The pursuit of ever-more efficient, reliable, and affordable solar cells has pushed the development of nano/micro-technological solutions capable of boosting photovoltaic (PV) performance without significantly increasing costs. One of the most relevant solutions is based on light management via photonic wavelength-sized structures, as these enable pronounced efficiency improvements by reducing reflection and by trapping the light inside the devices. Furthermore, optimized microstructured coatings allow self-cleaning functionality via effective water repulsion, which reduces the accumulation of dust and particles that cause shading. Nevertheless, when it comes to market deployment, nano/micro-patterning strategies can only find application in the PV industry if their integration does not require high additional costs or delays in high-throughput solar cell manufacturing. As such, colloidal lithography (CL) is considered the preferential structuring method for PV, as it is an inexpensive and highly scalable soft-patterning technique allowing nanoscopic precision over indefinitely large areas. Tuning specific parameters, such as the size of colloids, shape, monodispersity, and final arrangement, CL enables the production of various templates/masks for different purposes and applications. This review intends to compile several recent high-profile works on this subject and how they can influence the future of solar electricity.

Flux balance analysis is currently the standard method to compute metabolic fluxes in genome-scale networks. Several variations employing diverse objective functions and/or constraints have been published. Here we propose a hybrid semi-parametric version of flux balance analysis that combines mechanistic-level constraints (parametric) with empirical constraints (non-parametric), at the genome-scale. A CHO dataset with 27 measured exchange fluxes obtained from 21 reactor experiments served to evaluate the method. The reduced CHO genome-scale model comprehended 686 metabolites, 788 reactions and 210 degrees of freedom. The experimental flux dataset could be compressed to 6 principal components retaining 93.7% of explained variance. The conjugation of both types of constraints is coded as a linear program with comparable computational cost as standard flux balance analysis. The hybrid flux balance analysis showed a significant reduction in the specific growth rate prediction error in comparison to the non-hybrid version. The hybrid method was eventually used to design a metabolically efficient feed to extend cell expansion from 9.87 Mcell/ml to 22.48 Mcell/ml at the point of induction with minimal accumulation of byproducts. It is concluded that the predictive advantage of the hybrid method resulted from the statistical abstraction of regulatory mechanisms, which were absent in the standard flux balance analysis.