Modulating interactions between ligand-coated nanoparticles and phase-separated lipid bilayers by varying the ligand density and the surface charge
Bibliographic record
Abstract
The interactions between nanoparticles and lipid bilayers are critical in applications of nanoparticles in nanomedicine, cell imaging, toxicology, and elsewhere. Here, we investigate the interactions between nanoparticles coated with neutral and/or charged ligands and phase-separated lipid bilayers using coarse-grained molecular dynamics simulation. Both penetration and adsorption processes as well as the final distribution of the nanoparticles can be readily modulated by varying the ligand density and the surface charge of the nanoparticles. Completely hydrophobic (neutral) nanoparticles with larger size initially preferentially penetrate into the liquid-disordered region of the lipid bilayer and finally transfer into the liquid-ordered region; partially hydrophilic nanoparticles with low or moderate surface charge tend to either distribute in the liquid-disordered region or be adsorbed on the surface of the lipid bilayer, while strongly hydrophilic nanoparticles with high surface charge always reside on the surface of the lipid bilayer. Interactions of the nanoparticles with the lipid bilayers are affected by the surface charge of nanoparticles, hydrophobic mismatch, bending of the ligands, and the packing state of the lipids. Insight in these factors can be used to improve the efficiency of designing nanoparticles for specific applications.
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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 | 0.001 | 0.001 |
| 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".