Multi-Objective Design Automation for Microfluidic Capture Chips.

Microfluidic capture chips are useful for preparing or analyzing a wide range of different chemical, biological, and medical samples. A typical microfluidic capture chip contains features that capture certain targets (i.e. molecules, particles, cells) as they flow through the chip. However, creating optimal capture chip designs is difficult because of the inherent relationship between capture efficiency and flow resistance: as more capture features are added to the chip, the capture efficiency increases, but the additional features slow the flow of fluid through the chip. This paper introduces the use of multi-objective optimization to generate capture chip designs that balance the trade-off between maximizing target capture efficiency and minimizing resistance to fluid flow. Design automation for this important class of microfluidic chips has not been attempted previously. Our approach automatically produces a Pareto front of non-dominated chip designs in a reasonable amount of time, and most of these designs have comparable capture efficiency to hand-designed chips with far lower flow resistance. By choosing from the chip designs on the Pareto front, a user can obtain high capture efficiency without exceeding the flow resistance constraints of their application.

Rapid development and optimization of paper microfluidic designs using software automation.

Paper microfluidic or lateral flow devices have found many applications, especially in medical diagnostics. Their low cost and ease of use makes them particularly valuable in resource-limited and point-of-care applications. However, the process of developing new paper microfluidic devices is slowed by the need to find optimal values for their various design parameters, which determine the overall size and fluid volume requirements of the device. Often, researchers must design and test several different versions of a device to find a combination of parameters that functions as intended. To accelerate the development of new paper microfluidics, we developed a software framework that automatically designs custom paper microfluidic devices for a given application. Once the user specifies the desired device parameters, the software generates printable image files of the resulting devices, ready to output to a conventional wax ink color printer and test. As a proof-of-concept, we used our software to automatically design 51 different paper microfluidic devices we needed to create a functional lateral flow assay that detects protein and glucose in urine. These designs took only a few seconds to generate and were used in 120 lab experiments we performed in 16 h in the lab. Thus, with the help of our software framework, we went from an idea to a functional device in just two work days. By accelerating device development and enabling researchers without microfluidics experience to create custom devices, our software can help spread paper microfluidic technology to important new application areas.

A pneumatic random-access memory for controlling soft robots.

Pneumatically-actuated soft robots have advantages over traditional rigid robots in many applications. In particular, their flexible bodies and gentle air-powered movements make them more suitable for use around humans and other objects that could be injured or damaged by traditional robots. However, existing systems for controlling soft robots currently require dedicated electromechanical hardware (usually solenoid valves) to maintain the actuation state (expanded or contracted) of each independent actuator. When combined with power, computation, and sensing components, this control hardware adds considerable cost, size, and power demands to the robot, thereby limiting the feasibility of soft robots in many important application areas. In this work, we introduce a pneumatic memory that uses air (not electricity) to set and maintain the states of large numbers of soft robotic actuators without dedicated electromechanical hardware. These pneumatic logic circuits use normally-closed microfluidic valves as transistor-like elements; this enables our circuits to support more complex computational functions than those built from normally-open valves. We demonstrate an eight-bit nonvolatile random-access pneumatic memory (RAM) that can maintain the states of multiple actuators, control both individual actuators and multiple actuators simultaneously using a pneumatic version of time division multiplexing (TDM), and set actuators to any intermediate position using a pneumatic version of analog-to-digital conversion. We perform proof-of-concept experimental testing of our pneumatic RAM by using it to control soft robotic hands playing individual notes, chords, and songs on a piano keyboard. By dramatically reducing the amount of hardware required to control multiple independent actuators in pneumatic soft robots, our pneumatic RAM can accelerate the spread of soft robotic technologies to a wide range of important application areas.

ChemStor: Using Formal Methods To Guarantee Safe Storage and Disposal of Chemicals.

While safe chemical storage and disposal are simple in principle-users should read safety specifications and place chemicals in appropriate cabinets or collection points-high-profile incidents involving improper storage and disposal of chemicals continue to occur. This paper introduces ChemStor, an open-source, automated computational system that can guarantee (mathematically verify a system is correct with respect to its specification), with regard to prescribed constraints, safe storage and disposal of chemicals used in academic, industrial, and domestic settings. ChemStor borrows concepts from formal methods-a branch of computer science capable of mathematically proving a specification or software is correct-to safely store or dispose of chemicals. If two or more chemicals can be combined in the same cabinet without forming possibly dangerous combinations of chemicals (while observing cabinet/shelf space constraints), then ChemStor determines that the storage configuration is safe. Likewise, if chemicals can be added to an existing disposal container without forming possibly dangerous combinations of chemicals (or exceeding the volume of the container), then ChemStor determines that the disposal configuration is safe. ChemStor accomplishes this by first building a chemical interaction graph, a graph that describes which chemicals may interact with each other based on their reactivity groups as determined by the United States Environmental Protection Agency. Next, ChemStor computes the chromatic number of the graph, the smallest number of colors used to color the graph such that no two vertices (chemicals) that share an edge (an interaction) share the same color. ChemStor then assigns all the chemicals of each color to a storage or disposal container after confirming that there is enough space in the container. These steps are encoded into a series of satisfiability modulo theory equations, and ChemStor uses an industry-standard tool to try to find a valid solution to these equations. The result is either a solution which dictates exactly where to store or dispose of each chemical, or an indication that no safe storage or disposal configuration could be found. To demonstrate the feasibility of ChemStor, we used the tool to analyze ten real-world chemical storage and disposal incidents that led to injuries or destruction of property. In each case, ChemStor quickly and successfully identified a proper chemical disposal or storage configuration that would have prevented the incident. In the future, ChemStor may be integrated with electronic laboratory notebooks, voice assistants, and other emerging technology to protect users of chemicals in labs, workplaces, and homes.

Finding the optimal design of a passive microfluidic mixer.

The ability to thoroughly mix two fluids is a fundamental need in microfluidics. While a variety of different microfluidic mixers have been designed by researchers, it remains unknown which (if any) of these mixers are optimal (that is, which designs provide the most thorough mixing with the smallest possible fluidic resistance across the mixer). In this work, we automatically designed and rationally optimized a microfluidic mixer. We accomplished this by first generating a library of thousands of different randomly designed mixers, then using the non-dominated sorting genetic algorithm II (NSGA-II) to optimize the random chips in order to achieve Pareto efficiency. Pareto efficiency is a state of allocation of resources (e.g. driving force) from which it is impossible to reallocate so as to make any one individual criterion better off (e.g. pressure drop) without making at least one individual criterion (e.g. mixing performance) worse off. After 200 generations of evolution, Pareto efficiency was achieved and the Pareto-optimal front was found. We examined designs at the Pareto-optimal front and found several design criteria that enhance the mixing performance of a mixer while minimizing its fluidic resistance; these observations provide new criteria on how to design optimal microfluidic mixers. Additionally, we compared the designs from NSGA-II with some popular microfluidic mixer designs from the literature and found that designs from NSGA-II have lower fluidic resistance with similar mixing performance. As a proof of concept, we fabricated three mixer designs from 200 generations of evolution and one conventional popular mixer design and tested the performance of these four mixers. Using this approach, an optimal design of a passive microfluidic mixer is found and the criteria of designing a passive microfluidic mixer are established.

Programmable Electrofluidics for Ionic Liquid Based Neuromorphic Platform.

Due to the limit in computing power arising from the Von Neumann bottleneck, computational devices are being developed that mimic neuro-biological processing in the brain by correlating the device characteristics with the synaptic weight of neurons. This platform combines ionic liquid gating and electrowetting for programmable placement/connectivity of the ionic liquid. In this platform, both short-term potentiation (STP) and long-term potentiation (LTP) are realized via electrostatic and electrochemical doping of the amorphous indium gallium zinc oxide (aIGZO), respectively, and pulsed bias measurements are demonstrated for lower power considerations. While compatible with resistive elements, we demonstrate a platform based on transitive amorphous indium gallium zinc oxide (aIGZO) pixel elements. Using a lithium based ionic liquid, we demonstrate both potentiation (decrease in device resistance) and depression (increase in device resistance), and propose a 2D platform array that would enable a much higher pixel count via Active Matrix electrowetting.

Using printer ink color to control the behavior of paper microfluidics.

Paper microfluidic devices (including lateral flow assays) offer an excellent combination of utility and low cost. Many paper microfluidic devices are fabricated using the Xerox ColorQube line of commercial wax-based color printers; the wax ink serves as a hydrophobic barrier to fluid flow. These printers are capable of depositing four different colors of ink, cyan (C), magenta (M), yellow (Y), and black (K), plus 11 combinations of these colors (CM, CY, CK, MY, MK, YK, CMY, CMK, CYK, MYK, and CMYK), although most researchers use only black ink to print paper microfluidic devices. Recently, as part of a project to develop a computer-aided design framework for use with paper microfluidics devices, we unexpectedly observed that different colors of wax ink behave differently in paper microfluidics. We found that among the single colors of ink, black ink actually had the most barrier failures, and magenta ink had the fewest barrier failures. In addition, some combinations of colors performed even better than magenta: the combinations CY, MK, YK, CMY, CYK and MYK had no barrier failures in our study. We also found that the printer delivers significantly different amounts of ink to the paper for the different color combinations, and in general, the color combinations that formed the strongest barriers to fluid flow were the ones that had the most ink delivered to the paper. This suggests that by simply weighing paper samples printed with all 15 combinations of colors, one can easily find the color combinations most likely to form a strong barrier for a given printer. Finally, to show that deliberate choices of ink colors can actually be used to create new functions in paper microfluidics, we designed and tested a new color-based "antifuse" structure that protects paper microfluidic devices from a typical operator error (addition of too much fluid to the device). Our results provide a set of color choice guidelines that designers can use to control the behavior of their paper microfluidics.

Chronoprints: Identifying Samples by Visualizing How They Change over Space and Time.

The modern tools of chemistry excel at identifying a sample, but the cost, size, complexity, and power consumption of these instruments often preclude their use in resource-limited settings. In this work, we demonstrate a simple and low-cost method for identifying a sample based on visualizing how the sample changes over space and time in response to a perturbation. Different types of perturbations could be used, and in this proof-of-concept we use a dynamic temperature gradient that rapidly cools different parts of the sample at different rates. We accomplish this by first loading several samples into long parallel channels on a "microfluidic thermometer chip." We then immerse one end of the chip in liquid nitrogen to create a dynamic temperature gradient along the channels, and we use an inexpensive USB microscope to record a video of how the samples respond to the changing temperature gradient. The video is then converted into several bitmap images (one per sample) that capture each sample's response to the perturbation in both space (the y-axis; the distance along the dynamic temperature gradient) and time (the x-axis); we call these images "chronological fingerprints" or "chronoprints" of each sample. If two samples' chronoprints are similar, this suggests that the samples are the same chemical substance or mixture, but if two samples' chronoprints are significantly different, this proves that the samples are chemically different. Since chronoprints are just bitmap images, they can be compared using a variety of techniques from computer science, and in this work we use three different image comparison algorithms to quantify chronoprint similarity. As a demonstration of the versatility of chronoprints, we use them in three different applications: distinguishing authentic olive oil from adulterated oil (an example of the over $10 billion global problem of food fraud), identifying adulterated or counterfeit medication (which represents around 10% of all medication in low- and middle-income countries), and distinguishing the occasionally confused pharmaceutical ingredients glycerol and diethylene glycol (whose accidental or intentional substitution has led to hundreds of deaths). The simplicity and versatility of chronoprints should make them valuable analytical tools in a variety of different fields.

Instantaneous simulation of fluids and particles in complex microfluidic devices.

Microfluidics researchers are increasingly using computer simulation in many different aspects of their research. However, these simulations are often computationally intensive: simulating the behavior of a simple microfluidic chip can take hours to complete on typical computing hardware, and even powerful workstations can lack the computational capabilities needed to simulate more complex chips. This slows the development of new microfluidic chips for new applications. To address this issue, we present a microfluidic simulation method that can simulate the behavior of fluids and particles in some typical microfluidic chips instantaneously (in around one second). Our method decomposes the chip into its primary components: channels and intersections. The behavior of fluid in each channel is determined by leveraging analogies with electronic circuits, and the behavior of fluid and particles in each intersection is determined by querying a database containing nearly 100,000 pre-simulated channel intersections. While constructing this database takes a nontrivial amount of computation time, once built, this database can be queried to determine the behavior of fluids and particles in a given intersection in a fraction of a second. Using this approach, the behavior of a microfluidic chip can be simulated in just one second on a standard laptop computer, without any noticeable degradation in the accuracy of the simulation. While our current technique has some constraints on the designs of the chips it can simulate (namely, T- or cross-shaped intersections, 90 degree channel turns, a fixed channel width, fluid flow rates between 0 and 2 cm/s, and particles with diameters between 1 and 20 microns), we provide several strategies for increasing the range of possible chip designs that can be simulated using our technique. As a proof of concept, we show that our simulation method can instantaneously simulate the paths followed by particles in both simple and complex microfluidic chips, with results that are essentially indistinguishable from simulations that took hours or even days to complete using conventional approaches.

MOPSA: A microfluidics-optimized particle simulation algorithm.

Computer simulation plays a growing role in the design of microfluidic chips. However, the particle tracers in some existing commercial computational fluid dynamics software are not well suited for accurately simulating the trajectories of particles such as cells, microbeads, and droplets in microfluidic systems. To address this issue, we present a microfluidics-optimized particle simulation algorithm (MOPSA) that simulates the trajectories of cells, droplets, and other particles in microfluidic chips with more lifelike results than particle tracers in existing commercial software. When calculating the velocity of a particle, MOPSA treats the particle as a two-dimensional rigid circular object instead of a single point. MOPSA also checks for unrealistic interactions between particles and channel walls and applies an empirical correcting function to eliminate these errors. To validate the performance of MOPSA, we used it to simulate a variety of important features of microfluidic devices like channel intersections and deterministic lateral displacement (DLD) particle sorter chips. MOPSA successfully predicted that different particle sizes will have different trajectories in six published DLD experiments from three research groups; these DLD chips were used to sort a variety of different cells, particles, and droplets. While some of these particles are not actually rigid or spherical, MOPSA's approximation of these particles as rigid spheres nonetheless resulted in lifelike simulations of the behaviors of these particles (at least for the particle sizes and types shown here). In contrast, existing commercial software failed to replicate these experiments. Finally, to demonstrate that MOPSA can be extended to simulate other properties of particles, we added support for simulating particle density to MOPSA and then used MOPSA to simulate the operation of a microfluidic chip capable of sorting cells by their density. By enabling researchers to accurately simulate the behavior of some types of particles in microfluidic chips before fabricating the chips, MOPSA should accelerate the development of new microfluidic devices for important applications.

Random design of microfluidics.

In this work we created functional microfluidic chips without actually designing them. We accomplished this by first generating a library of thousands of different random microfluidic chip designs, then simulating the behavior of each design on a computer using automated finite element analysis. The simulation results were then saved to a database which a user can query via to find chip designs suitable for a specific task. To demonstrate this functionality, we used our library to select chip designs that generate any three desired concentrations of a solute. We also fabricated and tested 16 chips from the library, confirmed that they function as predicted, and used these chips to perform a cell growth rate assay. This is one of many different applications for randomly-designed microfluidics; in principle, any microfluidic chip that can be simulated could be designed automatically using our method. Using this approach, individuals with no training in microfluidics can obtain custom chip designs for their own unique needs in just a few seconds.

Recent developments in microfluidic large scale integration.

In 2002, Thorsen et al. integrated thousands of micromechanical valves on a single microfluidic chip and demonstrated that the control of the fluidic networks can be simplified through multiplexors [1]. This enabled realization of highly parallel and automated fluidic processes with substantial sample economy advantage. Moreover, the fabrication of these devices by multilayer soft lithography was easy and reliable hence contributed to the power of the technology; microfluidic large scale integration (mLSI). Since then, mLSI has found use in wide variety of applications in biology and chemistry. In the meantime, efforts to improve the technology have been ongoing. These efforts mostly focus on; novel materials, components, micromechanical valve actuation methods, and chip architectures for mLSI. In this review, these technological advances are discussed and, recent examples of the mLSI applications are summarized.