Flexible microfluidic colorimetric detection chip integrated with ABTS·+ and Co@MnO2 nanozyme catalyzed TMB reaction systems for bio-enzyme free detection of sweat uric acid.

BACKGROUND: The development of wearable detection devices that can achieve noninvasive, on-site and real-time monitoring of sweat metabolites is of great demand and practical significance for point-of-care testing and healthcare monitoring. Monitoring uric acid (UA) content in sweat provides a simple and promising way to reduce the risk of gout and hyperuricemia. Traditional bioenzyme based UA assays suffer from high cost, poor stability, inconvenience for storage and easy deactivation of bioenzymes. Wearable microfluidic colorimetric detection device for sweat UA detection has not been reported. The development of novel wearable microfluidic colorimetric detection chip with no requirement of bioenzymes for sweat UA detection is of great importance for health care monitoring. RESULTS: Firstly, Co@MnO2 nanozyme with high oxidase-like activity was synthesized and characterized. Co@MnO2 can catalyze the oxidation of 3,3',5,5'-tetramethylbenzidine (TMB) directly to generate blue-green colored ox-TMB. Green colored 2,2'-Azinobis-(3-ethylbenzthiazoline-6-sulphonate) radical (ABTS·+) was produced by the oxidation of ABTS by potassium persulfate. UA exhibits distinct quenching effect on Co@MnO2 catalyzed TMB colorimetric reaction system and ABTS·+ based colorimetric system, leading to obvious color fading of the two colorimetric systems. Then, a flexible microfluidic colorimetric detection chip for UA detection was fabricated by assembling Co@MnO2/TMB modified paper chips and ABTS·+ modified paper chips into a polydimethylsiloxane (PDMS) microfluidic chip. The fabricated microfluidic colorimetric detection chip exhibits good linear relationship for sweat UA detection. The linear range is from 20 to 200 µmol/L with detection limit as low as 6.6 µmol/L. Good results were obtained for the detection of UA in actual sweat from three volunteers. SIGNIFICANCE: This work provides two bio-enzyme free colorimetric detection systems for UA detection. Furthermore, a simple, low-cost and selective flexible wearable microfluidic colorimetric detection chip was fabricated for noninvasive and on-site detection of sweat UA, which holds great application potential for personal health monitoring and point-of-care testing.

Machine learning assisted discrimination and detection of antibiotics by using multicolor microfluidic chemiluminescence detection chip.

The fabrication of multicolor chemiluminescence (CL) sensing chip for the discrimination and detection of multianalytes remains a great challenge. Herein, machine learning assisted multicolor microfluidic CL detection chip for the identification and concentration prediction of antibiotics was presented. Firstly, a three-channel microfluidic CL detection chip was fabricated. The three detection zones of the microfluidic detection chip were modified with CL catalyst Co(II) and different CL reagents including luminol, luminol mixed with fluorescein, and luminol mixed with phloxine B, respectively. Strong blue, green and pink-purple colored light emissions can be generated from the three detection zones in the presence of H2O2 solution. The three multicolor CL emissions show different degrees of reduce in intensity and change in color in the presence of different antibiotics, including diethylstilbestro (DES), metronidazole (MNZ), kanamycin (KAN), isoniazide (INH), and ceftiofur sodium (CS), resulting in distinct fingerprint-like response patterns. The red (R), green (G), blue (B) and gray scale values of the three multicolor light emissions were extracted and ten characteristic sensing parameters were chosen to obtain multicolor CL response database. Then, machine learning assisted data analysis were carried out. The five antibiotics can be facilely classified by using principal component analysis (PCA) and hierarchical clustering analysis (HCA), and further quantified by using deep neural networks (DNN) algorithm. Good results were obtained for identification of binary antibiotic mixtures, spiked antibiotics in water samples, and unknown antibiotic samples. Satisfied results were obtained for concentration prediction of antibiotics. This work provides a simple machine learning assisted and multicolor microfluidic CL detection chip based CL sensing strategy for discrimination and quantitative detection of multiple analytes.

Bioenzyme-free colorimetric assay for creatinine determination based on Mn3O4 nanoparticles catalyzed oxidation of 3,3',5,5'-tetramethylbenzidine.

Mn3O4 nanozyme with good oxidase-like activity was successfully synthesized. The prepared Mn3O4 nanozyme can directly and effectively catalyze the oxidation of 3,3',5,5'-tetramethylbenzidine (TMB) to generate green-blue-colored ox-TMB. Creatinine exhibits distinct inhibition effect on Mn3O4 nanozyme-catalyzed TMB colorimetric reaction system, leading to obvious color fading and absorbance intensity decrease of the reaction system. Furthermore, interference from uric acid can be effectively eliminated by regulating the pH of TMB-Mn3O4 colorimetric reaction system to pH 2.0. Then, a simple and bioenzyme-free colorimetric assay for the determination of creatinine was developed based on TMB-Mn3O4 colorimetric reaction. The linear detection range is from 100 to 800 µM and from 1 to 20 mM. The lowest limit of detection is 35.3 µM. Satisfied results are obtained for the determination of creatinine in real urine and sweat samples. This work provides the synthesis of a good oxidase-like nanozyme Mn3O4 and presents the fabrication of an effective nanozyme-based bioenzyme-free colorimetric assay for the determination of creatinine.