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Record W2037292185 · doi:10.1002/cjce.20630

TIO<sub>2</sub>/Nanoclay nanocomposite for phenol degradation in sonophotocatalytic reactor

2011· article· en· W2037292185 on OpenAlexvenueaboutno aff
Shailesh Ghodke, Shirish H. Sonawane, Ravindra Gaikawad, K. C. Mohite

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2011
Typearticle
Languageen
FieldMaterials Science
TopicUltrasound and Cavitation Phenomena
Canadian institutionsnot available
Fundersnot available
KeywordsNanocompositeMaterials sciencePhenolPhotocatalysisFourier transform infrared spectroscopyDegradation (telecommunications)Chemical engineeringNanoparticleComposite materialNuclear chemistryCatalysisChemistryNanotechnologyOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract A TiO 2 –nanoclay nanocomposite was used as a photocatalyst for the degradation of phenol in presence of acoustic cavitation. TiO 2 –nanoclay nanocomposite was synthesised in benzyl alcohol medium wherein TiO 2 nanoparticles were formed between the nanoclay platelets. The synthesised product was characterised by using FTIR, XRD and TEM techniques. TEM image shows that TiO 2 –nanoclay nanocomposite particles were in the range of 30–40 nm. XRD gram confirms the formation of nanocomposite of TiO 2 nanoclay. The effect of cavitation and TiO 2 –nanoclay nanocomposite photocatalyst on phenol removal was investigated. The effects of various parameters such as nanocomposite loading, initial concentration, etc., have been studied. On comparing the results obtained with that of nanocomposite without UV, it was found for an initial concentration of 500 mg/L of phenol, the TiO 2 –nanoclay nanocomposite exhibited higher percentage of pollutant removal (59%) and for nanoclay it was 47%. © 2011 Canadian Society for Chemical Engineering

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 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 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 score0.294

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.014
GPT teacher head0.187
Teacher spread0.173 · 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 teacher head, 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

Citations22
Published2011
Admission routes2
Has abstractyes

Explore more

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