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Record W2000030776 · doi:10.3329/jbas.v37i1.15684

Effect of thickness on the optical properties of Gaas thin films

2013· article· en· W2000030776 on OpenAlexaff
Chitra Das, Jahanara Begum, Tahmina Begum, Shamima Choudhury

Bibliographic record

VenueJournal of Bangladesh Academy of Sciences · 2013
Typearticle
Languageen
FieldMaterials Science
TopicZnO doping and properties
Canadian institutionsAtomic Energy (Canada)
Fundersnot available
KeywordsRefractive indexMaterials scienceMolar absorptivityDielectricTransmittanceThin filmBand gapAttenuation coefficientOpticsAbsorption (acoustics)Substrate (aquarium)Fused quartzPhoton energyAnalytical Chemistry (journal)OptoelectronicsQuartzPhotonChemistryComposite materialNanotechnology

Abstract

fetched live from OpenAlex

Effect of thickness on the optical and electrical properties of gallium arsenide (GaAs) thin films were studied. The films of different thicknesses were prepared by vacuum evaporation method (~10-4 Pa) on glass substrates at a substrate temperature of 323 K. The film thickness was measured in situ by a frequency shift of quartz crystal. The thicknesses were 250, 300 and 500 nm. Absorption spectrum of this thin film had been recorded using UV-VIS-NIR spectrophotometer in the photon wavelength range of 300 - 2500 nm. The values of some important optical parameters of the studied films (absorption coefficient, optical band gap energy and refractive index; extinction co-efficient and real and imaginary parts of dielectric constant) were determined using these spectra. Transmittance peak was observed in the visible region of the solar spectrum. Here transmittance showed better result when thicknesses were being increased. The optical band gap energy was decreased by the increase of thickness. The refractive index increased by increasing thickness while extinction co-efficient and real and imaginary part of dielectric constant decreased. DOI: http://dx.doi.org/10.3329/jbas.v37i1.15684 Journal of Bangladesh Academy of Sciences, Vol. 37, No. 1, 83-91, 2013

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)0.005
metaresearch head score (Gemma)0.001
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 score0.005
Threshold uncertainty score0.768

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.039
GPT teacher head0.276
Teacher spread0.238 · 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

Citations24
Published2013
Admission routes1
Has abstractyes

Explore more

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