Bibliographic record
Abstract
Nanoparticle (NP) size and charge play key roles in bioconjugation chemistry, imaging and drug delivery. Although the electrophoretic mobility and hydrodynamic size are routinely measured, interpreting these data can be extremely difficult. Here, the challenge is addressed via an electrokinetic model for spheres bearing a soft amphoteric corona, the charge of which is regulated by a multi-component electrolyte. The model is applied to NPs with a metallic core to which are grafted poly(ethylene glycol) chains with either weak acid or amphiprotic end groups. The results elucidate the separate roles of electrolyte pH and ionic strength on the electrophoretic mobility and diffusion coefficient. In this study, the forces were evaluated directly, rather than from the Stokeslet velocity disturbances. While the second-order convergence was demonstrated by both methods, the direct approach, which uses only the inner part of the global solution, furnished superior accuracy and robustness. This may benefit future attempts to model the dielectric and electroacoustic properties of these complex nanoparticulates.
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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".