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Metal-organic framework materials for supercapacitors

2021· article· en· W4312788541 on OpenAlex
Xiaoyue Liu, Jianing Song

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Physics Conference Series · 2021
Typearticle
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsSupercapacitorMaterials scienceMetal-organic frameworkNanoporousEnergy storageNanotechnologyPorosityOxideCapacitorElectrodeComposite materialCapacitanceAdsorptionPower (physics)MetallurgyElectrical engineeringChemistryVoltage

Abstract

fetched live from OpenAlex

Supercapacitor (SCs), known as outstanding electrical storage capacity, which times more than the batteries and fuel cells, recharges with a large amount of released power, fast overpassing the gap between capacitors and batteries, and widely be employed in the field of energy storage. Metal-organic frameworks (MOFs) attract intensive attention as electrode materials in supercapacitor application, owing to their ultrahigh porosity, adjustable distribution of pore size, convenient synthesis, great structural adaptability, etc. Composited with different materials, such as carbons family, metal oxide, can improve pristine MOFs' conductivity and chemical stability, which are all-important as electrode materials in SCs. This review comprehensively summarizes the common synthesis methods of four kinds of MOFs materials: the pristine MOFs, MOFs composites, MOFs-derived nanoporous carbons, and MOFs-derived metal oxides. Also, the applications of these four kinds of materials in SCs electrode materials are systematically introduced. Furthermore, a perspective on MOFs materials in the field of SCs is discussed.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.037
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.261
Teacher spread0.229 · 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