Interactions between a Solid Spherical Particle and a Chemically Heterogeneous Planar Substrate
Bibliographic record
Abstract
The Derjaguin-Landau-Verwey-Overbeek (DLVO) interaction forces between a chemically heterogeneous substrate and a spherical particle approaching the surface are calculated using a simple model. This model decomposes the heterogeneous substrate into nanoscale subunits and applies a pairwise summation of forces between each subunit and the approaching particle to determine the net DLVO force. This approach leads to a three-dimensional description of the forces arising from the chemical heterogeneity of the surface. In this three-dimensional realm, we observe the emergence of a substantial lateral force at specific separation distances from the substrate. More specifically, the lateral forces become significantly larger than the normal forces at separation distances between those of the repulsive barrier and the secondary minimum of the DLVO interaction energy curve. These lateral forces are most pronounced at high electrolyte concentrations, particularly at biological salt concentrations of approximately 0.1 M. Furthermore, the lateral forces are found to be significantly higher when the particle is near the edge of a heterogeneous region of the substrate. On the basis of the evidence of this study, and depending on the characteristics of the system, both the physical roughness and the chemical heterogeneity of a surface can significantly affect how a particle will interact with it.
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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".