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Record W1985183096 · doi:10.1021/ic700915z

Fast Synthesis of ZnO Nanostructures by Laser-Induced Decomposition of Zinc Acetylacetonate

2007· article· en· W1985183096 on OpenAlexaff
Christian Fauteux, Rémi Longtin, Joseph Pegna, Daniel Therriault

Bibliographic record

VenueInorganic Chemistry · 2007
Typearticle
Languageen
FieldMaterials Science
TopicZnO doping and properties
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsWurtzite crystal structureNanorodZincChemistryRaman spectroscopyNanostructureNanoparticleChemical engineeringSubstrate (aquarium)IrradiationAqueous solutionNanowireHydrothermal circulationNanotechnologyLaserMaterials scienceOpticsOrganic chemistry

Abstract

fetched live from OpenAlex

A CO2 laser (lambda = 10.6 microm) was used to heat a solution of water and alcohol saturated by Zn(AcAc)2 on a fused quartz substrate in open air. After only a few seconds of irradiation, various zinc oxide (ZnO) nanostructures including nanorods and nanowires are formed near the center of the irradiated zone, surrounded by a porous thin film of ZnO nanoparticles. The type of structures produced and their localization on the substrate can be varied by selecting adequate irradiation time and laser power ranges. The deposits have been analyzed using SEM, TEM, EDS, XRD, and Raman spectroscopy, revealing that the nanorods (aspect ratio ~6) and nanowires (aspect ratio ~94) are single-crystalline structures which grow along the c axis of wurtzite ZnO. The nanoparticles are also single-crystalline and have an average diameter of 16 nm. A qualitative model for nanostructure growth is proposed, based on previous studies of aqueous solution and hydrothermal processing.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 head0.007
GPT teacher head0.233
Teacher spread0.226 · 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 source (direct Gemma or distilled Codex), 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

Citations64
Published2007
Admission routes1
Has abstractyes

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