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Record W2054335062 · doi:10.1166/jbn.2014.1805

Nanostructured Hybrid of Immiscible Gold and Silicon and Its Effect on Proliferation and Adhesion of Fibroblasts and Osteoblasts

2013· article· en· W2054335062 on OpenAlexfundno aff
Priyatha Premnath, Bo Tan, Krishnan Venkatakrishnan

Bibliographic record

VenueJournal of Biomedical Nanotechnology · 2013
Typearticle
Languageen
FieldEngineering
TopicLaser-Ablation Synthesis of Nanoparticles
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceNanotechnologyAdhesionBiomaterialNanostructureSiliconTissue engineeringHybrid materialCell adhesionBiomedical engineeringOptoelectronicsComposite material

Abstract

fetched live from OpenAlex

Hybrid biomaterials are a combination of two or more different materials that work synergistically to produce superior properties. Nano structuring of such hybrid materials has also posed complications. In this study, we present, for the first time a nanofibrous hybrid of gold and silicon fabricated by femtosecond laser synthesis for tissue engineering applications. The formation of a completely new phase, Au3Si (212) is reported. The formation mechanism is explained by vapor condensation. Particle sizes of 2-10 nm and 37-49 nm for gold and gold concentrations of 35-78% are achieved. The effect of this hybrid on cell growth was assessed using fibroblasts and osteoblasts. There was a significant decrease in both osteoblast and fibroblast proliferation with the increase of gold in the hybrid nanostructure. This novel hybrid nanofibrous matrix provides a method to effectively control the proliferation and adhesion of cells. Femtosecond laser synthesis presents a new standard by which not only a single element biomaterial but also multiple immiscible element hybrid biomaterials can be fabricated. This technique provides a paradigm shift in the fabrication of novel nanostructured immiscible hybrid biomaterials.

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 score0.041
Threshold uncertainty score0.398

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 studies0.0000.000
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 head0.004
GPT teacher head0.197
Teacher spread0.193 · 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

Citations2
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Biomedical NanotechnologySame topicLaser-Ablation Synthesis of NanoparticlesFrench-language works237,207