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Record W2027060553 · doi:10.3329/jbas.v36i2.12969

Substrate Temperature Effect on the Structural and Optical Properties of Znse Thin Films

2012· article· en· W2027060553 on OpenAlexaff
Mohammad Ruhul Amin Bhuiyan, M A H Miah, Jahanara Begum

Bibliographic record

VenueJournal of Bangladesh Academy of Sciences · 2012
Typearticle
Languageen
FieldEngineering
TopicChalcogenide Semiconductor Thin Films
Canadian institutionsAtomic Energy (Canada)
Fundersnot available
KeywordsMaterials scienceBand gapThin filmCrystalliteDiffractometerTransmittanceSubstrate (aquarium)Refractive indexAnalytical Chemistry (journal)Grain sizeAttenuation coefficientDirect and indirect band gapsDiffractionZinc selenideOpticsLattice constantOptoelectronicsChemistryComposite materialNanotechnologyScanning electron microscopeMetallurgy

Abstract

fetched live from OpenAlex

Zinc selenide (ZnSe) thin films were deposited on to chemically and ultrasonically cleaned glass substrates at different substrate temperatures from room temperature to 200°C keeping the thickness fixed at 300 nm by using thermal evaporation method in vacuum. The structural properties of the films were ascertained by X-ray diffraction (XRD) method utilizing a diffractometer. The optical properties were measured in the photon wavelength ranging between 300 and 2500 nm by using a UV-VIS-NIR spectrophotometer. The XRD patterns reveal that the films were polycrystalline in nature exhibiting f.c.c zincblende structure with average lattice parameter, a = 5.6873Å. The grain size, strain and dislocation densities of the films have bee calculated. The optical transmittance and reflectance were utilized to compute the absorption coefficient, band gap energy and refractive index of the films. The band gap energy of the films was extracted from the absorption spectra. The direct band gap energy of the films slightly increases with substrate temperature.DOI: http://dx.doi.org/10.3329/jbas.v36i2.12969Journal of Bangladesh Academy of Sciences, Vol. 36, No. 2, 233-240, 2012

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)0.001
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 score0.024
Threshold uncertainty score0.289

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.029
GPT teacher head0.250
Teacher spread0.221 · 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

Citations12
Published2012
Admission routes1
Has abstractyes

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