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Record W2801622265 · doi:10.1002/smll.201703879

Silver‐Assisted Synthesis of Gold Nanorods: the Relation between Silver Additive and Iodide Impurities

2018· article· en· W2801622265 on OpenAlexaff
Sarah Jessl, Moritz Tebbe, Luca Guerrini, Andreas Fery, Ramón A. Álvarez‐Puebla, Nicolás Pazos‐Pérez

Bibliographic record

VenueSmall · 2018
Typearticle
Languageen
FieldMaterials Science
TopicGold and Silver Nanoparticles Synthesis and Applications
Canadian institutionsUniversity of Toronto
FundersH2020 European Research CouncilEuropean Research CouncilMinisterio de Economía y CompetitividadGeneralitat de Catalunya
KeywordsNanorodIodideBromideYield (engineering)ReagentPulmonary surfactantImpurityMetalMaterials scienceInorganic chemistryChemistryChemical engineeringNanotechnologyOrganic chemistryMetallurgy

Abstract

fetched live from OpenAlex

Abstract Seed‐mediated methods employing cetyltrimethylammonium bromide (CTAB) as a surfactant, and silver salts as additives, are the most common synthetic strategies for high‐yield productions of quality Au nanorods. However, the mechanism of these reactions is not yet fully understood and, importantly, significant lab‐to‐lab reproducibility issues still affect these protocols. In this study, the direct correlation between the hidden content of iodide impurities in CTAB reagents, which can drastically differ from different suppliers or batches, and the optimal concentration of silver required to maximize the nanorods yield is demonstrated. As a result, high‐quality nanorods are obtained at different iodide contents. These results are interpreted based on the different concentrations of CTAB and cetyltrimethylammonium iodide (CTAI) complexes with Ag + and Au + metal ions in the growth solution, and their different binding affinity and reduction potential on distinct crystallographic planes. Notably, the exhaustive conversion of CTAI–Au + to CTAI–Ag + appears to be the key condition for maximizing the nanorod yield.

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 score0.163
Threshold uncertainty score0.299

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 head0.025
GPT teacher head0.235
Teacher spread0.210 · 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

Citations36
Published2018
Admission routes1
Has abstractyes

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