Tailoring electric field standing waves in reflection–absorption infrared spectroscopy to enhance absorbance from adsorbates on ice surfaces
Bibliographic record
Abstract
The spectroscopic detection of molecules adsorbed onto ice surfaces at coverages similar to those encountered under typical environmental conditions requires high surface selectivity and sensitivity that few techniques can afford. An experimental methodology allowing a significant enhancement in the absorbance from adsorbed molecules is demonstrated herein. It exploits Electric Field Standing Wave (EFSW) effects intrinsic to grazing incidence Reflection–Absorption Infrared (RAIR) spectroscopy, where film thickness dependent optical interferences occur between the multiple reflections of the IR beam at the film–vacuum and the substrate–film interfaces. In this case study, CH4 is used as a probe molecule and is deposited on a 20 ML coverage dense amorphous solid water film adsorbed onto solid Ar underlayers of various thicknesses. We observe that, at thicknesses where destructive interferences coincide with the absorption features from the CH stretching and HCH bending vibrational modes of methane, their intensity increases by a factor ranging from 10 to 25. Simulations of the RAIR spectra of the composite stratified films using a classical optics model reproduce the Ar underlayer coverage dependent enhancements of the absorbance features from CH4 adsorbed onto the ice surface. They also reveal that the enhancements occur when the square modulus of the total electric field at the film’s surface reaches its minimum value. Exploiting the EFSW effect allows the limit of detection to be reduced to a coverage of (0.2 ± 0.2) ML CH4, which opens up interesting perspectives for spectroscopic studies of heterogeneous atmospheric chemistry at coverages that are more representative of those found in the natural environment.
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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 | 0.000 | 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) | 0.001 | 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".