Widefield Super-Resolution Infrared Spectroscopy and Imaging of Autofluorescent Biological Materials and Photosynthetic Microorganisms Using Fluorescence Detected Photothermal Infrared (FL-PTIR)
Bibliographic record
Abstract
We have demonstrated high-speed, super-resolution infrared (IR) spectroscopy and chemical imaging of autofluorescent biomaterials and organisms using camera-based widefield photothermal detection that takes advantage of temperature-dependent modulations of autofluorescent emission. A variety of biological materials and photosynthetic organisms exhibit strong autofluorescence emission under ultraviolet excitation and the autofluorescent emission has a very strong temperature dependence, of order 1%/K. Illuminating a sample with pulses of IR light from a wavelength-tunable laser source causes periodic localized sample temperature increases that result in a corresponding transient decrease in autofluorescent emission. A low-cost light-emitting diode-based fluorescence excitation source was used in combination with a conventional fluorescence microscopy camera to detect localized variations in autofluorescent emission over a wide area as an indicator of localized IR absorption. IR absorption image stacks were acquired over a range of IR wavelengths, including the fingerprint spectral range, enabling extraction of localized IR absorption spectra. We have applied widefield fluorescence detected photothermal IR (FL-PTIR) to an analysis of autofluorescent biological materials including collagen, leaf tissue, and photosynthetic organisms including diatoms and green microalgae cells. We have also demonstrated the FL-PTIR on live microalgae in water, demonstrating the potential for label-free dynamic chemical imaging of autofluorescent cells.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".