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Record W2735839972 · doi:10.1149/2.1301709jes

Evaluation of Electrospun Fibrous Mats Targeted for Use as Flow Battery Electrodes

2017· article· en· W2735839972 on OpenAlex

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 The Electrochemical Society · 2017
Typearticle
Languageen
FieldEngineering
TopicAdvanced battery technologies research
Canadian institutionsUniversity of WaterlooMcGill University
Fundersnot available
KeywordsElectrospinningMaterials scienceElectrodePorositySpecific surface areaElectrochemistryNanotechnologyNanofiberBattery (electricity)CarbonizationConductivityComposite materialPolymerChemistry

Abstract

fetched live from OpenAlex

Electrospinning was used to create custom-made fibrous electrode materials for redox flow batteries with targeted structural properties. The aim was to increase the available surface area for electrochemical reaction without diminishing the transport properties of the electrode. Electrospinning conditions were identified that could produce fibers several times larger than those typically yielded by the technique, yet much smaller than in commercially available electrodes. These materials were subsequently carbonized using widely reported protocols. The resultant materials were subjected to a range of characterization tests to confirm that the feasibility of the target material, including surface area, pore and fiber sizes, porosity, conductivity, and permeability. The most promising material to emerge from this selection processes was then tested for electrochemical performance in a flow cell. The produced material performed markedly better than a commercially available material. Further optimizations such as improved consistency in the production and some surface activation treatments could provide significant advancements.

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)0.002
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 score0.110
Threshold uncertainty score0.501

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 science0.0010.000
Research integrity0.0000.001
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 head0.027
GPT teacher head0.305
Teacher spread0.278 · 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