PM IRRAS Studies of Organized Molecular Films at a Gold Electrode Surface
Bibliographic record
Abstract
This feature article illustrates the potential of polarization modulation infrared reflection absorption spectroscopy (PM IRRAS) to provide molecular-level information about the structure, orientation and conformation of constituents of thin films at electrode surfaces. PM IRRAS relies on the surface selection rules stating that the p-polarized IR beam is enhanced, while the s-polarized beam is attenuated at the metal surface. The difference between p- and s-polarized beams eliminates the background of the solvent and provides IR spectra at a single electrode potential. In contrast, two other popular in situ IR spectroscopic techniques, namely, subtractively normalized interfacial Fourier transform infrared spectroscopy (SNIFTIRS) and surface-enhanced infrared reflection absorption spectroscopy (SEIRAS), provide potential difference spectra to remove the signal from the bulk solution. In this feature article, we provide a brief tutorial on how to run the PM IRRAS experiment and describe the methods used for background elimination first. The application of the PM IRRAS in the biomimetic research is then illustrated by three examples: construction of a tethered bilayer, reconstitution of colicin into a phospholipid bilayer and determination of the orientation of nucleolipids in a monolayer assembled at a gold electrode surface. Finally, the structural changes of graphene oxide during its electrochemical reduction are described to highlight the promising application of PM IRRAS in materials science.
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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 | 0.001 | 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".