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Record W4382236761 · doi:10.1149/2754-2734/acd43d

Waste-to-Wealth: Wheat-Based Porous Electrodes for Electrochemical Energy Storage Devices

2023· article· en· W4382236761 on OpenAlexaff
Hamidreza Parsimehr, Parya Kazemzadeh, Ali Ehsani

Bibliographic record

VenueECS Advances · 2023
Typearticle
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsBiocharAnodeSupercapacitorEnergy storageMaterials scienceElectrochemistryCathodeEnvironmentally friendlyElectrodeNanotechnologyWaste managementPyrolysisChemistryElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

Energy production/storage as one of the world’s leading issues has been widely studied. This significant problem can be solved by using disposable/low-cost biomass materials. Electrochemical energy storage (EES) devices including supercapacitors, rechargeable batteries, and hybrid EES devices have been extensively developed in recent years. The EES devices have been recognized as a proper answer to the energy storage problem in the world. Numerous studies have been accomplished to develop biomass-based and biochar-based EES devices to decrease environmental pollution and production costs. The most important part of the EES devices are electrodes including the cathode and anode. According to recent studies, biochar-based electrodes have considerable electrochemical properties. Wheat is one of the most important parts of the human diet. The wheat wastes have amazing electrochemical properties to be used as a precursor of electrochemical energy storage (EES) electrodes including supercapacitors, batteries, and hybrid EES devices. The benign/low-cost wheat wastes especially wheat straw and wheat husk have been used to fabricate wheat-based biochar materials. The electrochemical properties of the wheat-based biochar electrodes (cathode and anode) in the EES devices have determined that these benign/low-cost EES electrodes reduce production costs and obtain acceptable electrochemical performance and environmentally friendly procedures.

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 score0.051
Threshold uncertainty score0.646

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 head0.014
GPT teacher head0.267
Teacher spread0.252 · 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

Citations4
Published2023
Admission routes1
Has abstractyes

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