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Record W4386823353 · doi:10.31438/trf.hh2022.105

PIEZOELECTRIC PAPER: LARGE-SCALE COMPATIBLE MANUFACTURING PROCESS AND SENSOR DEMONSTRATION

2022· article· en· W4386823353 on OpenAlexafffund
Kanagasubbulakshmi Sankaralingam, Ninweh Nina, Sajana Sumanasinghe, Anindya Roy, Konrad Walus, Boris Stoeber

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsPiezoelectricityMaterials sciencePolingPapermakingPiezoelectric sensorAccelerometerProcess (computing)Composite numberMechanical engineeringProcess engineeringComposite materialComputer scienceEngineeringOptoelectronicsDielectric

Abstract

fetched live from OpenAlex

Functionalized paper has been used as a substrate for flexible devices for several years now.However, the methods for preparing such paper substrates are typically too complex for integration with large-scale paper manufacturing processes.Here, we demonstrate a simple process for manufacturing piezoelectric paper that is compatible with the conventional papermaking process.This process utilizes microfibrillated wood pulp to achieve paper with high loading (69 wt%) of piezoelectric BaTiO3 particles (~300 nm diameter) that shows high piezoelectric coefficients up to d33 = 15.2 pC/N after corona poling.Such a piezoelectric paper composite is used as a simple accelerometer to demonstrate its utility in inertial sensing applications.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.007
GPT teacher head0.210
Teacher spread0.203 · 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 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 routes2
Has abstractyes

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