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The emergence of smart and nanobiosensor (NB) technologies has transformed the monitoring and management of bacterial infections. These developments offer remarkable accuracy and precision for detecting infectious pathogens. Smart artificial intelligence (AI)-assisted and NB-based methods are used as powerful tools in biomedicine for bacterial detection, combatting multidrug resistance, and diagnosing infections. In this study, we delve into the advancements in these technologies, focusing on AI-based techniques for NBs in detecting bacterial infections from 2019 to 2024. We analyze the contributions of machine learning and deep learning techniques to enhance performance and reliability. The new approaches to improve the effectiveness and versatility of antibacterial treatments are critically analyzed. Our study includes the observations of carbon nanoparticles that selectively target bacteria using photothermal properties and the production of hybrid hydrogel composites with capabilities. Furthermore, the study emphasizes the crucial significance of NBs in propelling the progress of diagnostic methods, biosensing technologies, and treatments, thereby transforming the healthcare industry and the way diseases are managed. In addition, we explore pathogen-based infections, bacterial diagnosis, and treatment using engineered NBs enhanced with various modalities such as electrochemistry, acoustics, electromagnetism, and photothermal resonance. Our comprehensive review highlights the potential and throws light on future research directions for effective management and control of bacterial infections.

Kinetic analysis was used to study the destabilization of mayonnaise by focusing on the fat crystals. Mayonnaise prepared from rapeseed oil and soybean oil was stored at temperatures ranging from −20 to −40 °C. The destabilization kinetic parameters were measured by observing oil separation over time. The destabilization rate constant, kd, increased with decreasing temperature. The highest value of kd was 1.28 × 10−3 min−1 at −40 °C for rapeseed oil mayonnaise (RoM) and the lowest was 1.95 × 10−6 min−1 at −20 °C for soybean oil mayonnaise (SoM). At each temperature, the kd value in RoM was higher than that in SoM. However, the order of destabilization, n, followed no specific pattern. The crystallization rate constant, Kc, and Avrami constant, n, were calculated using microscopic images of the fat crystals. The increase in crystallization kinetic parameters with decreasing temperature revealed changes in crystal behavior. Both the destabilization rate constant, kd, and the crystallization rate constant, Kc, depended on the temperature. This temperature dependency behavior showed a correlation between kd and Kc, suggesting that the destabilization rate depended on the rate of growth of fat crystals during the freeze-thawing of mayonnaise.