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Record W3105153431 · doi:10.1002/smll.202005754

Plasmonic Titanium Nitride Facilitates Indium Oxide CO<sub>2</sub> Photocatalysis

2020· article· en· W3105153431 on OpenAlexafffund
Nhat Truong Nguyen, Tingjiang Yan, Lu Wang, Joel Y. Y. Loh, Paul N. Duchesne, Chengliang Mao, Peicheng Li, Feysal M. Ali, Meikun Xia, Mireille Ghoussoub, Nazir P. Kherani, Zheng‐Hong Lu, Geoffrey A. Ozin

Bibliographic record

VenueSmall · 2020
Typearticle
Languageen
FieldEnergy
TopicAdvanced Photocatalysis Techniques
Canadian institutionsUniversity of Toronto
FundersNatural Science Foundation of Shandong ProvinceChina Scholarship CouncilChina Postdoctoral Science FoundationOntario Ministry of Research and InnovationNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsPhotocatalysisMaterials sciencePlasmonNitrideOxideIndiumNanotechnologyTitanium nitrideTitaniumTitanium oxideOptoelectronicsChemical engineeringCatalysisMetallurgyChemistry

Abstract

fetched live from OpenAlex

Abstract Nanoscale titanium nitride TiN is a metallic material that can effectively harvest sunlight over a broad spectral range and produce high local temperatures via the photothermal effect. Nanoscale indium oxide‐hydroxide, In2O3−x(OH)y, is a semiconducting material capable of photocatalyzing the hydrogenation of gaseous CO2; however, its wide electronic bandgap limits its absorption of photons to the ultraviolet region of the solar spectrum. Herein, the benefits of both nanomaterials in a ternary heterostructure: TiN@TiO2@In2O3−x(OH)y are combined. This heterostructured material synergistically couples the metallic TiN and semiconducting In2O3−x(OH)y phases via an interfacial semiconducting TiO2 layer, allowing it to drive the light‐assisted reverse water gas shift reaction at a conversion rate greatly surpassing that of its individual components or any binary combinations thereof.

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 score0.002
Threshold uncertainty score0.005

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)0.0020.000

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 head0.022
GPT teacher head0.242
Teacher spread0.220 · 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

Citations60
Published2020
Admission routes2
Has abstractyes

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