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Record W2330085231 · doi:10.1166/jnn.2010.2051

Room Temperature Surfactant Assisted Crystal Growth of Silver Nanoparticles to Nanoribbons

2010· article· en· W2330085231 on OpenAlexaff
Mandeep Singh Bakshi

Bibliographic record

VenueJournal of Nanoscience and Nanotechnology · 2010
Typearticle
Languageen
FieldMaterials Science
TopicGold and Silver Nanoparticles Synthesis and Applications
Canadian institutionsAcadia University
Fundersnot available
KeywordsMaterials scienceOstwald ripeningPulmonary surfactantAmmonium bromideCrystalliteBromideChemical engineeringCationic polymerizationHigh-resolution transmission electron microscopyAqueous solutionMicelleNanoparticlePhase (matter)NanotechnologyTransmission electron microscopyPolymer chemistryOrganic chemistryChemistryMetallurgy

Abstract

fetched live from OpenAlex

This study presents a simple and systematic growth of Ag nanoparticles (NPs) to nanoribbons by changing the hydrophobicity parameter of a series of cationic Gemini surfactants by using seed-mediated approach in aqueous phase at room temperature. At lowest hydrophobicity of a Gemini surfactant (i.e., dimethylene bis(decyldimethyl-ammonium bromide), 10-2-10), Ostwald ripening process was observed which caused fusion among growing Ag NPs. This process was quite prominent when 0.5 ml of Ag seed solution was used for the growth process but weakened as the amount of seed decreased to 0.125 ml. Similar behavior was demonstrated by Au NPs studied for comparison. The nanostructures were characterized by TEM, XRD, and UV-visible measurements. Further increase in the hydrophobicity of a Gemini surfactant from 10-2-10 to dimethylene bis(tetradecyldimethylammonium bromide) (14-2-14) through dimethylene bis(dodecyldimethylammonium bromide) (12-2-12), resulted in the participation of threads like micelles and liquid crystalline phase as soft-templates towards the nanoribbon formation. Fine polycrystalline Ag nanoribbons were obtained in the presence 14-2-14 and were characterized by the HRTEM and EDX analysis.

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)0.001
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 score0.007
Threshold uncertainty score0.408

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.009
GPT teacher head0.233
Teacher spread0.224 · 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

Citations32
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Nanoscience and NanotechnologySame topicGold and Silver Nanoparticles Synthesis and ApplicationsFrench-language works237,207