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Record W2800276982 · doi:10.1002/admi.201800269

Mesoporous Cobalt Tungsten Oxide Heterostructured Nanotoroids for Gas Sensing

2018· article· en· W2800276982 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueAdvanced Materials Interfaces · 2018
Typearticle
Languageen
FieldEngineering
TopicGas Sensing Nanomaterials and Sensors
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaNational Foundation for Science and Technology Development
KeywordsMaterials scienceMesoporous materialCobalt oxideCobaltElectrocatalystElectrochromismOxideNanomaterialsTungstenNanoparticleIonic bondingNanotechnologyHeterojunctionInorganic chemistryChemical engineeringCatalysisElectrodeElectrochemistryPhysical chemistryOptoelectronicsChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Mesoporous semiconducting nanotoroids of cobalt and tungsten oxides (CoO/Co 3 O 4 /WO 3 ) are made by a hydrothermal reaction of binary metal ionic precursors with aminocaproic acid. The toroidal organization of the primary nanoparticles into the cobalt tungsten oxide nanorings presents a straightforward, interesting approach for amino acid–induced self‐aggregation of hierarchical metal‐based nanostructures. The semiconducting properties of the as‐prepared nanotoroids are exploited in gas‐sensing experiments with three analytes (CO, H 2 , NH 3 ) to demonstrate the toroids as promising gas sensor components. Toroidal geometry, porosity, and semiconductor p–n heterojunction make the new mesoporous Co‐W oxide nanomaterials attractive as novel functional supports for host–guest chemistry, electrocatalysis, and electrochromism.

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 score0.023
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 head0.008
GPT teacher head0.224
Teacher spread0.217 · 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