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Controllable synthesis of graphene-based titanium dioxide nanocomposites by atomic layer deposition

2011· article· en· W2119855842 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

VenueNanotechnology · 2011
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsWestern University
Fundersnot available
KeywordsMaterials scienceGrapheneAtomic layer depositionTitanium dioxideNanocompositeLayer (electronics)Deposition (geology)Chemical engineeringNanotechnologyComposite material

Abstract

fetched live from OpenAlex

Atomic layer deposition (ALD) was used to synthesize graphene-based metal oxide nanocomposites. This strategy was fulfilled on the preparation of TiO(2)-graphene nanosheet (TiO(2)-GNS) nanocomposites using titanium isopropoxide and water as precursors. The synthesized nanocomposites demonstrated that ALD exhibited many benefits in a controllable means. It was found that the as-deposited TiO(2) was tunable not only in its morphologies but also in its structural phases. As for the former, TiO(2) was transferable from nanoparticles to nanofilms with increased cycles. With regard to the latter, TiO(2) was changeable from amorphous to crystalline phase, and even a mixture of the two with increased growth temperatures (up to 250 °C). The underlying growth mechanisms were discussed and the resultant TiO(2)-GNS nanocomposites have great potentials for many applications, such as photocatalysis, lithium-ion batteries, fuel cells, and sensors.

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 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 score0.005
Threshold uncertainty score0.456

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 head0.015
GPT teacher head0.234
Teacher spread0.220 · 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