Small-Pore Zeolite Membranes: A Review of Gas Separation Applications and Membrane Preparation

There have been significant advancements in small-pore zeolite membranes in recent years. With pore size closely related to many energy- or environment-related gas molecules, small-pore zeolite membranes have demonstrated great potential for the separation of some interested gas pairs, such as CO2/CH4, CO2/N-2 and N-2/CH4. Small-pore zeolite membranes share some characteristics but also have distinctive differences depending on their framework, structure and zeolite chemistry. Through this mini review, the separation performance of different types of zeolite membranes with respect to interested gas pairs will be compared. We aim to give readers an idea of membrane separation status. A few representative synthesis conditions are arbitrarily chosen and summarized, along with the corresponding separation performance. This review can be used as a quick reference with respect to the influence of synthesis conditions on membrane quality. At the end, some general findings and perspectives will be discussed

Capacity Pre-Log of Noncoherent SIMO Channels via Hironaka's Theorem

We find the capacity pre-log of a temporally correlated Rayleigh block-fading SIMO channel in the noncoherent setting. It is well known that for block-length L and rank of the channel covariance matrix equal to Q, the capacity pre-log in the SISO case is given by 1-Q/L. Here, Q/L can be interpreted as the pre-log penalty incurred by channel uncertainty. Our main result reveals that, by adding only one receive antenna, this penalty can be reduced to 1/L and can, hence, be made to vanish in the large-L limit, even if Q/L remains constant as L goes to infinity. Intuitively, even though the SISO channels between the transmit antenna and the two receive antennas are statistically independent, the transmit signal induces enough statistical dependence between the corresponding receive signals for the second receive antenna to be able to resolve the uncertainty associated with the first receive antenna's channel and thereby make the overall system appear coherent. The proof of our main theorem is based on a deep result from algebraic geometry known as Hironaka's Theorem on the Resolution of Singularities

Colloidal Silicalite Coating for Improving Ionic Liquid Membrane Loading on Macroporous Ceramic Substrate for Gas Separation

A thin layer of colloidal silicalite was coated on a macroporous alumina substrate to improve the effectiveness in loading and supporting ionic liquid (IL) membrane on macroporous ceramic substrate. The [bmim][BF4] IL and CO2 gas separation were used as the model system in this research. The colloidal silicalite top layer enabled the formation of a pinhole-free IL membrane with significantly reduced load of IL as compared to the bare alumina substrate because the former had a smaller and more uniform inter-particle pore size than the latter. The supported IL membrane was extensively studied for CO2 separation in conditions relevant to coal combustion flue gases. The silicalite-supported IL membrane achieved a CO2/N2 permselectivity of ~24 with CO2 permeance of ~1.0×10-8 mol/m2·s·Pa in dry conditions at 26˚C and reached a CO2/N2 separation factor of ~18 with CO2 permeance of ~1.56×10-8 mol/m2·s·Pa for a feed mixture containing ~11% CO2 and ~9% water vapor at 50oC. This supported IL membrane exhibited excellent stability under a 5-bar transmembrane pressure at 103˚C and chemical resistance to H2O, SO2, and air (O2). Results of this study also indicated that, in order to fully realize the advantages of using the colloidal silicalite support for IL membranes, it is necessary to develop macroporous ceramic supports with optimized pore size distribution so that the IL film can be retained in the micron-thin silicalite layer without penetrating into the base substrate

Differentially Private Numerical Vector Analyses in the Local and Shuffle Model

Numerical vector aggregation plays a crucial role in privacy-sensitive applications, such as distributed gradient estimation in federated learning and statistical analysis of key-value data. In the context of local differential privacy, this study provides a tight minimax error bound of $O(\frac{ds}{n\epsilon^2})$, where $d$ represents the dimension of the numerical vector and $s$ denotes the number of non-zero entries. By converting the conditional/unconditional numerical mean estimation problem into a frequency estimation problem, we develop an optimal and efficient mechanism called Collision. In contrast, existing methods exhibit sub-optimal error rates of $O(\frac{d^2}{n\epsilon^2})$ or $O(\frac{ds^2}{n\epsilon^2})$. Specifically, for unconditional mean estimation, we leverage the negative correlation between two frequencies in each dimension and propose the CoCo mechanism, which further reduces estimation errors for mean values compared to Collision. Moreover, to surpass the error barrier in local privacy, we examine privacy amplification in the shuffle model for the proposed mechanisms and derive precisely tight amplification bounds. Our experiments validate and compare our mechanisms with existing approaches, demonstrating significant error reductions for frequency estimation and mean estimation on numerical vectors.Comment: Full version of "Hiding Numerical Vectors in Local Private and Shuffled Messages" (IJCAI 2021

Spaceborne miniaturized UHF dual band helix antenna with a small frequency ratio

This study proposes a novel miniaturized circularly polarized (CP) ultrahigh frequency (UHF) quadrifilar helix antenna for spaceborne applications. The dual-band operation is realized using four inverted-U shaped helical strips (IUSHSs) that are rotated and alternately arranged on the four faces of a hollow polyimide cuboid in a sequential rotation manner, which effectively reduces the size of the antenna. Furthermore, the four IUSHSs are connected by a cross-shape strip at the top of the antenna to control the dual resonant frequencies, resulting in a small dual-band frequency ratio. The proposed antenna is both lightweight and robust when compared with the conventional miniaturized CP antennas operating at similar bands with similar performance. In particular, its compact radiator provides effective miniaturized spaceborne solution without the need of high-dielectric coefficient materials. A device for spaceborne application that operates at 402/505 MHz is designed, fabricated, measured, and in-orbit tested with a weight of 651 g and an effective size of 0.161 7 0.161 7 0.228 λ3402MHz (λ402MHz is the wavelength at 402 MHz). The measured gain and axial ratio of the proposed antenna are better than 5.32 dBi and 2.18 dB, respectively, within 2 and 12 MHz bandwidth for the two bands. The test results proved that the methods used to design the proposed antenna are effective

Fine-grained Private Knowledge Distillation

Knowledge distillation has emerged as a scalable and effective way for privacy-preserving machine learning. One remaining drawback is that it consumes privacy in a model-level (i.e., client-level) manner, every distillation query incurs privacy loss of one client's all records. In order to attain fine-grained privacy accountant and improve utility, this work proposes a model-free reverse $k$-NN labeling method towards record-level private knowledge distillation, where each record is employed for labeling at most $k$ queries. Theoretically, we provide bounds of labeling error rate under the centralized/local/shuffle model of differential privacy (w.r.t. the number of records per query, privacy budgets). Experimentally, we demonstrate that it achieves new state-of-the-art accuracy with one order of magnitude lower of privacy loss. Specifically, on the CIFAR-$10$ dataset, it reaches $82.1\%$ test accuracy with centralized privacy budget $1.0$; on the MNIST/SVHN dataset, it reaches $99.1\%$/$95.6\%$ accuracy respectively with budget $0.1$. It is the first time deep learning with differential privacy achieve comparable accuracy with reasonable data privacy protection (i.e., $\exp(\epsilon)\leq 1.5$). Our code is available at https://github.com/liyuntong9/rknn