Design and optimization of highly sensitive and tunable nanostructure biosensor for heavy metal detection using...

The third biggest concentration of metallic ions is traces of the element copper (Cu²⁺), which is crucial to all living creatures and plays a key role in several operations. However, deficiency or excessive copper ions may trigger a wide range of disorders, as determined by cellular requirements. To identify these factors, optical SPR-based refractive index sensors have emerged that concentrate on the swift identification of Cu2 + ions in the present moment, that has excellent selectivity and sensitivity. Here, this paper intends to design and discuss a Four-Quadrant Circular Grid Refractive Index Biosensor (FQCGRIB) with a machine learning approach for detecting heavy metals like Cu²⁺. The four-quadrant circular grid refractive index biosensor enhances conventional biosensor performance via improved accuracy, sensitivity, specificity, and detection efficiency. significant sensitivity values of 719.85 nm/RIU, 763.35 nm/RIU, 761.90 nm/RIU, and 734.52 nm/RIU are achieved for n2cu²⁺, n3cu²⁺, n4cu²⁺, and n5cu²⁺, respectively. Simultaneously, a greater detection range of 1175.46, 1175.14, 1176.47, 1189.56, and 1180.59, along with a greater quality factor of 835.35 nm/RIU, 828.85 nm/RIU, 827.72 nm/RIU, 843.21 nm/RIU, and 828.57 nm/RIU, for the n1cu²⁺, n2cu²⁺, n3cu²⁺, n4cu²⁺, and n5cu²⁺, respectively, is obtained. In addition, the minimal achieved detection limit is 0.000932 for n4cu²⁺, and a greater figure of merit is 382.86 for n4cu²⁺. The high predicted value of 0.981494 has been achieved by the machine learning approach for Cu²⁺ ions, and the mean square error value of 0.001987 for Cu²⁺ ions. Along with the results, this sensor has a greater capability with compactness in detecting heavy metal ions.