Machine Learning-Assisted Design of Thin-Film Composite Membranes for Solvent Recovery

Multiscale modelling of transport in polymer-based reverse-osmosis/nanofiltration membranes: present and future

Nanofiltration (NF) and reverse osmosis (RO) processes are physical separation technologies used to remove contaminants from liquid streams by employing dense polymer-based membranes with nanometric voids that confine fluids at the nanoscale. At this level, physical properties such as solvent and solute permeabilities are intricately linked to molecular interactions. Initially, numerous studies focused on developing macroscopic transport models to gain insights into separation properties at the nanometer scale. However, continuum-based models have limitations in nanoconfined situations that can be overcome by force field molecular simulations. Continuum-based models heavily rely on bulk properties, often neglecting critical factors like liquid structuring, pore geometry, and molecular/chemical specifics. Molecular/mesoscale simulations, while encompassing these details, often face limitations in time and spatial scales. Therefore, achieving a comprehensive understanding of transport requires a synergistic integration of both approaches through a multiscale approach that effectively combines and merges both scales. This review aims to provide a comprehensive overview of the state-of-the-art in multiscale modeling of transport through NF/RO membranes, spanning from the nanoscale to continuum media.

Design of Modified Polymer Membranes Using Machine Learning

Surface modification is an attractive strategy to adjust the properties of polymer membranes. Unfortunately, predictive structure-processing-property relationships between the modification strategies and membrane performance are often unknown. One possibility to tackle this challenge is the application of data-driven methods such as machine learning. In this study, we applied machine learning methods to data sets containing the performance parameters of modified membranes. The resulting machine learning models were used to predict performance parameters, such as the pure water permeability and the zeta potential of membranes modified with new substances. The predictions had low prediction errors, which allowed us to generalize them to similar membrane modifications and processing conditions. Additionally, machine learning methods were able to identify the impact of substance properties and process parameters on the resulting membrane properties. Our results demonstrate that small data sets, as they are common in materials science, can be used as training data for predictive machine learning models. Therefore, machine learning shows great potential as a tool to expedite the development of high-performance membranes while reducing the time and costs associated with the development process at the same time.

Is Cr(III) re-oxidation occurring in Cr-contaminated soils after remediation: Meta-analysis and machine learning prediction

Whether Cr(III) in Cr(III)-containing sites formed after Cr(VI) reduction and stabilization remediation are re-oxidized and pose toxicity risks again has been a growing concern. In this study, 1030 data were collected to perform a meta-analysis to clarify the effects of various factors (oxidant type, soil and Cr(III) solid compound properties, aging conditions, and testing methods) on Cr(III) oxidation. We observed that the soil properties of clay, pH ≥ 8, the lower CEC capacity, easily reducible Mn content, and Cr(III) content, and the higher Eh value and Fe content can promote the re-oxidation of Cr(III). Publication bias and sensitivity analyses confirmed the stability and reliability of the meta-analysis. Subsequently, we used five machine learning algorithms to construct and optimize the models. The prediction results of the RF model (RMSE <1.36, R2 >0.71) with good algorithm performance showed that after ten years of remediation, the extractable Cr(VI) concentration in the soil was 0.0087 mg/L, indicating a negligible secondary pollution risk of Cr(III) re-oxidation. This study provides theoretical support for subsequent risk management and control after Cr(VI) soil remediation and provides a solution for the quantitative prediction of Cr(III) re-oxidation.