Drop Deposition Technique on Low Energy Surface
Bibliographic record
Abstract
In this paper we propose a new technique for drop deposition on low energy surfaces, which addresses the limitations of the classical drop deposition technique. In this classical technique, a drop is deposited on a surface by bringing a needle, holding the drop, in proximity to the solid surface. Therefore, irrespective of whether the solid surface is in air or under a liquid, it becomes extremely difficult to deposit the drop on low energy surfaces owing to the large differences between the drop-needle and the drop-substrate adhesion forces (or surface energies). In our discussed method, we overcome this difficulty for low energy surfaces immersed in a liquid. For surfaces under liquid, there is an interface in addition to the solid-liquid interface: this interface is the air-liquid interface, where the liquid gets exhausted. In our technique, we cater the (un)favorable drop spreading dynamics at this interface to ensure that the drop gets deposited on the under-liquid surface.
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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) | 0.007 | 0.003 |
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; both teacher heads agree on what is shown here.
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".