Bibliographic record
Abstract
We report here on the characterization of two types of chiral molecules deposited onto a silicon surface. Chiral molecules are nonsuperimposable mirror images of each other. Other than the way they interact with biological systems, chiral molecules have the same physical properties which make them hard to separate. Since many important drug molecules are chiral, effective separation methods are required by industry. We are building a model system to study one separation method called chiral chromatography. In chiral chromatography, separation is achieved by immobilizing a chiral compound along a column and passing the desired chiral mixture through. One of the mirror image molecules of the mixture has a higher attraction to the immobilized phase which causes it to exit the column at a later time. In the model being studied, propranolol is the sample drug molecule and phenylethylpropylurea (PEPU) is the selector molecule. Derivatives of these compounds were deposited onto a flat silicon surface. The resulting samples were studied in order to gain insight into the surface morphology and characteristics of the assembled layers. Using a combination of infra red (IR) spectroscopy and computational analysis it was possible to infer the average bulk molecular orientation of the deposited propranolol molecules. Atomic force microscopy was used to ensure a uniform deposition as well as to quantify the surface roughness. Through Xray photoelectron spectroscopy (XPS) analysis it was shown that an average layer thickness of four molecules was deposited onto the silicon
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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".