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Takken M, Wille R. Accelerated Computational Fluid Dynamics Simulations of Microfluidic Devices by Exploiting Higher Levels of Abstraction. MICROMACHINES 2024; 15:129. [PMID: 38258248 PMCID: PMC11154455 DOI: 10.3390/mi15010129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 01/08/2024] [Accepted: 01/10/2024] [Indexed: 01/24/2024]
Abstract
The design of microfluidic devices is a cumbersome and tedious process that can be significantly improved by simulation. Methods based on Computational Fluid Dynamics (CFD) are considered state-of-the-art, but require extensive compute time-oftentimes limiting the size of microfluidic devices that can be simulated. Simulation methods that abstract the underlying physics on a higher level generally provide results instantly, but the fidelity of these methods is usually worse. In this work, a simulation method that accelerates CFD simulations by exploiting simulation methods on higher levels of abstraction is proposed. Case studies confirm that the proposed method accelerates CFD simulations by multiple factors (often several orders of magnitude) while maintaining the fidelity of CFD simulations.
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Affiliation(s)
- Michel Takken
- School of Computation, Information and Technology, Technical University of Munich, Arcisstraße 21, 80333 München, Germany
| | - Robert Wille
- School of Computation, Information and Technology, Technical University of Munich, Arcisstraße 21, 80333 München, Germany
- Software Competence Center Hagenberg GmbH (SCCH), Softwarepark 32a, 4232 Hagenberg, Austria
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2
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Lashkaripour A, McIntyre DP, Calhoun SGK, Krauth K, Densmore DM, Fordyce PM. Design automation of microfluidic single and double emulsion droplets with machine learning. Nat Commun 2024; 15:83. [PMID: 38167827 PMCID: PMC10761910 DOI: 10.1038/s41467-023-44068-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 11/29/2023] [Indexed: 01/05/2024] Open
Abstract
Droplet microfluidics enables kHz screening of picoliter samples at a fraction of the cost of other high-throughput approaches. However, generating stable droplets with desired characteristics typically requires labor-intensive empirical optimization of device designs and flow conditions that limit adoption to specialist labs. Here, we compile a comprehensive droplet dataset and use it to train machine learning models capable of accurately predicting device geometries and flow conditions required to generate stable aqueous-in-oil and oil-in-aqueous single and double emulsions from 15 to 250 μm at rates up to 12000 Hz for different fluids commonly used in life sciences. Blind predictions by our models for as-yet-unseen fluids, geometries, and device materials yield accurate results, establishing their generalizability. Finally, we generate an easy-to-use design automation tool that yield droplets within 3 μm (<8%) of the desired diameter, facilitating tailored droplet-based platforms and accelerating their utility in life sciences.
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Affiliation(s)
- Ali Lashkaripour
- Department of Bioengineering, Stanford University, Stanford, CA, USA.
- Department of Genetics, Stanford University, Stanford, CA, USA.
| | - David P McIntyre
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
- Biological Design Center, Boston University, Boston, MA, USA
| | | | - Karl Krauth
- Department of Genetics, Stanford University, Stanford, CA, USA
| | - Douglas M Densmore
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
- Biological Design Center, Boston University, Boston, MA, USA
- Department of Electrical & Computer Engineering, Boston University, Boston, MA, USA
| | - Polly M Fordyce
- Department of Bioengineering, Stanford University, Stanford, CA, USA.
- Department of Genetics, Stanford University, Stanford, CA, USA.
- Chan-Zuckerberg Biohub, San Francisco, CA, USA.
- Sarafan ChEM-H Institute, Stanford University, Stanford, CA, USA.
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3
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McIntyre D, Lashkaripour A, Arguijo D, Fordyce P, Densmore D. Versatility and stability optimization of flow-focusing droplet generators via quality metric-driven design automation. LAB ON A CHIP 2023; 23:4997-5008. [PMID: 37909215 PMCID: PMC10694034 DOI: 10.1039/d3lc00189j] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/02/2023]
Abstract
Droplet generation is a fundamental component of droplet microfluidics, compartmentalizing biological or chemical systems within a water-in-oil emulsion. As adoption of droplet microfluidics expands beyond expert labs or integrated devices, quality metrics are needed to contextualize the performance capabilities, improving the reproducibility and efficiency of operation. Here, we present two quality metrics for droplet generation: performance versatility, the operating range of a single device, and stability, the distance of a single operating point from a regime change. Both metrics were characterized in silico and validated experimentally using machine learning and rapid prototyping. These metrics were integrated into a design automation workflow, DAFD 2.0, which provides users with droplet generators of a desired performance that are versatile or flow stable. Versatile droplet generators with stable operating points accelerate the development of sophisticated devices by facilitating integration of other microfluidic components and improving the accuracy of design automation tools.
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Affiliation(s)
- David McIntyre
- Biomedical Engineering Department, Boston University, MA, USA.
- Biological Design Center, Boston University, Boston, MA, USA
| | - Ali Lashkaripour
- Department of Bioengineering, Stanford University, Stanford, CA, USA
- Department of Genetics, Stanford University, Stanford, CA, USA
| | - Diana Arguijo
- Biomedical Engineering Department, Boston University, MA, USA.
- Biological Design Center, Boston University, Boston, MA, USA
| | - Polly Fordyce
- Department of Bioengineering, Stanford University, Stanford, CA, USA
- Department of Genetics, Stanford University, Stanford, CA, USA
- Chan-Zuckerberg Biohub, San Francisco, CA, USA
| | - Douglas Densmore
- Biological Design Center, Boston University, Boston, MA, USA
- Electrical & Computer Engineering Department, Boston University, Boston, MA, USA
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Tsai HF, Podder S, Chen PY. Microsystem Advances through Integration with Artificial Intelligence. MICROMACHINES 2023; 14:826. [PMID: 37421059 DOI: 10.3390/mi14040826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 04/04/2023] [Accepted: 04/06/2023] [Indexed: 07/09/2023]
Abstract
Microfluidics is a rapidly growing discipline that involves studying and manipulating fluids at reduced length scale and volume, typically on the scale of micro- or nanoliters. Under the reduced length scale and larger surface-to-volume ratio, advantages of low reagent consumption, faster reaction kinetics, and more compact systems are evident in microfluidics. However, miniaturization of microfluidic chips and systems introduces challenges of stricter tolerances in designing and controlling them for interdisciplinary applications. Recent advances in artificial intelligence (AI) have brought innovation to microfluidics from design, simulation, automation, and optimization to bioanalysis and data analytics. In microfluidics, the Navier-Stokes equations, which are partial differential equations describing viscous fluid motion that in complete form are known to not have a general analytical solution, can be simplified and have fair performance through numerical approximation due to low inertia and laminar flow. Approximation using neural networks trained by rules of physical knowledge introduces a new possibility to predict the physicochemical nature. The combination of microfluidics and automation can produce large amounts of data, where features and patterns that are difficult to discern by a human can be extracted by machine learning. Therefore, integration with AI introduces the potential to revolutionize the microfluidic workflow by enabling the precision control and automation of data analysis. Deployment of smart microfluidics may be tremendously beneficial in various applications in the future, including high-throughput drug discovery, rapid point-of-care-testing (POCT), and personalized medicine. In this review, we summarize key microfluidic advances integrated with AI and discuss the outlook and possibilities of combining AI and microfluidics.
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Affiliation(s)
- Hsieh-Fu Tsai
- Department of Biomedical Engineering, Chang Gung University, Taoyuan City 333, Taiwan
- Department of Neurosurgery, Chang Gung Memorial Hospital, Keelung, Keelung City 204, Taiwan
- Center for Biomedical Engineering, Chang Gung University, Taoyuan City 333, Taiwan
| | - Soumyajit Podder
- Department of Biomedical Engineering, Chang Gung University, Taoyuan City 333, Taiwan
| | - Pin-Yuan Chen
- Department of Biomedical Engineering, Chang Gung University, Taoyuan City 333, Taiwan
- Department of Neurosurgery, Chang Gung Memorial Hospital, Keelung, Keelung City 204, Taiwan
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Oliveira SMD, Densmore D. Hardware, Software, and Wetware Codesign Environment for Synthetic Biology. BIODESIGN RESEARCH 2022; 2022:9794510. [PMID: 37850136 PMCID: PMC10521664 DOI: 10.34133/2022/9794510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Accepted: 08/10/2022] [Indexed: 10/19/2023] Open
Abstract
Synthetic biology is the process of forward engineering living systems. These systems can be used to produce biobased materials, agriculture, medicine, and energy. One approach to designing these systems is to employ techniques from the design of embedded electronics. These techniques include abstraction, standards, modularity, automated design, and formal semantic models of computation. Together, these elements form the foundation of "biodesign automation," where software, robotics, and microfluidic devices combine to create exciting biological systems of the future. This paper describes a "hardware, software, wetware" codesign vision where software tools can be made to act as "genetic compilers" that transform high-level specifications into engineered "genetic circuits" (wetware). This is followed by a process where automation equipment, well-defined experimental workflows, and microfluidic devices are explicitly designed to house, execute, and test these circuits (hardware). These systems can be used as either massively parallel experimental platforms or distributed bioremediation and biosensing devices. Next, scheduling and control algorithms (software) manage these systems' actual execution and data analysis tasks. A distinguishing feature of this approach is how all three of these aspects (hardware, software, and wetware) may be derived from the same basic specification in parallel and generated to fulfill specific cost, performance, and structural requirements.
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Affiliation(s)
- Samuel M. D. Oliveira
- Department of Electrical and Computer Engineering, Boston University, MA 02215, USA
- Biological Design Center, Boston University, MA 02215, USA
| | - Douglas Densmore
- Department of Electrical and Computer Engineering, Boston University, MA 02215, USA
- Biological Design Center, Boston University, MA 02215, USA
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McIntyre D, Lashkaripour A, Fordyce P, Densmore D. Machine learning for microfluidic design and control. LAB ON A CHIP 2022; 22:2925-2937. [PMID: 35904162 PMCID: PMC9361804 DOI: 10.1039/d2lc00254j] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/19/2022] [Accepted: 06/28/2022] [Indexed: 05/24/2023]
Abstract
Microfluidics has developed into a mature field with applications across science and engineering, having particular commercial success in molecular diagnostics, next-generation sequencing, and bench-top analysis. Despite its ubiquity, the complexity of designing and controlling custom microfluidic devices present major barriers to adoption, requiring intuitive knowledge gained from years of experience. If these barriers were overcome, microfluidics could miniaturize biological and chemical research for non-experts through fully-automated platform development and operation. The intuition of microfluidic experts can be captured through machine learning, where complex statistical models are trained for pattern recognition and subsequently used for event prediction. Integration of machine learning with microfluidics could significantly expand its adoption and impact. Here, we present the current state of machine learning for the design and control of microfluidic devices, its possible applications, and current limitations.
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Affiliation(s)
- David McIntyre
- Biomedical Engineering Department, Boston University, MA, USA
- Biological Design Center, Boston University, Boston, MA, USA.
| | - Ali Lashkaripour
- Department of Bioengineering, Stanford University, Stanford, CA, USA
- Department of Genetics, Stanford University, Stanford, CA, USA
| | - Polly Fordyce
- Department of Bioengineering, Stanford University, Stanford, CA, USA
- Department of Genetics, Stanford University, Stanford, CA, USA
- Chan-Zuckerberg Biohub, San Francisco, CA, USA
| | - Douglas Densmore
- Biological Design Center, Boston University, Boston, MA, USA.
- Electrical & Computer Engineering Department, Boston University, Boston, MA, USA
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From Theoretical Network to Bedside: Translational Application of Brain-Inspired Computing in Clinical Medicine. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12125788] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Advances in the brain-inspired computing space are growing at a rapid rate, and many of these emerging strategies are in the field of neuromorphic control, robotics, and sensor development, just to name a few. These innovations are disruptive in their own right and have numerous, multi-dimensional medical applications within precision medicine, telematics, device development, and informed clinical decision making. For this discussion, I will define brain-inspired computing in the scope of simulating the architecture of the brain and discuss the realization of integrating hardware and other technologies with the applications of medicine, along with the considerations for the regulatory pathway for approval and evaluating the risk/consequences of failure modes. This perspective is a call for continued discussion of the development of a pathway for translating these technologies into medical treatment and diagnostic strategies. The aim is to align with global regulatory bodies and ensure that regulation does not limit the capacity of these emerging innovations while ensuring patient safety and clinical efficacy. It is my perspective that it is and will continue to be critical that these technologies are correctly perceived and understood in the lens of multiple disciplines in order to reach their full potential for medical applications.
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Microfluidic Network Simulations Enable On-Demand Prediction of Control Parameters for Operating Lab-on-a-Chip-Devices. Processes (Basel) 2021. [DOI: 10.3390/pr9081320] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Abstract
Reliable operation of lab-on-a-chip systems depends on user-friendly, precise, and predictable fluid management tailored to particular sub-tasks of the microfluidic process protocol and their required sample fluids. Pressure-driven flow control, where the sample fluids are delivered to the chip from pressurized feed vessels, simplifies the fluid management even for multiple fluids. The achieved flow rates depend on the pressure settings, fluid properties, and pressure-throughput characteristics of the complete microfluidic system composed of the chip and the interconnecting tubing. The prediction of the required pressure settings for achieving given flow rates simplifies the control tasks and enables opportunities for automation. In our work, we utilize a fast-running, Kirchhoff-based microfluidic network simulation that solves the complete microfluidic system for in-line prediction of the required pressure settings within less than 200 ms. The appropriateness of and benefits from this approach are demonstrated as exemplary for creating multi-component laminar co-flow and the creation of droplets with variable composition. Image-based methods were combined with chemometric approaches for the readout and correlation of the created multi-component flow patterns with the predictions obtained from the solver.
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Vitorino R, Guedes S, da Costa JP, Kašička V. Microfluidics for Peptidomics, Proteomics, and Cell Analysis. NANOMATERIALS (BASEL, SWITZERLAND) 2021; 11:1118. [PMID: 33925983 PMCID: PMC8145566 DOI: 10.3390/nano11051118] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/13/2021] [Revised: 04/20/2021] [Accepted: 04/23/2021] [Indexed: 12/18/2022]
Abstract
Microfluidics is the advanced microtechnology of fluid manipulation in channels with at least one dimension in the range of 1-100 microns. Microfluidic technology offers a growing number of tools for manipulating small volumes of fluid to control chemical, biological, and physical processes relevant to separation, analysis, and detection. Currently, microfluidic devices play an important role in many biological, chemical, physical, biotechnological and engineering applications. There are numerous ways to fabricate the necessary microchannels and integrate them into microfluidic platforms. In peptidomics and proteomics, microfluidics is often used in combination with mass spectrometric (MS) analysis. This review provides an overview of using microfluidic systems for peptidomics, proteomics and cell analysis. The application of microfluidics in combination with MS detection and other novel techniques to answer clinical questions is also discussed in the context of disease diagnosis and therapy. Recent developments and applications of capillary and microchip (electro)separation methods in proteomic and peptidomic analysis are summarized. The state of the art of microchip platforms for cell sorting and single-cell analysis is also discussed. Advances in detection methods are reported, and new applications in proteomics and peptidomics, quality control of peptide and protein pharmaceuticals, analysis of proteins and peptides in biomatrices and determination of their physicochemical parameters are highlighted.
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Affiliation(s)
- Rui Vitorino
- UnIC, Departamento de Cirurgia e Fisiologia, Faculdade de Medicina da Universidade do Porto, 4785-999 Porto, Portugal
- iBiMED, Department of Medical Sciences, University of Aveiro, 00351234 Aveiro, Portugal
- LAQV/REQUIMTE, Department of Chemistry, University of Aveiro, 00351234 Aveiro, Portugal;
| | - Sofia Guedes
- LAQV/REQUIMTE, Department of Chemistry, University of Aveiro, 00351234 Aveiro, Portugal;
| | - João Pinto da Costa
- Department of Chemistry & Center for Environmental and Marine Studies (CESAM), University of Aveiro, 00351234 Aveiro, Portugal;
| | - Václav Kašička
- Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Flemigovo n. 542/2, 166 10 Prague 6, Czech Republic
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Lashkaripour A, Rodriguez C, Mehdipour N, Mardian R, McIntyre D, Ortiz L, Campbell J, Densmore D. Machine learning enables design automation of microfluidic flow-focusing droplet generation. Nat Commun 2021; 12:25. [PMID: 33397940 PMCID: PMC7782806 DOI: 10.1038/s41467-020-20284-z] [Citation(s) in RCA: 53] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2020] [Accepted: 11/10/2020] [Indexed: 02/08/2023] Open
Abstract
Droplet-based microfluidic devices hold immense potential in becoming inexpensive alternatives to existing screening platforms across life science applications, such as enzyme discovery and early cancer detection. However, the lack of a predictive understanding of droplet generation makes engineering a droplet-based platform an iterative and resource-intensive process. We present a web-based tool, DAFD, that predicts the performance and enables design automation of flow-focusing droplet generators. We capitalize on machine learning algorithms to predict the droplet diameter and rate with a mean absolute error of less than 10 μm and 20 Hz. This tool delivers a user-specified performance within 4.2% and 11.5% of the desired diameter and rate. We demonstrate that DAFD can be extended by the community to support additional fluid combinations, without requiring extensive machine learning knowledge or large-scale data-sets. This tool will reduce the need for microfluidic expertise and design iterations and facilitate adoption of microfluidics in life sciences.
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Affiliation(s)
- Ali Lashkaripour
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
- Biological Design Center, 610 Commonwealth Avenue, Boston, MA, USA
| | - Christopher Rodriguez
- Computational and Systems Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Noushin Mehdipour
- Biological Design Center, 610 Commonwealth Avenue, Boston, MA, USA
- Division of Systems Engineering, Boston University, Boston, MA, USA
| | - Rizki Mardian
- Biological Design Center, 610 Commonwealth Avenue, Boston, MA, USA
- Department of Electrical and Computer Engineering, Boston University, Boston, MA, USA
| | - David McIntyre
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
- Biological Design Center, 610 Commonwealth Avenue, Boston, MA, USA
| | - Luis Ortiz
- Biological Design Center, 610 Commonwealth Avenue, Boston, MA, USA
- Department of Molecular Biology, Cell Biology & Biochemistry, Boston University, Boston, MA, USA
| | | | - Douglas Densmore
- Biological Design Center, 610 Commonwealth Avenue, Boston, MA, USA.
- Department of Electrical and Computer Engineering, Boston University, Boston, MA, USA.
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Catalan-Carrio R, Akyazi T, Basabe-Desmonts L, Benito-Lopez F. Predicting Dimensions in Microfluidic Paper Based Analytical Devices. SENSORS (BASEL, SWITZERLAND) 2020; 21:E101. [PMID: 33375225 PMCID: PMC7794781 DOI: 10.3390/s21010101] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Revised: 12/19/2020] [Accepted: 12/23/2020] [Indexed: 11/29/2022]
Abstract
The main problem for the expansion of the use of microfluidic paper-based analytical devices and, thus, their mass production is their inherent lack of fluid flow control due to its uncontrolled fabrication protocols. To address this issue, the first step is the generation of uniform and reliable microfluidic channels. The most common paper microfluidic fabrication method is wax printing, which consists of two parts, printing and heating, where heating is a critical step for the fabrication of reproducible device dimensions. In order to bring paper-based devices to success, it is essential to optimize the fabrication process in order to always get a reproducible device. Therefore, the optimization of the heating process and the analysis of the parameters that could affect the final dimensions of the device, such as its shape, the width of the wax barrier and the internal area of the device, were performed. Moreover, we present a method to predict reproducible devices with controlled working areas in a simple manner.
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Affiliation(s)
- Raquel Catalan-Carrio
- Microfluidics Cluster UPV/EHU, Analytical Microsystems & Materials for Lab-on-a-Chip (AMMa-LOAC) Group, Analytical Chemistry Department, University of the Basque Country UPV/EHU, 01006 Vitoria-Gasteiz, Spain; (R.C.-C.); (T.A.)
- Microfluidics Cluster UPV/EHU, BIOMICs Microfluidics Group, Lascaray Research Center, University of the Basque Country UPV/EHU, 01006 Vitoria-Gasteiz, Spain
| | - Tugce Akyazi
- Microfluidics Cluster UPV/EHU, Analytical Microsystems & Materials for Lab-on-a-Chip (AMMa-LOAC) Group, Analytical Chemistry Department, University of the Basque Country UPV/EHU, 01006 Vitoria-Gasteiz, Spain; (R.C.-C.); (T.A.)
| | - Lourdes Basabe-Desmonts
- Microfluidics Cluster UPV/EHU, BIOMICs Microfluidics Group, Lascaray Research Center, University of the Basque Country UPV/EHU, 01006 Vitoria-Gasteiz, Spain
- Basque Foundation for Science, IKERBASQUE, 48013 Bilbao, Spain
- Bioaraba Health Research Institute, Microfluidics Cluster UPV/EHU, 01006 Vitoria-Gasteiz, Spain
- BCMaterials, Basque Centre for Materials, Micro and Nanodevices, UPV/EHU Science Park, 48940 Leioa, Spain
| | - Fernando Benito-Lopez
- Microfluidics Cluster UPV/EHU, Analytical Microsystems & Materials for Lab-on-a-Chip (AMMa-LOAC) Group, Analytical Chemistry Department, University of the Basque Country UPV/EHU, 01006 Vitoria-Gasteiz, Spain; (R.C.-C.); (T.A.)
- Bioaraba Health Research Institute, Microfluidics Cluster UPV/EHU, 01006 Vitoria-Gasteiz, Spain
- BCMaterials, Basque Centre for Materials, Micro and Nanodevices, UPV/EHU Science Park, 48940 Leioa, Spain
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