Urea-mediated growth engineering of Au@Ag core-shell nanostructures: an enzymatic detection strategy with machine learning-assisted comparative analysis.

A non-invasive, enzyme-based colorimetric biosensor was developed for urea detection in saliva, utilizing a growth-based method with Au@Ag core-shell nanostructures, including CTAB-coated gold nanoparticles (AuNPs) and CTAB-coated gold nanorods with short (SAuNRs) and high (HAuNRs) aspect ratios. The biosensing mechanism relies on urease-mediated hydrolysis of urea, which raises the pH and enhances the reduction capability of ascorbic acid, leading to the formation of a silver shell on the gold nanostructures, causing colorimetric changes correlated to urea concentration. A machine-learning comparative analysis was also performed to assess how the nanostructure morphology of AuNPs, SAuNRs, and HAuNRs affects sensor performance.

Synergistic applications of quantum dots and magnetic nanomaterials in pathogen detection: a comprehensive review.

The rapid and accurate detection of pathogens is crucial for effective disease prevention and management in healthcare, food safety, and environmental monitoring. While conventional pathogen detection methods like culture-based techniques and PCR are sensitive and selective, they are often time-consuming, require skilled operators, and are not suitable for point-of-care or on-site testing. To address these limitations, innovative sensor technologies have emerged that leverage the unique properties of nanomaterials.

Laboratory study: Synthesis and optimization of nano nisomes containing essential oil and investigating its toxicity on parasite and HFF cell line.

The use of nanotechnology can reduce the challenges facing the use of herbal compounds in the fight against infectious agents. The aim of the present research is to produce nano niosomes containing essential oil with high efficiency in the temperature and acidity of the living environment of parasite and to investigate its toxicity on this parasite. First, Essential oil compounds were identified using GC-Mass.

A simple smartphone-assisted paper-based colorimetric biosensor for the detection of urea adulteration in milk based on an environment-friendly pH-sensitive nanocomposite.

Urea is a common milk adulterant that falsely increases its protein content. Excessive consumption of urea is harmful to the kidney, liver, and gastrointestinal system. The conventional methods for urea detection in milk are time-consuming, costly, and require highly skilled operators.