Ketone Binding at Amino and Ureido Monolayer/Solvent Interfaces Studied by Nonlinear Optical Techniques
Bibliographic record
Abstract
Understanding the influence of immobilization is key to advancing efforts in green chemistry based on supported catalyst materials. The binding of a model reactant 4-nitroacetophenone with amino and ureido organocatalytic monolayers has been investigated at the acetonitrile/solid interface using a combination of second harmonic generation (SHG) and sum frequency generation (SFG). By changing the ketone concentration in the bulk solvent, binding isotherms at each interface were determined from SHG measurements. Langmuir fitting of these isotherms yielded binding energies consistent with hydrogen bond formation. Surprisingly, the ketone had a lower binding affinity for ureido monolayers compared with its binding behavior at amino-modified surfaces despite the fact that urea can form two hydrogen bonds with carbonyl groups. This lower binding affinity was attributed to the presence of a hydrogen bond network within the ureido monolayer that must be disrupted to facilitate ketone binding. Vibrational SFG measurements of the urea groups in the N–H stretching region revealed two new peaks upon introduction of the ketone that were attributed to the ketone-bound urea. An observed phase change in these peaks supported that ketone binding not only disrupted the hydrogen-bonded network within the ureido monolayer but also led to significant reorientation of the ureido groups.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".