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Flexible Thin Battery with Fast and Sensitive Voltage Control by a Simple Mechanical Bending: No Energy without Working

2019· article· en· W3042792154 on OpenAlexaff
Hendry Izaac Elim, Meilladelfia Rahman, Wanda Sari Latupoho, Randy Rasyid Latuconsina, Aprilia Angel Pattipeilohy, M. V. Reddy, Rajan Jose

Bibliographic record

VenueSCIENCE NATURE · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality Monitoring Technologies
Canadian institutionsHydro-Québec
Fundersnot available
KeywordsBattery (electricity)BendingVoltageElectrical engineeringSimple (philosophy)Energy (signal processing)Computer scienceLow voltageAutomotive engineeringControl (management)EngineeringStructural engineeringArtificial intelligencePower (physics)Physics

Abstract

fetched live from OpenAlex

The competition among scientists in providing the best need of mobile energy in society has been spread worldwide. This short communication shares a newly simple invention about thin battery with its voltage control simply adjusted by bending it. The changing in battery voltage is quite fast only in few seconds and very sensitive according to the mechanical bending treated into it. Furthermore, the thin battery was fabricated to be water resistant so that it can be applied under water with special technology purposes. This invention is a new beginning for flexible thin battery (FTB) technology which can be implemented in many different activities of daily life such as integrated technology use, medical energy supports, and education smart tools.

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: none
Teacher disagreement score0.004
Threshold uncertainty score0.012

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 communication0.0010.001
Open science0.0010.001
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 head0.006
GPT teacher head0.228
Teacher spread0.221 · 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

Citations11
Published2019
Admission routes1
Has abstractyes

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Same venueSCIENCE NATURESame topicWater Quality Monitoring TechnologiesFrench-language works237,207