From eyes to cameras: Computer vision for high-throughput liquid-liquid separation
Bibliographic record
Abstract
We present a high-throughput automation platform for screening liquid-liquid extraction (LLE) processes. Our hardware platform simultaneously screens up to 12 vials and is coupled with a computer vision (CV) system for real-time monitoring of macroscopic visual cues. Our CV system, named HeinSight3.0 , leverages machine learning and image analysis to classify and quantify multivariate visual cues such as liquid level(s), turbidity, homogeneity, volume, and color. These cues, combined with process parameters such as stir rate and temperature, enable real-time analysis of key workup processes (e.g., separation time, volume ratio of layers, and emulsion presence) to aid in the optimization of separation parameters. We demonstrate our system on three case studies: impurity recovery, excess reagent removal, and Grignard workup. Our application of HeinSight3.0 to literature data also suggests a high potential for generalizability and adaptability across different platforms and contexts. Overall, our work represents a step toward autonomous LLE optimization guided by visual cues.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".