Dual Source Fourier Transform Polarization Modulation Spectroscopy: An Improved Method for the Measurement of Circular and Linear Dichroism
Bibliographic record
Abstract
It is shown that the use of two sources in a four-port interferometer equipped with cube-corner mirrors leads to increased signal-to-noise ratios in Fourier transform (FT-IR) circular and linear dichroism spectra. The output beam to the sample is a superposition of two interferograms, one from each source, having opposite Fourier phases. These two interferograms cancel one another to the degree that the two sources are matched in intensity. If the radiation from each of the two sources is first polarized orthogonally with respect to the other and passed through a polarization modulator before reaching the sample, the resulting polarization-modulation interferograms are out of Fourier phase and out of polarization-modulation phase. As a result, the polarization-modulation interferograms, due to circular or linear dichroism in the sample, from the two sources combine positively rather than negatively. An improvement in signal-to-noise ratio of up to two (or a factor of four in scan-time reduction for the same signal-to-noise ratio) compared to single source operation can be realized, while at the same time, the potential for saturation of the detector signal is significantly reduced due to the reduction in magnitude of the combined ordinary infrared transmission interferogram. Absorption and circular dichroism spectra from a dual-source FT-IR spectrometer are presented and analyzed.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".