ReviewOrganic process research & development2023
Determining Phase Separation Dynamics with an Automated Image Processing Algorithm.
Review in Organic process research & development, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- From Eyes to Cameras to Kinetics: Computer Vision for Chemical Process Monitoring.Accounts of chemical research · 2026Article
- Solvent Screening for Separation Processes Using Machine Learning and High-Throughput Technologies.Chem & bio engineering · 2025Review
- Parallel and High Throughput Reaction Monitoring with Computer Vision.Angewandte Chemie (International ed. in English) · 2025Article
- A Coalescing Filter for Liquid-Liquid Separation and Multistage Extraction in Continuous-Flow Chemistry.Organic process research & development · 2024Article
- Keeping an "eye" on the experiment: computer vision for real-time monitoring and control.Chemical science · 2024Article
- Teaching old presumptive tests new digital tricks with computer vision for forensic applications.Digital discovery · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The problems of extracting products efficiently from reaction workups are often overlooked. Issues such as emulsions and rag layer formation can cause long separation times and slow production, thus resulting in manufacturing inefficiencies. To better understand science within this area and to support process development, an image processing methodology has been developed that can automatically track the interface between liquid-liquid phases and provide a quantitative measure of the separation rate of two immiscible liquids. The algorithm is automated and has been successfully applied to 29 cases. Its robustness has been demonstrated with a variety of different liquid mixtures that exhibit a wide range of separation behavior-making such an algorithm suited to high-throughput experimentation. The information gathered from applying the algorithm shows how issues resulting from poor separations can be detected early in process development.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.