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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 OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

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 categoriesMeta-epidemiology (narrow)
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 score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.007
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