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Record W3038973630 · doi:10.1016/j.xcrp.2020.100101

Standalone Solar Carbon-Based Fuel Production Based on Semiconductors

2020· article· en· W3038973630 on OpenAlexafffund
Chenyu Xu, Jianan Hong, Peng‐Fei Sui, Mengnan Zhu, Yanwei Zhang, Jing‐Li Luo

Bibliographic record

VenueCell Reports Physical Science · 2020
Typearticle
Languageen
FieldEnergy
TopicAdvanced Photocatalysis Techniques
Canadian institutionsUniversity of Alberta
FundersCanada First Research Excellence FundNatural Science Foundation of Zhejiang ProvinceNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsPhotovoltaicsSolar fuelSolar energyProcess engineeringCarbon fibersPhotovoltaic systemProduction (economics)Key (lock)Oxygen reduction reactionEnvironmental scienceComputer scienceNanotechnologyMaterials scienceEngineeringChemistryElectrical engineeringCatalysis

Abstract

fetched live from OpenAlex

Standalone solar carbon dioxide conversion without the use of any external energy is a primary goal of solar carbon-based fuel production. So far, researchers have performed many staged studies, for example, on the independent oxygen evolution reaction and CO2 reduction reaction, to focus on these key steps and strengthen specific characteristics, including photoresponse, energy carrier separation, and transfer. Noteworthy performance has been obtained for almost every stage. However, recent pioneering works have demonstrated that staged research cannot always be easily pieced together. Here, recent advances in standalone systems are collected and reviewed. We focus on the critical assessment of the key components requiring improvement. In addition, the opportunities for hybrid systems of photocatalysts with photothermocatalysts and photovoltaics with electrocatalysts are assessed. This review aims to discuss the direction of standalone solar carbon fuel production and potential knowledge transfer to intersecting fields.

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: none
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)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.015
GPT teacher head0.256
Teacher spread0.241 · 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

Citations26
Published2020
Admission routes2
Has abstractyes

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