A recyclable CNC-milled microfluidic platform for colorimetric assays and label-free aged-related macular degeneration detection

[Display omitted] •The μCM chip was a recyclable stand-alone 3D chemical sensing analytic platform.•The μCM chips yielded 3-fold faster flow rates compared to conventional μPAD chips.•The μCM chips successfully detected clinically relevant range of albumin and glucose.•SERS-imposed μCM chips can be...

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Veröffentlicht in:Sensors and actuators. B, Chemical Chemical, 2019-07, Vol.290, p.484-492
Hauptverfasser: Choi, Samjin, Moon, Sang Woong, Ho Lee, Seung, Kim, Wansun, Kim, Soogeun, Kang Kim, Su, Shin, Jae-Ho, Park, Young-Guk, Jin, Kyung-Hyun, Kim, Tae Gi
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Sprache:eng
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Zusammenfassung:[Display omitted] •The μCM chip was a recyclable stand-alone 3D chemical sensing analytic platform.•The μCM chips yielded 3-fold faster flow rates compared to conventional μPAD chips.•The μCM chips successfully detected clinically relevant range of albumin and glucose.•SERS-imposed μCM chips can be used as a biomarker to detect the presence of AMD. We report the development of a simple, low-cost, and eco-friendly stand-alone 3D microfluidic chemical sensing platform capable of colorimetric and biochemical analyses at the same time. The microfluidic cellulose microfiber (μCM) chip was prototyped by injecting 10% CM mixtures on computer numeric control (CNC)-milled substrates. We show that the μCM chip has a 3-fold faster flow rate than conventional microfluidic paper-based analytical devices and is a recyclable platform that could perform basic microfluidic experiments. The colorimetric assays of the μCM chip can successfully detect clinically relevant concentrations of albumin (R2 = 0.9994) and glucose (R2 = 0.9464). The gold nanoparticle-induced surface-enhanced Raman scattering (SERS) label-free bioassay of μCM chips can enhance the Raman signal by 5.15 × 108 and a sensitivity of 0.94 (10 pM–1 mM for CV molecules) with an excellent stability of 96% clinical sensitivity and >78% clinical specificity (87% accuracy) from principal component linear discriminant analysis (PC-LDA) model-based multivariate statistical analysis methods.
ISSN:0925-4005
1873-3077
DOI:10.1016/j.snb.2019.04.025