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Record W3107241673 · doi:10.1016/j.gee.2020.11.015

Efficient photoelectrochemical conversion of CO2 to syngas by photocathode engineering

2020· article· en· W3107241673 on OpenAlexafffund
Sheng Chu, Pengfei Ou, Roksana Tonny Rashid, Yuyang Pan, Daolun Liang, Huiyan Zhang, Jun Song

Bibliographic record

VenueGreen Energy & Environment · 2020
Typearticle
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaSoutheast UniversityNatural Science Foundation of Jiangsu ProvinceNational Natural Science Foundation of ChinaEmissions Reduction Alberta
KeywordsPhotocathodeSyngasMaterials scienceEnergy conversion efficiencyChemical engineeringPhotoelectrochemical cellNanotechnologyChemistryOptoelectronicsCatalysisPhysicsElectronOrganic chemistryElectrode

Abstract

fetched live from OpenAlex

The synthesis of renewable chemical fuels from CO2 and H2O via photoelectrochemical (PEC) route reprensents a promising room-temperature approach for transforming greenhouse gas into value-added chemicals (e.g., syngas), but to date it has been hampered by the lack of efficient photocathode for CO2 reduction. Herein, we report efficient PEC CO2 reduction into syngas by photocathode engineering. The photocathode is consisting of a planar p-n Si junction for strong light harvesting, GaN nanowires for efficient electron extraction and transfer, and Au/TiO2 for rapid electrocatalytic syngas production. The photocathode yields a record-high solar energy conversion efficiency of 2.3%. Furthermore, desirable syngas compositions with CO/H2 ratios such as 1:2 and 1:1 can be produced by simply varying the size of Au nanoparticle. Theoretical calculations reveal that the active sites for CO and H2 generation are the facet and undercoordinated sites of Au particles, respectively.

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 science0.0000.000
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 head0.006
GPT teacher head0.177
Teacher spread0.171 · 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

Citations29
Published2020
Admission routes2
Has abstractyes

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