Driving Adsorbed Gold Nanoparticle Assembly by Merging Lipid Gel/Fluid Interfaces
Bibliographic record
Abstract
Surface forces between inorganic nanoparticles and lipid bilayer is of great relevance to biophysics, medicine, and nanobiotechnology. Adsorbed nanoparticles may influence the fluidity of the underlying lipids, which may in turn influence nanoparticle assembly. Herein three types of lipids (DOPC, Tc = -20 °C; DMPC, Tc = 23 °C; and DPPC, Tc = 41 °C) are used, all with the same phosphocholine (PC) headgroup. Gold nanoparticle (AuNP) color change is monitored as a function of lipid phase transition temperature (Tc), surface ligands on AuNPs, and temperature. Liposomes with higher fluidity induce much faster aggregation of AuNPs. Aside from the kinetic aspect of faster diffusion on fluid bilayers, this faster color change is attributed to the local lipid gelation and merging of gelled regions to eliminate the interface between different lipid phases.
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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 | 0.001 |
| 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".