Is Surface Roughness a “Scapegoat” or a Primary Factor When Defining Particle−Substrate Interactions?
Bibliographic record
Abstract
Extended DLVO interaction potentials were determined for spherical particles approaching nanopatterned substrates using the numerical surface element integration (SEI) technique. In most cases, nanopatterned ("rough") surfaces produced smaller interaction potentials than chemically identical planar ("smooth") surfaces. For unfavorable scenarios, electrostatic double layer and acid-base potentials were reduced to a greater extent than van der Waals potentials, which made rough surfaces "more attractive" than smooth ones. Two influential surface morphological descriptors emerged: (1) the ratio of particle size to asperity size, a/r, and (2) the ratio of asperity separation to asperity size, p/r. As a/r increased, particle-substrate interaction energy decreased, while the opposite was true for p/r. The simple morphological descriptors gave rise to an analytical model based on the Derjaguin integration (DI) method that compared reasonably well with numerical SEI results, where the size and density of nanopatterned surface features dictated the magnitude of interaction potentials. In fact, changes in the size of nanopatterned surface features impacted the magnitudes of interaction potentials to the same extent as similar changes in the magnitudes of acid-base free energy and zeta potential, which begs the question, "is surface morphology a 'scapegoat' or a primary consideration when defining particle-substrate interactions?"
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 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".