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Record W3135730814 · doi:10.1038/s41598-021-84979-z

Integrated hybrid architecture of metal and biochar for high performance asymmetric supercapacitors

2021· article· en· W3135730814 on OpenAlexafffund
Omid Norouzi, S.E.M. Pourhosseini, Hamid Reza Naderi, Francesco Di Maria, Animesh Dutta

Bibliographic record

VenueScientific Reports · 2021
Typearticle
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsUniversity of Guelph
FundersOntario Ministry of Agriculture, Food and Rural Affairs
KeywordsSupercapacitorCapacitanceMaterials scienceChemical engineeringBiocharElectrolyteCurrent densityCobaltPyrolysisNanotechnologyElectrodeChemistryMetallurgy

Abstract

fetched live from OpenAlex

Abstract Two state-of-the-art electrodes were successfully synthesized and used to assemble both symmetric and asymmetric type supercapacitors. 3DFAB was fabricated by direct pyrolysis of green macroalgae in the presence of NaOH. Possible NaOH activation mechanisms are proposed, which explains the formation of oxygen functional groups through quick penetration of OH- and NaOH into the vacancies. To obtain CoTLM, the tile-like architecture of cobalt oxides was introduced to the 3D interconnected functional algal biochar (3DFAB) by a simple one-pot hydrothermal method under mild conditions. For the symmetric supercapacitors, the maximum specific capacitance of RAB, 3DFAB, and CoTLM were 158, 296, and 445 F g −1 at the current density of 1 A g −1 . Regarding cobalt-based asymmetric systems, the maximum capacitance for the 3DFAB//CoTLM was 411 F g −1 . This asymmetric supercapacitor device also retained 100.9% of its initial capacitance after 4000 cycles at the current density of 4 A g −1 . Unbuffered aqueous electrolyte and the unique morphological structure used in this study might catapult forward commercialization of such advanced energy storage devices.

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)0.001
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 score0.009
Threshold uncertainty score0.488

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.220
Teacher spread0.207 · 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

Citations66
Published2021
Admission routes2
Has abstractyes

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