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Record W3095114135 · doi:10.1177/1045389x20965704

Modeling of piezoelectric power nano-generators using Brownian particles

2020· article· en· W3095114135 on OpenAlexaff
Fang Sun, Mahi R. Singh

Bibliographic record

VenueJournal of Intelligent Material Systems and Structures · 2020
Typearticle
Languageen
FieldEngineering
TopicInnovative Energy Harvesting Technologies
Canadian institutionsWestern University
Fundersnot available
KeywordsPiezoelectricityElectricity generationVoltageGenerator (circuit theory)Nano-Energy harvestingPower (physics)NanogeneratorElectric generatorMaterials scienceElectrical engineeringMechanical engineeringNanotechnologyEngineeringPhysicsComposite material

Abstract

fetched live from OpenAlex

We have designed a piezoelectric power nano-generator unitcell (device) where gas molecules are moving in Brownian motion and these molecules collide with a piezoelectric material. Due to the collision by gas molecules on the piezoelectric material, a voltage difference is induced across the piezoelectric material. We developed a simple model to calculate the voltage and power generation by using the method of statistical mechanics. Here we considered that gas molecules are not interacting with each other. We have created a design to connect nano-generator unitcells to form a chip in series configurations to enhance the production of the power nano-generation. Our design for the new power generator shows that as the thickness of the piezoelectric material increases so does the voltage generation. Our design can continuously generate electricity and this feature is better than solar panels that only work during daytime and windmills that cannot work without wind. It can work in a natural atmosphere environment. In the future when a new way of printing chips at low costs is discovered, then generating electricity will be very cheap using our design.

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 score0.209
Threshold uncertainty score0.417

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 head0.028
GPT teacher head0.226
Teacher spread0.198 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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