A Novel High-Throughput Viscometer

Go with the flow: deep learning methods for autonomous viscosity estimations

Closed-loop experiments can accelerate material discovery by automating both experimental manipulations and decisions that have traditionally been made by researchers. Fast and non-invasive measurements are particularly attractive for closed-loop strategies. Viscosity is a physical property for fluids that is important in many applications. It is fundamental in application areas such as coatings; also, even if viscosity is not the key property of interest, it can impact our ability to do closed-loop experimentation. For example, unexpected increases in viscosity can cause liquid-handling robots to fail. Traditional viscosity measurements are manual, invasive, and slow. Here we use convolutional neural networks (CNNs) as an alternative to traditional viscometry by non-invasively extracting the spatiotemporal features of fluid motion under flow. To do this, we built a workflow using a dual-armed collaborative robot that collects video data of fluid motion autonomously. This dataset was then used to train a 3-dimensional convolutional neural network (3D-CNN) for viscosity estimation, either by classification or by regression. We also used these models to identify unknown laboratory solvents, again based on differences in fluid motion. The 3D-CNN model performance was compared with the performance of a panel of human participants for the same classification tasks. Our models strongly outperformed human classification in both cases. For example, even with training on fewer than 50 videos for each liquid, the 3D-CNN model gave an average accuracy of 88% for predicting the identity of five different laboratory solvents, compared to an average accuracy of 32% for human observation. For comparison, random category selection would give an average accuracy of 20%. Our method offers an alternative to traditional viscosity measurements for autonomous chemistry workflows that might be used both for process control (e.g., choosing not to pipette liquids that are too viscous) or for materials discovery (e.g., identifying new polymerization catalysts on the basis of viscosification).

Single Microdroplet Breakup-Assisted Viscosity Measurement

Recently, with the development of biomedical fields, the viscosity of prepolymer fluids, such as hydrogels, has played an important role in determining the mechanical properties of the extracellular matrix (ECM) or being closely related to cell viability in ECM. The technology for measuring viscosity is also developing. Here, we describe a method that can measure the viscosity of a fluid with trace amounts of prepolymers based on a simple flow-focused microdroplet generator. We also propose an equation that could predict the viscosity of a fluid. The viscosity of the prepolymer was predicted by measuring and calculating various lengths of the disperse phase at the cross junction of two continuous-phase channels and one disperse-phase channel. Bioprepolymer alginates and gelatin methacryloyl (GelMA) were used to measure the viscosity at different concentrations in a microdroplet generator. The break-up length of the dispersed phase at the cross junction of the channel gradually increased with increasing flow rate and viscosity. Additional viscosity analysis was performed to validate the standard viscosity calculation formula depending on the measured length. The viscosity formula derived based on the length of the alginate prepolymer was applied to GelMA. At a continuous phase flow rate of 400 uL/h, the empirical formula of alginate showed an error within about 2%, which was shown to predict the viscosity very well in the viscometer. Results of this study are expected to be very useful for hydrogel tuning in biomedical and tissue regeneration fields by providing a technology that can measure the dynamic viscosity of various prepolymers in a microchannel with small amounts of sample.

Development of Biphasic Formulations for Use in Electrowetting-Based Liquid Lenses with a High Refractive Index Difference

Commercial electrowetting-based liquid lenses are optical devices containing two immiscible liquids as an optical medium. The first phase is a droplet of a high refractive index oil phase placed in a ring-shaped chassis. The second phase is electrically conductive and has a similar density over a wide temperature range. Droplet curvature and refractive index difference of two liquids determine the optical strength of the lens. Liquid lenses take advantage of the electrowetting effect, which induces a change of the interface's curvature by applying a voltage, thereby providing a variable focal that is useful in autofocus applications. The first generation of lens modules were highly reliable, but the optical strength and application scope was limited by a low refractive index difference between the oil and conductive phase. Described herein is an effort to increase the refractive index difference between both phases, while maintaining other critical application characteristics of the liquids, including a low freezing point, viscosity, phase miscibility, and turbidity after thermal shock. An important challenge was the requirement that both phases have to have matching densities and hence had to be optimized simultaneously. Using high throughput experimentation in conjunction with statistical design of experiments (DOE), we have developed a series of empirical models to predict multiple physicochemical properties of both phases and derived ideal locations within the formulation space. This approach enabled the development of reliable liquid lenses with a previously unavailable refractive index difference of Δ n_D of ≥0.290, which enabled true optical zooming capability.

High-Throughput Industrial Coatings Research at The Dow Chemical Company

At The Dow Chemical Company, high-throughput research is an active area for developing new industrial coatings products. Using the principles of automation (i.e., using robotic instruments), parallel processing (i.e., prepare, process, and evaluate samples in parallel), and miniaturization (i.e., reduce sample size), high-throughput tools for synthesizing, formulating, and applying coating compositions have been developed at Dow. In addition, high-throughput workflows for measuring various coating properties, such as cure speed, hardness development, scratch resistance, impact toughness, resin compatibility, pot-life, surface defects, among others have also been developed in-house. These workflows correlate well with the traditional coatings tests, but they do not necessarily mimic those tests. The use of such high-throughput workflows in combination with smart experimental designs allows accelerated discovery and commercialization.