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Record W2316807338 · doi:10.1021/acssuschemeng.5b01795

P-Type Cu-Doped Zn<sub>0.3</sub>Cd<sub>0.7</sub>S/Graphene Photocathode for Efficient Water Splitting in a Photoelectrochemical Tandem Cell

2016· article· en· W2316807338 on OpenAlexaff
Yijie Wu, Zongkuan Yue, Aijuan Liu, Ping Yang, Mingshan Zhu

Bibliographic record

VenueACS Sustainable Chemistry & Engineering · 2016
Typearticle
Languageen
FieldEnergy
TopicAdvanced Photocatalysis Techniques
Canadian institutionsUniversity of Toronto
FundersPriority Academic Program Development of Jiangsu Higher Education InstitutionsNational Natural Science Foundation of China
KeywordsPhotocathodeWater splittingTandemGraphenePhotoelectrochemical cellMaterials scienceDopingPhotocatalysisOptoelectronicsChemistryNanotechnologyElectrodeCatalysisPhysicsElectrolytePhysical chemistry

Abstract

fetched live from OpenAlex

By doping Cu(I) ions in Zn 0.3 Cd 0.7 S, a novel p-type Cu doped Zn 0.3 Cd 0.7 S modified graphene (Zn 0.3 Cd 0.7 S (Cu)/GR) film photocathode was prepared. The as-prepared p-type Zn 0.3 Cd 0.7 S (Cu)/GR photocathode and an n-type WO 3 /graphene (WO 3 /GR) photoanode were used to assemble a photoelectrochemical tandem cell. Through examination of the optoelectronic and photoelectrochemical properties of Zn 0.3 Cd 0.7 S (Cu)/GR and WO 3 /GR photoelectrode, we evaluate the feasibility of the tandem cell for overall water splitting under UV–vis light irradiation. The optimal Cu doping in Zn 0.3 Cd 0.7 S photocathode concentration was found to be 6%. The rates of hydrogen and oxygen evolved from this tandem cell with the optimal electrodes were 65.6 and 12.3 μmol g –1 h –1 (80.5 and 15.1 μmol cm –2 h –1 ), respectively. This study suggests a promising method for constructing an efficient photoelectrochemical tandem device for overall water splitting.

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 score0.002

Distilled classifier scores by category (both heads)

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 science0.0010.000
Research integrity0.0000.000
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 head0.004
GPT teacher head0.207
Teacher spread0.203 · 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

Citations51
Published2016
Admission routes1
Has abstractyes

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Same venueACS Sustainable Chemistry & EngineeringSame topicAdvanced Photocatalysis TechniquesFrench-language works237,207