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Record W4413219374 · doi:10.1016/j.fuel.2025.136497

Experimental investigation and assessment of a newly designed hybrid hydrogen reactor for sustainable fuel production

2025· article· en· W4413219374 on OpenAlexaff
Mehmet Gursoy, İbrahim Dinçer

Bibliographic record

VenueFuel · 2025
Typearticle
Languageen
FieldEngineering
TopicNuclear reactor physics and engineering
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsHydrogen productionProduction (economics)HydrogenEnvironmental scienceNuclear engineeringProcess engineeringWaste managementMaterials scienceChemistryEngineeringOrganic chemistry

Abstract

fetched live from OpenAlex

• A new hybrid PEC-conventional cells facilitate continuous hydrogen generation. • A coated photocathode enhances catalytic activity and hydrogen production. • The Design Expert was utilized to optimize essential parameters using RSM modelling. • Maximum hydrogen generation attained: 1.226 µg/s over an area of 49 cm 2 . • The energy and exergy efficiencies attained were 3.78% and 3.86%, respectively. With the increasing need for clean and sustainable energy sources, hydrogen has become a crucial energy carrier owing to its high energy density and zero-emission properties. This study presents a novel hybrid photoelectrochemical (PEC)-conventional electrolysis system that integrates the advantageous features of both PEC and conventional electrolysis technologies, providing distinct benefits. A significant advantage is further the ability to constantly produce hydrogen, even without solar irradiation. An experimental examination and thermodynamic performance evaluation of a hybrid hydrogen reactor to produce clean hydrogen is presented. The Design Expert software and Response Surface Methodology (RSM) were used to assess the experimental data and simulate the operational conditions and factors, such as mass flow rate, operating temperature and sun irradiation. At a 49 cm 2 active electrode area, the system produced 1.226 µg/s of hydrogen with energy and exergy efficiencies of 3.78% and 3.86%, respectively. The results underscore the significance of coating material selection and process optimization in enhancing efficient and sustainable hydrogen generation methods.

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 score0.111
Threshold uncertainty score0.392

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 head0.237
Teacher spread0.226 · 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

Citations2
Published2025
Admission routes1
Has abstractyes

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