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

The third biggest concentration of metallic ions is traces of the element copper (Cu 2+ ), 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 Cu 2+ . 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 n2cu 2+ , n3cu 2+ , n4cu 2+ , and n5cu 2+ , 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 n1cu 2+ , n2cu 2+ , n3cu 2+ , n4cu 2+ , and n5cu 2+ , respectively, is obtained. In addition, the minimal achieved detection limit is 0.000932 for n4cu 2+ , and a greater figure of merit is 382.86 for n4cu 2+ . The high predicted value of 0.981494 has been achieved by the machine learning approach for Cu 2+ ions, and the mean square error value of 0.001987 for Cu 2+ ions. Along with the results, this sensor has a greater capability with compactness in detecting heavy metal ions.