Molecular modeling of interfacial layer-by-layer assembly towards functionalized capsule materials
Bibliographic record
Abstract
Encapsulated nanomaterials, such as polymer-coated nanoemulsions, have highly tunable properties leading to versatile applications. A current lack of understanding of the fundamentals governing the choice of "capsule" materials (polyelectrolyte + surfactant) and its ensuing performance effectively precludes their widespread use. Computational methods can start to redress this by discovering molecule-scale attributes that significantly control the design of capsule materials tuned to fit desired properties. We use molecular dynamics (MD) to carry out the layer-by-layer (LbL) assembly of six unique polyelectrolyte bilayer systems at a surfactant-mediated interface, modeling early-stage capsule synthesis. Monolayer thickness is related to layer density and polyelectrolyte/surfactant interaction energy through polyelectrolyte molecular weight and radius of gyration, respectively, yielding a simple relationship between absorption kinetics and layer structure. For the second monolayer, faster absorption kinetics are observed for pairings of polyelectrolytes with similarly sized functional groups. Surfactants with a more delocalized charge on the head-group catalyze the build-up of ions at the interface, resulting in faster absorption kinetics and greater confinement of the encapsulated material but leading to thicker, less uniform bilayers. These relationships between capsule building block molecules and nanomaterial capsule properties provide a foundation for property prediction and rational design of optimized multi-functional capsule materials.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".