MétaCan
Menu
Back to cohort
Record W4382402822 · doi:10.3390/cleantechnol5030040

Metal-Supported TiO2/SiO2 Core-Shell Nanosphere Photocatalyst for Efficient Sunlight-Driven Methanol Degradation

2023· article· en· W4382402822 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueClean Technologies · 2023
Typearticle
Languageen
FieldEnergy
TopicAdvanced Photocatalysis Techniques
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsPhotocatalysisCalcinationMaterials scienceChemical engineeringMetalDegradation (telecommunications)NanotechnologyCatalysisMetallurgyChemistryComputer science

Abstract

fetched live from OpenAlex

The development of novel and active photocatalysts to industrialize photocatalysis technology is still a challenging task. In this work, a novel method is presented to prepare TiO2/SiO2 NSs by covering SiO2 nanospheres (NSs) with titanate-nanodiscs (TNDs) followed by calcination. In this regard, SiO2 NSs are first synthesized and then TNDs are deposited on the SiO2 NSs using a layer-by-layer deposition technique. The morphology of the prepared samples is analyzed via SEM and TEM analyses before and after the deposition. The analysis of metal (Cu, Pt, and Ni) loading on calcined TNDs/SiO2 NSs reveals the highest specific surface area (109 m2/g), absorption wavelength extension (up to 420 nm), and photocatalytic activity for the Cu-loaded sample. In addition, studying the effect of metal content shows that loading 3% Cu leads to the highest photocatalytic activity. Finally, it is demonstrated that H2S treatment can improve the photocatalytic activity by around 15%. These findings suggest the calcined TNDs/SiO2 NSs are a versatile photocatalyst with potential applications in other processes such as hydrogen production and CO2 valorization.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.217
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.038
GPT teacher head0.302
Teacher spread0.263 · 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