DEVELOPMENT OF AN ELECTROCHEMICAL BIOSENSOR FOR URIC ACID DETECTION BASED ON ELECTRODEPOSITION IN A FLOW-CELL SYSTEM
The increase in uric acid levels in the body is generally associated with various serious diseases such as gout, hypertension, and chronic kidney disease, which have become global health concerns. Various methods for detecting uric acid levels have been developed; however, conventional methods of...
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| Format: | Theses |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/87624 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | The increase in uric acid levels in the body is generally associated with various
serious diseases such as gout, hypertension, and chronic kidney disease, which
have become global health concerns. Various methods for detecting uric acid levels
have been developed; however, conventional methods often require sophisticated
equipment, high costs, and long analysis times, making them less ideal for
application as point-of-care testing. Therefore, the development of non-enzymatic
electrochemical biosensors offers an alternative with high sensitivity, selectivity,
and efficiency. This study aims to develop an electrochemical biosensor based on a
screen-printed carbon electrode (SPCE) modified with gold nanoflowers (AuNF)
using electrodeposition techniques within a flow cell and to integrate it with a flowcell
system for uric acid detection. The electrodeposition process successfully
produced AuNF, as confirmed through SEM, EDS, and XRD characterizations,
which indicated dominant crystal structures on the (111), (200), (220), and (311)
planes. Optimization of the flow-cell chamber volume for the sensor yielded optimal
performance at 70 ?L.
Electrochemical characterization results showed that the AuNF-modified SPCE
sensor demonstrated a sensitivity of 0.079 ?M/?A with a limit of detection (LOD)
of 20.3 ?M in droplet-based testing and a sensitivity of 0.058 ?M/?A with an LOD
of 5.8 ?M in the flow-cell platform. The sensor also exhibited high selectivity
toward uric acid and good stability after 30 repeated scanning cycles using the
cyclic voltammetry (CV) method. With excellent sensitivity, selectivity, and stability,
iv
the developed biosensor holds significant potential to support clinical applications
and research in real-time and cost-effective uric acid monitoring. |
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