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Record W4405854093 · doi:10.18280/rcma.340603

Electricity Generation from Bamboo Nanoparticles Processed by High Energy Milling without Thermal Treatment Mixed with NaOH

2024· article· en· W4405854093 on OpenAlexvenueno aff
Megara Munandar, Eko Siswanto, Winarto Winarto, I.N.G. Wardana

Bibliographic record

VenueRevue des composites et des matériaux avancés · 2024
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsnot available
FundersDirectorate for Biological SciencesUniversitas Muhammadiyah SurakartaUniversitas Negeri MalangUniversitas Brawijaya
KeywordsBambooNanoparticleMaterials scienceElectricity generationThermalHigh energyChemical engineeringNanotechnologyComposite materialEngineering physicsEngineeringPower (physics)

Abstract

fetched live from OpenAlex

The purpose of this research is to utilize bamboo as an energy-harvesting material.Energy harvesters are produced from nanomaterial-sized bamboo.This research aims to make bamboo nanomaterials with a high-energy milling process without heat treatment as a producer of electrical energy.Bamboo nanomaterials contain a chemical composition of C, O, Si, and K with a more positive electrical charge.The addition of NaOH negatively charges the electrolyte solution due to the presence of OH-.The concentration of NaOH specifies the production of electricity because the energy gap decreases from 0.48 eV to 0.39 eV.During the electricity generation process, some of the Al electrodes dissolve into the electrolyte and form a more regular crystal structure so that the energy gap decreases further to 0.28 eV, which has a stronger electricity generation capacity.The electrical voltage produced in the battery cell is around 768 mV.

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 score0.003

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 head0.031
GPT teacher head0.226
Teacher spread0.195 · 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
Published2024
Admission routes1
Has abstractyes

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