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Record W4387067552 · doi:10.11159/ijmmme.2023.002

Nano-twinned Ag Thin Films on Graphene/ Si Photoelectrochemical Cell for CO<sub>2</sub> Reduction and Hydrogen Production

2023· article· en· W4387067552 on OpenAlexvenueno aff
Yen‐Ting Chen, Tsung-Hsin Liu, Chun‐Wei Chen, Tung-Han Tung-Han

Bibliographic record

VenueInternational Journal of Mining Materials and Metallurgical Engineering · 2023
Typearticle
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsnot available
FundersHsinchu Science Park Bureau, Ministry of Science and Technology, Taiwan
KeywordsGrapheneMaterials scienceNano-HydrogenHydrogen productionThin filmPhotoelectrochemical cellOptoelectronicsChemical engineeringNanotechnologyReduction (mathematics)Water splittingCatalysisPhotocatalysisElectrodeChemistryComposite materialPhysical chemistry

Abstract

fetched live from OpenAlex

This study proposes a novel approach for applying the nanotwinned Ag thin films in CO2 reduction.We focus on optimizing the sputter deposition process of nanocrystalline Ag structures on n+Si chips and preparing nanotwinned silver catalysts with good structure, adhesion, and stability.The effects of twinned structure catalysts on the photocatalytic performance were investigated.In this research, the n+Si/Gr/sputtered Ag structure was used as the photoelectrode for photoelectrochemical (PEC) CO2 reduction reaction (CO2RR) since the utilization of graphene can expedite carrier transport, thereby improving device stability and performance in electrolytes.Nanotwinned Ag films were successfully synthesized on graphene transferred n+Si substrates with DC magnetron sputtering.Focused ion beam analyses demonstrated that the addition of graphene did not diminish the nanotwin density; rather, it improved the quality of the sputtered Ag thin films, which leads the framework to a potential structure for PEC CO2RR.

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 score0.003
Threshold uncertainty score0.547

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.010
GPT teacher head0.232
Teacher spread0.222 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

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