MétaCan
Menu
Back to cohort
Record W4412067603 · doi:10.1016/j.seta.2025.104429

Development and simulation of new electrode designs for improved hydrogen production in alkaline electrolyzers

2025· article· en· W4412067603 on OpenAlexaff
Mohamed Ismail, Doğan Erdemir, İbrahim Dinçer

Bibliographic record

VenueSustainable Energy Technologies and Assessments · 2025
Typearticle
Languageen
FieldEnergy
TopicHybrid Renewable Energy Systems
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsHydrogen productionElectrodeProduction (economics)Alkaline water electrolysisProcess engineeringHydrogenComputer scienceElectrolysisMaterials scienceBiochemical engineeringEnvironmental scienceChemistryEngineeringElectrolyte

Abstract

fetched live from OpenAlex

This study develops multiple modified electrode surface geometries which aims at enhancing the hydrogen production rates of alkaline water electrolyzers (AWEs). Subsequently, the study investigates how the developed simulation model to determine is utilized to evaluate the flow characteristics and identify the optimal electrode surface modification. The model incorporates dimpled electrode surfaces with circular, triangular, and rectangular shapes. Furthermore, we study each dimple shape in its inward, outward, and alternating configurations. The simulations show that these surface modifications can introduce localized turbulence to the flow inside the gas evolution chamber. This, in turn, improves the gas bubbles’ separation from the electrode surface. The results demonstrate a correlation between the induced turbulence from electrode surface changes and an increase in the volume fraction of evolved gases. Notably, inward-facing rectangular dimples lead to an increment in hydrogen volume fraction of up to 9.7%. This study highlights the critical role of electrode geometry in improving AWE performance and provides insights for advancing green hydrogen production technologies.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.727
Threshold uncertainty score0.780

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.013
GPT teacher head0.281
Teacher spread0.268 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueSustainable Energy Technologies and AssessmentsSame topicHybrid Renewable Energy SystemsFrench-language works237,207