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.

The Impact of Surface Chemistry and Synthesis Conditions on the Adsorption of Antibiotics onto MXene Membranes.

MXene, a two-dimensional (2D) nanomaterial with diverse applications, has gained significant attention due to its 2D lamellar structure, abundance of surface groups, and conductivity. Despite various established synthesis methods since its discovery in 2011, MXenes produced through different approaches exhibit variations in structural and physicochemical characteristics, impacting their suitability for environmental application. This study delves into the effect of synthesis conditions on MXene properties and its adsorption capabilities for four commonly prescribed antibiotics. We utilized material characterization techniques to differentiate MXenes synthesized using three prevalent etchants: hydrofluoric acid (HF), mixed acids (HCl/HF), and fluoride salts (LiF/HCl). Our investigation of adsorption performance included isotherm and kinetic analysis, complemented by density functional theory calculations. The results of this research pinpointed LiF/HCl as an efficient etchant, yielding MXene with favorable morphology and surface chemistry. Electrostatic interactions and hydrogen bonding between MXene surface terminations and ionizable moieties of the antibiotic molecules emerge as pivotal factors in adsorption. Specifically, a higher presence of oxygen terminations increases the binding affinities. These findings provide valuable guidance for etchant selection in environmental applications and underscore the potential to tailor MXenes through synthesis conditions to design membranes capable of selectively removing antibiotics and other targeted substances.

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.

Incorporation of Cellulose Nanocrystals into Graphene Oxide Membranes for Efficient Antibiotic Removal at High Nutrient Recovery.

Two-dimensional (2D) material-based membranes hold great promise in wastewater treatment. However, it remains challenging to achieve highly efficient and precise small molecule/ion separation with pure 2D material-fabricated lamellar membranes. In this work, laminated graphene oxide (GO)-cellulose nanocrystal (CNC) hybrid membranes (GO/CNC) were fabricated by taking advantages of the unique structures and synergistic effects generated from these two materials. The characterization results in physiochemical properties, and the structure of the as-synthesized hybrid membranes displayed enhanced membrane surface hydrophilicity, enhanced crumpling surface structure, and slightly enlarged interlayer-spacing with the incorporation of CNCs. Water permeability increases by two to four times with the addition of different CNC weight ratios in comparison to a pristine GO membrane. The optimal GO/CNC membrane achieved efficient rejection toward three typical antibiotics at 74.8, 90.9, and 97.2% for sulfamethoxazole (SMX), levofloxacin (Levo), and norfloxacin (Nor), respectively, while allowing a high passage of desirable nutrients such as NO3- and H2PO4-. It was found that SMX removal is primarily governed by electrostatic repulsion, while adsorption plays a crucial role in removing Levo and Nor. Moreover, the density functional theory calculations confirmed the increased antibiotic removal in the presence of an organic foulant, humic acid. Such a 2D material-based hybrid membrane offers a new strategy to develop fit-for purpose membranes for resource recovery and water separation.