Functionalization of mesoporous silica nanoparticles and their applications in organo-, metallic and organometallic catalysis

Mesoporous silica nanoparticles (MSN) with high surface area, tunable pore size and very narrow pore size distribution were functionalized by organic acid, organic base, metallic nanoparticles and organometallic complexes through co-condensation methods and/or post-synthesis grafting methods. And these surface-functionalized mesoporous materials were applied as heterogeneous catalysts in organocatalysis, metallic catalysis and organometallic catalysis. Organocatalysis. First, Brynsted acid and base were confined into the mesoporous channels of MSN and they could co-exist as compatible catalysts for one-pot reaction cascades without neutralizing each other because they were confined in different MSN particles\u27 channels. Brynsted acid and base also were site-separated by MSN\u27s internal surface and external surface through co-condensation method to functionalize MSN\u27s internal surface followed by grafting method to functionalize MSN\u27s external surface. These internal and external surface-separated Brynsted acid and base could co-exist as compatible catalysts too. Metallic catalysis. Water-soluble rhodium nanoparticles with well defined particle size were synthesized and immobilized on MSN during in situ MSN\u27s synthesis. The obtained material (MSNRhNPs) had homogeneous rhodium nanoparticle size, homogeneous rhodium nanoparticle distribution in MSN, typical MSN\u27s highly ordered structure and surface area and narrow pore size distribution as well. After MSNRhNPs were modified by manganese oxide, it could catalyze the hydrogenation of CO to produce the renewable energy alternative - ethanol with high selectivity and high activity. Additionally, after MSNRhNPs were functionalized by some chiral agents such as (-)-cinchonidine, it can used as a solid chiral catalyst which can be recycled and reused without any loss of reactivity and enantioselectivity. Organometallic catalysis. Wilkinson-type rhodium phosphine complex was homogeneously immobilized on MSN surface by co-condensation method. This MSN- immobilized rhodium-phosphine complex (RhPMSN) had a new and total different catalytic performance: RhPMSN could enantioselectively catalyze the hydrogenation of pyruvate when (-)-cinchonidine was adsorbed on RhPMSN surface. However, RhCl(TPP)3 (TPP: triphenylphosphine) and (-)-cinchonidine could not enantioselectively catalyze the same reaction in homogeneous system. An in-depth solid-state NMR study of RhPMSN has been presented. Functionalization of the ligand was confirmed by the presence of T sites in the 29Si CPMASNMR spectrum and quantification of these sites was achieved via integration of the 29Si DPMAS NMR spectrum. Both 1D and 2D SSNMR experiments showed that covalent attachment of the rhodium- phosphine ligand to the MSN surfaces was successful. Both 13C-1H and 31P-1H idHETCOR experiments provided structural details of oxidized and non-oxidized phosphine ligands, otherwise indiscernible in a conventional 1D CPMAS NMR experiments. Organometallic complex (salen)Cr on MSN was synthesized and applied in the oxidation of tetramethylbenzidine (TMB) with iodosobenzene. MSN-(salen)CrIII as a heterogeneous catalyst exhibited both similarities and differences with the analogous (salen)CrIII(H2O)+ as catalyst in aqueous acetonitrile (10% H2O). It was shown that the covalently attached catalyst in mesoporous channels of MSN was still easily accessible to the reactants without diffusion problem. Aminopropyl-functionalized MSN was synthesized and applied in the selective sequestration of carboxylic acids from biomass fermentation. Aminopropyl-functionalized MSN with a designed loading of functional groups could have a very high selectivity for carboxylic acid instead for ethanol, glucose, and protein. The regeneration of aminopropyl-functionalized MSN could be done easily by increasing pH to 10.5 because the adsorption reaction between carboxylic acids and aminopropyl-functionalized MSN was pH-dependent. And the regenerated aminopropyl-functionalized MSN showed adsorption capacity equivalent to the original

Multi-Visual-Inertial System: Analysis, Calibration and Estimation

In this paper, we study state estimation of multi-visual-inertial systems (MVIS) and develop sensor fusion algorithms to optimally fuse an arbitrary number of asynchronous inertial measurement units (IMUs) or gyroscopes and global and(or) rolling shutter cameras. We are especially interested in the full calibration of the associated visual-inertial sensors, including the IMU or camera intrinsics and the IMU-IMU(or camera) spatiotemporal extrinsics as well as the image readout time of rolling-shutter cameras (if used). To this end, we develop a new analytic combined IMU integration with intrinsics-termed ACI3-to preintegrate IMU measurements, which is leveraged to fuse auxiliary IMUs and(or) gyroscopes alongside a base IMU. We model the multi-inertial measurements to include all the necessary inertial intrinsic and IMU-IMU spatiotemporal extrinsic parameters, while leveraging IMU-IMU rigid-body constraints to eliminate the necessity of auxiliary inertial poses and thus reducing computational complexity. By performing observability analysis of MVIS, we prove that the standard four unobservable directions remain - no matter how many inertial sensors are used, and also identify, for the first time, degenerate motions for IMU-IMU spatiotemporal extrinsics and auxiliary inertial intrinsics. In addition to the extensive simulations that validate our analysis and algorithms, we have built our own MVIS sensor rig and collected over 25 real-world datasets to experimentally verify the proposed calibration against the state-of-the-art calibration method such as Kalibr. We show that the proposed MVIS calibration is able to achieve competing accuracy with improved convergence and repeatability, which is open sourced to better benefit the community

A deep deformable residual learning network for SAR images segmentation

Reliable automatic target segmentation in Synthetic Aperture Radar (SAR) imagery has played an important role in the SAR fields. Different from the traditional methods, Spectral Residual (SR) and CFAR detector, with the recent adavance in machine learning theory, there has emerged a novel method for SAR target segmentation, based on the deep learning networks. In this paper, we proposed a deep deformable residual learning network for target segmentation that attempts to preserve the precise contour of the target. For this, the deformable convolutional layers and residual learning block are applied, which could extract and preserve the geometric information of the targets as much as possible. Based on the Moving and Stationary Target Acquisition and Recognition (MSTAR) data set, experimental results have shown the superiority of the proposed network for the precise targets segmentation

Probabilistic Contrastive Learning for Long-Tailed Visual Recognition

Long-tailed distributions frequently emerge in real-world data, where a large number of minority categories contain a limited number of samples. Such imbalance issue considerably impairs the performance of standard supervised learning algorithms, which are mainly designed for balanced training sets. Recent investigations have revealed that supervised contrastive learning exhibits promising potential in alleviating the data imbalance. However, the performance of supervised contrastive learning is plagued by an inherent challenge: it necessitates sufficiently large batches of training data to construct contrastive pairs that cover all categories, yet this requirement is difficult to meet in the context of class-imbalanced data. To overcome this obstacle, we propose a novel probabilistic contrastive (ProCo) learning algorithm that estimates the data distribution of the samples from each class in the feature space, and samples contrastive pairs accordingly. In fact, estimating the distributions of all classes using features in a small batch, particularly for imbalanced data, is not feasible. Our key idea is to introduce a reasonable and simple assumption that the normalized features in contrastive learning follow a mixture of von Mises-Fisher (vMF) distributions on unit space, which brings two-fold benefits. First, the distribution parameters can be estimated using only the first sample moment, which can be efficiently computed in an online manner across different batches. Second, based on the estimated distribution, the vMF distribution allows us to sample an infinite number of contrastive pairs and derive a closed form of the expected contrastive loss for efficient optimization. Our code is available at https://github.com/LeapLabTHU/ProCo.Comment: Accepted by IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI

Temporal Aware Mixed Attention-based Convolution and Transformer Network (MACTN) for EEG Emotion Recognition

Emotion recognition plays a crucial role in human-computer interaction, and electroencephalography (EEG) is advantageous for reflecting human emotional states. In this study, we propose MACTN, a hierarchical hybrid model for jointly modeling local and global temporal information. The model is inspired by neuroscience research on the temporal dynamics of emotions. MACTN extracts local emotional features through a convolutional neural network (CNN) and integrates sparse global emotional features through a transformer. Moreover, we employ channel attention mechanisms to identify the most task-relevant channels. Through extensive experimentation on two publicly available datasets, namely THU-EP and DEAP, our proposed method, MACTN, consistently achieves superior classification accuracy and F1 scores compared to other existing methods in most experimental settings. Furthermore, ablation studies have shown that the integration of both self-attention mechanisms and channel attention mechanisms leads to improved classification performance. Finally, an earlier version of this method, which shares the same ideas, won the Emotional BCI Competition's final championship in the 2022 World Robot Contest

Effect of doubly fed induction generatortidal current turbines on stability of a distribution grid under unbalanced voltage conditions

This paper analyses the effects of doubly fed induction generator (DFIG) tidal current turbines on a distribution grid under unbalanced voltage conditions of the grid. A dynamic model of an electrical power system under the unbalanced network is described in the paper, aiming to compare the system performance when connected with and without DFIG at the same location in a distribution grid. Extensive simulations of investigating the effect of DFIG tidal current turbine on stability of the distribution grid are performed, taking into account factors such as the power rating, the connection distance of the turbine and the grid voltage dip. The dynamic responses of the distribution system are examined, especially its ability to ride through fault events under unbalanced grid voltage conditions. The research has shown that DFIG tidal current turbines can provide a good damping performance and that modern DFIG tidal current power plants, equipped with power electronics and low-voltage ride-through capability, can stay connected to weak electrical grids even under the unbalanced voltage conditions, whilst not reducing system stability