Yew, M., Ren, Y., Koh, K. S., Sun, C. & Snape, C. A review of state-of-the-art microfluidic technologies for environmental applications: Detection and remediation. Global Chall. 3, 1800060. https://doi.org/10.1002/gch2.201800060 (2019).
Google Scholar
Dittrich, P. S. & Manz, A. Lab-on-a-Chip: Microfluidics in drug discovery. Nat. Rev. Drug Discov. 5, 210–218 (2006).
Google Scholar
Wu, M.-H., Huang, S.-B. & Lee, G.-B. Microfluidic cell culture systems for drug research. Lab Chip 10, 939 (2010).
Google Scholar
Whitesides, G. M. The origins and the future of microfluidics. Nature 442, 368–373 (2006).
Google Scholar
Cui, X. et al. Smartphone-based rapid quantification of viable bacteria by single-cell microdroplet turbidity imaging. Analyst 143, 3309–3316. https://doi.org/10.1039/C8AN00456K (2018).
Google Scholar
Evanko, D. Living droplets. Nat. Methods 5, 580 (2008).
Google Scholar
Li, H. et al. Application of droplet digital PCR to detect the pathogens of infectious diseases. Biosci. Rep. 38, BSR20181170 (2018).
Google Scholar
Barea, J. S., Lee, J. & Kang, D.-K. Recent advances in droplet-based microfluidic technologies for biochemistry and molecular biology. Micromachines 10, 412. https://doi.org/10.3390/mi10060412 (2019).
Google Scholar
Ngernsutivorakul, T., Steyer, D. J., Valenta, A. C. & Kennedy, R. T. In vivo chemical monitoring at high spatiotemporal resolution using microfabricated sampling probes and droplet-based microfluidics coupled to mass spectrometry. Anal. Chem. 90, 10943–10950. https://doi.org/10.1021/acs.analchem.8b02468 (2018).
Google Scholar
Slaney, T. R. et al. Push–pull perfusion sampling with segmented flow for high temporal and spatial resolution in vivo chemical monitoring. Anal. Chem. 83, 5207–5213. https://doi.org/10.1021/ac2003938 (2011).
Google Scholar
Wang, M., Roman, G. T., Schultz, K., Jennings, C. & Kennedy, R. T. Improved temporal resolution for in vivo microdialysis by using segmented flow. Anal. Chem. 80, 5607–5615. https://doi.org/10.1021/ac800622s (2008).
Google Scholar
Hindson, B. J. et al. High-throughput droplet digital PCR system for absolute quantitation of DNA copy number. Anal. Chem. 83, 8604–8610. https://doi.org/10.1021/ac202028g (2011).
Google Scholar
Schuler, F. et al. Digital droplet lamp as a microfluidic app on standard laboratory devices. Anal. Methods 8, 2750–2755. https://doi.org/10.1039/C6AY00600K (2016).
Google Scholar
Monat, C., Domachuk, P. & Eggleton, B. J. Integrated optofluidics: A new river of light. Nat. Photonics 1, 106–114. https://doi.org/10.1038/nphoton.2006.96 (2007).
Google Scholar
Vallejo, D., Nikoomanzar, A., Paegel, B. M. & Chaput, J. C. Fluorescence-activated droplet sorting for single-cell directed evolution. ACS Synth. Biol. 8, 1430–1440. https://doi.org/10.1021/acssynbio.9b00103 (2019).
Google Scholar
Vallejo, D., Nikoomanzar, A. & Chaput, J. C. Directed evolution of custom polymerases using droplet microfluidics. In Methods in Enzymology (eds Colowick, S. P. et al.) 227–253 (Elsevier, 2020). https://doi.org/10.1016/bs.mie.2020.04.056.
Google Scholar
Tu, R. et al. Droplet-based microfluidic platform for high-throughput screening of streptomyces. Commun. Biol. 4, 1–9. https://doi.org/10.1038/s42003-021-02186-y (2021).
Google Scholar
Paiè, P., Vázquez, R. M., Osellame, R., Bragheri, F. & Bassi, A. Microfluidic based optical microscopes on chip. Cytom. Part A 93, 987–996. https://doi.org/10.1002/cyto.a.23589 (2018).
Google Scholar
Fu, J.-L., Fang, Q., Zhang, T., Jin, X.-H. & Fang, Z.-L. Laser-induced fluorescence detection system for microfluidic chips based on an orthogonal optical arrangement. Anal. Chem. 78, 3827–3834. https://doi.org/10.1021/ac060153q (2006).
Google Scholar
Zhang, P., Kaushik, A., Hsieh, K. & Wang, T.-H. Customizing droplet contents and dynamic ranges via integrated programmable picodroplet assembler. Microsyst. Nanoeng. 5, 1–12. https://doi.org/10.1038/s41378-019-0062-5 (2019).
Google Scholar
Fu, X., Zhang, Y., Xu, Q., Sun, X. & Meng, F. Recent advances on sorting methods of high-throughput droplet-based microfluidics in enzyme directed evolution. Front. Chem. 9, 666867. https://doi.org/10.3389/fchem.2021.666867 (2021).
Google Scholar
Booth, M. A. et al. Fiber-based electrochemical biosensors for monitoring pH and transient neurometabolic lactate. Anal. Chem. 93, 6646–6655. https://doi.org/10.1021/acs.analchem.0c05108 (2021).
Google Scholar
Frank, J. A. et al. In vivo photopharmacology enabled by multifunctional fibers. ACS Chem. Neurosci. 11, 3802–3813. https://doi.org/10.1021/acschemneuro.0c00577 (2020).
Google Scholar
Yankelevich, D. R. et al. Design and evaluation of a device for fast multispectral time-resolved fluorescence spectroscopy and imaging. Rev. Sci. Instrum. 85, 034303. https://doi.org/10.1063/1.4869037 (2014).
Google Scholar
Marsden, M. et al. FLImBrush: Dynamic visualization of intraoperative free-hand fiber-based fluorescence lifetime imaging. Biomed. Opt. Express 11, 5166. https://doi.org/10.1364/boe.398357 (2020).
Google Scholar
Thiberville, L. et al. Human in vivo fluorescence microimaging of the alveolar ducts and sacs during bronchoscopy. Eur. Respir. J. 33, 974–985. https://doi.org/10.1183/09031936.00083708 (2009).
Google Scholar
Mills, B. et al. Molecular detection of gram-positive bacteria in the human lung through an optical fiber-based endoscope. Eur. J. Nucl. Med. Mol. Imaging 48, 800–807. https://doi.org/10.1007/s00259-020-05021-4 (2020).
Google Scholar
Etcheverry, S., Russom, A., Laurell, F. & Margulis, W. Fluidic trapping and optical detection of microparticles with a functional optical fiber. Opt. Express 25, 33657. https://doi.org/10.1364/oe.25.033657 (2017).
Google Scholar
Sudirman, A., Etcheverry, S., Stjernström, M., Laurell, F. & Margulis, W. A fiber optic system for detection and collection of micrometer-size particles. Opt. Express 22, 21480. https://doi.org/10.1364/oe.22.021480 (2014).
Google Scholar
Etcheverry, S. et al. High performance micro-flow cytometer based on optical fibres. Sci. Rep. 7, 1–8. https://doi.org/10.1038/s41598-017-05843-7 (2017).
Google Scholar
Kumar, T. et al. Optofluidic fiber component for separation and counting of micron-sized particles. bioRxiv. https://doi.org/10.1101/2021.04.13.439593 (2021).
Google Scholar
Yan, D., Popp, J., Pletz, M. W. & Frosch, T. Highly sensitive broadband Raman sensing of antibiotics in step-index hollow-core photonic crystal fibers. ACS Photonics 4, 138–145. https://doi.org/10.1021/acsphotonics.6b00688 (2017).
Google Scholar
Förster, R. et al. Tracking and analyzing the Brownian motion of nano-objects inside hollow core fibers. ACS Sens. 5, 879–886. https://doi.org/10.1021/acssensors.0c00339 (2020).
Google Scholar
Jiang, S. et al. Three-dimensional spatiotemporal tracking of nano-objects diffusing in water-filled optofluidic microstructured fiber. Nanophotonics 9, 4545–4554. https://doi.org/10.1515/nanoph-2020-0330 (2020).
Google Scholar
Schmidt, M. A. et al. Optofluidic fiber-based nanoparticle tracking analysis: Tool to characterize diffusing nanoscale specimen such as SARS-CoV-2. In Micro-structured and Specialty Optical Fibres VII (eds Peterka, P., Kalli, K. & Mendez, A.) (SPIE, 2021). https://doi.org/10.1117/12.2597806.
Kim, J. et al. The optofluidic light cage—On-chip integrated spectroscopy using an antiresonance hollow core waveguide. Anal. Chem. 93, 752–760. https://doi.org/10.1021/acs.analchem.0c02857 (2020).
Google Scholar
MacKay, M. J. et al. The Covid-19 xprize and the need for scalable, fast, and widespread testing. Nat. Biotechnol. 38, 1021–1024. https://doi.org/10.1038/s41587-020-0655-4 (2020).
Google Scholar
Dinnes, J. et al. Rapid, point-of-care antigen and molecular-based tests for diagnosis of SARS-CoV-2 infection. Cochrane Database Syst. Rev. https://doi.org/10.1002/14651858.cd013705 (2020).
Google Scholar
Ibrahim, A. M., Padovani, J. I., Howe, R. T. & Anis, Y. H. Modeling of droplet generation in a microfluidic flow-focusing junction for droplet size control. Micromachines 12, 590 (2021).
Google Scholar
Zhu, P. & Wang, L. Passive and active droplet generation with microfluidics: A review. Lab Chip 17, 34–75. https://doi.org/10.1039/c6lc01018k (2017).
Google Scholar
Virtanen, P. et al. SciPy 1.0: Fundamental algorithms for scientific computing in Python. Nat. Methods 17, 261–272 (2020).
Google Scholar
Harris, C. R. et al. Array programming with NumPy. Nature 585, 357–362 (2020).
Google Scholar
Soares, R. R. G. et al. Sample-to-answer COVID-19 nucleic acid testing using a low-cost centrifugal microfluidic platform with bead-based signal enhancement and smartphone read-out. Lab Chip. https://doi.org/10.1039/d1lc00266j (2021).
Google Scholar
Yu, L. et al. Rapid detection of COVID-19 coronavirus using a reverse transcriptional loop-mediated isothermal amplification (RT-LAMP) diagnostic platform. Clin. Chem. 66, 975–977. https://doi.org/10.1093/clinchem/hvaa102 (2020).
Google Scholar
Vazquez, B., Qureshi, N., Oropeza-Ramos, L. & Olguin, L. F. Effect of velocity on microdroplet fluorescence quantified by laser-induced fluorescence. Lab Chip 14, 3550–3555. https://doi.org/10.1039/c4lc00654b (2014).
Google Scholar
Stone, J. M., Wood, H. A. C., Harrington, K. & Birks, T. A. Low index contrast imaging fibers. Opt. Lett. 42, 1484–1487 (2017).
Google Scholar
Parker, H. E., Perperidis, A., Stone, J. M., Dhaliwal, K. & Tanner, M. G. Core crosstalk in ordered imaging fiber bundles. Opt. Lett. 45, 6490. https://doi.org/10.1364/OL.405764 (2020).
Google Scholar

