Schulich School of Engineering
Spray coated bentonite/MWCNT composite enzymatic biosensor for uric acid detection
Abstract
dc:description.abstractBiosensors are widely used in healthcare and diagnostics, offering rapid and reliable detection of biological analytes. This study presents the development of an enzymatic uric acid (UA) biosensor based on a nanocomposite of bentonite (BT) and multi-walled carbon nanotubes (MWCNTs) fabricated via spray coating. The composite utilizes the high conductivity and surface area of MWCNTs along with the ion-exchange capacity and antifouling properties of BT to improve both electron transfer and long-term stability. Uricase (UOX) was immobilized on the BT/MWCNTs modified electrode using glutaraldehyde (GA) crosslinking, forming a stable enzyme matrix with strong adhesion and reduced leaching. The fabricated BT/MWCNT/GA/UOX biosensor was systematically characterized through morphological, chemical, and electrochemical analyses. Optimizing spray parameters enabled uniform film deposition and consistent surface roughness, thereby improving reproducibility across electrodes. The biosensor exhibited a broad linear detection range, high sensitivity, and a low detection limit of 5.31 µM in phosphate buffer, outperforming the control sensors prepared with BT-only and MWCNT-only films. Excellent selectivity was demonstrated against common interferents such as ascorbic acid and dopamine. The proposed sensor also exhibited good antifouling ability and was applicable to hydrodynamic conditions. Stability studies indicated that after 60 days of storage at 4 °C, the biosensor retained 92% of its initial response, showing minimal signal drift and structural degradation. Furthermore, real-sample validation using spiked human serum demonstrated reliable recovery (92.2–110.8%) and strong correlation (R² = 0.997) with the standard calibration, confirming the biosensor’s clinical applicability. These results confirm the mechanical, electrochemical, and operational robustness of the BT/MWCNT-based biosensor platform and suggest its strong potential for future integration into point-of-care (POC) diagnostic systems for UA and related metabolites.
Degree
thesis:*- Name thesis:degree_name
- Master of Science (MSc)
- Discipline thesis:degree_discipline
- Engineering – Biomedical
- Grantor
- Schulich School of Engineering
- Year dc:date.issued
- 2026
Author and committee
dc:creator, dc:contributor.*- Author dc:creator
-
- Park, Seohyun
- Advisor dc:contributor.advisor
-
- Kim, Keekyoung
- Committee members dc:contributor.committeemember
-
- Pandey, Richa
- Natale, Giovanniantonio
Subjects
dc:subject × 6Rights
dc:rights- Statement dc:rights
-
- Unless otherwise indicated, this material is protected by copyright and has been made available with authorization from the copyright owner. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.
- Language dc:language.iso
- en
Identifiers
dc:identifier.*- OAI identifier oai:identifier
- oai:ucalgary.scholaris.ca:1880/123826