In the present research work is demonstrated a cross-scale manufacturing approach for the production of multifunctional glass fiber reinforced polymer (GFRP) composite tubes with a purposely redesigned filament winding process. Up until now, limited studies have been reported towards the multiscale reinforcement direction of continuous fibers for the manufacturing of hierarchical composites at the industrial level. This study involved the development of two different multi-walled carbon nanotube (MWCNT) aqueous-based inks, which were employed for the modification of commercial glass fiber (GF) reinforcing tows via a bath coating unit in a pilot production line. The obtained multifunctional GFRP tubes presented a variety of characteristics in relation to their final mechanical, hydrothermal aging, electrical, thermal and thermoelectric properties. Results revealed that the two individual systems exhibited pronounced differences both in crushing behavior and durability performance. Interestingly, for lateral compression the MWCNT coatings comprising a polymeric dispersant minorly affected the mechanical response of the produced tubes. The crashworthiness indicators of the multifunctional tubes displayed a slight 5% variation to the respective reference values, combined with a more ductile behavior. Moreover, regarding the bulk electrical and thermal conductivity values, as well as the Seebeck coefficient factor, the corresponding tubes displayed a variance of 233% and 19% and an opposite semi-conducting sign denoting a p- and n-type character, respectively.

With the expansion of the green products market and the worldwide policies and strategies directed toward a green revolution and ecological transition, the demand for innovative approaches is always on the rise. Among the sustainable agricultural approaches, microbial-based products are emerging over time as effective and feasible alternatives to agrochemicals. However, the production, formulation, and commercialization of some products can be challenging. Among the main challenges are the industrial production processes that ensure the quality of the product and its cost on the market. In the context of a circular economy, solid-state fermentation (SSF) might represent a smart approach to obtaining valuable products from waste and by-products. SSF enables the growth of various microorganisms on solid surfaces in the absence or near absence of free-flowing water. It is a valuable and practical method and is used in the food, pharmaceutical, energy, and chemical industries. Nevertheless, the application of this technology in the production of formulations useful in agriculture is still limited. This review summarizes the literature dealing with SSF agricultural applications and the future perspective of its use in sustainable agriculture. The survey showed good potential for SSF to produce biostimulants and biopesticides useful in agriculture.

In this study, we are reporting a novel prediction model for forecasting the carbon dioxide (CO2) fixation of microalgae which is based on the hybrid approach of adaptive neuro-fuzzy inference system (ANFIS) and genetic algorithm (GA). The CO2 fixation rate of various algal strains was collected and the cultivation conditions of the microalgae such as temperature, pH, CO2 %, and amount of nitrogen and phosphorous (mg/L) were taken as the input variables, while the CO2 fixation rate was taken as the output variable. The optimization of ANFIS parameters and the formation of the optimized fuzzy model structure were performed by genetic algorithm (GA) using MATLAB in order to achieve optimum prediction capability and industrial applicability. The best-fitting model was figured out using statistical analysis parameters such as root mean square error (RMSE), coefficient of regression (R2), and average absolute relative deviation (AARD). According to the analysis, GA-ANFIS model depicted a greater prediction capability over ANFIS model. The RMSE, R2, and AARD for GA-ANFIS were observed to be 0.000431, 0.97865, and 0.044354 in the training phase and 0.00056, 0.98457, and 0.032156 in the testing phase, respectively, for the GA-ANFIS Model. As a result, it can be concluded that the proposed GA-ANFIS model is an efficient technique having a very high potential to accurately predict the CO2 fixation rate.

Native starches have limited applications in the food industry due to their unreactive and insoluble nature. Cold plasma technology, including plasma-activated water (PAW), has been explored to modify starches to enhance their functional, thermal, molecular, morphological, and physicochemical properties. Atmospheric cold plasma and low-pressure plasma systems have been used to alter starches and have proven successful. This review provides an in-depth analysis of the different cold plasma setups employed for starch modifications. The effect of cold plasma technology application on starch characteristics is summarized. We also discussed the potential of plasma-activated water as a novel alternative for starch modification. This review provides information needed for the industrial scale-up of cold plasma technologies as an eco-friendly method of starch modification.