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Record W3139211912 · doi:10.1021/acsanm.1c00230

Insight into the Role of Ag in the Seed-Mediated Growth of Gold Nanorods: Implications for Biomedical Applications

2021· article· en· W3139211912 on OpenAlexafffund
Jun Zhu, R. Bruce Lennox

Bibliographic record

VenueACS Applied Nano Materials · 2021
Typearticle
Languageen
FieldMaterials Science
TopicGold and Silver Nanoparticles Synthesis and Applications
Canadian institutionsMcGill University
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of Canada
KeywordsNanorodNanotechnologyLimitingAscorbic acidReagentSolubilityCombinatorial chemistryMaterials scienceReducing agentChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

The synthesis of gold nanorods (AuNRs) with a specific length/diameter aspect ratio is crucial for their use in imaging, sensing, drug delivery, and biological applications. However, the most commonly used silver-aided seed-mediated synthesis method still suffers from poor outcome predictability and hence, in an overall sense, reproducibility. To address this information gap, the mechanism of the seed-mediated synthesis has been investigated, particularly with regard to the possible existence of limiting reagents or intermediates in the reaction. The key silver intermediate which controls the AuNR aspect ratio has thus been identified as a CTA–Ag–Br complex. The AuNR growth solution preparation process has been systematically investigated and the solubility of the CTA–Ag–Br complex is established to be the limiting agent in the preparation and growth of the resulting AuNR. The sequence of reagent addition is shown to also be a determinant in the evolution of a resulting gold nanorod. The importance of the CTA–Ag–Br complex in nanorod syntheses is supported by the observation of gold NP formation when a reductant (ascorbic acid) is added before the CTA–Ag–Br complex has formed. This result informs the understanding of the role of the silver ion in the AuNR synthesis and provides a much-needed entry to the synthesis of AuNRs with custom aspect ratios, including those with much sought after large aspect ratios. This result will benefit research involving AuNRs, especially that in biomedical applications.

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 score0.142
Threshold uncertainty score0.385

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 head0.011
GPT teacher head0.239
Teacher spread0.228 · 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

Citations24
Published2021
Admission routes2
Has abstractyes

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