Reflected light microspectroscopy for single-nanoparticle biosensing
Bibliographic record
Abstract
Conventional and dark-field microscopy in the transmission mode is extensively used for single plasmonic nanoparticle (NP) imaging and spectral analysis. However, application of the transmission mode for realtime biosensing to single NP poses strict limitations on the size and material properties of the microfluidic system. This article proposes a simple optical technique based on reflected light microscopy to perform microspectroscopy of a single NP placed in a conventional, nontransparent liquid delivery system. The insertion of a variable spot diaphragm in the optical path reduces the interference effect that occurs at the NP-substrate interface and improves the signal-to-noise ratio in NP imaging. Using this method, we demonstrated spatial imaging and spectral analyses of 60-, 80-, and 100-nm single gold NPs. A single-NP sensor based on a 100-nm NP was used for real-time measurement of bulk refractive index changes in the microfluidic channel and for detection of fast dynamic poly(ethylene glycol) attachment to the NP surface. Finally, electrochemical single-particle microspectroscopy was demonstrated by using a methylene blue electroactive redox tag. The proposed optical approach is expected to significantly improve the miniaturization and multiplexing capabilities of high-throughput biosensing based on single NP.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| 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".