Two Dimensional Acoustofluidic Manipulation of Microparticles

Acoustically actuated microfluidic devices for bio diagnostics have gained the attention of the research community, due to the gentle and non-contact characteristics of the actuation force. The existing acoustofluidic devices have been primarily made off materials with high acoustic impedance such as glass (Z_ac≅12 MRayls ), and silicon (Z_ac≅19 MRayls ). In terms of fabrication costs and fabrication time, plastics would have been more preferable due to their rapid and easy fabrication methods such as milling, and molding into microfluidic chips. However, the drawback of using plastics in acoustofluidics is that plastics tend to have lower acoustic impedance than glass, or silicon, and are considered as lower quality resonators than e.g. glass. Nonetheless, poly-methyl methacrylate (PMMA) has a moderate acoustic impedance (Z_ac≅3 MRayls), which is higher than e.g. PDMS where its acoustic impedance is (Z_ac≅1.03 MRayls), and that has encouraged this work. In this thesis, the employment of bulk acoustic waves (BAW) on a two-dimensional acoustofluidic resonators made from glass and PMMA were compared for acoustofluidic micromanipulation applications. Polystyrene microparticles were used in the manipulation experiments for visualizing the acoustic modes of the acoustofluidic chips, where their frequency responses were recorded by the magnitude of motion achieved from particle tracking velocimetry (PTV). Firstly, a PMMA microfluidic device was fabricated by laser engraving, and ethanol thermal bonding. As a control experiment, a glass acoustofluidic chip was fabricated using femtosecond ablation (at Technical University of Braunschweig) and tested using the same parameters as the PMMA chip. Secondly, simulations using COMSOL Multiphysics have been carried out to determine numerical values of resonance frequencies of the devices, and their corresponding acoustic resonance mode shapes. In addition to glass and PMMA, simulations were conducted for an acoustofluidic device made out of PDMS. The simulation results showed that PDMS performance is poor for particle manipulation. Thirdly, preliminary experiments were contacted, where a beta version of our particle tracking velocimetry (PTV) was used and, we were able to obtain a rough frequency profile of the devices in the frequency range 60 kHz and 460 kHz. The results of the preliminary frequency response of the PMMA chip showed good agreement with the predicted frequency response using multiphysics simulations ,where its performance in both cases (simulations and experiments) was comparable to results obtained by the glass chip. Simulated results shown that PMMA has the potential to performs similar to the glass chip. However, in the preliminary experiments was observed that the power requirements needed for a particle motion of 0.5 mm in PMMA are much greater than in glass. This has caused overheating of the aqueous solution at antiresonances, due to the enormous amount of power fed to the actuator, which this did not happen in the glass chip. Additionally, the actual acoustic modes of both glass and PMMA chips do not agree with the simulated results, where the frequency difference in actual modes from theoretical is ~100 kHz for the glass chip

Programmable Droplet Microfluidics Based on Machine Learning and Acoustic Manipulation

Typical microfluidic devices are application-specific and have to be carefully designed to implement the necessary functionalities for the targeted application. Programmable microfluidic chips try to overcome this by offering reconfigurable functionalities, allowing the same chip to be used in multiple different applications. In this work, we demonstrate a programmable microfluidic chip for the two-dimensional manipulation of droplets, based on ultrasonic bulk acoustic waves and a closed-loop machine-learning-based control algorithm. The algorithm has no prior knowledge of the acoustic fields but learns to control the droplets on the fly. The manipulation is based on switching the frequency of a single ultrasonic transducer. Using this method, we demonstrate 2D transportation and merging of water droplets in oil and oil droplets in water, and we performed the chemistry that underlies the basis of a colorimetric glucose assay. We show that we can manipulate drops with volumes ranging from ∼200 pL up to ∼30 nL with our setup. We also demonstrate that our method is robust, by changing the system parameters and showing that the machine learning algorithm can still complete the manipulation tasks. In short, our method uses ultrasonics to flexibly manipulate droplets, enabling programmable droplet microfluidic devices.publishedVersionPeer reviewe

Biodegradable, Flexible and Transparent Tactile Pressure Sensor Based on Rubber Leaf Skeletons

Capacitive sensors have many applications in tactile sensing, human-machine interfaces, on-body sensors, and patient monitoring. Particularly in biomedical applications, it would be beneficial if the sensor is disposable and readily degradable for efficient recycling. In this study, we report a biodegradable capacitive tactile pressure sensor based on sustainable and bio resourced materials. Silver-nanowire-coated rubber tree leaf skeletons are used as transparent and flexible electrodes while a biodegradable clear tape is used as the dielectric layer. The fabricated sensor is sensitive and can respond to low pressures (7.9 mN when pressed with a probe with a surface area of 79 mm2 / 0.1 kPa) ranging to relatively high pressures (37 kPa), with a sensitivity up to ≈ 4.5×10-3 kPa-1. Owing to all bio resourced constituents, the sensor is biodegradable and does not create electronic waste.publishedVersionPeer reviewe

Performance Comparison of Fast, Transparent, and Biotic Heaters Based on Leaf Skeletons

Bioinspired, highly flexible, fast, and biodegradable heaters are fabricated based on Ag nanowires and leaf skeletons of different plant species. The leaf skeletons act as transparent substrates with a high surface-area-to-volume ratio and allow a uniform dispersion of the Ag nanowires through the surface. Ag nanowires adhered to the leaf skeletons display very good transmittance (up to ≈87%) and mechanical (flexibility) properties (curvature values >800 m−1) without any post-treatment. The flexible leaf skeleton-based heaters reach high temperatures very quickly, with very low voltages (<4 V). The performance of the bioinspired heater surface is dependent on the types of fractal structures at the microscale. The morphology of the leaf skeletons is studied in detail and is corelated with the transmittance, flexibility, and sheet resistances. Bioinspired heater surfaces based on different leaf skeletons are compared based on their multiscale morphology, and the different heating performance parameters are screened. Based on the study conducted, insights on the best-performing biotic design for the fabrication of the heaters that are useful in practical wearable, medical, or industrial heating applications are provided.publishedVersionPeer reviewe

Integrated stretchable pneumatic strain gauges for electronics-free soft robots

In soft robotics, actuators, logic and power systems can be entirely fluidic and electronics-free. However, sensors still typically rely on electric or optical principles. This adds complexity to fluidic soft robots because transducers are needed, and electrical materials have to be incorporated. Herein, we show a highly-stretchable pneumatic strain gauge based on a meandering microchannel in a soft elastomer material thus eliminating the need for an electrical signal in soft robots. Using such pneumatic sensors, we demonstrate an all-pneumatic gripper with integrated pneumatic strain gauges that is capable of autonomous closure and object recognition. The gauges can measure at least up to 300% engineering strains. The sensor exhibits a very stable signal over a 12-hour measurement period with no hysteresis. Using pneumatic sensors, all four major components of robots—actuators, logic, power, and sensors—can be pneumatic, enabling all-fluidic soft robots with proprioception and exteroception.publishedVersionPeer reviewe

Breathable, Flexible, Transparent, Hydrophobic, and Biotic Sustainable Electrodes for Heating and Biopotential Signal Measurement Applications

Pressure to reduce the global amount of e-waste has increased in recent years. The optimal use of natural resources is a demanding area especially due to the overabundance of the use of resources and challenges with after-life disposal. Herein, an easy method is developed to fabricate an improved version of leaf skeleton-based biodegradable, transparent, flexible, and hydrophobic electrodes. A fractal-like rubber leaf skeleton is used as the substrate, physical vapor deposited Au interlayer to promote adhesion, and uniform deposition of overlayer silver nanowires. The fabricated surfaces present a high level of electrical stability, optical transparency, hydrophobicity, and robust mechanical properties. The prepared electrodes demonstrate a comparable level of optical transmittance to the virgin leaf skeleton. The mechanical sturdiness of the electrodes is verified by 1k bending cycles. To demonstrate the functionality of these hybrid biotic conductive network (HBCN) electrodes, their performance is evaluated as flexible transparent heating elements and as biosignal measurement electrodes. The heater can reach a temperature of 140 °C with only 2.5 V in ≈5 s and Ag nanowire loading of ≈160 μg cm−2. Likewise, electrocardiogram (ECG) and electromyogram (EMG) signals are successfully obtained from the electrodes without using any electrode gel or other electrolytes.publishedVersionPeer reviewe

Two Dimensional Acoustofluidic Manipulation of Microparticles

Acoustically actuated microfluidic devices for bio diagnostics have gained the attention of the research community, due to the gentle and non-contact characteristics of the actuation force. The existing acoustofluidic devices have been primarily made off materials with high acoustic impedance such as glass (Z_ac≅12 MRayls ), and silicon (Z_ac≅19 MRayls ). In terms of fabrication costs and fabrication time, plastics would have been more preferable due to their rapid and easy fabrication methods such as milling, and molding into microfluidic chips. However, the drawback of using plastics in acoustofluidics is that plastics tend to have lower acoustic impedance than glass, or silicon, and are considered as lower quality resonators than e.g. glass. Nonetheless, poly-methyl methacrylate (PMMA) has a moderate acoustic impedance (Z_ac≅3 MRayls), which is higher than e.g. PDMS where its acoustic impedance is (Z_ac≅1.03 MRayls), and that has encouraged this work. In this thesis, the employment of bulk acoustic waves (BAW) on a two-dimensional acoustofluidic resonators made from glass and PMMA were compared for acoustofluidic micromanipulation applications. Polystyrene microparticles were used in the manipulation experiments for visualizing the acoustic modes of the acoustofluidic chips, where their frequency responses were recorded by the magnitude of motion achieved from particle tracking velocimetry (PTV). Firstly, a PMMA microfluidic device was fabricated by laser engraving, and ethanol thermal bonding. As a control experiment, a glass acoustofluidic chip was fabricated using femtosecond ablation (at Technical University of Braunschweig) and tested using the same parameters as the PMMA chip. Secondly, simulations using COMSOL Multiphysics have been carried out to determine numerical values of resonance frequencies of the devices, and their corresponding acoustic resonance mode shapes. In addition to glass and PMMA, simulations were conducted for an acoustofluidic device made out of PDMS. The simulation results showed that PDMS performance is poor for particle manipulation. Thirdly, preliminary experiments were contacted, where a beta version of our particle tracking velocimetry (PTV) was used and, we were able to obtain a rough frequency profile of the devices in the frequency range 60 kHz and 460 kHz. The results of the preliminary frequency response of the PMMA chip showed good agreement with the predicted frequency response using multiphysics simulations ,where its performance in both cases (simulations and experiments) was comparable to results obtained by the glass chip. Simulated results shown that PMMA has the potential to performs similar to the glass chip. However, in the preliminary experiments was observed that the power requirements needed for a particle motion of 0.5 mm in PMMA are much greater than in glass. This has caused overheating of the aqueous solution at antiresonances, due to the enormous amount of power fed to the actuator, which this did not happen in the glass chip. Additionally, the actual acoustic modes of both glass and PMMA chips do not agree with the simulated results, where the frequency difference in actual modes from theoretical is ~100 kHz for the glass chip

Acoustic Manipulation of Particles in Microfluidic Chips with an Adaptive Controller that Models Acoustic Fields

Acoustic manipulation is a technique that uses sound waves to move particles, droplets, or cells. Closed-loop control methods based on complex, time-varying acoustic fields have been demonstrated, but usually require accurate models of the acoustic fields or many training experiments for successful manipulation. Herein, a new adaptive control method is proposed for the acoustic manipulation of single and multiple particles inside microfluidic chips. The method is based on online machine learning of the acoustic fields. Starting with no knowledge of the fields, the controller can manipulate particles even on the first attempt, and its performance improves in subsequent attempts, yet can still readapt if the models are invalidated by a sudden change in system parameters. The controller can generalize: it can use information learned from one task to improve its performance in other tasks. Despite the machine-learning nature of the controller, the internal models of the controller have a physical interpretation and correspond to the experimentally observed acoustic fields. The online adaptiveness of the controller should make it easier to use in practical applications, such as particle and cell sorting, microassembly, labs-on-chips, and diagnostic devices, as the method does not require extensive training or prior models.Peer reviewe

Controlled Manipulation and Active Sorting of Particles Inside Microfluidic Chips Using Bulk Acoustic Waves and Machine Learning

Manipulation of cells, droplets, and particles via ultrasound within microfluidic chips is a rapidly growing field, with applications in cell and particle sorting, blood fractionation, droplet transport, and enrichment of rare or cancerous cells, among others. However, current methods with a single ultrasonic transducer offer limited control of the position of single particles. In this paper, we demonstrate closed-loop two-dimensional manipulation of particles inside closed-channel microfluidic chips, by controlling the frequency of a single ultrasound transducer, based on machine-vision-measured positions of the particles. For the control task, we propose using algorithms derived from the family of multi-armed bandit algorithms. We show that these algorithms can achieve controlled manipulation with no prior information on the acoustic field shapes. The method learns as it goes: there is no need to restart the experiment at any point. Starting with no knowledge of the field shapes, the algorithms can (eventually) move a particle from one position inside the chamber to another. This makes the method very robust to changes in chip and particle properties. We demonstrate that the method can be used to manipulate a single particle, three particles simultaneously, and also a single particle in the presence of a bubble in the chip. Finally, we demonstrate the practical applications of this method in active sorting of particles, by guiding each particle to exit the chip through one of three different outlets at will. Because the method requires no model or calibration, the work paves the way toward the acoustic manipulation of microparticles inside unstructured environments.publishedVersionPeer reviewe

Data and code related to the paper: "Programmable Droplet Microfluidics Based on Machine Learning and Acoustic Manipulation"

This archive contains the raw data and Matlab scripts to reproduce the plots and supplementary movies for the paper: Kyriacos Yiannacou, Vipul Sharma and Veikko Sariola, "Programmable Droplet Microfluidics Based on Machine Learning and Acoustic Manipulation", Langmuir 2022, 38, 38, 11557–11564. Link to the paper. The scripts were tested on Matlab R2021a on Windows. The acoustofluidic controller software is the same as in our previous paper and is archived here. Generally speaking, there is a folder containing the plotting scripts for each figure(s) and/or movie(s). Within each folder, the raw data files are under the folder `data/`. Once ran, the scripts produce another folder called `output/`, to which they place the created plots and movies. Most folder contain a script name `plot_*.m` that makes the figure(s) and `video_*.m` that generates the video(s). You will need `ffmpeg` installed to convert the serial images into a video.This work was funded by Academy of Finland (projects #299087, #311415 and #343408) and Walter Ahlström Foundation (grant #20220061)