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Record W2883413140 · doi:10.5539/jmsr.v7n4p1

Optical and Photoacoustic Properties of Colloidal Silver Nanoparticles Solutions

2018· article· en· W2883413140 on OpenAlexvenueno aff
F. M. Aldosari, Abdallah M. Azzeer, A. M. Hassib

Bibliographic record

VenueJournal of Materials Science Research · 2018
Typearticle
Languageen
FieldEngineering
TopicPhotoacoustic and Ultrasonic Imaging
Canadian institutionsnot available
Fundersnot available
KeywordsAbsorbanceMaterials scienceColloidAbsorption (acoustics)NanoparticleAnalytical Chemistry (journal)FluorescenceTransmission electron microscopySilver nanoparticlePhase (matter)Absorption spectroscopyPhotoacoustic imaging in biomedicineChemical engineeringOpticsNanotechnologyChromatographyComposite materialChemistry

Abstract

fetched live from OpenAlex

The study has aimed to investigate optical and photoacoustic properties of some colloidal silver nanoparticles at different concentration over the time. The phase purity with crystal structure of silver nanoparticles were studied using X-ray diffraction (XRD) and was confirmed by Transmission Electron Microscopy (TEM). Optical property was changed during the variable concentration of nanoparticles and duration time of sample. The absorbance of silver colloidal solutions is increased, when concentration increases but decreased with respective to the longer period of sample. Absorption peaks and stability of particles in solutions is influenced by the duration time of solution and its concentration. The low concentration-based solutions fall in the absorbance over the times. The observed PA signal is subjected by the factor of parameters; such as, pump wavelengths, energy, repetition rate, concentrations, and temperature. Stability of fluorescence and absorption peaks are influenced by the concentration of particles and time period of colloidal solution. Effects on PA signal with fluorescent and absorption peaks upon the concentration was also significant.

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)0.004
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 score0.790

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
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 head0.067
GPT teacher head0.315
Teacher spread0.248 · 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

Citations4
Published2018
Admission routes1
Has abstractyes

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