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Synthesis, Comparative Characterization and Photocatalytic Application of SnO2/MWCNT Nanocomposite Materials

2014· article· en· W1637613966 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Coating Science and Technology · 2014
Typearticle
Languageen
FieldEngineering
TopicGas Sensing Nanomaterials and Sensors
Canadian institutionsnot available
FundersUniversitas Pelita Harapan
KeywordsNanocompositePhotocatalysisCharacterization (materials science)Materials scienceNanotechnologyComposite materialChemical engineeringChemistryEngineeringOrganic chemistry

Abstract

fetched live from OpenAlex

Two different preparation methods were developed to cover successfully multi-walled carbon nanotubes (MWCNT) with tin-dioxide (SnO2) nanoparticles using SnCl2·2H2O as precursor under different solvent conditions. The applied mass ratios of the components were 1:4, 1:8, 1:16, 1:32 and 1:64, respectively. As-prepared tin-dioxide coverages were characterized by TEM, SEM, SEM-EDX, Raman microscopy, BET and X-ray diffraction techniques. Photocatalytic efficiencies of selected composites were investigated in a self-made photoreactor, equipped with UV-A fluorescence lamps. Photocatalytic degradation of phenol solution was followed by using HPLC. Observations revealed that using hydrothermal method we can easily control the layer of SnO2 nanoparticles on the surface of MWCNTs. Using various solvents SnO2 nanoparticles with different morphologies formed. The nanocomposites have low photocatalytic efficiencies under conditions used generally (when λ>300 nm).

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 score0.006
Threshold uncertainty score0.219

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.007
GPT teacher head0.217
Teacher spread0.210 · 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