Corncobs of four different corn varieties were physically segregated into two different anatomical portions, namely the corncob outer (CO) and corncob pith (CP). The biomass composition analysis of both the CO and CP was performed by four different methods. The CP showed a higher carbohydrate and lower lignin content (83.32% and 13.58%, respectively) compared with the CO (79.93% and 17.12%, respectively) in all of the methods. The syringyl/guaiacyl (S/G) ratio was observed to be higher in the CP (1.34) than in the CO (1.28). The comprehensive physical characterization of both samples substantiated the lower crystallinity and lower thermal stability that was observed in the CP compared to the CO. These properties make the CP more susceptible to glycanases, as evident from the enzymatic saccharification of CP carried out with a commercial cellulase and xylanase in this work. The yields obtained were 70.57% and 88.70% of the respective theoretical yields and were found to be equal to that of pure cellulose and xylan substrates. These results support the feasibility of the tailored valorization of corncob anatomical portions, such as enzymatic production of xylooligosaccharides from CP without pretreatment combined with the bioethanol production from pretreated CO to achieve an economical biorefinery output from corncob feedstock.

The increasing global demand for energy and the urgent need to mitigate climate change have driven the search for sustainable alternatives to fossil fuels, with biofuels emerging as a promising solution. However, the low yields and inefficiencies in biofuel production processes necessitate advanced strategies to enhance their commercial viability. Metabolic engineering has become a pivotal tool in optimizing microbial pathways to improve biofuel production, addressing these challenges through innovative genetic and synthetic biology approaches. This review highlights the role of metabolic engineering in enhancing biofuel production by focusing on microbial engineering for lignocellulosic biomass utilization, strategies to overcome inhibitor effects, and pathway optimization for biofuels like n-butanol and iso-butanol. It also explores the production of advanced biofuels from fatty acid and isoprenoid pathways, emphasizing the use of model organisms such as Escherichia coli and Saccharomyces cerevisiae. Key insights include the application of CRISPR/Cas9 and multiplex automated genome engineering for precise genetic modifications, as well as metabolic flux analysis to optimize pathway efficiency. Additionally, the review discusses synthetic biology methodologies to rewire metabolic networks and improve biofuel yields, providing a comprehensive overview of current advancements and their implications for industrial-scale production.

This study presents a statistically optimized protocol for the green synthesis of gold nanoparticles (Au NPs) using aqueous Kalanchoe pinnata leaf extract (AKPLE). An integrated experimental strategy, transitioning from preliminary one-factor-at-a-time (OFAT) screening to a five-factor Box–Behnken Design, was employed to model and simultaneously optimize two critical optical responses derived from surface plasmon resonance: the peak position (λ_max) and its absorbance intensity. Highly predictive quadratic models (R² > 0.97) revealed that synthesis outcomes are governed by significant nonlinear curvature, with minimal interaction effects. Multi-response optimization via a desirability function identified a harmonized set of conditions (HAuCl₄: 0.44 mM, AKPLE: 3.50% v/v, temperature: 80.6 °C, pH: 7.2, time: 66.7 min) predicted to minimize λ_max at 540 nm while maximizing absorbance to 0.61. Synthesis under these optimized conditions successfully produced spherical, crystalline Au NPs, as confirmed by characterization (average TEM size: 26.3 ± 4.1 nm; zeta potential: –30.45 mV). This work demonstrates that a hybrid OFAT-RSM approach is superior for the precise, multivariate optimization of plant-mediated Au NP synthesis, providing a validated and scalable framework to balance nanoparticle size and plasmonic intensity—an outcome unattainable through conventional OFAT methods.