Bibliographic record
Abstract
This article describes efforts to build a model biological membrane at a surface of a gold electrode. In this architecture, the membrane may be exposed to static electric fields on the order of 10(7) to 10(8) V m(-1). These fields are comparable in magnitude to the static electric field acting on a natural biological membrane. The field may be conveniently used to manipulate organic molecules within the membrane. By turning a knob on the control instrument one can deposit or lift the membrane from the gold surface. Electrochemical techniques can be used to control the physical state of the film while the infrared reflection absorption spectroscopy (IRRAS), surface imaging by STM and AFM and neutron scattering techniques can be employed to study conformational changes of organic molecules and their ordering within the membrane. This is shown on examples of membranes built of a simple zwitterionic phospholipid such as 1,2-dimyristoyl-sn-glycero-3-phosphatidylcholine (DMPC) and a mixed membrane composed of DMPC and cholesterol. The results illustrate the tremendous effect of cholesterol on the membrane structure. Two methods of membrane deposition at the electrode surface, namely by unilamellar vesicles fusion and using the Langmuir-Blodgett technique, are compared. Applications of these model systems to study interactions of small antibiotic peptides with lipids are discussed.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".