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Record W2783545156 · doi:10.1002/aenm.201702277

Highly Efficient Ambient Temperature CO<sub>2</sub> Photomethanation Catalyzed by Nanostructured RuO<sub>2</sub> on Silicon Photonic Crystal Support

2018· article· en· W2783545156 on OpenAlexafffund
Feysal M. Ali, Kulbir Kaur Ghuman, Paul G. O’Brien, Mohamad Hmadeh, Amit Sandhel, Doug D. Perovic, Chandra Veer Singh, Charles A. Mims, Geoffrey A. Ozin

Bibliographic record

VenueAdvanced Energy Materials · 2018
Typearticle
Languageen
FieldEnergy
TopicAdvanced Photocatalysis Techniques
Canadian institutionsYork UniversityUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceSiliconCatalysisPhotonic crystalCrystal (programming language)WaferAbsorption (acoustics)MethanationChemical engineeringNanotechnologyOptoelectronicsOrganic chemistryComposite material

Abstract

fetched live from OpenAlex

Abstract Sunlight‐driven catalytic hydrogenation of CO 2 is an important reaction that generates useful chemicals and fuels and if operated at industrial scales can decrease greenhouse gas CO 2 emissions into the atmosphere. In this work, the photomethanation of CO 2 over highly dispersed nanostructured RuO 2 catalysts on 3D silicon photonic crystal supports, achieving impressive conversion rates as high as 4.4 mmol g cat −1 h −1 at ambient temperatures under high‐intensity solar simulated irradiation, is reported. This performance is an order of magnitude greater than photomethanation rates achieved over control samples made of nanostructured RuO 2 on silicon wafers. The high absorption and unique light‐harvesting properties of the silicon photonic crystal across the entire solar spectral wavelength range coupled with its large surface area are proposed to be responsible for the high methanation rates of the RuO 2 photocatalyst. A density functional theory study on the reaction of CO 2 with H 2 revealed that H 2 splits on the surface of the RuO 2 to form hydroxyl groups that participate in the overall photomethanation process.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesMeta-epidemiology (narrow)
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 score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.005
GPT teacher head0.241
Teacher spread0.236 · 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; both teacher heads agree on what is shown here.

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

Citations81
Published2018
Admission routes2
Has abstractyes

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