MXene-Enabled Electrochemical Microfluidic Biosensor: Applications toward Multicomponent Continuous Monitoring in Whole Blood

A new biophotonic sensor based on photonic crystal (PC) has been designed for the detection of creatinine concentration in blood, and is considered an important small molecule biomarker of renal dysfunction. Based on the transfer matrix method (TMM), we theoretically investigated the transmittance spectra of a one dimensional alternating dielectric photonic crystal (PC) designed as (AB)7/C/(AB)7 made of MgF2 (A), CeO2 (B) and creatinine concentration present in blood (C). The transmission spectra exhibit resonant peaks within the photonic band gap (PBG) indicative of so-called defect modes, which depend on parameters, such as concentration of creatinine in blood, thickness of defect layer and incident angle. The proposed sensor can determine the physiological levels of creatinine in human blood serum samples. The estimated parameters realize an efficient biophotonic sensor wherein sensitivity was tuned from 136.4 nm per RIU to 306.25 nm per RIU and is very useful for the detection of creatinine.

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Biophotonic sensor for the detection of creatinine concentration in blood serum based on 1D photonic crystal

Microfluidic paper-based platform for whole blood creatinine detection

Biosensing methods for determination of creatinine: A review

Modern creatinine (Bio)sensing: Challenges of point-of-care platforms.

A disposable non-enzymatic sensor for creatinine was developed by electrodepositing copper on screen printed carbon electrodes. The sensor was characterized using electrochemical and microscopic techniques. Electrochemical detection of creatinine was carried out in phosphate buffer solution of pH 7.4. The estimation was based on the formation of soluble copper-creatinine complex. The formation of copper-creatinine complex was established using the pseudoperoxidase activity of copper-creatinine complex. The sensor showed a detection limit of 0.0746 μM with a linear range of 6–378 μΜ. The sensor exhibited a stable response to creatinine and found to be free from interference from molecules like urea, glucose, ascorbic acid and dopamine. Real sample analysis was carried out with blood serum.

Fabrication of a disposable non-enzymatic electrochemical creatinine sensor

Electrochemical synthesis of graphene and its application in electrochemical sensing of glucose

Screen-printed carbon electrode for the electrochemical detection of conjugated bilirubin

Computational simulation for the fluid flow in a paper based microfluidic system was performed and was experimentally validated in this work. Comsol Multiphysics based simulation was performed and the module used was species transport in porous medium. Two separate simulation for the reagent distribution and the interaction of sample with the immobilized reagent in paper based microfluidics devices were carried out. This was performed by varying the parameters such as adsorption constant, diffusivity and average velocity of fluid in the porous medium for finding the concentration profile. Finer mesh were used for the simulation which gives more accurate results with less computational time. The reagent distribution was experimentally validated by dropping methylorange indicator over Whatman filter paper.

https://iopscience.iop.org/article/10.1088/1757-899X/577/1/012104/pdf

Design and simulation of fluid flow in paper based microfluidic platforms

Development of a paper-based analytical device for the colourimetric detection of alanine transaminase and the application of deep learning for image analysis.

P.E. Resmi

Papers

Fabrication of a disposable non-enzymatic electrochemical creatinine sensor

Electrochemical synthesis of graphene and its application in electrochemical sensing of glucose

Screen-printed carbon electrode for the electrochemical detection of conjugated bilirubin

Design and simulation of fluid flow in paper based microfluidic platforms

Development of a paper-based analytical device for the colourimetric detection of alanine transaminase and the application of deep learning for image analysis.