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Record W2318577112 · doi:10.1021/nn103464r

Nanoparticle Films and Photonic Crystal Multilayers from Colloidally Stable, Size-Controllable Zinc and Iron Oxide Nanoparticles

2011· article· en· W2318577112 on OpenAlexaff
Engelbert Redel, Peter Mirtchev, Huai Chen, Srebri Petrov, Geoffrey A. Ozin

Bibliographic record

VenueACS Nano · 2011
Typearticle
Languageen
FieldEnergy
TopicIron oxide chemistry and applications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMaterials scienceNanoparticleChemical engineeringPhotocatalysisPhotonic crystalOxideNanotechnologyColloidPorosityZincCatalysisOrganic chemistryOptoelectronicsComposite materialChemistry

Abstract

fetched live from OpenAlex

We report a facile sol-gel synthesis of colloidally stable Fe(2)O(3) and ZnO nanoparticles in alcoholic solvents, ROH, where R = methyl, ethyl, n-propyl, isopropyl, and tert-butyl. We show that nanoparticles of ZnO (4-42) nm and Fe(2)O(3) (4-38 nm) monotonically increase in size upon increasing the alkyl chain length and branching of the alcohol solvent. These colloidally stable and size-controllable metal oxide nanoparticles enable the formation of high optical quality films and photonic crystal multilayers whose component layer thickness, refractive index, porosity, and surface area are found to scale with the nature of the alcohol. Utility of these colloidally stable nanoparticles is demonstrated by preparation of one-dimensional porous photonic crystals comprising ncZnO/ncWO(3) and ncFe(2)O(3)/ncWO(3) multilayers whose photonic stop band can be tuned by tailoring nanoparticle size. Myriad applications can be envisaged for these nanoparticle films in, for example, heterogeneous catalysis, photocatalysis, electrocatalysis, chemical sensors, and solar cells.

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 score0.008
Threshold uncertainty score0.882

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)0.0010.000

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 head0.012
GPT teacher head0.205
Teacher spread0.193 · 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

Citations61
Published2011
Admission routes1
Has abstractyes

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