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A thermal study on the structural changes of bimetallic ZrO<sub>2</sub>-modified TiO<sub>2</sub>nanotubes synthesized using supercritical CO<sub>2</sub>

2009· article· en· W1992145788 on OpenAlexaff
Rahima A. Lucky, Paul A. Charpentier

Bibliographic record

VenueNanotechnology · 2009
Typearticle
Languageen
FieldChemical Engineering
TopicCatalysis and Oxidation Reactions
Canadian institutionsWestern University
Fundersnot available
KeywordsMaterials scienceCrystalliteCalcinationChemical engineeringAnataseSupercritical fluidBimetallic stripThermal stabilityHigh-resolution transmission electron microscopySol-gelPhase (matter)Carbon nanotubeComposite materialNanotechnologyTransmission electron microscopyCatalysisOrganic chemistryMetallurgyPhotocatalysisMetal

Abstract

fetched live from OpenAlex

In this study the thermal behavior of bimetallic ZrO(2)-TiO(2) (10/90 mol/mol) nanotubes are discussed which were synthesized via a sol-gel process in supercritical carbon dioxide (scCO(2)). The effects of calcination temperature on the morphology, phase structure, mean crystallite size, specific surface area and pore volume of the nanotubes were investigated by using a variety of physiochemical techniques. We report that SEM and TEM images showed that the nanotubular structure was preserved at up to 800 degrees C calcination temperature. When exposed to higher temperatures (900-1000 degrees C) the ZrO(2)-TiO(2) tubes deformed and the crystallites fused together, forming larger crystallites, and a bimetallic ZrTiO(4) species was detected. These results were further examined using TGA, FTIR, XRD and HRTEM analysis. The BET textural properties demonstrated that the presence of a small amount of Zr in the TiO(2) matrix inhibited the grain growth, stabilized the anatase phase and increased the thermal stability.

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 head0.024
GPT teacher head0.263
Teacher spread0.239 · 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

Citations12
Published2009
Admission routes1
Has abstractyes

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