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Water Adsorption Properties of Titanium(IV) Oxide Embedded in Multiwalled Carbon Nanotubes (CNT)

2019· article· en· W2945632026 on OpenAlex
Haynes Fernando, Praveena Raveendran, Alfred A. Christy, Dhayalan Velauthapillai

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

VenueKey engineering materials · 2019
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsWestern University
Fundersnot available
KeywordsAdsorptionTitanium oxideMaterials scienceTitaniumCarbon nanotubeOxideChemical engineeringGravimetric analysisRelative humidityInfrared spectroscopyInorganic chemistryComposite materialChemistryOrganic chemistryMetallurgy

Abstract

fetched live from OpenAlex

Titanium(IV) oxide was embedded into carbon nanotubes through sonication. The water adsorption properties of the carbon nanotubes, titanium(IV) oxide and the titanium(IV) oxide embedded carbon nanotubes have been studied using near infrared spectroscopy and second derivative techniques. Each sample was evacuated, then exposed to 40% and 60% relative humidity to adsorb water molecules and the evolving adsorption near infrared spectra were studied. Adsorption properties were further studied by gravimetric analysis. Near infrared spectroscopic and Scanning Electron Microscopic studies confirm that the titanium(IV) oxide has been embedded into the carbon nanotube samples. The water adsorption profiles show that the TiO 2 adsorbs more water at a relative humidity of 60% than at relative humidity of 40%. However, the titanium(IV) oxide embedded in CNTs loses its ability to adsorb water. Embedding of titanium(IV) oxide in CNT has altered the adsorption properties of pure TiO 2 .

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.005
Threshold uncertainty score0.929

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.193
Teacher spread0.185 · 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