Rapid Evaluation Method of Post Dialysate Urea Nitrogen Measurement for Chronic Kidney Disease Patients By Using Non-Enzymatic N-Doped Carbon Supported Nickel Based Biosensor
Authors: Janyasupab Metini , Asavakijthananont Narawee , Zhang Yuan , Chanlek Narong , Chio-Srichan Sirinart , Surareungchai Werasak , Liu Chung-Chiun 
Published: May 2020

An effective hemodialysis is essential for patients with chronic kidney diseases to maintain their kidney’s functionalities and health conditions. Not only the treatment usually takes 3-4 times a week, but a monthly measurement of blood urea nitrogen (BUN) is also a time-consuming requirement process, which in turn, provides a rough estimation of blood quality with respect to blood life cycle. However, a lack of feedback for each treatment’s efficacy still remains a major challenge for prognostic hemodialysis. Consequently, self-precaution or spontaneous adjustment on patients’ dietary can be difficult to achieve with little predicted quantitative information. Herein, we present a rapid evaluation method for the first time to quantify dialysate urea nitrogen (DUN), considering as a body waste information after hemodialysis treatment with our proposed model of estimation. Our systematic design on sample preparation with optimized supporting electrolyte enables a suitable linear range from 1 to 5 mM to predict urea concentration in patients’ post-treatment dialysate solution on a scalable non-enzymatic N-doped Carbon (NC) supported nickel (Ni) catalyst. Mechanistically, Ni in alkaline medium can form hydroxyl layers, especially Ni(I/II) redox couple to bind with urea molecules. Although this commonly known process is often exploited in many electrochemical reactions, the strong alkaline electrolyte often diminishes a weak target output, particularlly in sensing applications, thereby requiring secondary layers such as organic or macromolecular polymers to capture the target molecules. In this work, we optimize the alkaline medium and dialysate volume ratio and include three important factors: (i) reference dialysate sensitivity (mref), (ii), roughness (Rf), and (iii) capacitive current (Ic), respectively, into our predicted model. Each factor is also experimentally carried out. In detail, mref is -1660.40 A.mol-1cm, obtained from a current density and urea concentration calibration plot by using differential pulse voltammetry (DPV) on Ni/NC in our simulated reference dialysate solution with successive addition of urea concentration from 1-5 mM on 900 rpm rotating disk electrode. In addition, Rf is estimated to be 2.56 from electrochemical surface area (ECSA) in Randles-Sevcik equation, that is calculated by anodic peaks of 10 mM ferricyanide redox couple in 1 M potassium chloride supporting electrolyte. Furthermore, Ic is calculated to be the capacitive current density of 1.57 mA.cm-2, based on Gouy-Chapman double layer at the same scan rate of DPV (0.05 V/s) and the roughness of Ni, assessed by cyclic voltammetry from -0.35 to -0.05 V versus Ag/AgCl in 0.5 M potassium hydroxide. As shown in Figure 1, our proposed model is illustrated in equation (1) with parameter described in Table below. For further validation, we estimate urea concentrations in post-treatment dialysate, obtained from three patients by DPV on Ni/NC biosensor for 5 independent trials in comparison with their clinical standard results. It was found that percentage of relative error between our predicted urea concentrations and the clinical results in real samples are those of 1.30 %, 0.25 %, and 0.52 % for patient I-III, respectively, providing a highly accurate and rapid assessment for this important indicator in hemodialysis. Based on our observation, DUN evaluation has an inversely proportional relationship with the difference of BUN values between before and after hemodialysis: 48.3, 41.2, and 53.2 mg.dL-1 for patient I-III, accordingly. With enzyme-free, scalable, and economical choice of sensing design, this finding can become appreciable to further develop as an on-site monitoring prognostic tool and provide instantaneous feedback on treatment’s efficacy and daily dietary management of chronic kidney disease patients.

Figure 1