Computer-Aided Design of Microfluidic Circuits

Tsur, Elishai Ezra

doi:10.1146/annurev-bioeng-082219-033358

Cited by 22 publications

(21 citation statements)

References 108 publications

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“…On the other hand, at temperatures above 125 °C, the paper substrate suffered degradation over time. Zhong et al found out that, for temperatures above 150 °C, the paper curled up, and the color of the paper changed [50]. Additionally, the barriers were not fully homogeneous from device to device, obtaining that more than 60% of the devices were not operative.…”

Section: Resultsmentioning

confidence: 99%

“…The design of paper-based devices is generally made by ordinary designing software, such as AutoCAD or Adobe Illustrator. However, specific software has been developed for the selection of the shape, width of the barrier and width of the channel [14,50,51]. In particular, during the patterning process of the paper substrate, it is important to be able to obtain the desired dimensions of the wax barriers with well-controlled and uniform features in the final printed and heated device.…”

Section: Introductionmentioning

confidence: 99%

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Predicting Dimensions in Microfluidic Paper Based Analytical Devices

Catalan-Carrio

Akyazi

Basabe‐Desmonts

et al. 2020

Sensors

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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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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predicting Dimensions in Microfluidic Paper Based Analytical Devices

Catalan-Carrio

Akyazi

Basabe‐Desmonts

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…AI requires appropriate training datasets and algorithms to improve results before testing, similar to traditional statistical methods [ 213 , 214 , 215 , 216 , 217 ]. AI focuses on building automated decision systems, unlike traditional statistical approaches that rely on rule-based systems [ 215 , 218 ]. AI methods are categorized into three types of learning techniques: supervised, unsupervised, or semi-supervised or augmented learning.…”

Section: The Future Of Microfluidicsmentioning

confidence: 99%

“…Sequence-based AI approaches can be used for selection and testing in a wet-lab experiment to refine the design of therapeutic peptides before they are synthesized. This complicates their usefulness to the end-user and highlights the need to systematically evaluate and develop these approaches, taking into account the state of the art in methodology and predictive performance [ 216 , 218 ].…”

Section: The Future Of Microfluidicsmentioning

confidence: 99%

Microfluidics for Peptidomics, Proteomics, and Cell Analysis

et al. 2021

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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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“…This can be attributed to the phenomenological complexity of droplet formation 16,17 , lack of predictive understanding [18][19][20] , high fabrication cost inherent to photolithography 21 , and unreliability of numerical simulations to capture the intricate dynamics of multiphase flows 20 . Consequently, expertise and an iterative design process is required to achieve the desired performance 22,23 . Several geometries including T-junction 7 , step-emulsification 24 , co-flow 25 , and flow-focusing 26 can be used to generate droplets.…”

mentioning

confidence: 99%

Machine learning enables design automation of microfluidic flow-focusing droplet generation

et al. 2021

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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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Computer-Aided Design of Microfluidic Circuits

Cited by 22 publications

References 108 publications

Predicting Dimensions in Microfluidic Paper Based Analytical Devices

Predicting Dimensions in Microfluidic Paper Based Analytical Devices

Microfluidics for Peptidomics, Proteomics, and Cell Analysis

Machine learning enables design automation of microfluidic flow-focusing droplet generation

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