Modular Optofluidic Systems (MOPS)

Ackermann, Tobias N.; Dietvorst, Jiri; Sanchís, Ana; Salvador, J.‐Pablo; Muñoz-Berbel, Xavier; Alvarez-Conde, Erica; Köpp, Daniel; Zappe, Hans; Marco, M.‐Pilar; Llobera, Andreu

doi:10.1117/12.2242997

Cited by 1 publication

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References 24 publications

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“…This may include sampling, sample pre-treatment, chemical reactions, analytical separations, analyte detection, product isolation, and data analysis (Ríos et al 2012). Even though the achievements reported in the literature are numerous, apparently the transition of these devices from lab prototyping to market is still limited, if not negligible (Volpatti and Yetisen 2014;Mohammed et al 2015;Ackermann et al 2016a), and the remaining challenges are still considerable. The main challenge is to identify and optimize strategies for the integration of all analysis functions into a cost-efficient, technician-free, robust microstructure, to develop fully-autonomous micro-analysis systems.…”

Section: Introductionmentioning

confidence: 99%

Miniaturization of fluorescence sensing in optofluidic devices

Măriuța

Colin

Barrot-Lattes

et al. 2020

Microfluid Nanofluid

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Successful development of a micro-total-analysis system (µTAS, lab-on-a-chip) is strictly related to the degree of miniaturization, integration, autonomy, sensitivity, selectivity, and repeatability of its detector. Fluorescence sensing is an optical detection method used for a large variety of biological and chemical assays, and its full integration within lab-on-a-chip devices remains a challenge. Important achievements were reported during the last few years, including improvements of previously reported methodologies, as well as new integration strategies. However, a universal paradigm remains elusive. This review considers achievements in the field of fluorescence sensing miniaturization, starting from off-chip approaches, representing miniaturized versions of their lab counter-parts, continuing gradually with strategies that aim to fully integrate fluorescence detection on-chip, and reporting the results around integration strategies based on optical-fiber-based designs, optical layer integrated designs, CMOS-based fluorescence sensing, and organic electronics. Further successful development in this field would enable the implementation of sensing networks in specific environments that, when coupled to Internet-of-Things (IoT) and artificial intelligence (AI), could provide real-time data collection and, therefore, revolutionize fields like health, environmental, and industrial sensing.

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