectric nano-film/substrate systems

2018· article· en· W2782775094 on OpenAlexaff
Shengkai Su, Huaiwei Huang, Yijie Liu, Zheng Zhu

Bibliographic record

VenueJournal of Physics D Applied Physics · 2018
Typearticle
Languageen
FieldEngineering
TopicAdvanced Materials and Mechanics
Canadian institutionsYork University
FundersNational Natural Science Foundation of China
KeywordsNano-Materials scienceSubstrate (aquarium)NanotechnologyFlexoelectricityComposite materialPiezoelectricityGeology

Abstract

fetched live from OpenAlex

Abstract The study of wrinkling mechanisms essentially helps to establish stable and controllable performance in electronic products. To gain some basic understanding of the wrinkling process in flexoelectric dielectrics, this paper models the wrinkling of nano-film/substrate systems, typically seen in stretchable electronics, subjected to substrate prestrain and voltage loading on electrodes. Flexoelectricity is considered through the constitutive equations proposed by Shen and Hu, and Euler–Bernoulli beam theory is applied to formulate the expressions of wrinkling wavelength and amplitude through the Ritz method. The effects of flexoelectricity, surface parameters, prestrain, applied voltage, structural scale etc on wrinkling behaviors, including wrinkling deformation and the wrinkling critical condition, are discussed. Results reveal that the action of both flexoelectric and surface effects is significant over only a small scale range, with film thickness less than 10 nm. Alongside these issues, the fundamental difference between flexoelectric and piezoelectric effects on wrinkling behaviors is highlighted. Piezoelectricity may act as a promoter or suppressor of wrinkling initiation and amplitude, depending on the applied voltage, while flexoelectricity not only reduces the critical prestrain or voltage required for wrinkling, but also decreases the wrinkling wavelength and amplitude.

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 score0.372
Threshold uncertainty score0.794

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 head0.011
GPT teacher head0.211
Teacher spread0.200 · 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

Citations10
Published2018
Admission routes1
Has abstractyes

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