From eyes to cameras: Computer vision for high-throughput liquid-liquid separation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 |
| 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) | 0.000 | 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 it