WO2019227126A1 - Procédé et appareil de commande et de manipulation d'écoulement multiphase dans la microfluidique à l'aide d'intelligence artificielle - Google Patents
Procédé et appareil de commande et de manipulation d'écoulement multiphase dans la microfluidique à l'aide d'intelligence artificielle Download PDFInfo
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- WO2019227126A1 WO2019227126A1 PCT/AU2019/050220 AU2019050220W WO2019227126A1 WO 2019227126 A1 WO2019227126 A1 WO 2019227126A1 AU 2019050220 W AU2019050220 W AU 2019050220W WO 2019227126 A1 WO2019227126 A1 WO 2019227126A1
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Definitions
- a general disadvantage of conventional droplet sorting systems based on electric actuation is that the biological samples have to be labelled with fluorescence dyes.
- the labelling practice can be time consuming and not very desirable as dyes are not compatible with all cells types and cell behaviour may be affected during live cell imaging. In certain cases, the labelling of cells with fluorescence can only be done with dead cells.
- the controller may include an artificial intelligence controller incorporating, for example, a machine learning classifier.
- the controller may include a pressure-driven flow controller or a syringe pump.
- the controller is configured to control the microfluidic device so that the microfluidic device generates a mono-disperse emulsion of the micro droplets or bubbles.
- the sensor may include an optical system.
- the optical system may acquire information such as sizes, shapes, colours of a gas, liquid or solid phase in a fluid flow of the micro droplets or bubbles.
- the parameters may include any one or more of:
- the step of controlling may include a training phase involving building a model using training data from a feedback sensor.
- the training phase may involve determining a training region on a pressure characteristic of a generator performing the step of generating.
- the training region is between parallel flow and stable region transition lines.
- the training phase may involve querying in a serpentine manner against a training boundary.
- Figure 6 shows a comparison between detected droplet area against user input expected droplet area, where two separate experiments (a & b) were carried out to compare the results with separate training sessions and parameters optimization;
- the microfluidic generator 102 is a microfluidic flow-focusing device fabricated in polydimethylsiloxane (PDMS, Dow Corning) using standard photolithography and soft lithography procedures.
- the controller 108 is configured to control the microfluidic device 102 so that the microfluidic device 102 generates a mono-disperse emulsion of the microfluidic droplets 104.
- FIG. 1 Another factor that influences the performance of the predictor 107 is the selection of kernels.
- a nonlinear Gaussian kernel is used. This is selected after comparison with both linear kernel and nonlinear kernels including polynomial and Gaussian kernels. Experimental results show that the Gaussian kernel is the best one out of all options.
- predictions can be made by applying the model to unseen testing samples and calculating the required oil and water pressures given desired droplet size with the separation distance fixed.
- Figure 4 shows the 3D diagram 400 illustrating the droplet areas at different P1 and P2 obtained from the training data.
- Figure 12 shows a flow regime diagram 1200 obtained to determine the range for droplet generation.
- the lower blue line 1202 gives the pressure values at stable interface (blue bordered inset 1204) before transitioning into droplet generation (green bordered inset 1206).
- the red line 1208 depicts the pressure values for onset of parallel flow (red bordered inset 1210). Scale bars in Figure 12 represent dimensions of 100 um.
- the black squares points are the pressure combinations used for subsequent study of spacer oil influence.
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- Chemical & Material Sciences (AREA)
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- Dispersion Chemistry (AREA)
- General Health & Medical Sciences (AREA)
- Analytical Chemistry (AREA)
- Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Chemical Kinetics & Catalysis (AREA)
- Hematology (AREA)
- Software Systems (AREA)
- Clinical Laboratory Science (AREA)
- Theoretical Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Pathology (AREA)
- Biochemistry (AREA)
- Immunology (AREA)
- General Engineering & Computer Science (AREA)
- Evolutionary Computation (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Computing Systems (AREA)
- Data Mining & Analysis (AREA)
- Artificial Intelligence (AREA)
- Medical Informatics (AREA)
- Mathematical Physics (AREA)
- Fluid Mechanics (AREA)
- Investigating Or Analysing Biological Materials (AREA)
- Automatic Analysis And Handling Materials Therefor (AREA)
- Apparatus Associated With Microorganisms And Enzymes (AREA)
- Networks Using Active Elements (AREA)
Abstract
Priority Applications (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
AU2019277193A AU2019277193A1 (en) | 2018-05-28 | 2019-03-12 | Method and apparatus for controlling and manipulation of multi-phase flow in microfluidics using artificial intelligence |
CN201980009308.3A CN112566721A (zh) | 2018-05-28 | 2019-03-12 | 利用人工智能控制并操纵微流体中多相流的方法和装置 |
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Application Number | Priority Date | Filing Date | Title |
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AU2018901863 | 2018-05-28 | ||
AU2018901863A AU2018901863A0 (en) | 2018-05-28 | Method and appratus for controlling and manipulation of multi-phase flow in microfluidics using artificial intelligence |
Publications (2)
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WO2019227126A1 true WO2019227126A1 (fr) | 2019-12-05 |
WO2019227126A9 WO2019227126A9 (fr) | 2020-06-25 |
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PCT/AU2019/050220 WO2019227126A1 (fr) | 2018-05-28 | 2019-03-12 | Procédé et appareil de commande et de manipulation d'écoulement multiphase dans la microfluidique à l'aide d'intelligence artificielle |
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CN (1) | CN112566721A (fr) |
AU (2) | AU2019101835A4 (fr) |
WO (1) | WO2019227126A1 (fr) |
Cited By (3)
Publication number | Priority date | Publication date | Assignee | Title |
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CN112070711A (zh) * | 2020-06-04 | 2020-12-11 | 清华大学 | 一种微液滴图像检测法中微液滴的分析方法 |
WO2022197921A1 (fr) * | 2021-03-18 | 2022-09-22 | Brown University | Prédiction de la vélocimétrie à l'aide de modèles d'apprentissage automatique |
NL1043994B1 (en) * | 2021-04-14 | 2022-10-25 | Digi Bio B V | A method for identifying the best therapeutics producing candidates using a digital microfuidics based lab-on-a-chip platform |
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WO2023086963A1 (fr) * | 2021-11-12 | 2023-05-19 | Raven Industries, Inc. | Détermination de caractéristiques de gouttelettes |
CN114505105B (zh) * | 2022-01-13 | 2022-11-11 | 电子科技大学 | 一种基于内存计算的微流控芯片 |
Citations (2)
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WO2017192286A1 (fr) * | 2016-05-02 | 2017-11-09 | Purdue Research Foundation | Systèmes et procédés de production de produit chimique |
WO2018051242A1 (fr) * | 2016-09-14 | 2018-03-22 | Ecole Polytechnique Federale De Lausanne (Epfl) | Dispositif destiné à des études à haut débit de cellules uniques |
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CN101500694B (zh) * | 2006-05-09 | 2012-07-18 | 先进液体逻辑公司 | 液滴操纵系统 |
EP2411148B1 (fr) * | 2009-03-23 | 2018-02-21 | Raindance Technologies, Inc. | Manipulation de gouttelettes microfluidiques |
CN104736725A (zh) * | 2012-08-13 | 2015-06-24 | 加利福尼亚大学董事会 | 用于检测生物组分的方法和系统 |
CN111508489B (zh) * | 2017-12-19 | 2022-10-18 | 深圳市欧瑞博科技股份有限公司 | 语音识别方法、装置、计算机设备和存储介质 |
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- 2019-03-12 CN CN201980009308.3A patent/CN112566721A/zh active Pending
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NL1043994B1 (en) * | 2021-04-14 | 2022-10-25 | Digi Bio B V | A method for identifying the best therapeutics producing candidates using a digital microfuidics based lab-on-a-chip platform |
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WO2019227126A9 (fr) | 2020-06-25 |
CN112566721A (zh) | 2021-03-26 |
AU2019277193A1 (en) | 2020-10-29 |
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