Influence of ligands within Al-based metal-organic frameworks for selective separation of methane from unconventional natural gas.

Efficient CH4/N2 separation from unconventional natural gas is vital for both energy recycling and climate change control. Figuring out the reason for the disparity between ligands in the framework and CH4 is the crucial problem for developing adsorbents in PSA progress. In this study, a series of eco-friendly Al-based MOFs, including Al-CDC, Al-BDC, CAU-10, and MIL-160, were synthesized to investigate the influence of ligands on CH4 separation through experimental and theoretical analyses. The hydrothermal stability and water affinity of synthetic MOFs were explored through experimental characterization. The active adsorption sites and adsorption mechanisms were investigated via quantum calculation. The results manifested that the interactions between CH4 and MOFs materials were affected by the synergetic effects of pore structure and ligand polarities, and the disparities of ligands within MOFs determined the separation efficiency of CH4. Especially, the CH4 separation performance of Al-CDC with high sorbent selection (68.56), moderate isosteric adsorption heat for CH4 (26.3 kJ/mol), and low water affinity (0.1 g/g at 40% RH) was superior to most porous adsorbents, which was attributed to its nanosheet structure, proper polarity, reduced local steric hindrance, and extra functional groups. The analysis of active adsorption sites indicated that hydrophilic carboxyl groups and hydrophobic aromatic ring were the dominant CH4 adsorption sites for liner ligands and bent ligands, respectively. The methylene groups with saturated C-H bonds enhanced the wdV interaction between ligands and CH4, resulting in the highest binding energy of CH4 for Al-CDC. The results provided valuable guidance for the design and optimization of high-performance adsorbents for CH4 separation from unconventional natural gas.

Inhibitory effects of compounds from the roots of Potentilla longifolia on lipid accumulation.

Potentilla longifolia is a kind of Chaoyao medicine, which is a branch of traditional Chinese medicine. The plant is often referred to as ganyancao or ganyearmcao, which means that it has a significant therapeutic effect on liver inflammation. In previous experiments, we found that a water extract of ganyearmcao inhibited lipid accumulation. In the present study, we isolated one new (ganyearmcaoone A, 1) and eight known compounds (2-9) from a water extract of the dried roots of ganyearmcao; all of the compounds were isolated for the first time from this medicinal plant. We elucidated the chemical structures of these compounds using comprehensive analyses of HR-ESI-MS and 1D, 2D NMR. We evaluated the inhibitory effects of the nine compounds on lipid accumulation in 3T3-L1 cells; we did so using photographic and quantitative assessments of the lipid content with oil red O staining and by measuring triglyceride levels. Compared with the control, compounds 6 and 9 significantly inhibited differentiation of 3T3-L1 cells and lipid accumulation. Compound 1 showed potential inhibitory effects on lipid accumulation. Molecular docking results indicated that compounds 6 and 9 may efficiently bind to AMPK and its downstream kinase (SCD1), thereby inhibiting lipid accumulation. Our results demonstrate that ganyearmcao and its components may play an important role in treating diseases related to lipid accumulation in the future.

An Improved Imaging Algorithm for High-Resolution Spotlight SAR with Continuous PRI Variation Based on Modified Sinc Interpolation.

This paper focuses on an improved imaging algorithm for spotlight synthetic aperture radar (SAR) with continuous Pulse Repetition Interval (PRI) variation in extremely high-resolution. Conventional SAR systems are limited in that a wide swath cannot be achieved with a high azimuth resolution in the meantime. This limitation can be overcome by Pulse Repetition Frequency (PRF) variation in a SAR system. However, there are problems such as the ambiguities of point targets or extended targets caused by nonuniform sampling. A reconstructive method, Nonuniform Discrete Fourier Transform (NUDFT) has been presented in the current literature, but it is rather computationally expensive. In this paper, a modified sinc interpolation based on NUDFT is proposed, which is used to reconstruct the uniformly sampled echo in time domain. Since the interpolation kernel length is relatively short, it is more computationally efficient. Then, the two-step processing approach combined with the modified sinc interpolation is further presented, which has much better accuracy than that combined with the conventional sinc interpolation. Both the simulated data and the extracted GF-3 data experiment demonstrate the validity and accuracy of the proposed approach.

SeqViews2SeqLabels: Learning 3D Global Features via Aggregating Sequential Views by RNN With Attention.

Learning 3D global features by aggregating multiple views has been introduced as a successful strategy for 3D shape analysis. In recent deep learning models with end-to-end training, pooling is a widely adopted procedure for view aggregation. However, pooling merely retains the max or mean value over all views, which disregards the content information of almost all views and also the spatial information among the views. To resolve these issues, we propose Sequential Views To Sequential Labels (SeqViews2SeqLabels) as a novel deep learning model with an encoder-decoder structure based on recurrent neural networks (RNNs) with attention. SeqViews2SeqLabels consists of two connected parts, an encoder-RNN followed by a decoder-RNN, that aim to learn the global features by aggregating sequential views and then performing shape classification from the learned global features, respectively. Specifically, the encoder-RNN learns the global features by simultaneously encoding the spatial and content information of sequential views, which captures the semantics of the view sequence. With the proposed prediction of sequential labels, the decoder-RNN performs more accurate classification using the learned global features by predicting sequential labels step by step. Learning to predict sequential labels provides more and finer discriminative information among shape classes to learn, which alleviates the overfitting problem inherent in training using a limited number of 3D shapes. Moreover, we introduce an attention mechanism to further improve the discriminative ability of SeqViews2SeqLabels. This mechanism increases the weight of views that are distinctive to each shape class, and it dramatically reduces the effect of selecting the first view position. Shape classification and retrieval results under three large-scale benchmarks verify that SeqViews2SeqLabels learns more discriminative global features by more effectively aggregating sequential views than state-of-the-art methods.

A High-Resolution SAR Focusing Experiment Based on GF-3 Staring Data.

Spotlight synthetic aperture radar (SAR) is a proven technique, which can provide high-resolution images as compared to those produced by traditional stripmap SAR. This paper addresses a high-resolution SAR focusing experiment based on Gaofen-3 satellite (GF-3) staring data with about 55 cm azimuth resolution and 240 MHz range bandwidth. In staring spotlight (ST) mode, the antenna always illuminates the same scene on the ground, which can extend the synthetic aperture. Based on a two-step processing algorithm, some special aspects such as curved-orbit model error correction, stop-and-go correction, and antenna pattern demodulation must be considered in image focusing. We provide detailed descriptions of all these aspects and put forward corresponding solutions. Using these suggested methods directly in an imaging module without any modification for other data processing software can make the most of the existing ground data processor. Finally, actual data acquired in GF-3 ST mode is used to validate these methodologies, and a well-focused, high-resolution image is obtained as a result of this focusing experiment.