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Record W2011964831 · doi:10.1088/0957-4484/23/6/065305

The effect of hydrogen nanobubbles on the morphology of gold–gelatin bionanocomposite films and their optical properties

2012· article· en· W2011964831 on OpenAlexaff
Mohammed Alsawafta, Simona Bǎdilescu, Vo‐Van Truong, Muthukumaran Packirisamy

Bibliographic record

VenueNanotechnology · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicMinerals Flotation and Separation Techniques
Canadian institutionsConcordia University
Fundersnot available
KeywordsGelatinMaterials scienceSodium borohydrideColloidal goldSurface plasmon resonanceAqueous solutionNanoparticleHydrogenPlasmonNanotechnologyDipoleDiscrete dipole approximationChemical engineeringOptoelectronicsPhysical chemistryOrganic chemistryChemistry

Abstract

fetched live from OpenAlex

Gold-gelatin bionanocomposite films are prepared by the reduction of gold ions by sodium borohydride in an aqueous solution. It is shown that both the solution and the films on glass substrates contain entrapped hydrogen micro- and nanobubbles with diameters in the range of 200 nm-3 μm. The optical properties of gold nanoparticles in the presence of gelatin and hydrogen nanobubbles are measured and simulated by using the discrete dipole approximation method. The composite films having micro- and nanobubble inclusions have been found to be very stable. The calculated localized surface plasmon resonance band is found in agreement with the experimental band position only when the presence of hydrogen bubbles around the gold nanoparticles is taken into account. The different morphological features engendered by the presence of the bubbles in the film (gelatin receptacles for the nanoparticles, gelatin hemispheres raised by the bubbles under the surface, cavities on the surface of the film, etc) are described in detail and considered for potential applications. This work is highly relevant to the new and exciting topic of nanobubbles on surfaces and interfaces.

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 score0.014
Threshold uncertainty score0.355

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 studies0.0000.001
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 head0.010
GPT teacher head0.219
Teacher spread0.209 · 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

Citations6
Published2012
Admission routes1
Has abstractyes

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