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Record W4205839417 · doi:10.1002/adem.202101509

A New Photoelectrochemical Reactor with Special Photocathode Design for Hydrogen Production

2022· article· en· W4205839417 on OpenAlexaff
Ali M.M.I. Qureshy, İbrahim Dinçer

Bibliographic record

VenueAdvanced Engineering Materials · 2022
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsPhotocathodeHydrogen productionMaterials scienceHydrogenElectrolyteOpticsLight intensityOxideOptoelectronicsElectrodeChemistryPhysicsElectronMetallurgy

Abstract

fetched live from OpenAlex

Both electrodeposition and performance assessment of new photocathode design are carried out using cuprous oxide for solar hydrogen production. The maximum delivered light can be collected using the mesh dome design of the photocathode, and the light can also reach the farthest photocathode surfaces via the mesh shape. The stainless‐steel mesh dome electrode is coated with cuprous oxide using the electrodeposition method. This photocathode is examined in an alkaline electrolyte solution of potassium dioxide for hydrogen production. Two light angles are studied to explore the effects of the new photocathode design on the hydrogen yield rate and associated energy and exergy system efficiencies during the daytime, where the light angles change. The maximum hydrogen production rates are generated experimentally at 0.1 m KOH at the 45° tilt light, and vertical light positions are 5.13 and 5.58 μg s−1, respectively. Moreover, the highest exergy and energy system efficiencies are 0.82% and 1.18%, respectively, under vertical light, while they are 0.75% and 1.09% under tilted light conditions. The improvement of hydrogen production rates at tilted light can reach around 75.6% of the enhancement by vertical light due to the new photocathode design.

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 score0.001
Threshold uncertainty score0.003

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 studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.198
Teacher spread0.190 · 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

Citations4
Published2022
Admission routes1
Has abstractyes

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