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Record W2048280077 · doi:10.1117/12.981727

Tip-enhanced Raman spectroscopy: application to the study of single silicon nanowire and functionalized gold surface

2012· article· en· W2048280077 on OpenAlexafffund
Nastaran Kazemi‐Zanjani, Farshid Pashaee, François Lagugné‐Labarthet

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2012
Typearticle
Languageen
FieldPhysics and Astronomy
TopicForce Microscopy Techniques and Applications
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNanoprobeMaterials scienceRaman spectroscopySiliconNanowireRaman scatteringRaman microscopeOpticsPlasmonMicroscopeOptoelectronicsCoherent anti-Stokes Raman spectroscopyNanotechnologyNanoparticle

Abstract

fetched live from OpenAlex

Tip-Enhanced Raman spectroscopy is used to probe isolated silicon nanowires and functionalized gold surface with the goal to evaluate the improvements in lateral resolution and surface sensitivity of this method. The setup that involves the combination of and atomic force microscope and a confocal microscope in back-scattering geometry is described together with the optical alignment procedure used to ideally excite the localized surface plasmon of the metallized tip that acts as the nanoprobe. Once aligned, the tip, in feedback with the sample surface, is positioned at a given point and the Raman spectrum is acquired. The sample is then scanned point-by-point and a TERS map is generated for the object or surface of interest. This approach shows an improved lateral spatial resolution for the single silicon nanowires together with relevant information on the induced stress on the nanowire. Last, we show that the proximity of the TERS tip over an ultraflat gold nanoplate functionalized with an azobenzene thiol molecule, largely enhance the vibrational signal from a single monolayer.

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 score0.219
Threshold uncertainty score0.668

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 head0.009
GPT teacher head0.244
Teacher spread0.235 · 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

Citations2
Published2012
Admission routes2
Has abstractyes

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Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicForce Microscopy Techniques and ApplicationsFrench-language works237,207