Machine learning powered CN-coordinated cobalt nanoparticles embedded cellulosic nanofibers to assess meat quality via clenbuterol monitoring.

The World Anti-Doping Agency (WADA) has prohibited the use of clenbuterol (CLN) because it induces anabolic muscle growth while potentially causing adverse effects such as palpitations, anxiety, and muscle tremors. Thus, it is vital to assess meat quality because, athletes might have positive test for CLN even after consuming very low quantity of CLN contaminated meat. Numerous materials applied for CLN monitoring faced potential challenges like sluggish ion transport, non-uniform ion/molecule movement, and inadequate electrode surface binding. To overcome these shortcomings, herein we engineered bimetallic zeolitic imidazole framework (BM-ZIF) derived N-doped porous carbon embedded Co nanoparticles (CN-CoNPs), dispersed on conductive cellulose acetate-polyaniline (CP) electrospun nanofibers for sensitive electrochemical monitoring of CLN. Interestingly, the smartly designed CN-CoNPs wrapped CP (CN-CoNPs-CP) electrospun nanofibers offers rapid diffusion of CLN molecules to the sensing interface through amine and imine groups of CP, thus minimizing the inhomogeneous ion transportation and inadequate electrode surface binding. Additionally, to synchronize experiments, machine learning (ML) algorithms were applied to optimize, predict, and validate voltametric current responses. The ML-trained sensor demonstrated high selectivity, even amidst interfering substances, with notable sensitivity (4.7527 µA/µM/cm2), a broad linear range (0.002-8 µM), and a low limit of detection (1.14 nM). Furthermore, the electrode exhibited robust stability, retaining 98.07% of its initial current over a 12-h period. This ML-powered sensing approach was successfully employed to evaluate meat quality in terms of CLN level. To the best of our knowledge, this is the first study of using ML powered system for electrochemical sensing of CLN.

Biomass and domestic waste: a potential resource combination for bioenergy generation and water treatment via benthic microbial fuel cell.

The benthic microbial fuel cell (BMFC) is one of the most efficient types of bioelectrochemical fuel cell systems. Modern bioelectrochemical fuel cells have several drawbacks, including an unstable organic substrate and a microorganism-unfriendly atmosphere. The recent literature to encounter such issues is one of the emerging talks. Researchers are focusing on the utilization of biomass and waste to encounter such challenges and make the technique more feasible at the pilot scale. This study investigated the combination of local bakery waste as an organic substrate with lignocellulosic biomass material. The whole experiment was conducted for 45 days. At an external resistance of 1000 ῼ and an internal resistance of 677 ῼ, the power density was found to be 3.51 mW/m2. Similarly, for Pb2+, Cd2+, Cr3+, Ni2+, and Co2+, the degradation efficiency was 84.40%, 81.21%, 80%, 89.50%, and 86.0%, respectively. The bacterial identification results showed that Liquorilactobacillus nagelii, Proteus mirabilis, Pectobacterium punjabense, and Xenorhabdus thuongxuanensis are the most prominent species found on anode biofilm. The method of electron generation in this study, which includes the degradation of metal ions, is also well described. Lastly, optimising the parameters showed that pH 7 provides a feasible environment for operation. A few future suggestions for practical steps are enclosed for the research community.