查看原文
其他

中科大何立群等 | 基于微流控输运网络节点阻力特征的微流控芯片精准设计方法

The following article is from 生物设计与制造BDM Author 生物设计与制造


内容简介


本研究论文聚焦基于微流控输运网络节点阻力特征的微流控芯片精准设计方法。微流控通道内流体的流动特征为层流,故通道内压降与流量呈线性相关,其比值称为阻力,取决于通道的尺寸与动力粘度。通常,微流控芯片设计方法类似于电路设计,主要关注直管段尺寸布置。其实,水力网络设计与电路设计略有不同。体现在节点上,电路局部电压损失可以忽略不计,但流体局部压降相对于直管段压降不能忽略。本文着重解决常见节点局部阻力计算方法,通过数值计算建立了微流体局部阻力构件库。据此,建立了模块化的微流控芯片设计方法。我们设计几组实验检验所建立的方法。其中,通过阿霉素刺激内皮细胞后观察和分析内皮细胞的反应来进行最终的判断。结果表明,考虑局部阻力后,微流控芯片分配组分的精度获得极大提高,表明在设计中考虑局部阻力是十分必要的。本文所建立的设计方针有助于微流控芯片更加准确地用于单分子水平的分析与检测,甚至设计更为精准的器官芯片。


引用本文(点击最下方阅读原文可下载PDF)

Lin Y, He D, Wu Z, et al., 2022. Junction matters in hydraulic circuit bio-design of microfluidics. Bio-des Manuf (Early Access). https://doi.org/10.1007/s42242-022-00215-1

文章导读



图1 微流控芯片设计和制造过程


图2 实验系统搭建


图3 双聚焦微流控芯片用于单细胞分析的精准设计


图4 染料(试剂)在浓度梯度网络的微流控芯片中输运


图5 通过输运网络产生设计的均匀浓度阿霉素来刺激内皮细胞

参考文献

上下滑动以阅览

1. Whitesides GM (2006) The origins and the future of microfluidics. Nature 442(7101):368–373. https://doi.org/10.1038/nature05058

2. Dai L, Zhao X, Guo J et al (2020) Microfluidics-based microwave sensor. Sens Actuat A Phys 309:111910. https://doi.org/10.1016/j.sna.2020.111910

3. She X, Wang X, Niu P et al (2022) Miniature sono-electrochemical platform enabling effective and gentle electrode biofouling removal for continuous sweat measurements. Chem Eng J 431:133354. https://doi.org/10.1016/j.cej.2021.133354

4. Yu Y, Guo J, Ma B et al (2020) Liquid metal-integrated ultra-elastic conductive microfibers from microfluidics for wearable electronics. Sci Bull 65(20):1752–1759. https://doi.org/10.1016/j.scib.2020.06.002

5. Guo J, Yu Y, Zhang D et al (2021) Morphological hydrogel microfibers with MXene encapsulation for electronic skin. Research 2021:7065907. https://doi.org/10.34133/2021/7065907

6. Zilionis R, Nainys J, Veres A et al (2017) Single-cell barcoding and sequencing using droplet microfluidics. Nat Protoc 12(1):44–73. https://doi.org/10.1038/nprot.2016.154

7. Hindson BJ, Ness KD, Masquelier DA et al (2011) High-throughput droplet digital PCR system for absolute quantitation of DNA copy number. Anal Chem 83(22):8604–8610. https://doi.org/10.1021/ac202028g

8. Liu Y, Cheng Y, Zhao C et al (2022) Nanomotor-derived porous biomedical particles from droplet microfluidics. Adv Sci 9(4):e2104272. https://doi.org/10.1002/advs.202104272

9. Cai L, Chen G, Wang Y et al (2021) Boston ivy-inspired disc-like adhesive microparticles for drug delivery. Research 2021:9895674. https://doi.org/10.34133/2021/9895674

10. Fraser LA, Cheung YW, Kinghorn AB et al (2019) Microfluidic technology for nucleic acid aptamer evolution and application. Adv Biosyst 3(5):1900012. https://doi.org/10.1002/adbi.201900012

11. Bhatia SN, Ingber DE (2014) Microfluidic organs-on-chips. Nat Biotechnol 32(8):760–772. https://doi.org/10.1038/nbt.2989

12. Chu LY, Wan W (2017) Microfluidics for advanced functional polymeric materials (1st Ed.), Wiley-VCH. https://doi.org/10.1002/9783527803637

13. Damiri HS, Bardaweel HK (2015) Numerical design and optimization of hydraulic resistance and wall shear stress inside pressure-driven microfluidic networks. Lab Chip 15(21):4187–4196. https://doi.org/10.1039/c5lc00578g

14. Salva ML, Temiz Y, Rocca M et al (2019) Programmable hydraulic resistor for microfluidic chips using electrogate arrays. Sci Rep 9:17242. https://doi.org/10.1038/s41598-019-53885-w

15. Wang JC, Brisk P, Grover WH (2016) Random design of microfluidics. Lab Chip 16(21):4212–4219. https://doi.org/10.1039/c6lc00758a

16. Wang J, Zhang N, Chen J et al (2019) Finding the optimal design of a passive microfluidic mixer. Lab Chip 19(21):3618–3627. https://doi.org/10.1039/c9lc00546c

17. Wang J, Zhang N, Chen J et al (2021) Predicting the fluid behavior of random microfluidic mixers using convolutional neural networks. Lab Chip 21(2):296–309. https://doi.org/10.1039/d0lc01158d

18. Sanka R, Lippai J, Samarasekera D et al (2019) 3DμF - interactive design environment for continuous flow microfluidic devices. Sci Rep 9(1):9166. https://doi.org/10.1038/s41598-019-45623-z

19. Parker RW, Wilson DJ, Mace CR (2020) Open software platform for automated analysis of paper-based microfluidic devices. Sci Rep 10(1):11284. https://doi.org/10.1038/s41598-020-67639-6

20. Lashkaripour A, Rodriguez C, Mehdipour N et al (2021) Machine learning enables design automation of microfluidic flow-focusing droplet generation. Nat Commun 12(1):25. https://doi.org/10.1038/s41467-020-20284-z

21. Tanev G (2017) A correct-by-construction design and programming approach for open paper-based digital microfluidics. In: Symposium on design, test, integration and packaging of MEMS/MOEMS. https://doi.org/10.1109/dtip.2017.7984476

22. Gleichmann N, Malsch D, Horbert P et al (2014) Toward microfluidic design automation: a new system simulation toolkit for the in silico evaluation of droplet-based lab-on-a-chip systems. Microfluid Nanofluid 18(5–6):1095–1105. https://doi.org/10.1007/s10404-014-1502-z

23. Oh KW, Lee K, Ahn B et al (2012) Design of pressure-driven microfluidic networks using electric circuit analogy. Lab Chip 12(3):515–545. https://doi.org/10.1039/c2lc20799k

24. Cornish RJ (1928) Flow in a pipe of rectangular gross-section. Proc R Soc Lond 120(786):691–700. https://doi.org/10.1098/rspa.1928.0175

25. Choi S, Lee MG, Park JK (2010) Microfluidic parallel circuit for measurement of hydraulic resistance. Biomicrofluidics 4(3):034110. https://doi.org/10.1063/1.3486609

26. Vanapalli SA, Banpurkar AG, van den Ende D et al (2009) Hydrodynamic resistance of single confined moving drops in rectangular microchannels. Lab Chip 9(7):982–990. https://doi.org/10.1039/b815002h

27. Miguel AF (2010) Dendritic structures for fluid flow: laminar, turbulent and constructal design. J Fluids Struct 26(2):330–335. https://doi.org/10.1016/j.jfluidstructs.2009.11.004

28. Miguel AF (2018) A general model for optimal branching of fluidic networks. Phys A Stat Mech Appl 512:665–674. https://doi.org/10.1016/j.physa.2018.07.054

29. Razavi MS, Shirani E (2013) Development of a general method for designing microvascular networks using distribution of wall shear stress. J Biomech 46(13):2303–2309. https://doi.org/10.1016/j.jbiomech.2013.06.005

30. Reyes DR, van Heeren H, Guha S et al (2021) Accelerating innovation and commercialization through standardization of microfluidic-based medical devices. Lab Chip 21(1):9–21. https://doi.org/10.1039/d0lc00963f

31. Sayed Razavi M, Shirani E (2013) Development of a general method for designing microvascular networks using distribution of wall shear stress. J Biomech 46(13):2303–2309. https://doi.org/10.1016/j.jbiomech.2013.06.005

32. Li L, Wu P, Luo Z et al (2019) Dean flow assisted single cell and bead encapsulation for high performance single cell expression profiling. ACS Sens 4(5):1299–1305. https://doi.org/10.1021/acssensors.9b00171

33. Schmandt B, Herwig H (2015) The head change coefficient for branched flows: why “losses” due to junctions can be negative. Int J Heat Fluid Flow 54:268–275. https://doi.org/10.1016/j.ijheatfluidflow.2015.06.004

34. Schmandt B, Herwig H (2013) Performance evaluation of the flow in micro junctions: head change versus head loss coefficients. ASME 11th Internatinoal Conference on Nanochannels, Microchannels, and Minichannels. https://doi.org/10.1115/icnmm2013-73031

35. Schmandt B, Iyer V, Herwig H (2014) Determination of head change coefficients for dividing and combining junctions: a method based on the second law of thermodynamics. Chem Eng Sci 111:191–202. https://doi.org/10.1016/j.ces.2014.02.035

36. Bhargava KC, Thompson B, Malmstadt N (2014) Discrete elements for 3D microfluidics. Proc Natl Acad Sci USA 111(42):15013–15018. https://doi.org/10.1073/pnas.1414764111

37. Dai B, Long Y, Wu J et al (2021) Generation of flow and droplets with an ultra-long-range linear concentration gradient. Lab Chip 21(22):4390–4400. https://doi.org/10.1039/d1lc00749a

38. Tseng TM, Li M, Zhang Y et al (2019) Cloud Columba: accessible design automation platform for production and inspiration. IEEE/ACM International Conference on Computer-Aided Design. https://doi.org/10.1109/iccad45719.2019.8942104

39. Biral A (2013) Microfluidic networking: modelling and analysis. MS Thesis, Università degli Studi di Padova

40. Delplace F (2018) Laminar flow of newtonian liquids in ducts of rectangular cross-section an interesting model for both physics and mathematics. Int J Theor Math Phys 8(2):4. https://doi.org/10.15406/oajmtp.2018.01.00034

41. Zhuang QC, Ning RZ, Ma Y et al (2016) Recent developments in microfluidic chip for in vitro cell-based research. Chin J Anal Chem 44(4):522–532. https://doi.org/10.1016/s1872-2040(16)60919-2

42. Fu H, Liu X, Li S (2017) Mixing indexes considering the combination of mean and dispersion information from intensity images for the performance estimation of micromixing. RSC Adv 7(18):10906–10914. https://doi.org/10.1039/c6ra23783e

43. Lee CY, Chang CL, Wang YN et al (2011) Microfluidic mixing: a review. Int J Mol Sci 12(5):3263–3287. https://doi.org/10.3390/ijms12053263

44. Mahmud F, Tamrin KF (2020) Method for determining mixing index in microfluidics by RGB color model. Asia-Pac J Chem Eng 15(2):e2407. https://doi.org/10.1002/apj.2407


关于本刊

Bio-Design and Manufacturing(中文名《生物设计与制造》),简称BDM,是浙江大学主办的专业英文季刊,主编杨华勇院士、崔占峰院士,2018年新创,目前已被SCI-E等检索,最新影响因子为5.887。


初审迅速:初审快速退稿,不影响作者投其它期刊。

审稿速度快:过去两年平均录用时间约40天;平均退稿时间约10天。文章录用后及时在线SpringerLink。一般两周左右即被SCI-E检索。

收稿方向 :机械工程(3D打印及生物处理工程等)、生物墨水与配方、组织与器官工程、医学与诊断装置、生物产品设计等。

文章类型:Research Article, Review, Short Paper (包括Editorial, Perspective, Letter, Technical Note, Case Report, Lab Report, Negative Result等)。


期刊主页:

http://www.springer.com/journal/42242

http://www.jzus.zju.edu.cn/ (国内可下载全文)

在线投稿地址:

http://www.editorialmanager.com/bdmj/default.aspx


入群交流

围绕BDM刊物的投稿方向,本公众号建有“生物设计与制造”学术交流群,加小编微信号icefires212入群交流,或扫以下二维码

您可能也对以下帖子感兴趣

文章有问题?点此查看未经处理的缓存