Two-step optimization of pressure and recovery of reverse osmosis desalination process

Exploring the Knowledge Attained by Machine Learning on Ion Transport across Polyamide Membranes Using Explainable Artificial Intelligence

Recent studies have increasingly applied machine learning (ML) to aid in performance and material design associated with membrane separation. However, whether the knowledge attained by ML with a limited number of available data is enough to capture and validate the fundamental principles of membrane science remains elusive. Herein, we applied explainable artificial intelligence (XAI) to thoroughly investigate the knowledge learned by ML on the mechanisms of ion transport across polyamide reverse osmosis (RO) and nanofiltration (NF) membranes by leveraging 1,585 data from 26 membrane types. The Shapley additive explanation method based on cooperative game theory was used to unveil the influences of various ion and membrane properties on the model predictions. XAI shows that the ML can capture the important roles of size exclusion and electrostatic interaction in regulating membrane separation properly. XAI also identifies that the mechanisms governing ion transport possess different relative importance to cation and anion rejections during RO and NF filtration. Overall, we provide a framework to evaluate the knowledge underlying the ML model prediction and demonstrate that ML is able to learn fundamental mechanisms of ion transport across polyamide membranes, highlighting the importance of elucidating model interpretability for more reliable and explainable ML applications to membrane selection and design.

Development and characterisation of self-assembled graphene hydrogel-based anodes for bioelectrochemical systems

In this work, we report a simple and scalable method to produce high efficiency 3D graphene-based electrodes (GH) for bioelectrochemical systems. GH were obtained by self-assembly of graphene oxide, through slow reduction with ascorbic acid over conductive mesh-works (carbon cloth and stainless-steel). The GH structure and composition were characterised by electron microscopy (SEM) and spectroscopy (FTIR and Raman), whereas the electrodes' performance was tested by chronoamperometry and cyclic voltammetry in a microbial electrolysis cell (MEC) inoculated with a pure culture of G. sulfurreducens. The hydrogel had a broad pore size distribution (>1 μm), which allowed bacterial colonisation within the framework. The macro-porous structure and chemical properties of the hydrogel rendered a higher bacterial loading capacity and substrate oxidation rate than other carbonaceous materials, including different reported graphene electrodes, which significantly increased MEC performance.

A cheap, robust and versatile hydrogel-electrode is easily obtained by reduction of graphene-oxide; its colonisation by Geobacter resulted in high current densities.