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Record W3186902319 · doi:10.1149/ma2021-01391261mtgabs

(Invited) A Stable Integrated Photoelectrochemical Reactor for Hydrogen Production from Water

2021· article· en· W3186902319 on OpenAlexaff
Hicham Idriss, Mohd Adnan Khan, Ahmed Ziani, Ibraheam Al‐Shankiti

Bibliographic record

VenueECS Meeting Abstracts · 2021
Typearticle
Languageen
FieldEnergy
TopicTiO2 Photocatalysis and Solar Cells
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsElectrolyteSuns in alchemyMaterials scienceCatalysisPhotovoltaic systemWater splittingCorrosionHydrogen productionOxygenHydrogenSolar cellDegradation (telecommunications)Chemical engineeringChemistryOptoelectronicsElectrodeMetallurgyElectrical engineeringEngineeringPhotocatalysis

Abstract

fetched live from OpenAlex

Among the major challenges in solar water, splitting to molecular hydrogen and oxygen is making a stable and affordable system for largescale applications. In this work we present results of the design, fabrication, and testing a photoelectrochemical reactor composed of the following. 1) An integrated device to reduce the balance of the system cost. 2) A concentrated sunlight to reduce the photoabsorber cost. 3) An alkaline electrolyte to reduce catalyst cost and eliminate external thermal management needs. The system consists of an III-V-based photovoltaic cell integrated with Ni foil as catalyst for oxygen production that also protects the cell from corrosion. At low light concentration and without the use of optical lenses, the solar-to-hydrogen (STH) efficiency was found to be 18.3%, while at high light concentration (up to 207 suns) with the use of optical lenses, the STH efficiency was 13%. Catalytic tests conducted for over 100 hours at 100–200 suns showed no sign of degradation nor deviation from product stoichiometry (H 2 /O 2 =2). Further tests projected a system stability of over nine years. Figure 1

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)0.001
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 score0.133
Threshold uncertainty score0.748

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.014
GPT teacher head0.221
Teacher spread0.207 · 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
Published2021
Admission routes1
Has abstractyes

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