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Record W1993620750 · doi:10.1021/cm100297q

Au Nanoparticles in Nanocrystalline TiO<sub>2</sub>−NiO Films for SPR-Based, Selective H<sub>2</sub>S Gas Sensing

2010· article· en· W1993620750 on OpenAlexaff
Enrico Della Gaspera, M. Guglielmi, Stefano Agnoli, Gaetano Granozzi, Michael L. Post, Valentina Bello, G. Mattei, Alessandro Martucci

Bibliographic record

VenueChemistry of Materials · 2010
Typearticle
Languageen
FieldEngineering
TopicGas Sensing Nanomaterials and Sensors
Canadian institutionsNational Research Council Canada
FundersFondazione Cassa di Risparmio di Padova e Rovigo
KeywordsMaterials scienceCrystallinityNon-blocking I/ONanocrystalline materialSurface plasmon resonanceNanoparticleHydrogen sulfideThin filmChemical engineeringNanocompositeCatalysisNanotechnologyComposite materialChemistryMetallurgyOrganic chemistry

Abstract

fetched live from OpenAlex

Thin films composed of Au nanoparticles dispersed inside a TiO 2 −NiO mixed oxide matrix are prepared by the sol−gel method, resulting in nanostructured composites with a morphology and crystallinity that depend on synthesis parameters and thermal treatment. Their functional activity as hydrogen sulfide optical sensors is due to Au-localized surface plasmon resonance (SPR) which is reversible. The detection sensitivity is shown to be down to a few parts per million of H 2 S, and almost no interference in response is observed during simultaneous exposure to CO or H 2, resulting in a highly sensitive and selective sensor for hydrogen sulfide detection. For mechanistic studies, experimental evidence using reaction product analysis and thin film surface characterization suggests a direct catalytic oxidation of H 2 S over the Au−TiO 2 −NiO nanocomposite film.

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 score0.003

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 head0.007
GPT teacher head0.194
Teacher spread0.188 · 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

Citations106
Published2010
Admission routes1
Has abstractyes

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