Ultrathin, molecular-sieving membranes have great potential to realize high-flux, high-selectivity mixture separation at low energy cost. Current microporous membranes [pore size < 1 nanometer (nm)], however, are usually relatively thick. With the use of current membrane materials and techniques, it is difficult to prepare microporous membranes thinner than 20 nm without introducing extra defects. Here, we report ultrathin graphene oxide (GO) membranes, with thickness approaching 1.8 nm, prepared by a facile filtration process. These membranes showed mixture separation selectivities as high as 3400 and 900 for H2/CO2 and H2/N2 mixtures, respectively, through selective structural defects on GO.

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UV-irradiation has been utilized as an effective and facile approach to tune the underwater oleophobicity of GO coatings/membranes by gradually modifying GO flake composition and surface morphology.

This work investigates mechanical properties of styrene-butadiene rubber (SBR) composites incorporating magadiite (MGD), a synthetic layered silicate (Na 2 Si 14 O 29 Á9H 2 O) with surface chemistry similar to precipitated silica used in tire tread formulations. Treatment with cetyltrimethylammonium (CTA 1 ) expands the MGD layers and makes the interlayer face surfaces accessible to sulfur-functional silane TESPT (Si69) and SBR, primarily during batch mixing. DMA and tensile testing of cured CMGD/SBR composites show that CTA-treated MGD (CMGD) provides substantially higher levels of mechanical reinforcement than equivalent amounts of silica. However, CMGD/SBR composites exhibit larger loss tangent values above T g , probably due to lower SBR-SBR crosslink density resulting from interlayer trapping of sulfur released by Si69 during vulcanization. DMA and tensile testing also demonstrate Si69's critical role in forming MGD-SBR graft sites essential to mechanical reinforcement. Replacing silica with CMGD reduces composite weight without sacrificing tensile modulus, suggesting that use of CMGD in tire rubber formulations could improve vehicle energy efficiency.

As a key component in combination therapy for acquired immunodeficiency syndrome (AIDS), non-nucleoside reverse transcriptase inhibitors (NNRTIs) have been proven to be an essential way in stopping HIV-1 replication. In the present work, in silico studies were conducted on a series of 119 NNRTIs, including 1-(2-hydroxyethoxymethyl)-6-(phenylthio)thymine (HEPT) and dihydroalkoxybenzyloxopyrimidine (DABO) derivatives by using the comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA), docking simulations and molecular dynamics (MD). The statistical results of the optimal model, the ligand-based CoMSIA one (Q(2) = 0.48, R(ncv)(2) =0.847, R(pre)(2) = 0.745) validates its satisfactory predictive capacity both internally and externally. The contour maps, docking and MD results correlate well with each other, drawing conclusions as follows: 1) Compounds with bulky substituents in position-6 of ring A, hydrophobic groups around position- 1, 2, 6 are preferable to the biological activities; 2) Two hydrogen bonds between RT inhibitor and the Tyr 318, Lys 101 residues, respectively, and a π-π bond between the inhibitor and Trp 188 are formed and crucial to the orientation of the active conformation of the molecules; 3) The binding pocket is essentially hydrophobic, which are determined by residues such as Trp 229, Tyr 318, Val 179, Tyr 188 and Val 108, and hydrophobic substituents may bring an improvement to the biological activity; 4) DABO and HEPT derivatives have different structures but take a similar mechanism to inhibit RT. The potency difference between two isomers in HEPTs can be explained by the distinct locations of the 6-naphthylmethyl substituent and the reasons are explained in details. All these results could be employed to alter the structural scaffold in order to develop new HIV-1 RT inhibitors that have an improved biological property. To the best of our knowledge, this is the first report on 3D-QSAR modeling of this series of HEPT and DABO NNRTs. The QSAR model and the information derived, we hope, will be of great help in presenting clear guidelines and accurate activity predictions for newly designed HIV-1 reverse transcriptase (RT) inhibitor.