Maize root behavior as three-inputs-three-outputs logical gates due to positive gravitropism and nutritropism.

Root growth and their interactions can provide valuable information for the development of asynchronous logic systems. Here, maize root behavior due to positive gravitropism and nutritropism is evaluated as three-inputs-three-outputs logical gates. Using plant roots as the element for unconventional computing, the Boolean functions of each root tropism were constructed through arithmetic-logical operations. One gravity gate (rGG) and two nutrient gates (rNG1 and rNG2) were fabricated using additive manufacturing. The rGG platform was oriented with roots directly pulled down by gravity which computes (x, y, z) = (xz + yz, x + y¯z+yz¯, xy + yz), whereas specific output channels in rNG1 and rNG2 were fertigated with high phosphorus concentration resulting in (x, y, z) = (x + y + z, xy + xz, xyz) for rNG1 and (x, y, z) = (xyz, xy¯z+xyz¯, x + y + z) for rNG2. For rGG, rNG1, and rNG2, the symbols x, y, and z pertain to "root presence" in the related channel, whereas top bar on the symbols indicates "root absence". Anatomical traits of roots were evaluated to assess possible differences in vascular tissues due to gravitropic and nutritropic responses. Overall, maize primary roots showed prominent positive gravitropism and nutritropism, and the roots that were most attracted by gravitational or nutritional stimuli showed an increase in the diameter of phloem and xylem. The logic exhibited by roots was dependent on the gravitropic and nutritropic stimuli to which they were exposed in the different logic gates. The responsiveness of maize roots to environmental stimuli such as gravity and nutrients provided valuable information to be used in computational bioelectronics.

Smart sustainable biorefineries for lignocellulosic biomass.

Lignocellulosic biomass (LCB) is considered as a sustainable feedstock for a biorefinery to generate biofuels and other bio-chemicals. However, commercialization is one of the challenges that limits cost-effective operation of conventional LCB biorefinery. This article highlights some studies on the sustainability of LCB in terms of cost-competitiveness and environmental impact reduction. In addition, the development of computational intelligence methods such as Artificial Intelligence (AI) as a tool to aid the improvement of LCB biorefinery in terms of optimization, prediction, classification, and decision support systems. Lastly, this review examines the possible research gaps on the production and valorization in a smart sustainable biorefinery towards circular economy.