A Combined Vibrational Sum Frequency Generation Spectroscopy and Atomic Force Microscopy Study of Sphingomyelin–Cholesterol Monolayers
Bibliographic record
Abstract
A combination of vibrational sum frequency generation spectroscopy and atomic force microscopy is used to study the changes in morphology and conformational order in monolayers prepared from three natural sphingomyelin (SM) mixtures as a function of surface pressure and cholesterol concentration. The most homogeneous SM gave monolayers with well-ordered acyl chains and few gauche defects with relatively small effects of either increasing surface pressure or cholesterol addition. Heterogeneous SM mixtures with a mixture of acyl chain lengths or with significant fractions of unsaturated acyl chains had much larger contributions from gauche defects at low surface pressure and gave increasingly well-ordered monolayers as the surface pressure increased. They also showed substantial increases in lipid chain order after cholesterol addition. Overall, these results are consistent with the strong hydrogen bonding capacity of SM leading to well-ordered monolayers over a range of surface pressures. The changes in acyl chain order for natural SMs as a function of cholesterol are relevant to formation of sphingolipid-cholesterol enriched domains in cell membranes.
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 | 0.001 | 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".