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Record W2936251745 · doi:10.1002/ppap.201800213

One‐step deposition of nano‐Ag‐TiO<sub>2</sub> coatings by atmospheric pressure plasma jet for water treatment: Application to trace pharmaceutical removal using solar photocatalysis

2019· article· en· W2936251745 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

VenuePlasma Processes and Polymers · 2019
Typearticle
Languageen
FieldEnergy
TopicAdvanced Photocatalysis Techniques
Canadian institutionsUniversity of Windsor
FundersFP7 Nanosciences, Nanotechnologies, Materials and new Production TechnologiesMitacs
KeywordsMaterials sciencePhotocatalysisAtmospheric-pressure plasmaAnataseChemical engineeringPhotodegradationCoatingDeposition (geology)Rhodamine BNanoparticleNanotechnologyPlasmaCatalysisChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract In this study, micrometer thick Ag‐TiO 2 coatings were deposited in a single and facile step by spraying the precursor in an atmospheric pressure plasma jet with different concentrations of Ag nanoparticles. The homogenous distribution of Ag decreased the TiO 2 crystal size and increased the surface area. The coatings were characterized to be porous with an anatase phase with improved charge separation and visible light absorption. The photocatalytic activity of the materials was investigated for degrading rhodamine B using a white lamp as a screening method to optimize Ag‐TiO 2 coatings. Then the photodegradation of trace pharmaceutical compounds (TrPCs) was investigated by using a solar light simulator at the optimal condition of a TiO 2 coating with 0.4wt% Ag.

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 score0.032
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 head0.011
GPT teacher head0.260
Teacher spread0.249 · 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