In the current study the application of organo‐modified bentonite for the adsorption of mycotoxins (aflatoxin B1, citrinin, patulin, and zearalenone) is presented. The modification of clays is carried out using benzyl‐tri‐n‐butyl ammonium bromide (BTB), benzethonium chloride (BTC), and dioctyl sodium sulfosuccinate (DSS). Various experimental parameters such as pH, time, adsorbent dose, and mycotoxins concentration are thoroughly studied. The modified clays (B‐BTB, B‐BTC and B‐DSS) are characterized by X‐ray fluorescence, X‐ray diffraction, and Fourier transform infrared spectroscopy. The results depicted the high detoxification efficiency (≈99%) of modified clays for the removal of mycotoxins under optimized conditions (pH 5, time: 30 min, adsorbent amount: 50 mg). The adsorption capacities of modified clays are found in the order of: B‐BTC (AFB1: 18.02, CIT: 18.35, PAT: 18.21, ZEA: 18.09 mg g−1) > B‐BTB (AFB1: 17.7, CIT: 18.11, PAT: 17.95, ZEA: 17.90 mg g−1) > B‐DSS (AFB1: 17.5, CIT: 18.02, PAT: 17.86, ZEA: 17.80 mg g−1). The obtained results fitted well with thermodynamic, isothermal (Langmuir) and pseudo‐second order kinetics. Low cost organo‐modified bentonite shows the promise in mitigating mycotoxin contamination, which could improve food safety and reduce environmental contamination.
This research combines two emerging technologies, the IoT and blockchain, and investigates their potential and use in the healthcare sector. In healthcare, IoT technology can be utilized for purposes such as remotely monitoring patients’ health. This paper details ongoing research towards individualized health monitoring using wearable gadgets. The goal of improving healthcare facilities and improvement of the quality of life of citizens naturally brings up Internet of Things (IoT) technologies for consideration. Health observation is exceptionally critical in terms of avoidance, especially since the early determination of illnesses can minimize trouble and treatment costs. The cornerstones of intelligent, integrated, and individualized healthcare are continuous monitoring of physical signs and evaluation of medical data. To build a more reliable and robust IoMT model, the study will monitor the application of blockchain technology in federated learning (FL). A viable way to address the heterogeneity problem in federated learning is to design the system, data, and model tiers to lessen heterogeneity and produce a high-quality, tailored model for each endpoint. Blockchain-based federated learning allows for smarter simulations, lower latency, and lower power consumption while maintaining privacy at the same time. This solution provides another immediate benefit: in addition to having a shared model upgrade, the updated model on phones will now be used automatically, giving personalized knowledge about the phone is used.
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