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Record W4404851496 · doi:10.6000/1929-5995.2024.13.25

Nanostructure Polyaniline (PANI) Decorated TiO2 Nanocomposite as Efficient Heterogeneous Catalyst

2024· article· en· W4404851496 on OpenAlexvenueno aff
Kalpana N. Handore, Sumit Sharma, Smita Jagtap, Vasant Chabukswar, M Sable, Ramkailash Gujar, S. H. Gawande

Bibliographic record

VenueJournal of Research Updates in Polymer Science · 2024
Typearticle
Languageen
FieldChemistry
TopicMulticomponent Synthesis of Heterocycles
Canadian institutionsnot available
Fundersnot available
KeywordsPolyanilineMaterials scienceNanocompositeNanostructureCatalysisPolyaniline nanofibersNanotechnologyChemical engineeringComposite materialPolymerOrganic chemistryPolymerization

Abstract

fetched live from OpenAlex

The paper describes the synthesis of conducting polyaniline-TiO2 (PANI-TiO2) nanocomposite by chemical oxidative polymerization method using aniline as a monomer. TiO2 nanoparticles were synthesized by sol gel method using TiCl4 and ethanol. Ultrasound synthesized TiO2 nanoparticles and nanocomposite were characterized by various spectroscopic techniques UV, FT-IR, XRD, SEM and TGA. The XRD pattern confirms the appearance of sharp diffraction patterns indicating the small sized, high purity TiO2 nano particles are highly agglomerated with spherical morphology. The PANI-TiO2 nanocomposite was employed as a promising heterocyclic, reusable catalyst for most of the organic synthesis. The advantages of this methodology are mild reaction conditions with short reaction time, excellent yields, low loading and reusability of catalyst.

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 score0.002

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 head0.023
GPT teacher head0.355
Teacher spread0.332 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Research Updates in Polymer ScienceSame topicMulticomponent Synthesis of HeterocyclesFrench-language works237,207