Electrochemical ATR-SEIRAS Using Low-Cost, Micromachined Si Wafers
Bibliographic record
Abstract
Thin, micromachined Si wafers, designed as internal reflection elements (IREs) for attenuated total reflectance infrared spectroscopy, are adapted to serve as substrates for electrochemical ATR surface enhanced infrared absorption spectroscopy (ATR-SEIRAS). The 500 μm thick wafer IREs with groove angles of 35° are significantly more transparent at long mid-IR wavelengths as compared to conventional large Si hemisphere IREs. The appeal of greater transparency is mitigated by smaller optical throughput at larger grazing angles and steeper angles of incidence at the reflecting plane that reduce the enhancement factor. Through use of the potential dependent adsorption of 4-methoxypyridine (MOP) as a test system, the microgroove IRE is shown to provide relatively strong electrochemical ATR-SEIRAS responses when the angle of incident radiation is between 50 and 55°, corresponding to refracted angles through the crystal of ∼40°. The higher than expected enhancement is attributed to attenuation of the reflection loss of p-polarized light and multiple reflections within the wafer-based IRE. The micromachined IREs are shown to outperform a 25 mm radius hemisphere in terms of S/N at wavenumbers less than ca. 1400 cm –1 despite the weaker signal enhancement derived from the steeper angle incident on the IRE/sample interface. The high optical transparency of the new IREs allows the spectral observation of displaced water libration bands at ca. 730 cm –1 upon solvent replacement by adsorbed MOP. The results are highly encouraging for the further development of low-cost, Si wafer-based IREs for electrochemical ATR-SEIRAS applications.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".