Fourier Transform Infrared Spectrochemical Imaging: Review of Design and Applications with a Focal Plane Array and Multiple Beam Synchrotron Radiation Source
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it