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Record W2073770432 · doi:10.1088/1757-899x/46/1/012018

The Optical Properties of CdSe Quantum Dots by Using Spray-Atomization Method

2013· article· en· W2073770432 on OpenAlexaff
C.H. Rosmani, S. Abdullah, M. Rusop

Bibliographic record

VenueIOP Conference Series Materials Science and Engineering · 2013
Typearticle
Languageen
FieldMaterials Science
TopicQuantum Dots Synthesis And Properties
Canadian institutionsSciencetech (Canada)
Fundersnot available
KeywordsCadmium selenideQuantum dotBand gapCadmium sulfideMaterials scienceCadmiumSeleniumParticle sizeSodium sulfitePhotoluminescenceCadmium chlorideCadmium oxideParticle (ecology)NanotechnologyCadmium acetateSelenideChemistryOptoelectronicsSodiumMetallurgyPhysical chemistry

Abstract

fetched live from OpenAlex

Cadmium Selenide (CdSe) quantum dots (QDs) is inorganic material by using spray-atomization method which is the novelty to find out the optical properties for the CdSe QDs. The Selenium (Se) precursor and Cadmium (Cd) precursor were prepared first. Se precursor by using sodium sulfite aqueous was mixed with selenium (Se) powder. For Cd precursor was used cadmium chloride (CdCI) as the Cd precursor. From previous research, CdSe QDs was obtained by using capping agent such as tri-n-octylphosphine oxide (TOPO) and trioctylphosphine (TOP). These capping agent are hazardous to environment and human. By using spray-atomization method it is more safe and economically. The photoluminescence (PL) was used to investigate the optical properties and to investigate the energy band gap from PL result. The field emission scanning electron microscopy (FESEM) was used to know the surface morphology of CdSe QDs. By PL result, the energy band gap was calculate and the comparison was investigate between the size of particle and the energy band gap. This important in this paper is to investigate the optical properties of CdSe QDs by using sprays-atomization method and to relate with the particle size.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
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 score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.002
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 head0.028
GPT teacher head0.232
Teacher spread0.205 · 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.

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

Citations0
Published2013
Admission routes1
Has abstractyes

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