A Systematic Analysis of Microstructured Silicon ATR-FTIR Reflection Elements: Operating Parameters, Performance, and Underlying Phenomena
Bibliographic record
Abstract
Microstructured silicon internal reflection elements (μSi-IREs) have the potential to revolutionize attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy. This study compares the analytical performance of leading μSi-IREs from three prominent providers, examining the influence of ridge angle and sensor footprint on key figures of merit. All μSi-IREs delivered high-quality spectra and calibration curves, enabling quantitative concentration measurements and spectral line shape analysis. μSi-IREs with a 35 degree ridge angle and large sensor area exhibited sensitivities approaching 10 –4 mM –1 and detection limits as low as 0.3 mM, outperforming smaller μSi-IREs with a 55 degree ridge angle. Theoretical analysis investigated the effects of beam alignment and revealed fundamental structure-performance relationships, providing design guidelines for next-generation spectroscopic systems. The analysis was benchmarked against a commercial diamond ATR accessory. The inherent advantages of μSi-IREs─including low cost, ease of use, and system integrability─combined with this rigorous performance evaluation, position these microstructured ATR components for novel and impactful applications in analytical spectroscopy.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".