Microfluidics is an emerging technology that can be employed as a powerful tool for designing lipid nano-microsized structures for biological applications. Those lipid structures can be used as carrying vehicles for a wide range of drugs and genetic materials. Microfluidic technology also allows the design of sustainable processes with less financial demand, while it can be scaled up using parallelization to increase production. From this perspective, this article reviews the recent advances in the synthesis of lipid-based nanostructures through microfluidics (liposomes, lipoplexes, lipid nanoparticles, core-shell nanoparticles, and biomimetic nanovesicles). Besides that, this review describes the recent microfluidic approaches to produce lipid micro-sized structures as giant unilamellar vesicles. New strategies are also described for the controlled release of the lipid payloads using microgels and droplet-based microfluidics. To address the importance of microfluidics for lipid-nanoparticle screening, an overview of how microfluidic systems can be used to mimic the cellular environment is also presented. Future trends and perspectives in designing novel nano and micro scales are also discussed herein.
Brewer's spent grain (BSG) is the main by‐product of the brewing industry. BSG can have diverse end‐users and has a high moisture level. To guarantee good conditions for storage and trade, it is necessary to remove the moisture from the material and use the proper method for the drying process. The phenomenon of water content removal is represented by mathematical models. Empirical and phenomenological models, as well as artificial neural networks (ANN) can be employed for this purpose. Here, we compare the fitted curves between empirical models (such as Page, Midilli–Kucuk, and Newton models) and an artificial neural network. The fits were investigated quantitatively by the analysis of the mean squared error (MSE) and determination coefficient (R2). The residues generated were analyzed qualitatively by the plots of histograms and qqplots (quantil vs. quantil). From the results that were obtained, it is possible to conclude that the ANN model had the best performance when compared to the empirical and semi‐empirical models studied, with the lowest values of MSE and the highest values of the R2 (0.999). The net presented adequate estimations for intermediate data as well, proving its use as a proper empirical model for the prediction of moisture data at several temperatures.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.