MXene-Coated Ion-Selective Electrode Sensors for Highly Stable and Selective Lithium Dynamics Monitoring

Lithium holds immense significance in propelling sustainable energy and environmental systems forward. However, existing sensors used for lithium monitoring encounter issues concerning their selectivity and long-term durability. Addressing these challenges is crucial to ensure accurate and reliable lithium measurements during the lithium recovery processes. In response to these concerns, this study proposes a novel approach involving the use of an MXene composite membrane with incorporated poly(sodium 4-styrenesulfonate) (PSS) as an antibiofouling layer on the Li+ ion selective electrode (ISE) sensors. The resulting MXene-PSS Li+ ISE sensor demonstrates exceptional electrochemical performance, showcasing a superior slope (59.42 mV/dec), lower detection limit (10–7.2 M), quicker response time (∼10 s), higher selectivity to Na+ (−2.37) and K+ (−2.54), and reduced impedance (106.9 kΩ) when compared to conventional Li+ ISE sensors. These improvements are attributed to the unique electronic conductivity and layered structure of the MXene-PSS nanosheet coating layer. In addition, the study exhibits the long-term accuracy and durability of the MXene-PSS Li+ ISE sensor by subjecting it to real wastewater testing for 14 days, resulting in sensor reading errors of less than 10% when compared to laboratory validation results. This research highlights the great potential of MXene nanosheet coatings in advancing sensor technology, particularly in challenging applications, such as detecting emerging contaminants and developing implantable biosensors. The findings offer promising prospects for future advancements in sensor technology, particularly in the context of sustainable energy and environmental monitoring

Revolutionizing Membrane Design Using Machine Learning-Bayesian Optimization

Polymeric membrane design is a multidimensional process involving selection of membrane materials and optimization of fabrication conditions from an infinite candidate space. It is impossible to explore the entire space by trial-and-error experimentation. Here, we present a membrane design strategy utilizing machine learning-based Bayesian optimization to precisely identify the optimal combinations of unexplored monomers and their fabrication conditions from an infinite space. We developed ML models to accurately predict water permeability and salt rejection from membrane monomer types (represented by the Morgan fingerprint) and fabrication conditions. We applied Bayesian optimization on the built ML model to inversely identify sets of monomer/fabrication condition combinations with the potential to break the upper bound for water/salt selectivity and permeability. We fabricated eight membranes under the identified combinations and found that they exceeded the present upper bound. Our findings demonstrate that ML-based Bayesian optimization represents a paradigm shift for next-generation separation membrane design

Revolutionizing Membrane Design Using Machine Learning-Bayesian Optimization

Polymeric membrane design is a multidimensional process involving selection of membrane materials and optimization of fabrication conditions from an infinite candidate space. It is impossible to explore the entire space by trial-and-error experimentation. Here, we present a membrane design strategy utilizing machine learning-based Bayesian optimization to precisely identify the optimal combinations of unexplored monomers and their fabrication conditions from an infinite space. We developed ML models to accurately predict water permeability and salt rejection from membrane monomer types (represented by the Morgan fingerprint) and fabrication conditions. We applied Bayesian optimization on the built ML model to inversely identify sets of monomer/fabrication condition combinations with the potential to break the upper bound for water/salt selectivity and permeability. We fabricated eight membranes under the identified combinations and found that they exceeded the present upper bound. Our findings demonstrate that ML-based Bayesian optimization represents a paradigm shift for next-generation separation membrane design