Search for: All records

An air gap membrane distillation (AGMD) module was developed by incorporating a poly(etheretherketone) (PEEK) hollow fiber membrane (HFM) having a nonporous wall. This PEEK HFM was placed inside a polyvinylidene fluoride (PVDF) hydrophobic porous wall HFM with a larger bore diameter. The outside diameter (OD) of PVDF HFM is 925 μm, small enough to be capable of achieving a high surface area packing density of 1297 m2/m3. The air gap thickness was very small, 121 μm. Hot brine flowed on the outside of the PVDF HFM; the colder liquid was passed through the lumen of the PEEK-based condenser hollow fibers. Water vapor condensed in the air gap formed between the inner surface of the porous PVDF HFM and the outer surface of the nonporous condenser PEEK fiber. With 85o C hot brine flowing at 40 mL•min􀀀1 and 5o C coolant flowing at 8 mL•min􀀀1, the water vapor flux was 9.05 kg/m2•h with a salt rejection of 98.7 %. Simulation by COMSOL Multiphysics predicted water flux and interfacial temperature of HFM, which supported the experimental observations. Moreover, the influence of module geometry, membrane characteristics and internal flow configuration on permeate flux, thermal efficiency, gained output ratio (GOR), and temperature and concentration polarization were evaluated. Principal component analysis (PCA) was used to illustrate the interconnections among various parameters and their respective contributions to water flux and other performance indicators. Air gap thickness had the strongest influence on temperature polarization. 

Per- and polyfluoroalkyl substances (PFAS) have garnered attention as a pressing environmental issue due to their enduring presence and suspected adverse health effects. This study assessed the rejection or removal ef- ficacy of PFAS by commercial reverse osmosis (RO) and nanofiltration (NF) membranes and examined the im- pacts of surfactants, ion valency and solution temperature that are inadequately explored. The results reveal that the presence of cationic surfactants such as cetyltrimethylammonium bromide (CTAB) increased the rejection of two selected PFAS compounds, perfluorooctanoic acid (PFOA) and perfluorobutanoic acid (PFBA), by binding with negatively charged PFAS and preventing them from passing through membrane pores via size exclusion, whereas the presence of anionic surfactants such as sodium dodecyl sulfate (SDS) increased the PFAS rejection because the increased electrostatic repulsion prevented PFAS from approaching and adsorbing onto the mem- brane surface. Moreover, aqueous ions (e.g., Al³⁺ and PO³−) with higher ion valency enabled higher rejection of PFOA and PFBA through increased effective molecular size and increased electronegativity. Finally, only high solution temperature at 45 ◦C significantly reduced PFAS rejection efficiency because of the thermally expanded membrane pores and thus the increased leakage of PFAS. Overall, this research provides valuable insights into the various factors impacting PFAS rejection in commercial RO and NF processes. These findings are crucial for developing efficient PFAS removal methods and optimizing existing treatment systems, thereby contributing significantly to the ongoing efforts to combat PFAS contamination. 

We consider the problem of sequential multiple hypothesis testing with nontrivial data collection costs. This problem appears, for example, when conducting biological experiments to identify differentially expressed genes of a disease process. This work builds on the generalized α-investing framework which enables control of the marginal false discovery rate in a sequential testing setting. We make a theoretical analysis of the long term asymptotic behavior of α-wealth which motivates a consideration of sample size in the α-investing decision rule. Posing the testing process as a game with nature, we construct a decision rule that optimizes the expected α-wealth reward (ERO) and provides an optimal sample size for each test. Empirical results show that a cost-aware ERO decision rule correctly rejects more false null hypotheses than other methods for $n=1$ where n is the sample size. When the sample size is not fixed cost-aware ERO uses a prior on the null hypothesis to adaptively allocate of the sample budget to each test. We extend cost-aware ERO investing to finite-horizon testing which enables the decision rule to allocate samples in a non-myopic manner. Finally, empirical tests on real data sets from biological experiments show that cost-aware ERO balances the allocation of samples to an individual test against the allocation of samples across multiple tests. 

Despite the enormous developments in asymmetric catalysis, the basis for asymmetric induction is largely limited to the spatial interaction between the substrate and catalyst. Consequently, asymmetric discrimination between two sterically similar groups remains a challenge. This is particularly formidable for enantiodifferentiation between two aryl groups without a directing group or electronic manipulation. Here we address this challenge by using a robust organocatalytic system leading to excellent enantioselection between aryl and heteroaryl groups. With versatile 2-indole imine methide as the platform, an excellent combination of a superb chiral phosphoric acid and the optimal hydride source provided efficient access to a range of highly enantioenriched indole-containing triarylmethanes. Control experiments and kinetic studies provided important insights into the mechanism. DFT calculations also indicated that while hydrogen bonding is important for activation, the key interaction for discrimination of the two aryl groups is mainly π–π stacking. Preliminary biological studies also demonstrated the great potential of these triarylmethanes for anticancer and antiviral drug development.