Hamaker Constants for Bionano Interactions in Water
Bibliographic record
Abstract
Bionano complex formation and interaction at the bionano interface are important features to understand in detail, so that a firm relationship between the nanomaterials (NMs) of any size, shape and surface charge and their biological activity in water can be established.The bulk of the NM interacts via the long-range van der Waals interaction, which is a major contribution in the calculation of the adsorption energies of biomolecules in water.Therefore, Hamaker constants are advanced descriptors of the bionano interface.We evaluate the bionano interactions through an atomistic Force Field (FF) approach.For metals we use CHARMM FF parameters [1], and for metal oxides and carbon materials as well as amino acids, lipids and sugars the FFs developed and reported [2].All FF parameters have been applied in molecular dynamics simulations for many properties, including potentials of mean forces.In this work, we present a methodology to estimate Hamaker constants of the bionano interface in water or just the interaction of the NM with itself in water.The long-range dispersion interaction is calculated using the Lorentz-Berthelot rules for and [3], i.e. combining rules that provide the interaction energy between two non-bonded atoms.By summing up all atom-atom interactions to a single parameter, the Hamaker constant between two molecular entities of the same nanoparticle (NP) can be approximated [4] by: 11 = 4 2 ( ) 2 ∑ ≠ ( ( ) 6 ), where is the number density of the NP, is related to the induced dipole interactions between two particles, is the effective radius between
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".