The utility of polarization modulation infrared reflection absorption spectroscopy (PM-IRRAS) in surface and <i>in situ</i> studies: new data processing and presentation approach
Bibliographic record
Abstract
Infrared spectroscopy is a powerful non-destructive technique for the identification and quantification of organic molecules widely used in scientific studies. For many years, efforts have been made to adopt this technique for the in situ monitoring of reactions. From these efforts, polarization modulation infrared reflection absorption spectroscopy (PM-IRRAS) was developed three decades ago. Unfortunately, because of the complexity of data processing and interpretation, PM-IRRAS had been avoided in lieu of the single potential alteration infrared spectroscopy (SPAIRS) and subtractively normalized interfacial Fourier transform infrared (SNIFTIR). In this work, we present a new approach for PM-IRRAS data processing and presentation, which provides more insight into in situ and surface studies besides dramatically improving the S/N. In this new approach, we recommend three complementary methods of data treatment (eqn (7), (9) and (10)) as the new protocols for presenting PM-IRRAS data. These equations are robust in visualising the surface processes at the solid-liquid and solid-gas interphases. Eqn (7) contrasts the surface adsorbed species with respect to the isotropic background with or without the influence of the applied potential. Eqn (9) highlights the surface potential-driven changes between the sample and the reference spectra. Eqn (10) focuses on the bulk-phase (solution/gas and surface species) potential-driven changes between the sample and the reference spectra, and hence it can be used to track the production of species, which desorb from the surface upon their formation. Examples of ethanol electro-oxidation reaction are provided as a test system for in situ studies and PVP deposited on glassy carbon for thin-film studies to illustrate the utility of the new PM-IRRAS data handling protocol, which is poised to improve the understanding of the chemistry and physics of surface processes.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".