Accessible Double Nanohole Raman Tweezer Analysis of Single Nanoparticles
Bibliographic record
Abstract
Raman spectroscopy allows for material characterization of nanoparticles; however, probing individual nanoparticles requires an efficient way of isolating and enhancing the signal. Past works have used optical trapping with nanoapertures in metal films to measure the Raman spectra of individual nanoparticles; however, those works required custom laser tweezer systems that provided a transmission signal to verify trapping events as well as costly top-down nanofabrication. Here, we trapped Titania nanoparticles in a commercial Raman system using double nanoholes (DNH) and measured their spectra while trapped. The microscope camera allowed for measuring the trapping event in reflection mode, and a simultaneous Raman spectrum was recorded to allow for material characterization. The Raman signal was comparable to a past work that used particles a million times larger in volume without utilizing double nanoholes, and all other features were similar. The DNHs were created with a colloidal lithography technique and identified in the microscope, as confirmed by electron microscopy registration. Therefore, this approach allows a simple way of characterizing the Raman signal of individual nanoparticles while in solution by using existing commercial Raman systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".