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Record W2546505123 · doi:10.1139/cjp-2016-0578

Structural and optical properties of TiO<sub>2</sub>–Ge nanoparticles prepared through laser ablation in liquid medium

2016· article· en· W2546505123 on OpenAlexvenueno aff
Shaista Shahzada, Anbarin Fatima, Asma Nazir, Arshad Mehmood, Mazhar Mehmood, Ali Nadeem

Bibliographic record

VenueCanadian Journal of Physics · 2016
Typearticle
Languageen
FieldEngineering
TopicLaser-Ablation Synthesis of Nanoparticles
Canadian institutionsnot available
Fundersnot available
KeywordsScanning electron microscopeCrystalliteRaman spectroscopyLaser ablationNanoparticleMaterials scienceTransmission electron microscopyGermaniumSpectroscopyAnalytical Chemistry (journal)Particle sizeLaserComposite numberBand gapOpticsNanotechnologyOptoelectronicsChemical engineeringPhysicsComposite materialSiliconChemistry

Abstract

fetched live from OpenAlex

Quantum dot (QD) composite nanoparticles of titania germanium (TiO 2 –Ge) were fabricated by pulsed laser ablation of composite targets of TiO 2 –Ge immersed in deionized water. The single phase crystallanity, lattice parameters, and the evaluation of crystallite size of nanoparticles was investigated by X-ray diffraction (XRD). The morphology of the samples was analyzed through scanning electron microscopic studies. The average particle size calculated through XRD and measured through scanning electron microscope is in the range from 18 to 29 nm and are in agreement with each other. Compositional study was carried out through electron dispersive X-ray analysis showing that the ablation rate of Ge is smaller than TiO 2 . Optical properties were measured by UV–visible transmission spectra and Raman spectroscopy. The band gap of composite particles varies from the UV to the visible range by varying concentration of Ge QDs. This work shows that pulsed laser ablation in liquid media is an easy approach to synthesize ultrafine, contamination-free nanosized material, which is difficult to produce by other conventional methods.

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 score0.010
Threshold uncertainty score0.315

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 communication0.0000.001
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 head0.017
GPT teacher head0.197
Teacher spread0.180 · 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
Published2016
Admission routes1
Has abstractyes

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