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Record W2953507624 · doi:10.1021/acs.cgd.9b00502

Nucleation Control of Oriented Titania Nanofibers

2019· article· en· W2953507624 on OpenAlexafffund
Dmitry Maznichenko, Bo Tan, Krishnan Venkatakrishnan

Bibliographic record

VenueCrystal Growth & Design · 2019
Typearticle
Languageen
FieldEngineering
TopicLaser-Ablation Synthesis of Nanoparticles
Canadian institutionsToronto Metropolitan University
FundersUniversity of Toronto
KeywordsNucleationNanofiberMaterials scienceAnataseNanotechnologyNanoscopic scaleNanoparticleCrystalliteRutileNanostructurePhase (matter)FemtosecondChemical engineeringLaserPhotocatalysisChemistryOrganic chemistryOpticsMetallurgy

Abstract

fetched live from OpenAlex

A multitude of commercial products and synthetic processes are seeking an advance from dispersive to assembled nanoscale systems. In the case of widely produced nanoparticles such as those of titania, there is a further demand to understand their interactions as a system that will reveal sophisticated large-scale designs. In this study, we have developed oriented nanofibers in a three-dimensional web system comprising titania nanoparticles. The phase nucleation of these titania nanofibers are controllable by femtosecond laser pulses in ambient air. Oriented attachment of the nanofibers during phase nucleation and after processing is maintained by the atomic coordination of rutile (110) crystal facets. The outcome of this study firmly suggests a controllable route to polycrystalline nanofiber synthesis for multidimensional systems. A water quality experiment is additionally conducted with this nanoscale system in selection of application between organic molecule detection and degradation.

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 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 score0.236
Threshold uncertainty score0.488

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 head0.007
GPT teacher head0.180
Teacher spread0.172 · 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 teacher head, 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

Citations1
Published2019
Admission routes2
Has abstractyes

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