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Record W2766712171 · doi:10.1039/c7cp06533g

Understanding the role of co-catalysts on silicon photocathodes using intensity modulated photocurrent spectroscopy

2017· article· en· W2766712171 on OpenAlexafffund
James E. Thorne, Yanyan Zhao, Da Wei He, Shizhao Fan, Srinivas Vanka, Zetian Mi, Dunwei Wang

Bibliographic record

VenuePhysical Chemistry Chemical Physics · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsMcGill University
FundersDivision of Materials ResearchNatural Sciences and Engineering Research Council of CanadaNational Science Foundation
KeywordsPhotocurrentRecombinationSpectroscopySiliconCatalysisOptoelectronicsIntensity (physics)Materials scienceLight intensityPhotoconductivityChemical physicsChemistryNanotechnologyPhysicsOptics

Abstract

fetched live from OpenAlex

The addition of a co-catalyst onto the surface of a photocathode often greatly enhances the harvested photovoltage of the system. However, the true nature of how the catalyst improves the onset potential remains poorly understood. As a result, how to best utilize effective co-catalysts is still a limiting factor in achieving high performance earth abundant photoelectrochemical hydrogen evolution. Using intensity modulated photocurrent spectroscopy (IMPS), we have probed charge behaviors at the photoelectrode co-catalyst interface. We find that Pt drastically reduces charge recombination at the semiconductor liquid interface (SCLI). Further studies reveal that the onset potentials can be improved either by accelerating the reaction kinetics or reducing the recombination at the SCLI. The knowledge permits us to understand how earth abundant HER catalysts, such as CoP, behave at the SCLI. It is found that CoP is more effective at accelerating the reaction kinetics than reducing recombination.

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 score0.014
Threshold uncertainty score0.916

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.047
GPT teacher head0.327
Teacher spread0.280 · 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

Citations42
Published2017
Admission routes2
Has abstractyes

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