Relating the Phase in Vibrational Sum Frequency Spectroscopy and Second Harmonic Generation with the Maximum Entropy Method
Bibliographic record
Abstract
Nonlinear optical methods such as vibrational sum frequency generation (vSFG) and second harmonic generation (SHG) are powerful techniques to study the elusive structures at charged buried interfaces such as the silica/water interface. However, for an accurate evaluation of the structure formed at these buried interfaces, the complex vSFG spectra and hence the absolute phase needs to be retrieved. The maximum entropy method is a useful tool for the retrieval of complex spectra from the intensity spectra; however, one caveat is that an understanding of the error phase is required. Here we provide a physically motivated understanding of the error phase, where we show that for broadband vSFG spectra such as the silica/water, the good spectral overlap between water in the diffuse and Stern (or bonded interfacial) layers results in the absolute phase correlating with the error phase. This correlation makes the error phase sensitive to changes in Debye length from varying the ionic strength amongst other variations at the interface. Furthermore, the change in the magnitude error phase can be related to the absolute SHG phase permitting the use of an error phase model that can utilize the SHG phase to predict the error phase and hence the complex vSFG spectra. We highlight limitations of the model for narrow vSFG spectra with poor overlap between the diffuse and Stern layer spectra, such as the silica/HOD in D2O system.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".