In this study, crude extracellular keratinase obtained from a novel keratin-degrading bacterial strain, Bacillus safensis LAU 13 (GenBank accession No. KJ461434) was used for the synthesis of silver nanoparticles (AgNPs). The particles were characterised by UV–Visible spectroscopy, Fourier transform infrared (FTIR) spectroscopy, and transmission electron microscopy. The biosynthesised AgNPs exhibited maximum absorbance at 409 nm. They are spherical in shape with the size ranging 5–30 nm. The FTIR spectrum showed peaks at 3410, 2930, 1664, 1618, 1389 and 600 cm⁻¹, indicating that proteins were the capping and stabilisation molecules in the synthesis of AgNPs. Data obtained from XRD showed that the particles have face-centred cubic phase and are crystalline in nature with average size of ~8.3 nm. The particles showed effective inhibitory activity against five clinical isolates of Escherichia coli. Therefore, the keratinase of this strain could be used to develop an environmental friendly method for the rapid synthesis of AgNPs. To the best of our knowledge, this is the first report of green synthesis of AgNPs using the metabolite of B. safensis, and the report adds to the growing relevance of B. safensis as a potential industrially viable organism.

In view of the looming energy crisis as a result of depleting fossil fuel resources and environmental concerns from greenhouse gas emissions, the need for sustainable energy sources has secured global attention. Research is currently focused towards renewable sources of energy due to their availability and environmental friendliness. Biofuel production like other bioprocesses is controlled by several process parameters including pH, temperature and substrate concentration; however, the improvement of biofuel production requires a robust process model that accurately relates the effect of input variables to the process output. Artificial neural networks (ANNs) have emerged as a tool for modelling complex, non-linear processes. ANNs are applied in the prediction of various processes; they are useful for virtual experimentations and can potentially enhance bioprocess research and development. In this study, recent findings on the application of ANN for the modelling and optimization of biohydrogen, biogas, biodiesel, microbial fuel cell technology and bioethanol are reviewed. In addition, comparative studies on the modelling efficiency of ANN and other techniques such as the response surface methodology are briefly discussed. The review highlights the efficiency of ANNs as a modelling and optimization tool in biofuel process development.

A newly isolated bacterium identified as Bacillus safensis based on biochemical tests and 16S rRNA analysis and its mutant variant created by exposure to ultraviolet radiation at 254 nm were investigated for keratinolytic activity. The wild-type strain produced 35.4–50.4 U/mL keratinase over a period of 120 h, while the mutant one yielded 64.4–108.5 U/mL keratinase for the same period of 120 h. The optimal conditions for the enzyme activities were pH 7.5 and 40 °C. The mutant and wild-type strain keratinases retained 59% and 54% of their activity after 12 h pretreatment at 40 °C, and 64% and 60% of their activity after 12 h at pH 7.5, respectively. The keratinases showed high substrate specificity for feathers, but low specificity for human and bovine hairs. The enzymes were activated by Na⁺, Ca²⁺, Fe²⁺ and Mg²⁺. However, while Mn²⁺ activated the enzyme from the mutant strain, it inhibited that of the wild type. The mutant and wild-type strain completely degraded whole chicken feathers after 6 and 9 days at 30 ± 2 °C, and also completely dehaired goat skin within 12 and 16 h, respectively, without damage to the skin. Similarly, remarkable destaining of blood-stained cloth occurred within 2–3 h. The obtained results showed an improvement in the properties of the mutant strain for use of the micro-organism or its enzyme as biocatalysts.