Role of Precipitating Solute Curvature on Microdrops and Nanodrops during Concentrating Processes: The Nonideal Ostwald–Freundlich Equation
Bibliographic record
Abstract
The microdrop concentrating process, which is one of the manipulations in the microdrop platform of microfluidic technologies, is a useful technique, especially in biological applications. This process may encounter a solute precipitation within the droplet if the solute reaches its solubility limit. In the case of very small solid precipitates, the solid particle size will affect the solubility limit, and the Ostwald-Freundlich equation (OFE) describes this dependency. Including the OFE in analysis affects the design parameters for this type of system and the system's thermodynamic stability. Here, by means of Gibbsian surface thermodynamics, we provide the thermodynamic description and stability analysis of this system considering the role of the Ostwald-Freundlich equation. Previously we have investigated the stability of the system without considering the OFE, which is equivalent to using a constant solubility limit. Herein it is shown that the OFE significantly affects the results for nanometer drop sizes and that various stability behaviors are possible.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".