Device-Physics Realization of ZnO-MWCNT Nanostructure-Based Field-Effect Biosensor for Ultrasensitive Simultaneous Genomic Detection of Foodborne Pathogens.

The complex and versatile interactions among the wide variety of the nanostructures and the target analytes have primarily limited the detailed investigation of the transduction mechanism of nanomaterial-assisted electrical signal-based biosensors despite their high sensitivity, low-cost, portability, and ease of deployment. Hence, no common ground is formed detailing the principle of operation, demanding a strong need for systematic examination instead of hit and trial. Therefore, a maiden mechanistic investigation has been carried out in this paper for a field-effect-based biosensor device relying on the energy band diagram and the surface potential profile. To demonstrate the experimental evidence and appreciate the importance of food safety, three hazardous foodborne pathogens (Proteus mirabilis, Escherichia coli, and Clostridium botulinum) have been detected herein. The biosensor device, built on a hydrothermally synthesized zinc oxide and MWCNT (ZnO-MWCNT) composite nanostructure, simultaneously incorporates three fairly specific ss-DNA probes. Furthermore, the unmet challenge of biosensor device variability is addressed through the optimum selection of operating voltage of the device via a unique "voltage-selection-algorithm". We believe that the rigorous experimentation and the insightful device-physics realization demonstrated in this work will pave the way for a future decisive biosensor platform.

Near perfect classification of cardiac biomarker Troponin-I in human serum assisted by SnS2-CNT composite, explainable ML, and operating-voltage-selection-algorithm.

The high worldwide mortality and disproportionate impact of cardiovascular diseases have emerged as the most significant global health burden, unfortunately, unmet by the traditional detection methods. Therefore, developing a rapid, sensitive, selective, and rugged biosensor for the precise classification/quantification of cardiac biomarkers is a stepping stone for the future generation of cardiac healthcare. We demonstrate a facile, time-efficient, and scalable biosensor for classifying the FDI approved gold standard cardiac biomarker Troponin-I (cTnI) in untreated human serum matrix, built-on 2-D SnS2 and 1-D MWCNT composite transducer and decision-tree based explainable machine learning (ML) algorithm. The proposed methodology is further enhanced using an inimitable Operating-Voltage-Selection-Algorithm (OVSA), which boosts ML accuracy to ∼100%. The near-perfect classification is realized by strategically incorporating this two-step algorithm-first the OVSA, then the heuristic and ML approaches on the selected dataset. Dynamic concentrations of the biomarker (100 fg/mL to 100 ng/mL) are estimated with high sensitivity, ∼71 (ΔR/R) (ng/mL)-1cm-2 and low limit of detection (0.02 fg/mL), aiding to the prediction and prognosis of acute myocardial infarction. The hyperparameter tuning and feature engineering improve the decision process of the ML algorithm, fostering robustness against data variability. Feature importance indices, namely the Gini index, Permutation Importance, and SHAP values, portray 'Voltage' as the most important feature, further justifying our insight into the OVSA. The biosensor's specificity, selectivity, reproducibility and stability are effectively demonstrated with the sampling to result reporting time of just 20 min, establishing it as a potential candidate for clinical testing.

Ultrasonic biodiesel synthesis from crude Jatropha curcas oil with heterogeneous base catalyst: mechanistic insight and statistical optimization.

This paper reports studies in ultrasound-assisted heterogeneous solid catalyzed (CaO) synthesis of biodiesel from crude Jatropha curcas oil. The synthesis has been carried out in two stages, viz. esterification and trans-esterification. The esterification process is not influenced by ultrasound. The transesterification process, however, shows marked enhancement with ultrasound. A statistical experimental design has been used to optimize the process conditions for the synthesis. XRD analysis confirms formation of Ca(OMe)2, which is the active catalyst for transesterification reaction. The optimum values of parameters for the highest yield of transesterification have been determined as follows: alcohol to oil molar ratio ≈ 11, catalyst concentration ≈ 5.5 wt.%, and temperature ≈ 64°C. The activation energy of the reaction is calculated as 133.5 kJ/mol. The heterogeneity of the system increases mass transfer constraints resulting in approx. 4 × increase in activation energy as compared to homogeneous alkali catalyzed system. It is also revealed that intense micro-convection induced by ultrasound enhances the mass transfer characteristics of the system with ∼ 20% reduction in activation energy, as compared to mechanically agitated systems. Influence of catalyst concentration and alcohol to oil molar ratio on the transesterification yield is inter-linked through formation of methoxy ions and their diffusion to the oil-alcohol interface, which in turn is determined by the volume fractions of the two phases in the reaction mixture. As a result, the highest transesterification yield is obtained at the moderate values of catalyst concentration and alcohol to oil molar ratio.