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Record W4396707090 · doi:10.1002/cphc.202400146

An Exploration of Cysteamine as a Subphase Additive for the Fabrication of Uniform Gold Nanorod Arrays using Langmuir‐Blodgett Deposition

2024· article· en· W4396707090 on OpenAlexafffund
Najwan Albarghouthi, Sumayyah A. B. Chotoye, Christa L. Brosseau

Bibliographic record

VenueChemPhysChem · 2024
Typearticle
Languageen
FieldMaterials Science
TopicGold and Silver Nanoparticles Synthesis and Applications
Canadian institutionsSaint Mary's University
FundersResearch Nova ScotiaNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsCanada Foundation for Innovation
KeywordsNanorodDeposition (geology)CysteamineLangmuir–Blodgett filmFabricationLangmuirNanotechnologyMaterials scienceChemical engineeringChemistryMonolayerPhysical chemistryAdsorptionOrganic chemistryGeology

Abstract

fetched live from OpenAlex

Gold nanorods (AuNRs) have attracted significant attention over the past several decades for a variety of applications and there has been steady progress with regards to their synthesis and modification. Despite these advances, the assembly of AuNRs into well-organized hierarchical assemblies remains a formidable challenge. Specifically, there is a need for tools that can fabricate assemblies of nanorods over large length scales at low cost with the potential for high-throughput manufacturing. Langmuir-Blodgettry is a monolayer deposition technique which has been primarily applied to amphiphilic molecules, but which has recently shown promise for the ordering of functionalized nanoparticles residing at the air-water interface. In this work, Langmuir-Blodgett deposition is explored for the formation of AuNR arrays for enhanced surface-enhanced Raman spectroscopy (SERS) sensing. In particular, both surface modification of the AuNRs as well as subphase modification with cysteamine were evaluated for AuNR array fabrication.

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 score0.042
Threshold uncertainty score0.246

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 head0.042
GPT teacher head0.299
Teacher spread0.256 · 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

Citations0
Published2024
Admission routes2
Has abstractyes

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