Bimetallic organic framework as non-enzymatic biosensor for high sensitivity of UA detection

Xiaoting Zha; Zili Yang; Zheyao Zhang; Yajie Yang

doi:10.1117/12.2652439

2 November 2022 Bimetallic organic framework as non-enzymatic biosensor for high sensitivity of UA detection

Xiaoting Zha, Zili Yang, Zheyao Zhang, Yajie Yang

Proceedings Volume 12351, International Conference on Advanced Sensing and Smart Manufacturing (ASSM 2022); 1235111 (2022) https://doi.org/10.1117/12.2652439
Event: International Conference on Advanced Sensing and Smart Manufacturing (ASSM 2022), 2022, Nanjing, China

Abstract

The imbalance of uric acid concentration in the blood can lead to serious related diseases. Therefore, accurate detection of uric acid plays an important role in human health management. However, uricase is used to detect uric acid. As is known to all, enzymes are easily affected by temperature, humidity and pH and thus lose their activity, resulting in invalid test results. Enzyme free electrochemical biosensor can make up for these defects of enzyme sensor. Optimization of the electrode of non-enzyme sensor is the main mean to improve its detection performance. Thus, in this work, the porous bimetallic MOF material, which formed three-dimensional folds on the rhombic dodecahedron surface by morphology control strategy, was successfully designed. Electrochemical test shows that the designed material has good detection performance of UA, due to the great surface morphology and chemical composition. Detailly, the NiCo-MOF sensor displays the performance with the ultra-wide linear range of 2.5-2500 μM and the excellent sensitivity of 428 μA mM^-1 cm^-2 (2.5-20 μM). These results provide an effective method for preparing MOF-based electrode materials with excellent sensing performance.

Citation Download Citation

Xiaoting Zha, Zili Yang, Zheyao Zhang, and Yajie Yang "Bimetallic organic framework as non-enzymatic biosensor for high sensitivity of UA detection", Proc. SPIE 12351, International Conference on Advanced Sensing and Smart Manufacturing (ASSM 2022), 1235111 (2 November 2022); https://doi.org/10.1117/12.2652439

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