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Record W4319834111 · doi:10.1002/aesr.202200188

Optimizing Various Operational Conditions of Hydrazine Single Cell for a Short Stack System

2023· article· en· W4319834111 on OpenAlexaff
Jihyeon Park, Jaeyoung Lee

Bibliographic record

VenueAdvanced Energy and Sustainability Research · 2023
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsMcMaster University
FundersKorea Institute of Energy Technology Evaluation and PlanningMinistry of Science and ICT, South KoreaNational Research Foundation of KoreaNational Research Foundation
KeywordsStack (abstract data type)Hydrazine (antidepressant)Process engineeringFuel cellsMaterials scienceChemical engineeringComputer scienceNuclear engineeringChemistryEngineeringChromatography

Abstract

fetched live from OpenAlex

Although hydrazine fuel cells (HzFCs) have various advantages such as a high theoretical potential, low operating temperature, and no carbon dioxide emission, only a couple of studies on HzFC stack have been reported due to the peculiarity of using an anion exchange membrane and the toxic issue of highly concentrated hydrazine fuel. Herein, how the power output performance in a single‐cell system is affected by various operational factors of cell temperature, humidification level, pressurization, fuel concentration, and stoichiometric value is investigated and then a home‐made short stack consisting of five single cells (HzFC‐5S) to evaluate the difference between the single cell and the stack is built up. Confirmation that the optimization point in the single‐cell does not apply to the short stack can be meaningful in accessing its possible commercialization process for portable and mobile devices.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.020
GPT teacher head0.299
Teacher spread0.279 · 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 source (direct Gemma or distilled Codex), 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

Citations7
Published2023
Admission routes1
Has abstractyes

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