Washless Method Enables Multilayer Coating of an Aggregation‐Prone Nanoparticulate Drug Delivery System with Enhanced Yields, Colloidal Stability, and Scalability
Bibliographic record
Abstract
Aggregation is frequently encountered during coating nanoparticles, especially when the core is not solid and the coating polyelectrolytes are weak. Here, the coating of a nanoliposome with two weak polyelectrolytes, alginate and chitosan, is investigated. First, quartz crystal microbalance with dissipation, atomic force microscopy, scanning electron microscopy, and energy dispersive spectroscopy analyses confirm the feasibility of firm adsorption of up to 16 layers of weak polyelectrolytes to the liposomal surface. Titrations are then performed to identify the lowest amounts of polyelectrolytes required to make eight saturated coating layers using the washless method. Significantly improved yields and reproducibility (almost 100%) are achieved, in addition to control over layer thickness. Attenuated total reflectance Fourier transform infrared spectroscopy studies confirm the success of layering. This is special since scientists always attempt to reduce nanoparticle aggregation by substituting the soft core, using one strong polyelectrolyte, or contending with lower yields or numbers of coating layers.
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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) | 0.001 | 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 | 0.001 |
| 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".