Determining Phase Separation Dynamics with an Automated Image Processing Algorithm

Solvent Screening for Separation Processes Using Machine Learning and High-Throughput Technologies

As the chemical industry shifts toward sustainable practices, there is a growing initiative to replace conventional fossil-derived solvents with environmentally friendly alternatives such as ionic liquids (ILs) and deep eutectic solvents (DESs). Artificial intelligence (AI) plays a key role in the discovery and design of novel solvents and the development of green processes. This review explores the latest advancements in AI-assisted solvent screening with a specific focus on machine learning (ML) models for physicochemical property prediction and separation process design. Additionally, this paper highlights recent progress in the development of automated high-throughput (HT) platforms for solvent screening. Finally, this paper discusses the challenges and prospects of ML-driven HT strategies for green solvent design and optimization. To this end, this review provides key insights to advance solvent screening strategies for future chemical and separation processes.

Coronary Computed Tomography Angiography Using an Optimal Acquisition Time Window Based on Heart Rate Determined During Breath-Holding Following Free Breathing

OBJECTIVES

To compare the image quality and radiation dose in coronary computed tomography angiography (CCTA) based on different acquisition time windows corresponding to the heart rate of breath-holding after free breathing.

METHODS

Two hundred patients who underwent CCTA with a basal heart rate between 70 and 85 beats/min were divided into groups A and B, with 100 patients in each group. Patients in groups A and B were scanned with the acquisition time window corresponding to the heart rate determined during a breath hold obtained after free breathing and the basal heart rate during free breathing, respectively. Computed tomography (CT) attenuation values of the coronary artery, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were calculated. The subjective image scores of the groups were assessed blindly by 2 experienced physicians using a 4-point system, and score consistency was compared using the κ test. The volume CT dose index and dose-length product were recorded for each patient, and the effective dose (ED) was calculated. The Kruskal-Wallis H test was performed to evaluate differences in age, heart rate, and body mass index. A χ2 test was used to evaluate sex differences. An independent-sample t test was employed to compare objective and subjective data such as dose-length product, volume CT dose index, ED, SNR, CNR, and averaged subjective assessment scores. Statistical significance was set at P  < 0.05.

RESULTS

No statistically significant differences occurred in sex, age, or body mass index between patients in group A and group B (all P  > 0.05). No significant differences occurred in the mean CT values, mean SNR values, mean CNR values, or mean subjective scores of CCTA images between the patients in groups A and B ( P  > 0.05). The ED values of the patients in group A were 52.93% lower than those in group B ( P  < 0.001).

CONCLUSION

The radiation dose in CCTA examinations can be significantly reduced while maintaining image quality by narrowing the acquisition time window for breath-holding after free breathing.

Parallel and High Throughput Reaction Monitoring with Computer Vision

We report the development and applications of a computer vision based reaction monitoring method for parallel and high throughput experimentation (HTE). Whereas previous efforts reported methods to extract bulk kinetics of one reaction from one video, this new approach enables one video to capture bulk kinetics of multiple reactions running in parallel. Case studies, in and beyond well-plate high throughput settings, are described. Analysis of parallel dye-quenching hydroxylations, DMAP-catalysed esterification, solid-liquid sedimentation dynamics, metal catalyst degradation, and biologically-relevant sugar-mediated nitro reduction reactions have each provided insight into the scope and limitations of camera-enabled high throughput kinetics as a means of widening known analytical bottlenecks in HTE for reaction discovery, mechanistic understanding, and optimisation. It is envisaged that the nature of the multi-reaction time-resolved datasets made available by this analytical approach will later serve a broad range of downstream efforts in machine learning approaches to exploring chemical space.

A Coalescing Filter for Liquid-Liquid Separation and Multistage Extraction in Continuous-Flow Chemistry

Presented here is the design and performance of a coalescing liquid-liquid filter, based on low-cost and readily available meltblown nonwoven substrates for separation of immiscible phases. The performance of the coalescer was determined across three broad classes of fluid mixtures: (i) immiscible organic/aqueous systems, (ii) a surfactant laden organic/aqueous system with modification of the type of emulsion and interfacial surface tension through the addition of sodium chloride, and (iii) a water-acetone/toluene system. The first two classes demonstrated good performance of the equipment in effecting separation, including the separation of a complex emulsion system for which a membrane separator, operating through transport of a preferentially wetting fluid through the membrane, failed entirely. The third system was used to demonstrate the performance of the separator within a multistage liquid-liquid counterflow extraction system. The performance, robust nature, and scalability of coalescing filters should mean that this approach is routinely considered for liquid-liquid separations and extractions within the fine chemical and pharmaceutical industry.

Keeping an "eye" on the experiment: computer vision for real-time monitoring and control

This work presents a generalizable computer vision (CV) and machine learning model that is used for automated real-time monitoring and control of a diverse array of workup processes. Our system simultaneously monitors multiple physical outputs (e.g., liquid level, homogeneity, turbidity, solid, residue, and color), offering a method for rapid data acquisition and deeper analysis from multiple visual cues. We demonstrate a single platform (consisting of CV, machine learning, real-time monitoring techniques, and flexible hardware) to monitor and control vision-based experimental techniques, including solvent exchange distillation, antisolvent crystallization, evaporative crystallization, cooling crystallization, solid-liquid mixing, and liquid-liquid extraction. Both qualitative (video capturing) and quantitative data (visual outputs measurement) were obtained which provided a method for data cross-validation. Our CV model's ease of use, generalizability, and non-invasiveness make it an appealing complementary option to in situ and real-time analytical monitoring tools and mathematical modeling. Additionally, our platform is integrated with Mettler-Toledo's iControl software, which acts as a centralized system for real-time data collection, visualization, and storage. With consistent data representation and infrastructure, we were able to efficiently transfer the technology and reproduce results between different labs. This ability to easily monitor and respond to the dynamic situational changes of the experiments is pivotal to enabling future flexible automation workflows.

Teaching old presumptive tests new digital tricks with computer vision for forensic applications

Presumptive (or 'spot') tests have served forensic scientists, law enforcement, and legal practitioners for over a hundred years. Yet, the intended design of such tests, enabling quick identification of drugs by-eye, also hides their full potential. Here, we report the development and application of time-resolved imaging methods of reactions attending spot tests for amphetamines, barbiturates, and benzodiazepines. Analysis of the reaction videos helps distinguish drugs within the same structural class that, by-eye, are judged to give the same qualitative spot test result. It is envisaged that application of these results will bridge the existing suite of field and lab-based confirmatory forensic tests, and support a broader range of colorimetric sensing technologies.