Spindle shaped Fe-Ni metal organic frameworks wrapped with f-MWCNTs for the efficacious sensing of tartrazine.

A facile hydrothermal route was employed for the synthesis of iron-nickel bimetal organic frameworks (Fe-Ni bi-MOFs) and composite with an acid functionalized multi-walled carbon nanotubes (Fe-Ni MOF/f-MWCNTs) for electrochemical detection of tartrazine. The as-prepared Fe-Ni MOF/f-MWCNTs was confirmed by the several physicochemical studies. A micro spindle shaped, highly porous, and crystalline Fe-Ni MOF/f-MWCNTs was noticed. The high sensitivity and stability of Fe-Ni MOF/f-MWCNTs/GCE modified electrode was analyzed. Due to its high porosity nature, the analyte molecule effectively gets adsorbed on the modified electrode and undergo electrochemical oxidation effectively. The modified electrode exhibits low limit of detection (LOD) and limit of quantification (LOQ) as 0.04 × 10-6 mol/L and 0.13 × 10-6 mol/L towards tartrazine. These results reveal the potential applications of Fe-Ni MOF/f-MWCNTs/GCE as modified electrode material for sensitive detection of tartrazine along with its robust reproducibility, stability, and effective sensing properties.

IOTEML: An Internet of Things (IoT)-Based Enhanced Machine Learning Model for Tumour Investigation.

In the current age of technology, various diseases in the body are also on the rise. Tumours that cause more discomfort in the body are set to increase the discomfort of most patients. Patients experience different effects depending on the tumour size and type. Future developments in the medical field are moving towards the development of tools based on IoT devices. These advances will in the future follow special features designed based on multiple machine learning developed by artificial intelligence. In that order, an improved algorithm named Internet of Things-based enhanced machine learning is proposed in this paper. What makes it special is that it involves separate functions to diagnose each type of tumour. It analyzes and calculates things like the size, shape, and location of the tumour. Cure from cancer is determined by the stage at which we find cancer. Early detection of cancer has the potential to cure quickly. At a saturation point, the proposed Internet of Things-based enhanced machine learning model achieved 94.56% of accuracy, 94.12% of precision, 94.98% of recall, 95.12% of F1-score, and 1856 ms of execution time. The simulation is conducted to test the efficacy of the model, and the results of the simulation show that the proposed Internet of Things-based enhanced machine learning obtains a higher rate of intelligence than other methods.