Free Energy of Nanoparticle Binding to Multivalent Polymeric Substrates
Bibliographic record
Abstract
Characterization of the interactions between nanosize ligands and polymeric substrates is important for predictive design of nanomaterials and in biophysical applications. The multivalent nature of the polymer-nanoparticle interaction and the dynamics of multiple internal conformations of the polymer chains makes it difficult to infer microscopic interactions from macroscopic binding assays. Using coarse-grained simulations, we estimate the free energy of binding between a nanoparticle and a surface-grafted polymeric substrate as a function of pertinent parameters such as polymer chain length, nanoparticle size, and microscopic polymer-nanoparticle attraction. We also investigate how the presence of the nanoparticle affects the internal configurations of the polymeric substrate, and estimate the entropic cost of binding. The results have important implications for the understanding of complex macromolecular assemblies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".