Extracting multivalent detachment rates from heterogeneous nanoparticle populations
Bibliographic record
Abstract
Nanoparticles can form multiple bonds with target surfaces, thereby increasing adhesion strength and internalization rate into cells. This property has helped to drive interest in nanoparticles as delivery vehicles for drugs and imaging agents, but significant gaps in our understanding of multivalent adhesion make it difficult to control and optimize binding dynamics. In previous work, we experimentally observed that multivalent nanoparticle adhesion can exhibit a time-dependent detachment rate. However, simulations later indicated that the underlying cause was variability in the number of bonds that formed between individual nanoparticles within the population. Here, we use this insight to develop a simple model to isolate a series of constant detachment rates from such heterogeneous populations. Using simulations of experimental data to train the model, we first classified nanoparticles within a given population based on the most likely equilibrium bond number, which we termed the bond potential. We then assumed that each bond potential category would follow standard first-order kinetics with constant detachment rates. Model results matched the population binding data, but only if we further divided each bond potential category into two sub-components, the second of which did not detach. We then utilized bonding rates from the simulation to estimate detachment rates for the second, slower detaching sub-component. These results confirm our hypothesis that nanoparticle populations can be sub-divided based on bond potential, each of which could be characterized by a constant detachment rate. Finally, we established relationships between the new heterogeneous population detachment model and a time-dependent, empirical detachment model that we developed in previous work. This could make it possible to determine bond potential distributions directly from experimental data without computationally costly simulations, which will be explored in future work.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".