Particle Deposition onto Charge-Heterogeneous Substrates
Bibliographic record
Abstract
The deposition of model colloidal particles onto striped charge-heterogeneous surfaces was studied to determine the influence of surface chemical heterogeneity on the deposit morphology. The charge heterogeneity was created employing self-assembled monolayers of carboxyl- and amine-terminated alkanethiols using a soft lithographic technique. Polystyrene sulfate microspheres and fluorescent polystyrene nanoparticles were sequentially deposited onto the patterned substrate under no flow (quiescent) condition. The deposited structures and the micropatterns were imaged using a combination of phase contrast and fluorescence microscopy. The experimental particle deposition behavior was compared to predictions based on random sequential adsorption (RSA) employing a Monte Carlo technique. Comparison of radial distribution obtained from experimental data was made with the theoretical results and found to be in good agreement despite the use of a simple binary probabilistic model in the simulations. The primary conclusion from the study is that particles tend to preferentially deposit at the edges of the favorable stripes. However, the extent of this bias can be controlled by the proximity of consecutive favorable stripes (or width of the intervening unfavorable stripes) as well as the particle size relative to the stripe width. Second, a simple binary probability distribution-based Monte Carlo RSA deposition model adequately predicts the deposit structure, particularly the periodicity of the underlying patterns on the substrate. These observations suggest that the patterns could be encrypted by the deposited particles, which can subsequently be decoded, given the proper "key" or information that is based on analyzing the deposit morphology.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".