MétaCan
Menu
Back to cohort
Record W1977333299 · doi:10.1039/c4nr05080k

Real-time monitoring for the morphological variations of single gold nanorods

2014· article· en· W1977333299 on OpenAlexaff
Tao Xie, Chao Jing, Wei Ma, Zhifeng Ding, Andrew J. Gross, Yi‐Tao Long

Bibliographic record

VenueNanoscale · 2014
Typearticle
Languageen
FieldMaterials Science
TopicGold and Silver Nanoparticles Synthesis and Applications
Canadian institutionsWestern University
Fundersnot available
KeywordsNanorodRayleigh scatteringMaterials scienceScatteringTransverse planeEtching (microfabrication)SpectroscopyNanomaterialsOpticsNanotechnologyPhysics

Abstract

fetched live from OpenAlex

The morphological characteristics of metal nanoparticles, particularly the shape, play an essential role in the optical, physical and chemical properties. In this work, we reported a transverse etching process to investigate the morphological variations of single gold nanorods (GNRs). Dark-field microscopy and Rayleigh scattering spectroscopy were used as complementary technologies to track the transverse etching process. Dark-field imaging with high spatial and temporal resolution could easily monitor the transverse etching process of GNRs in situ and in real time. Interactions between the scattering spectrum and the morphological variations were judiciously calculated within the dipole approximation by the Drude function. The calculated peak shift of GNRs (Δλmax = 17 nm) was obtained via the ratio of the long axis and short axis (aspect ratio) of GNRs from transmission electron microscopy. The average scattering peak shift (Δλmax = 22 nm) from Rayleigh scattering spectroscopy was in good agreement with the calculated peak shift. Monitoring the morphological variations of single GNRs enables us to track the transverse etching of GNRs at arbitrary time. This promises to be a useful method for the study of different nanomaterials and their spectral properties.

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 score0.004
Threshold uncertainty score0.174

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 head0.027
GPT teacher head0.257
Teacher spread0.231 · 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

Citations27
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueNanoscaleSame topicGold and Silver Nanoparticles Synthesis and ApplicationsFrench-language works237,207