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Record W4232673304 · doi:10.26434/chemrxiv.12376067.v1

Molecular Cu(I)-Cu(II) Photosensitizer-Catalyst Photoelectrode for Water Oxidation

2020· preprint· en· W4232673304 on OpenAlexaff
Zujhar Singh, Pedro Donnarumma, Marek B. Majewski

Bibliographic record

VenueChemRxiv · 2020
Typepreprint
Languageen
FieldEnergy
TopicAdvanced Photocatalysis Techniques
Canadian institutionsConcordia University
Fundersnot available
KeywordsAqueous solutionCatalysisPhotochemistryOxidizing agentWater splittingPhotoexcitationAcceptorChemistryTin oxidePhotosensitizerOxideInorganic chemistryMaterials sciencePhotocatalysisExcited statePhysical chemistry

Abstract

fetched live from OpenAlex

Photochemical splitting of H 2 O to H 2 and O 2 is one approach to generate "solar fuels." Cu(II)-based electrocatalysts for water oxidation in aqueous solution have been studied previously, but photodriving these systems still remains a challenge. Light harvesting units can be employed for this purpose, that upon photoexcitation generate a high energy excited state and give rise to a charge separated state. In this work, a bis-diimine Cu(I)-based donor-chromophore-acceptor (D-C-A) system is synthesized, characterized, and applied as the light harvesting component of a photoanode. Here, this molecular assembly was integrated onto a zinc oxide (ZnO) nanowire surface on a fluorine-doped tin oxide (FTO) glass slide. Upon photoexcitation, chronoamperometric studies reveal that the integrated triad can inject electrons directly into the conduction band of zinc oxide generating oxidizing equivalents that are then transferred to a Cu(II) water oxidation catalyst in aqueous solution yielding O 2 from H 2 O with a Faradaic efficiency of 76%.

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 score0.004

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 science0.0000.000
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 head0.017
GPT teacher head0.269
Teacher spread0.252 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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