Issue 2, 2019

A field amplification enhanced paper-based analytical device with a robust chemiluminescence detection module

Abstract

A sensitive detection method combined with an effective on-line concentration may improve the analytical performance of a paper-based analytical device (PAD), and its merits of low cost and portability in POCT are fully demonstrated. Here, a sensitive PAD system with chemiluminescence (CL) detection and electrokinetic preconcentration was introduced, and the performance was demonstrated by the detection of hemin. A commercially available low cost and miniaturized optical detection module was used for the CL detection. Firstly, hemin was stacked on a simple paper fluidic channel based on field amplified stacking (FAS), and then a CL reagent (luminol–H2O2) was loaded on the stacked band to initiate the CL reaction. The photons were directly detected using the detection module. With optimization of the background electrolyte (BGE), voltage and CL reagent, a limit of detection (LOD) of 0.58 nM for hemin was obtained with a linear range of 1–1000 nM (R2 = 0.995). With FAS, the signal intensity was about 13-fold enhanced. This PAD also exhibited satisfactory selectivity over possible interfering components at a 104 times higher concentration. The applicability of the PAD was demonstrated by the detection of hemin from iron supplements and human serum samples. With total manual operation, recovery rates of 84.8–115.6% were obtained with an RSD of less than 14.3%. With the introduction of the optical detection model, and together with FAS, both the LOD and dynamic range of this PAD were effectively improved.

Graphical abstract: A field amplification enhanced paper-based analytical device with a robust chemiluminescence detection module

Supplementary files

Article information

Article type
Paper
Submitted
27 Sep 2018
Accepted
02 Nov 2018
First published
06 Nov 2018

Analyst, 2019,144, 498-503

A field amplification enhanced paper-based analytical device with a robust chemiluminescence detection module

X. Zhang, J. Liu, Y. Cai, S. Zhao and Z. Wu, Analyst, 2019, 144, 498 DOI: 10.1039/C8AN01859F

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