Hydrothermally Synthesized Z-Scheme Nanocomposite of ZIF-9 Modified MXene for Photocatalytic Degradation of 4-Chlorophenol.

4-Chlorophenol (4CP) is a well-known environmental contaminant often detected in wastewater, generally arising from industrial processes such as chemical manufacture, pharmaceutical production, and pesticide formulation. 4CP is a matter of great concern since it is persistent and has the potential to have harmful impacts on both aquatic ecosystems and human health, owing to its hazardous and mutagenic properties. Hence, degradation of 4CP is of utmost significance. This research investigates the photocatalytic degradation of 4CP using a novel Z-scheme heterojunction nanocomposite composed of MXene and ZIF-9. The nanocomposite is synthesized through a two-step hydrothermal method and thoroughly characterized by using XRD, SEM, UV-visible spectroscopy, zeta potential, and electrochemical impedance spectroscopy studies, confirming successful fabrication with improved surface properties. The comparative photocatalytic degradation studies between pristine materials and the nanocomposite were performed, and significant enhancement in performance was observed. The effect of pH on the degradation efficiency is also explored and correlated with the surface charge. The Z-scheme photocatalysis mechanism is proposed, which is supported by time-resolved photoluminescence studies and scavenger experiments. The reusability of the nanocomposite is also evaluated. The study contributes to the development of efficient and sustainable photocatalysts for wastewater treatment.

Electronic Tongue Based on ZnO/ITO@glass for Electrochemical Monitoring of Spiciness Levels.

Capsaicin, a chemical compound present in chili peppers, is widely acknowledged as the main contributor to the spicy and hot sensations encountered during consumption. Elevated levels of capsaicin can result in meals being excessively spicy, potentially leading to health issues, such as skin burning, irritation, increased heart rate and circulation, and discomfort in the gastrointestinal system and even inducing nausea or diarrhea. The level of spiciness that individuals can tolerate may vary, so what may be considered incredibly hot for one person could be mild for another. To ensure food safety, human healthcare, regulatory compliance, and quality control in spicy food products, capsaicin levels must be measured. For these purposes, a reliable and stable sensor is required to quantify the capsaicin level. To leverage the effect of zinc oxide (ZnO), herein, we demonstrated the one-step fabrication process of an electronic tongue (E-Tongue) based on an electrochemical biosensor for the determination of capsaicin. ZnO was electrodeposited on the indium tin oxide (ITO) surface. The biosensor demonstrated the two notable linear ranges from 0.01 to 50 µM and from 50 to 500 µM with a limit of detection (LOD) of 2.1 nM. The present study also included the analysis of real samples, such as green chilis, red chili powder, and dried red chilis, to evaluate their spiciness levels. Furthermore, the E-Tongue exhibited notable degrees of sensitivity, selectivity, and long-term stability for a duration of more than a month. The development of an E-Tongue for capsaicin real-time monitoring as a point-of-care (POC) device has the potential to impact various industries and improve safety, product quality, and healthcare outcomes.

Detection of Pancreatic Cancer in CT Scan Images Using PSO SVM and Image Processing.

A diagnosis of pancreatic cancer is one of the worst cancers that may be received anywhere in the world; the five-year survival rate is very less. The majority of cases of this condition may be traced back to pancreatic cancer. Due to medical image scans, a significant number of cancer patients are able to identify abnormalities at an earlier stage. The expensive cost of the necessary gear and infrastructure makes it difficult to disseminate the technology, putting it out of the reach of a lot of people. This article presents detection of pancreatic cancer in CT scan images using machine PSO SVM and image processing. The Gaussian elimination filter is utilized during the image preprocessing stage of the removal of noise from images. The K means algorithm uses a partitioning technique to separate the image into its component parts. The process of identifying objects in an image and determining the regions of interest is aided by image segmentation. The PCA method is used to extract important information from digital photographs. PSO SVM, naive Bayes, and AdaBoost are the algorithms that are used to perform the classification. Accuracy, sensitivity, and specificity of the PSO SVM algorithm are better.