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Record W2901039102 · doi:10.1039/c8nr06497k

Appearance of SERS activity in single silver nanoparticles by laser-induced reshaping

2018· article· en· W2901039102 on OpenAlexafffund
Kamalesh Chaudhari, Tripti Ahuja, Vasanthanarayan Murugesan, Vidhya Subramanian, Mohd Azhardin Ganayee, Thomas Thundat, Thalappil Pradeep

Bibliographic record

VenueNanoscale · 2018
Typearticle
Languageen
FieldMaterials Science
TopicGold and Silver Nanoparticles Synthesis and Applications
Canadian institutionsAlberta Hospital EdmontonUniversity of Alberta
FundersCanada Research ChairsCanada Excellence Research Chairs, Government of CanadaDepartment of Science and Technology, Ministry of Science and Technology, IndiaUniversity of Alberta
KeywordsSilver nanoparticleLaserNanoparticleMaterials scienceNanotechnologyOpticsPhysics

Abstract

fetched live from OpenAlex

We report simultaneous plasmonic scattering and Raman spectroscopic observations of single citrate capped silver nanoparticles (AgNPs) which exhibit surface enhanced Raman scattering (SERS) upon meeting specific conditions induced by laser (532 nm) exposure. We show that nanoparticles which are not initially SERS active become SERS active by laser-induced reshaping/reorientation. A set-up developed for these observations enabled in situ high speed time-lapse characterization using plasmonic and Raman spectroscopies in conjunction with dark-field microscopy (DFM). Changes in the AgNPs were confirmed by monitoring plasmonic scattering spectra and DFM images. Time-lapse observations have shown that laser-induced changes in the plasmonic properties of AgNPs resulted in the appearance of SERS. Spectral matching between plasmon resonance and downward molecular vibronic transitions for molecules adsorbed on the surface of plasmonic nanomaterials is attributed to the nanoparticle SERS. We have further shown that the release of silver ions by silver nanoparticles can be the probable reason for their plasmonic changes. Gold nanoparticles inert to such mild (850 μW, 532 nm) laser-induced changes do not exhibit the appearance of SERS.

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 score0.003
Threshold uncertainty score0.362

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 head0.026
GPT teacher head0.255
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

Citations26
Published2018
Admission routes2
Has abstractyes

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