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Record W4240682303 · doi:10.26434/chemrxiv.11369820

Effective Plasmonic Coupling and Propagation Facilitates Ultrasensitive and Remote Sensing Using Surface Enhanced Raman Spectroscopy

2019· preprint· en· W4240682303 on OpenAlexaff
Collins Nganou, Andrew Carrier, Dongchang Yang, Yongli Chen, Naizhen Yu, Doug Richards, Craig Bennett, Ken D. Oakes, Stephanie MacQuarrie, Xu Zhang

Bibliographic record

VenueChemRxiv · 2019
Typepreprint
Languageen
FieldMaterials Science
TopicGold and Silver Nanoparticles Synthesis and Applications
Canadian institutionsCape Breton University
Fundersnot available
KeywordsDetection limitRaman scatteringRaman spectroscopyPlasmonSpectroscopyAnalytical Chemistry (journal)Materials scienceSurface-enhanced Raman spectroscopyAnalyteNanoparticleAqueous solutionOptoelectronicsChemistryOpticsNanotechnologyPhysicsChromatography

Abstract

fetched live from OpenAlex

Surface-enhanced Raman spectroscopy (SERS) is a sensitive technique for the detection of analytes through light scattering that is enhanced by chemical and electromagnetic effects through interactions on surfaces, particularly in nano-gaps. Herein we show that dissolved oxygen is the strongest attenuator of the SERS response in aqueous solution and its removal by chemical means can lower the detection limit by 109–1010 times, to achieve unprecedented sensitivity, i.e., detection of a single molecule in ~300 µL of sample solution. It also enables remote detection of the analyte outside of the field of view of the incident laser beam, e.g., over a distance of 1 m, which we propose is due to the coupling of the plasmonic field within and between nanoparticle aggregates, allowing for signal transmission throughout the sample volume.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.009

Distilled classifier scores by category (both heads)

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 communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.016
GPT teacher head0.257
Teacher spread0.241 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2019
Admission routes1
Has abstractyes

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