Oiling-out Crystallization on Solid Surfaces Controlled by Solvent Exchange
Bibliographic record
Abstract
Oiling-out, also termed as liquid-liquid separation (LLPS), is a phenomenon well observed in cooling crystallization when the solution becomes cloudy due to the formation of a second liquid phase. One of the methods to control LLPS is solvent exchange where surface droplets are produce in bottom up approach. In this process, a good solvent for oil is displaced by a poor one, leading to oil nanodroplet nucleation and subsequent growth. The nanodroplets are immobilized on the surface therefore dynamics of the droplet formation and growth from LLPS can be monitored with time and quantitatively studied. We investigated oiling-out of a model component (Beta-alanine) in the mixture of isopropanol and water. The aqueous solution is displaced by isopropanol in a microchamber at controlled flow conditions. We followed the solute-rich droplet on the various substrates during the process of oiling-out. We expect that this study will give us new understanding on the dynamics of oiling-out phenomenon and the crystallization process. The knowledge may be valuable for controlling the oiling-out crystallization in the processes, separation, and purification of crystals for pharmaceutical use and other applications such as the use in functional surfaces and crystal shape engineering.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".