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Record W3121256672 · doi:10.1021/acsaelm.0c00971

High-Temperature Annealed Biochar as a Conductive Filler for the Production of Piezoresistive Materials for Energy Conversion Application

2021· article· en· W3121256672 on OpenAlexaff
Mauro Giorcelli, Mattia Bartoli, Alessandro Sanginario, Elisa Padovano, Carlo Rosso, Massimo Rovere, Alberto Tagliaferro

Bibliographic record

VenueACS Applied Electronic Materials · 2021
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsPiezoresistive effectMaterials scienceComposite materialElectrical resistivity and conductivityElectrical conductorComposite numberBiocharConductivityElectrical engineering

Abstract

fetched live from OpenAlex

Abstract In this research work, we develop a prototype that is able to convert mechanical strain into an electrical signal. To reach this scope, we evaluated the electrical properties of a thermally annealed biochar-based silicon composite. The great elasticity range of silicon will provide the mechanical properties for the realization of an effective piezoresistive material. For the fulfillment of this aim, we annealed olive biochar at 1500 °C in order to achieve a good degree of graphitization and an electrical conductivity close to 103 S/m. The electrical conductivity under the mechanical stress of composites was deeply investigated through experiments and simulation to achieve a comprehensive knowledge. Furthermore, a real device based on these composites was designed and realized to demonstrate one of the prospective exploitations of the composite piezoresistive properties.

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 head0.209
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

Citations44
Published2021
Admission routes1
Has abstractyes

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