Fourier Transform Infrared Spectrochemical Imaging: Review of Design and Applications with a Focal Plane Array and Multiple Beam Synchrotron Radiation Source
Bibliographic record
Abstract
The beamline design, microscope specifications, and initial results from the new mid-infrared beamline (IRENI) are reviewed. Synchrotron-based spectrochemical imaging, as recently implemented at the Synchrotron Radiation Center in Stoughton, Wisconsin, demonstrates the new capability to achieve diffraction limited chemical imaging across the entire mid-infrared region, simultaneously, with high signal-to-noise ratio. IRENI extracts a large swath of radiation (320 hor. × 25 vert. mrads(2)) to homogeneously illuminate a commercial infrared (IR) microscope equipped with an IR focal plane array (FPA) detector. Wide-field images are collected, in contrast to single-pixel imaging from the confocal geometry with raster scanning, commonly used at most synchrotron beamlines. IRENI rapidly generates high quality, high spatial resolution data. The relevant advantages (spatial oversampling, speed, sensitivity, and signal-to-noise ratio) are discussed in detail and demonstrated with examples from a variety of disciplines, including formalin-fixed and flash-frozen tissue samples, live cells, fixed cells, paint cross-sections, polymer fibers, and novel nanomaterials. The impact of Mie scattering corrections on this high quality data is shown, and first results with a grazing angle objective are presented, along with future enhancements and plans for implementation of similar, small-scale instruments.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".