Experimental Realization of Few Layer Two-Dimensional MoS2 Membranes of Near Atomic Thickness for High Efficiency Water Desalination.

A globally imminent shortage of freshwater has been demanding viable strategies for improving desalination efficiencies with the adoption of cost- and energy-efficient membrane materials. The recently explored 2D transition metal dichalcogenides (2D TMDs) of near atomic thickness have been envisioned to offer notable advantages as high-efficiency membranes owing to their structural uniqueness; that is, extremely small thickness and intrinsic atomic porosity. Despite theoretically projected advantages, experimental realization of near atom-thickness 2D TMD-based membranes and their desalination efficiency assessments have remained largely unexplored mainly due to the technical difficulty associated with their seamless large-scale integration. Herein, we report the experimental demonstration of high-efficiency water desalination membranes based on few-layer 2D molybdenum disulfide (MoS2) of only ∼7 nm thickness. Chemical vapor deposition (CVD)-grown centimeter-scale 2D MoS2 layers were integrated onto porous polymeric supports with well-preserved structural integrity enabled by a water-assisted 2D layer transfer method. These 2D MoS2 membranes of near atomic thickness exhibit an excellent combination of high water permeability (>322 L m-2 h-1 bar-1) and high ionic sieving capability (>99%) for various seawater salts including Na+, K+, Ca2+, and Mg2+ with a range of concentrations. Moreover, they present near 100% salt ion rejection rates for actual seawater obtained from the Atlantic coast, significantly outperforming the previously developed 2D MoS2 layer membranes of micrometer thickness as well as conventional reverse osmosis (RO) membranes. Underlying principles behind such remarkably excellent desalination performances are attributed to the intrinsic atomic vacancies inherent to the CVD-grown 2D MoS2 layers as verified by aberration-corrected electron microscopy characterization.

On the accuracy and potential of Google Maps location history data to characterize individual mobility for air pollution health studies.

Appropriately characterizing spatiotemporal individual mobility is important in many research areas, including epidemiological studies focusing on air pollution. However, in many retrospective air pollution health studies, exposure to air pollution is typically estimated at the subjects' residential addresses. Individual mobility is often neglected due to lack of data, and exposure misclassification errors are expected. In this study, we demonstrate the potential of using location history data collected from smartphones by the Google Maps application for characterizing historical individual mobility and exposure. Here, one subject carried a smartphone installed with Google Maps, and a reference GPS data logger which was configured to record location every 10 s, for a period of one week. The retrieved Google Maps Location History (GMLH) data were then compared with the GPS data to evaluate their effectiveness and accuracy of the GMLH data to capture individual mobility. We also conducted an online survey (n = 284) to assess the availability of GMLH data among smartphone users in the US. We found the GMLH data reasonably captured the spatial movement of the subject during the one-week time period at up to 200 m resolution. We were able to accurately estimate the time the subject spent in different microenvironments, as well as the time the subject spent driving during the week. The estimated time-weighted daily exposures to ambient particulate matter using GMLH and the GPS data logger were also similar (error less than 1.2%). Survey results showed that GMLH data may be available for 61% of the survey sample. Considering the popularity of smartphones and the Google Maps application, detailed historical location data are expected to be available for large portion of the population, and results from this study highlight the potential of these location history data to improve exposure estimation for retrospective epidemiological studies.

A QSAR model for predicting rejection of emerging contaminants (pharmaceuticals, endocrine disruptors) by nanofiltration membranes.

A quantitative structure activity relationship (QSAR) model has been produced for predicting rejection of emerging contaminants (pharmaceuticals, endocrine disruptors, pesticides and other organic compounds) by polyamide nanofiltration (NF) membranes. Principal component analysis, partial least square regression and multiple linear regressions were used to find a general QSAR equation that combines interactions between membrane characteristics, filtration operating conditions and compound properties for predicting rejection. Membrane characteristics related to hydrophobicity (contact angle), salt rejection, and surface charge (zeta potential); compound properties describing hydrophobicity (log K(ow), log D), polarity (dipole moment), and size (molar volume, molecular length, molecular depth, equivalent width, molecular weight); and operating conditions namely flux, pressure, cross flow velocity, back diffusion mass transfer coefficient, hydrodynamic ratio (J(o)/k), and recovery were identified as candidate variables for rejection prediction. An experimental database produced by the authors that accounts for 106 rejection cases of emerging contaminants by NF membranes as result of eight experiments with clean and fouled membranes (NF-90, NF-200) was used to produce the QSAR model. Subsequently, using the QSAR model, rejection predictions were made for external experimental databases. Actual rejections were compared against predicted rejections and acceptable R(2) correlation coefficients were found (0.75 and 0.84) for the best models. Additionally, leave-one-out cross-validation of the models achieved a Q(2) of 0.72 for internal validation. In conclusion, a unified general QSAR equation was able to predict rejections of emerging contaminants during nanofiltration; moreover the present approach is a basis to continue investigation using multivariate analysis techniques for understanding membrane rejection of organic compounds.