Assessing the fate and behaviour of plant nutrients in aquaponic systems by chemical equilibrium modelling: A meta-analytical approach.

Aquaponic systems differ from hydroponics by a higher pH and higher concentrations of dissolved organic matter (DOM). This study assessed whether plant nutrient deficiencies in aquaponics are caused by lacking input of the deficient nutrients or their chemical saturation. Nine scenarios with nutrient concentrations based on Hoagland's solution and different pH (5.5, 6.5, 7.5) and DOM concentrations (0 mg L-1, 20 mg L-1) were constructed, representing theoretical hydroponic and aquaponic systems. Eventually, nutrient concentrations at equilibrium were calculated. In addition, a meta-analysis was conducted to assess whether nutrient concentrations reported in aquaponic studies could be predicted by equilibrium calculations. Theoretical results indicate that solubility thresholds cause deficiencies of P, Ca, Fe, and Cu at equilibrium due to the higher pH in aquaponics compared with hydroponics. Deficiencies in K and other plant nutrients are, meanwhile, likely caused by lacking supply through nutrient inputs at equilibrium. The presence of DOM can increase Fe and Cu solubility. However, equilibrium calculations could not predict nutrient concentrations found in literature. P was present at higher concentrations (max. 0.3 mmol L-1) than predicted (10-3-10-6 mmol L-1), indicating chemical equilibrium was not reached in the assessed systems (average hydraulic retention time = 17 d). Future studies should consider reaction rates. Furthermore, considering the low concentrations of dissolved P in all studies, a system scaling based on P instead of N might be considered.

Better, Faster, and Less Biased Machine Learning: Electromechanical Switching in Ferroelectric Thin Films.

Machine-learning techniques are more and more often applied to the analysis of complex behaviors in materials research. Frequently used to identify fundamental behaviors within large and multidimensional datasets, these techniques are strictly based on mathematical models. Thus, without inherent physical or chemical meaning or constraints, they are prone to biased interpretation. The interpretability of machine-learning results in materials science, specifically materials' functionalities, can be vastly improved through physical insights and careful data handling. The use of techniques such as dimensional stacking can provide the much needed physical and chemical constraints, while proper understanding of the assumptions imposed by model parameters can help avoid overinterpretation. These concepts are illustrated by application to recently reported ferroelectric switching experiments in PbZr0.2 Ti0.8 O3 thin films. Through systematic analysis and introduction of physical constraints, it is argued that the behaviors present are not necessarily due to exotic mechanisms previously suggested, but rather well described by classical ferroelectric switching superimposed by non-ferroelectric phenomena, such as electrochemical deformation, electrostatic interactions, and/or charge injection.