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Record W2753057718 · doi:10.1049/mnl.2017.0414

Nanocomposite of Ni–Ti‐layered double hydroxide and graphene for enhanced vis‐light photocatalysis

2017· article· en· W2753057718 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

VenueMicro & Nano Letters · 2017
Typearticle
Languageen
FieldEnergy
TopicAdvanced Photocatalysis Techniques
Canadian institutionsMinistry of Education and Child Care
FundersNatural Science Foundation of Jiangsu ProvinceNational Natural Science Foundation of China
KeywordsPhotocatalysisNanocompositeGrapheneMaterials scienceHydroxideLayered double hydroxidesChemical engineeringVisible spectrumNanotechnologyOptoelectronicsCatalysisChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Novel nanocomposite, in which nickel–titanium (Ni–Ti)‐layered double hydroxide (LDH) nanoplates were deposited on the surface of graphene nanosheets (GNSs), was synthesised by a simple in situ crystallisation technique. Owing to the introduction of GNS and the formation of layered heterostructure, the visible (vis)‐light absorption was enhanced, while the recombination probability of photogenerated electron–hole pairs was decreased. As a result, the obtained Ni–Ti LDH/GNS composite showed an enhanced vis‐light photocatalytic performance for the degradation of methylene blue. The corresponding photocatalytic mechanism was discussed according to the active species capture experimental results, which indicated that the active species of • OH was the most crucial one while O 2 •− and h + were the less crucial ones. This work may provide an insight for designing graphene‐based layered photocatalysts with a high performance.

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)0.000
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 score0.015
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.010
GPT teacher head0.259
Teacher spread0.249 · 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