We report a modular construction of a new metal-organic framework (MOF) by strategically incorporating a number of water repellent functional groups in the frameworks. These MOFs demonstrate both open structure for high sorption capability and strong water resistance.

Metal-organic frameworks have been proposed as useful sorbents for the capture of a variety of compounds. In this work, inverse gas chromatography (IGC) utilizing micropacked capillary columns was used to probe the adsorption of more than 30 volatile organic compounds (VOCs) on IRMOF-1. In an attempt to study the effect of structural degradation upon VOC adsorption, multiple samples of IRMOF-1 with widely ranging properties were investigated. Trends in the differential enthalpies and equilibrium constants for the adsorption of VOCs were determined on the basis of the molecular properties of the adsorbate and the structural properties of the MOF sample. The results indicate that samples of IRMOF-1 that are affected by a moderate amount of structural degradation interact with adsorbed species more strongly than does a sample with fewer defects, resulting in higher heats of adsorption. Samples of IRMOF-1 with specific surface areas of around 1000 m(2)/g show heats of adsorption for alkanes that are higher than those estimated previously via Monte Carlo calculations. Although the data for nonpolar (and weakly polar) species showed many of the anticipated trends for the interactions with IRMOF-1, the equilibrium behavior of polar VOCs did not correlate well with the molecular properties of the adsorbate (i.e., vapor pressure and deformation polarizability), leaving some uncertainty about the nature of the interaction mechanism. The equilibrium data and the heats of adsorption were found to fit well to a small group of molecular descriptors through the application of the Abraham linear free-energy relationship, thus providing insight into the complex interactions between the MOF structure and the VOC compounds. Hydrogen bonding interactions were determined to be the primary contributors to specific interactions between adsorbates and the MOF surface. Size exclusion also seems to play a role in the adsorption of larger species. These results show that the interaction of VOCs with MOFs is more complex than previously assumed and that more work is needed to probe the mechanisms of these processes.

Resonance Raman spectra of individual strained ultralong single-wall carbon nanotubes (SWNTs) are studied. Torsional and uniaxial strains are introduced by atomic force microscopy manipulation. Torsional strain strongly affects the Raman spectra, inducing a large downshift in the E2 symmetry mode in the G+ band, but a slight upshift for the rest of the G modes and also an upshift in the radial breathing mode (RBM). Whereas uniaxial strain has no effect on the frequency of either the E2 symmetry mode in the G+ band or the RBM, it downshifts the rest of the G modes. The Raman intensity change reflects the effect of these strains on the SWNT electronic band structure.

In this work, the adsorption behavior of a range of organic vapors and gases on the zeolitic imidazolate framework, ZIF-8, is investigated using an inverse gas chromatography (IGC) methodology at the zero-coverage limit and elevated temperatures. The measured thermodynamic values and surface energies for the adsorption of n-alkanes on ZIF-8 are found to be reduced from those previously reported for IRMOF-1. This reduction is most likely an effect of the predominately organic accessible surface of ZIF-8 and the resulting weaker interactions in comparison to IRMOF-1. The pore aperture size of ZIF-8, which is significantly reduced from that of IRMOF-1, is seen to introduce molecular sieving effects for branched alkanes, aromatics, and heavily halogenated compounds. Deformation polarizabilities of the adsorbates were used to calculate the specific adsorption free energy, and it is determined that the specific effects account for around 1-5 kJ/mol, or between 10% and 70% of the total free energy of adsorption for the sorbates studied (at 250 °C). The importance of electrostatic forces was seen in the significantly enhanced adsorption of propylene and ethylene in comparison to their respective alkanes and in the direct correlation shown between the specific components of the free energy of adsorption and the adsorbate's dipole moment.

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We present herein a rational approach to probe the torsional strain-induced electronic transition energy Eii variation of individual SWNTs by resonant Raman spectroscopy (RRS). When a SWNT was manipulated by AFM tip through a path perpendicular to SWNT axis, both torsional and uniaxial strain would be introduced in SWNTs. Under the torsional strain, resonant Raman spectral mapping along a SWNT detected an M-shaped frequency (omegaRBM) and W-shaped intensity (IS) variation of radial breathing mode (RBM) spectra, which were induced by the elastic retraction of the nanotubes in combination with the friction after the tip has been removed. The electronic transition energy Eii variation along SWNTs by torsional strain follows a family pattern based on q=(n - m) mod 3: for semiconducting SWNTs, E33S increases for q=+1, E33S decreases and E22S increases for q=-1, and for metallic SWNTs, E11M always increases.

The doctor determines whether there are lesions in the human body through the diagnosis of medical images, and classifies and identifies the lesions. Therefore, the automatic classification and recognition of medical images has received extensive attention. Since the inflammatory phenomenon of vascular endothelial cells is closely related to the varicose veins of the lower extremities, in order to realize the automatic classification and recognition of varicose veins of the lower extremities, this paper proposes a varicose vein recognition algorithm based on vascular endothelial cell inflammation images and multi-scale deep learning, called MSDCNN. First, we obtained images of vascular endothelial cells in patients with varicose veins of the lower extremities and normal subjects. Second, multiple convolutional layers extract multi-scale features of vascular endothelial cell images. Then, the MFM activation function is used instead of the ReLU activation function to introduce a competitive mechanism that extracts more features that are compact and reduces network layer parameters. Finally, the network uses a 3 × 3 convolution kernel to improve the network feature extraction capability and use the 1 × 1 convolution kernel for dimensionality reduction to further streamline network parameters. The experimental results tell us that the network has the advantages of high recognition accuracy, fast running speed, few network parameters, and is suitable for small-embedded devices. INDEX TERMS Vascular endothelial cells, inflammation, multi-scale deep learning, varicose veins of the lower extremities.