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Record W4389335662 · doi:10.1002/batt.202300451

All‐gel Proton‐conducting Batteries with BiOCl and VOSO<sub>4</sub> as Active Materials

2023· article· en· W4389335662 on OpenAlexafffund
Prathap Iyapazham Vaigunda Suba, Shoaib Muhammad, Oanh Hoang Nguyen, Kunal Karan, Steve Larter, Venkataraman Thangadurai

Bibliographic record

VenueBatteries & Supercaps · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvanced battery technologies research
Canadian institutionsAlberta EnergyUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaCanada First Research Excellence Fund
KeywordsRedoxSeparator (oil production)Energy storageMaterials scienceBattery (electricity)Renewable energyNanotechnologyElectrolyteComputer scienceChemical engineeringChemistryElectrical engineeringElectrodePower (physics)Engineering

Abstract

fetched live from OpenAlex

Abstract Flexible, scalable, and low‐cost energy storage solutions are required for the widespread use of renewable energy and the mitigation of climate change. State‐of‐the‐art lithium‐ion batteries provide high specific energy density; however, designing a safe and cost‐effective grid‐scale lithium‐ion battery is still a major challenge. Redox flow batteries are scalable due to their ability to decouple power and energy; however, the commercial applications of these batteries are limited because of expensive ion‐selective membranes. In this paper, we report a modified battery design approach in which Bi/BiOCl and V 4+ /V 5+ reaction‐based redox couples are utilized while employing a gel‐based architecture. We show, for the first time, that Bi/BiOCl conversion reaction based redox couple can reversibly work against traditional vanadium‐based redox pair in an aqueous electrolyte. Redox active materials in this cell design are in the gel form, and a traditional membrane or a separator is not required. This proof‐of‐concept battery delivers 0.9 V with a volumetric energy density of 22.14 Wh/L.

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 categoriesMeta-epidemiology (narrow)
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 score0.010
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.029
GPT teacher head0.265
Teacher spread0.236 · 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.

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

Citations3
Published2023
Admission routes2
Has abstractyes

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