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Multimodal characterization of carbon electrodes' thermal activation for vanadium redox flow batteries

2023· article· en· W4362454129 on OpenAlexaff
Kerstin Köble, Maximilian Jaugstetter, Monja Schilling, Michael Braig, Thomas Diemant, Kristina Tschulik, Roswitha Zeis

Bibliographic record

VenueJournal of Power Sources · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvanced battery technologies research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsVanadiumWettingDielectric spectroscopyMaterials scienceRedoxCarbon fibersChemical engineeringElectrodeElectrochemistryFlow batteryContact angleContext (archaeology)Analytical Chemistry (journal)ChemistryComposite materialMetallurgyPhysical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Thermal activation has proven to be a valuable procedure to improve the performance of carbon electrodes in vanadium redox flow batteries (VRFBs). This work investigates how different activation temperatures impact the rayon-based carbon felt's structure, surface composition, wettability, and electrochemical activity. A unique combination of non-standard techniques, including atomic force microscopy (AFM), dynamic vapor sorption (DVS), and electrochemical impedance spectroscopy (EIS) combined with the distribution of relaxation times (DRT) analysis, was used for the first time in the context of VRFB electrodes. The wettability of the carbon felts improved, and the process impedances decreased with higher activation temperatures. However, severe carbon decomposition occurs at high activation temperatures. The optimum electrochemical performance of the carbon felts in the vanadium(IV)/vanadium(V) redox reaction was observed after activation at 400 °C. Thus, we conclude that the optimum activation temperature for this type of carbon felt concerning the investigated properties is around 400 °C. Furthermore, we want to highlight the successful approach of using AFM, DVS, and EIS combined with DRT analysis for an integral investigation of key properties such as structure, wettability, and performance of VRFB electrodes.

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 score0.044
Threshold uncertainty score0.366

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 head0.010
GPT teacher head0.243
Teacher spread0.233 · 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

Citations31
Published2023
Admission routes1
Has abstractyes

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