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Record W2784027388 · doi:10.1002/adma.201705778

A Light Harvesting, Self‐Powered Monolith Tactile Sensor Based on Electric Field Induced Effects in MAPbI<sub>3</sub> Perovskite

2018· article· en· W2784027388 on OpenAlexafffund
Rohit Saraf, Long Pu, Vivek Maheshwari

Bibliographic record

VenueAdvanced Materials · 2018
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsNational Institute for NanotechnologyUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of WaterlooCanada Foundation for Innovation
KeywordsMaterials scienceMonolithPerovskite (structure)Electric fieldNanotechnologyField (mathematics)OptoelectronicsChemical engineeringCatalysis

Abstract

fetched live from OpenAlex

Abstract Organolead trihalide perovskite MAPbI3 shows a distinctive combination of properties such as being ferroelectric and semiconducting, with ion migration effects under poling by electric fields. The combination of its ferroelectric and semiconducting nature is used to make a light harvesting, self‐powered tactile sensor. This sensor interfaces ZnO nanosheets as a pressure‐sensitive drain on the MAPbI3 film and once poled is operational for at least 72 h with just light illumination. The sensor is monolithic in structure, has linear response till 76 kPa, and is able to operate continuously as the energy harvesting mechanism is decoupled from its pressure sensing mechanism. It has a sensitivity of 0.57 kPa−1, which can be modulated by the strength of the poling field. The understanding of these effects in perovskite materials and their application in power source free devices are of significance to a wide array of fields where these materials are being researched and applied.

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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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 head0.006
GPT teacher head0.212
Teacher spread0.206 · 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

Citations59
Published2018
Admission routes2
Has abstractyes

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