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Record W4386466584 · doi:10.1002/cssc.202301063

Investigating the Influence of Treatments on Carbon Felts for Vanadium Redox Flow Batteries

2023· article· en· W4386466584 on OpenAlexafffund
Monja Schilling, László Eifert, Kerstin Köble, Maximilian Jaugstetter, Nico Bevilacqua, Kieran F. Fahy, Kristina Tschulik, Aimy Bazylak, Roswitha Zeis

Bibliographic record

VenueChemSusChem · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvanced battery technologies research
Canadian institutionsUniversity of Toronto
FundersNational Research Council CanadaCenter of Excellence for Learning in Education, Science and TechnologyCanadian Institutes of Health ResearchUniversity of SaskatchewanDeutsche ForschungsgemeinschaftNatural Sciences and Engineering Research Council of CanadaFonds der Chemischen Industrie
KeywordsDielectric spectroscopyMaterials scienceVanadiumCyclic voltammetryRedoxDesorptionElectrochemistryFlow batteryChemical engineeringSorptionScanning electron microscopeCarbon fibersElectrolyteAnalytical Chemistry (journal)ElectrodeComposite materialChemistryMetallurgyAdsorptionOrganic chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

Vanadium redox flow battery (VRFB) electrodes face challenges related to their long-term operation. We investigated different electrode treatments mimicking the aging processes during operation, including thermal activation, aging, soaking, and storing. Several characterization techniques were used to deepen the understanding of the treatment of carbon felts. Synchrotron X-ray imaging, electrochemical impedance spectroscopy (EIS) with the distribution of relaxation times analysis, and dynamic vapor sorption (DVS) revealed differences between the wettability of felts. The bulk saturation after electrolyte injection into the carbon felts significantly differed from 8 % to 96 %. DVS revealed differences in the sorption/desorption behavior of carbon felt ranging from a slight change of 0.8 wt % to over 100 wt %. Additionally, the interactions between the water vapor and the sample change from type V to type H2. After treatment, morphology changes were observed by atomic force microscopy and scanning electron microscopy. Cyclic voltammetry and EIS were used to probe the electrochemical performance, revealing different catalytic activities and transport-related impedances for the treated samples. These investigations are crucial for understanding the effects of treatments on the performance and optimizing materials for long-term operation.

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 score0.141
Threshold uncertainty score0.460

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 head0.025
GPT teacher head0.273
Teacher spread0.249 · 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

Citations14
Published2023
Admission routes2
Has abstractyes

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