High-precision digital light processing (DLP) printing of microstructures for microfluidics applications based on a machine learning approach

References

• Scott SM, Ali Z. Fabrication methods for microfluidic devices: an overview. Micromachines. 2021;12(3). doi: 10.3390/mi12030319
   PubMed Web of Science ®Google Scholar
• Hansen CL, Skordalakes E, Berger JM, et al. A robust and scalable microfluidic metering method that allows protein crystal growth by free interface diffusion. Proc Natl Acad Sci U S A. 2002;99(26):16531–16536. doi: 10.1073/pnas.262485199
   PubMed Web of Science ®Google Scholar
• Takayama S, Ostuni E, LeDuc P, et al. Subcellular positioning of small molecules. Nature. 2001;411(6841):1016–1016. doi: 10.1038/35082637
   PubMed Web of Science ®Google Scholar
• Guo MT, Rotem A, Heyman JA, et al. Droplet microfluidics for high-throughput biological assays. Lab Chip. 2012;12(12):2146–2155. doi: 10.1039/c2lc21147e
   PubMed Web of Science ®Google Scholar
• Xinhui Wang JL, Zhang Y, Kristiansen PM, et al. Advances in precision microfabrication through digital light processing: system development, material and applications. Virtual Phys Prototyp. 2023;18(1). doi: 10.1080/17452759.2023.2248101
   Google Scholar
• Whitesides GM. The origins and the future of microfluidics. Nature. 2006;442(7101):368–373. doi: 10.1038/nature05058
   PubMed Web of Science ®Google Scholar
• Wei CW, Cheng JY, Huang CT, et al. Using a microfluidic device for 1 microl DNA microarray hybridization in 500 s. Nucleic Acids Res. 2005;33(8):e78. doi: 10.1093/nar/gni078
   PubMed Web of Science ®Google Scholar
• Zhang P, He M, Zeng Y. Ultrasensitive microfluidic analysis of circulating exosomes using a nanostructured graphene oxide/polydopamine coating. Lab Chip. 2016;16(16):3033–3042. doi: 10.1039/c6lc00279j
   PubMed Web of Science ®Google Scholar
• Reyes DR, Iossifidis D, Auroux P-A, et al. Micro total analysis systems. 1. Introduction, theory, and technology. Anal Chem. 2002;74(12):2623–2636. doi: 10.1021/ac0202435
   PubMed Web of Science ®Google Scholar
• Duffy DC, McDonald JC, Schueller OJA, et al. Rapid prototyping of microfluidic systems in poly (dimethylsiloxane). Anal Chem. 1998;70(23):4974–4984. doi: 10.1021/ac980656z
   PubMed Web of Science ®Google Scholar
• Amini A, Guijt RM, Themelis T, et al. Recent developments in digital light processing 3D-printing techniques for microfluidic analytical devices. J Chromatogr A. 2023;1692:463842. doi: 10.1016/j.chroma.2023.463842
   PubMed Web of Science ®Google Scholar
• Gonzalez G, Roppolo I, Pirri CF, et al. Current and emerging trends in polymeric 3D printed microfluidic devices. Add Manuf. 2022;55. doi: 10.1016/j.addma.2022.102867
   PubMed Web of Science ®Google Scholar
• Yazdi AA, Popma A, Wong W, et al. 3D printing: an emerging tool for novel microfluidics and lab-on-a-chip applications. Microfluid Nanofluid. 2016;20:3. doi: 10.1007/s10404-016-1715-4
   Web of Science ®Google Scholar
• Kim P, Lee SE, Jung HS, et al. Soft lithographic patterning of supported lipid bilayers onto a surface and inside microfluidic channels. Lab Chip. 2006;6(1):54–59. doi: 10.1039/b512593f
   PubMed Web of Science ®Google Scholar
• Zheng X, Lee H, Weisgraber TH, et al. Ultralight, ultrastiff mechanical metamaterials. Science. 2014;(6190):1373–1377. doi: 10.1126/science.1252291
   PubMed Web of Science ®Google Scholar
• Au AK, Huynh W, Horowitz LF, et al. 3D-Printed Microfluidics. Angew Chem Int Ed Engl. 2016;55(12):3862–3881. doi: 10.1002/anie.201504382
   PubMed Web of Science ®Google Scholar
• Shallan AI, Smejkal P, Corban M., et al. 2014. Cost-effective three-dimensional printing of visibly transparent microchips within minutes. Anal Chem. (6):3124–3130. doi: 10.1021/ac4041857
   PubMed Web of Science ®Google Scholar
• Macdonald NP, Cabot JM, Smejkal P, et al. Comparing microfluidic performance of three-dimensional (3D) printing platforms. Anal Chem. 2017;(7):3858-3866. doi: 10.1021/acs.analchem.7b00136
   PubMed Web of Science ®Google Scholar
• Razavi Bazaz S, Rouhi O, Raoufi MA, et al. 3D Printing of inertial microfluidic devices. Sci Rep. 2020;(1):5929. doi: 10.1038/s41598-020-62569-9
   PubMed Web of Science ®Google Scholar
• Razavi Bazaz S, Kashaninejad N, Azadi S, et al. Rapid softlithography using 3D-printed molds. Adv Mat Technol. 2019;4(10). doi: 10.1002/admt.201900425
   Web of Science ®Google Scholar
• Gong H, Bickham BP, Woolley AT, et al. Custom 3D printer and resin for 18 mu m x 20 mu m microfluidic flow channels. Lab Chip. 2017;17:2899–2909. doi: 10.1039/c7lc00644f
   PubMed Web of Science ®Google Scholar
• Bucciarelli A, Paolelli X, De Vitis E, et al. VAT photopolymerization 3D printing optimization of high aspect ratio structures for additive manufacturing of chips towards biomedical applications. Add Manufact. 2022;60. doi: 10.1016/j.addma.2022.103200
   Web of Science ®Google Scholar
• Cheng L, De Vos J, Zhao P, et al. Examining non-linear built environment effects on elderly’s walking: a random forest approach. Transp Res D Transp Environ. 2020;88. doi: 10.1016/j.trd.2020.102552
   Web of Science ®Google Scholar
• Caraviello DZ, Weigel KA, Craven M, et al. Analysis of reproductive performance of lactating cows on large dairy farms using machine learning algorithms. J Dairy Sci. 2006;89(12):4703–4722. doi: 10.3168/jds.S0022-0302(06)72521-8
   PubMed Web of Science ®Google Scholar
• Vinay A, Shekhar VS, Rituparna J, et al. Cloud based big data analytics framework for face recognition in social networks using machine learning. Procedia Comput Sci. 2015;50:623–630. doi: 10.1016/j.procs.2015.04.095
   Google Scholar
• He H, Yang Y, Pan Y. Machine learning for modeling of printing speed in continuous projection stereolithography. Paper presented at the 2018 International Solid Freeform Fabrication Symposium. 2018.
   Google Scholar
• You S, Guan J, Alido J, et al. Mitigating scattering effects in light-based three-dimensional printing using machine learning. J Manuf Sci Eng. 2020;142(8). doi: 10.1115/1.4046986
   Web of Science ®Google Scholar
• Sun X, Yue L, Yu L, et al. Machine learning-evolutionary algorithm enabled design for 4D-printed active composite structures. Adv Funct Mater. 2021;32:10. doi: 10.1002/adfm.202109805
   Web of Science ®Google Scholar
• Zhao B, Zhang M, Dong L, et al. Design of grayscale digital light processing 3D printing block by machine learning and evolutionary algorithm. Compos Commun. 2022;36. doi: 10.1016/j.coco.2022.101395
   Google Scholar
• Kiratitanaporn W, Guan J., Berry DB, et al. Multimodal three-dimensional printing for micro-modulation of scaffold stiffness through machine learning. Tissue Eng Part A. 2023. doi: 10.1089/ten.TEA.2023.0193
   PubMedGoogle Scholar
• Montgomery SM, Demoly F, Zhou, K, et al. Pixel-level grayscale manipulation to improve accuracy in digital light processing 3D printing. Adv Funct Mater. 2023;33(17). doi: 10.1002/adfm.202213252
   Web of Science ®Google Scholar
• Jacobs PF. Rapid Prototyping Manufacturing Fundamentals of StereoLithography by Jacobs P.F. (z-lib.org).pdf (Vol. “Working cure model” Most classical). 1992.
   Google Scholar
• Gong H, Beauchamp M, Perry S, et al. Optical approach to resin formulation for 3D printed microfluidics. RSC Adv. 2015;5:106621–106632. doi: 10.1039/C5RA23855B
   PubMed Web of Science ®Google Scholar
• Li Y, Mao Q, Li X, et al. High-fidelity and high-efficiency additive manufacturing using tunable pre-curing digital light processing. Add Manufact. 2019. doi: 10.1016/j.addma.2019.100889
   Web of Science ®Google Scholar
• Benjamin AD, Abbasi R, Owens M, et al. Light-based 3D printing of hydrogels with high-resolution channels. Biomed Phys Eng Express. 2019;5(2). doi: 10.1088/2057-1976/aad667
   Web of Science ®Google Scholar
• Swineharf DF. Beer-lambert law.pdf. J Chem Educ. 1962. doi: 10.1021/ed039p333
   Google Scholar
• Smith PF, Ganesh S, Liu P. A comparison of random forest regression and multiple linear regression for prediction in neuroscience. J Neurosci Methods. 2013;220(1):85–91. doi: 10.1016/j.jneumeth.2013.08.024
   PubMed Web of Science ®Google Scholar
• Ostertagová E. Modelling using polynomial regression. Procedia Eng. 2012;48:500–506. doi: 10.1016/j.proeng.2012.09.545
   Google Scholar
• Aczel AD. Complete Business Statistics (Vol. p. 1056). ISBN 0-256-05710-8. 1989.
   Google Scholar
• Ostertagová E. Applied Statistic (in Slovak) (Vol. p. 161,). Elfa Košice, Slovakia: SBN 978-80-8086-171-1. 2011.
   Google Scholar
• Breiman L. Random forests. Mach Learn. 2001;45:5–32. doi: 10.1023/A:1010933404324
   Web of Science ®Google Scholar
• Guo L, Chehata N, Mallet C, et al. Relevance of airborne lidar and multispectral image data for urban scene classification using Random Forests. ISPRS J Photogramm Remote Sens. 2011;66(1):56–66. doi: 10.1016/j.isprsjprs.2010.08.007
   Web of Science ®Google Scholar
• Rodriguez-Galiano VF, Chica-Rivas M. Evaluation of different machine learning methods for land cover mapping of a Mediterranean area using multi-seasonal Landsat images and Digital Terrain Models. Int J Digital Earth. 2014;7(6):492–509. doi: 10.1080/17538947.2012.748848
   Web of Science ®Google Scholar
• Rodriguez-Galiano V, Sanchez-Castillo M, Chica-Olmo M, et al. Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines. Ore Geol Rev. 2015;71:804–818. doi: 10.1016/j.oregeorev.2015.01.001
   Web of Science ®Google Scholar
• Breiman L. Bagging predictors. Mach Learn. 1996;24:123–140.
   Web of Science ®Google Scholar
• Ahangar-Asr A, Faramarzi A, Mottaghifard N, et al. Modeling of permeability and compaction characteristics of soils using evolutionary polynomial regression. Comput Geosci. 2011;37(11):1860–1869. doi: 10.1016/j.cageo.2011.04.015
   Web of Science ®Google Scholar
• Hu J, Szymczak S. A review on longitudinal data analysis with random forest. Brief Bioinform. 2023;24(2). doi: 10.1093/bib/bbad002
   Web of Science ®Google Scholar
• Sun Y, Cheng H, Zhang S, et al. Prediction & optimization of alkali-activated concrete based on the random forest machine learning algorithm. Constr Build Mater. 2023;385. doi: 10.1016/j.conbuildmat.2023.131519
   Web of Science ®Google Scholar
• Ahmad GN, Fatima H, Ullah S, et al. Efficient medical diagnosis of human heart diseases using machine learning techniques with and without GridSearchCV. IEEE Access 2022;10:80151–80173. doi: 10.1109/ACCESS.2022.3165792
   Web of Science ®Google Scholar
• Ranjan GSK, Verma AK, Radhika S. K-nearest neighbors and grid search cv based real time fault monitoring system for industries. 2019 IEEE 5th international conference for convergence in technology (I2CT), IEEE, 2019.
   Google Scholar
• Shin H, Lee HJ, Chae U, et al. Neural probes with multi-drug delivery capability. Lab Chip. 2015;15(18):3730–3737. doi: 10.1039/C5LC00582E
   PubMed Web of Science ®Google Scholar
• Maeki M, Saito T, Sato Y, et al. A strategy for synthesis of lipid nanoparticles using microfluidic devices with a mixer structure. RSC Adv. 2015;5(57):46181–46185. doi: 10.1039/C5RA04690D
   Web of Science ®Google Scholar
• Zhigaltsev IV, Belliveau N, Hafez I, et al. Bottom-up design and synthesis of limit size lipid nanoparticle systems with aqueous and triglyceride cores using millisecond microfluidic mixing. Langmuir. 2012;28(7):3633–3640. doi: 10.1021/la204833h
   PubMed Web of Science ®Google Scholar