Investigating the Performances of Wide-Field Raman Microscopy with Stochastic Optical Reconstruction Post-Processing
Bibliographic record
Abstract
Super-resolution fluorescence microscopy based on localization algorithms has tremendously impacted the field of imaging by improving the spatial resolution of optical measurements with specific blinking fluorophores and concomitant reduction of acquisition time. In vibrational spectroscopy and imaging, various methods have been developed to surpass the diffraction limit including near-field scattering methods, such as in tip-enhanced Raman and infrared spectroscopies. Although these scanning-probe techniques can provide exquisite spatial resolution, they often require long acquisition times and tedious fabrication of nano-scale scanning probes. Herein, stochastic optical reconstruction microscopy (STORM) protocol is applied on Raman measurements acquired using a wide-field home-built microscopy setup. We explore how the fluctuations of the Raman signal acquired over a series of time-lapse images at specific spectral ranges can be exploited with STORM processing, possibly revealing details with improved spatial resolution, under lower irradiance and with faster acquisition speed that cannot be achieved in point scanning mode over the same field of view. Samples studied here include patterned silicon, polystyrene microspheres on a silicon wafer, and graphene on a silicon/silicon dioxide substrate. The outcome presents an effective way to collect Raman images at selected spectral ranges with spatial resolutions of ∼200 nm over a large field of view under 532 nm excitation together with an acquisition speed improved by two orders of magnitude and under a significantly reduced irradiance compared to confocal laser scanning acquisition.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".