Preparation of Microporous Molding Activated Carbon Derived from Bamboo Pyrolysis Gasification Byproducts for Toluene Gas Adsorption.

The effective utilization of charcoal and tar byproducts is a challenge for pyrolysis gasification of bamboo. Herein, the bamboo tar was modified via polymerization and acted as a new adhesive for the preparation of excellent bamboo-charcoal-derived molding activated carbon (MBAC). As compared with pristine tar and other adhesives, the aromatization of tar with phenol increased its molecular weight, oxygenic functional groups, and thermal stability, leading to the decreased blocking impact of charcoal pore and improved bonding and pyrolytic crosslinking effect between charcoal particles. These further contribute to the high mechanical strength, specific surface area, pore volume, and amount of oxygenic functional groups for fabricated MBAC. Owing to the high microporous volume of MBAC, it exhibited 385 mg·g-1 toluene and 75.2% tetrachloride gas adsorption performances. Moreover, the pseudo-first-order, pseudo-second-order, and Bangham models were used to evaluate the kinetic data. The toluene adsorption process conforms to the Bangham kinetic model, suggesting that the diffusion mechanism of toluene adsorption mainly followed intraparticle diffusion.

Curvature Effect of Pyridinic N-Modified Carbon Atom Sites for Electrocatalyzing CO2 Conversion to CO.

Carbon material is considered a promising electrocatalyst for the CO2 reduction reaction (CO2RR); especially, N-doped carbon material shows high CO Faradic efficiency (FECO) when using pyridinic N species as the active site. However, in the past decade, more efforts were focused on the preparation of various carbon nanostructures containing abundant pyridinic N species and few researchers studied the electronic structure modulation of the pyridinic N site. The curvature of the carbon substrate is an easily controllable parameter for modulating the local electronic environment of catalytic sites. In this research, carbon nanotubes (CNTs) with different diameters are applied to modulate the electronic environment of pyridinic N by the curvature effect. The pyridinic N sites doped on CNTs with the average curvature of 0.04 show almost 100% FECO at the current density of 3 mA cm-2 at -0.6 V vs RHE and 91% FECO retention after 12 h test, which is superior to most of the carbon-based electrocatalysts. As demonstrated by density functional theory simulation, the pyridinic N site forms a strong local electric field around the nearby C active site and protrudes out of the curved CNT surface like a tip, which remarkably enriches the protons around the adsorbed CO2 molecule.

MSTGC: Multi-Channel Spatio-Temporal Graph Convolution Network for Multi-Modal Brain Networks Fusion.

Multi-modal brain networks characterize the complex connectivities among different brain regions from structure and function aspects, which have been widely used in the analysis of brain diseases. Although many multi-modal brain network fusion methods have been proposed, most of them are unable to effectively extract the spatio-temporal topological characteristics of brain network while fusing different modalities. In this paper, we develop an adaptive multi-channel graph convolution network (GCN) fusion framework with graph contrast learning, which not only can effectively mine both the complementary and discriminative features of multi-modal brain networks, but also capture the dynamic characteristics and the topological structure of brain networks. Specifically, we first divide ROI-based series signals into multiple overlapping time windows, and construct the dynamic brain network representation based on these windows. Second, we adopt adaptive multi-channel GCN to extract the spatial features of the multi-modal brain networks with contrastive constraints, including multi-modal fusion InfoMax and inter-channel InfoMin. These two constraints are designed to extract the complementary information among modalities and specific information within a single modality. Moreover, two stacked long short-term memory units are utilized to capture the temporal information transferring across time windows. Finally, the extracted spatio-temporal features are fused, and multilayer perceptron (MLP) is used to realize multi-modal brain network prediction. The experiment on the epilepsy dataset shows that the proposed method outperforms several state-of-the-art methods in the diagnosis of brain diseases.

Deep Multi-Modal Discriminative and Interpretability Network for Alzheimer's Disease Diagnosis.

Multi-modal fusion has become an important data analysis technology in Alzheimer's disease (AD) diagnosis, which is committed to effectively extract and utilize complementary information among different modalities. However, most of the existing fusion methods focus on pursuing common feature representation by transformation, and ignore discriminative structural information among samples. In addition, most fusion methods use high-order feature extraction, such as deep neural network, by which it is difficult to identify biomarkers. In this paper, we propose a novel method named deep multi-modal discriminative and interpretability network (DMDIN), which aligns samples in a discriminative common space and provides a new approach to identify significant brain regions (ROIs) in AD diagnosis. Specifically, we reconstruct each modality with a hierarchical representation through multilayer perceptron (MLP), and take advantage of the shared self-expression coefficients constrained by diagonal blocks to embed the structural information of inter-class and the intra-class. Further, the generalized canonical correlation analysis (GCCA) is adopted as a correlation constraint to generate a discriminative common space, in which samples of the same category gather while samples of different categories stay away. Finally, in order to enhance the interpretability of the deep learning model, we utilize knowledge distillation to reproduce coordinated representations and capture influence of brain regions in AD classification. Experiments show that the proposed method performs better than several state-of-the-art methods in AD diagnosis.

Stacked Topological Preserving Dynamic Brain Networks Representation and Classification.

In recent years, numerous studies have adopted rs-fMRI to construct dynamic functional connectivity networks (DFCNs) and applied them to the diagnosis of brain diseases, such as epilepsy and schizophrenia. Compared with the static brain networks, the DFCNs have a natural advantage in reflecting the process of brain activity due to the time information contained in it. However, most of the current methods for constructing DFCNs fail to aggregate the brain topology structure and temporal variation of the functional architecture associated with brain regions, and often ignore the inherent multi-dimensional feature representation of DFCNs for classification. In order to address these issues, we propose a novel DFCNs construction and representation method and apply it to brain disease diagnosis. Specifically, we fuse the blood oxygen level dependent (BOLD) signal and interactions between brain regions to distinguish the brain topology within each time domain and across different time domains, by embedding block structure in the adjacency matrix. After that, a sparse tensor decomposition method with sparse local structure preserving regularization is developed to extract DFCNs features from a multi-dimensional perspective. Finally, the kernel discriminant analysis is employed to provide the decision result. We validate the proposed method on epilepsy and schizophrenia identification tasks, respectively. The experimental results show that the proposed method outperforms several state-of-the-art methods in the diagnosis of brain diseases.

Study on the Effect of Torrefaction on Pyrolysis Kinetics and Thermal Behavior of Cornstalk Based On a Combined Approach of Chemical and Structural Analyses.

In this study, the effects of torrefaction pretreatment on physicochemical characteristics and pyrolysis behavior of cornstalk were investigated based on the changes in its chemical and structural characteristics. The results indicated that torrefaction treatment improved the fuel properties with elevated torrefaction temperature, including the lower volatile content, higher carbon content, and higher heating value. In addition, serious torrefaction promoted complete degradation of hemicellulose, while the lignin was increased obviously. The crystallinity degree of cornstalk increased first and then reduced with the torrefaction temperature. Slight torrefaction enhanced the devolatilization and thermochemical reactivity of cornstalk, but serious torrefaction discouraged the volatile release. Kinetic parameter analysis indicated that the Ozawa-Flynn-Wall model was more accurate in calculating the activation energy, and the average activation energy gradually increased from 196.06 to 199.21, 203.17, and 217.58 kJ/mol. Furthermore, the thermodynamic parameters also showed an increasing trend with elevated torrefaction temperature. These results provide important basic data support for the thermochemical conversion of cornstalk to energy and chemicals.

Decarboxylative thiolation of redox-active esters to free thiols and further diversification.

Thiols are important precursors for the synthesis of a variety of pharmaceutically important sulfur-containing compounds. In view of the versatile reactivity of free thiols, here we report the development of a visible light-mediated direct decarboxylative thiolation reaction of alkyl redox-active esters to free thiols based on the abundant carboxylic acid feedstock. This transformation is applicable to various carboxylic acids, including primary, secondary, and tertiary acids as well as natural products and drugs, forging a general and facile access to free thiols with diverse structures. Moreover, the direct access to free thiols affords an advantage of rapid in situ diversification with high efficiency to other important thiol derivatives such as sulfide, disulfide, thiocyanide, thioselenide, etc.

Decarboxylative Thiolation of Redox-Active Esters to Thioesters by Merging Photoredox and Copper Catalysis.

Thioesters and related thiols are critically important to biological systems and also widely employed in the synthesis of pharmaceutically important molecules and polymeric materials. However, known synthetic methods often suffer from the disadvantage of being specific only to certain substrates. Herein, we describe a facile decarboxylative thioesterification of alkyl acid derived redox-active esters by merging photoredox catalysis and copper catalysis. This reaction is applicable to a wide range of carboxylic acids, as well as natural products and drugs, allowing for the synthesis of various thioesters with diverse structures, including tertiary ones that are not accessible via traditional nucleophilic substitution from tertiary halides. Moreover, product utilization is demonstrated with a direct transformation of thioesters to sulfonyl fluorides.

A rapid access to aliphatic sulfonyl fluorides.

The past few years have witnessed a fast-growing research interest on the study of sulfonyl fluorides as reactive probes in chemical biology and molecular pharmacology, which raises an urgent need for the development of effective synthetic methods to expand the toolkit. Herein, we present the invention of a facile and general approach for the synthesis of aliphatic sulfonyl fluorides via visible-light-mediated decarboxylative fluorosulfonylethylation. The method is based on abundant carboxylic acid feed stock, applicable to various alkyl carboxylic acids including primary, secondary, and tertiary acids, and is also suitable for the modification of natural products like amino acids, peptides, as well as drugs, forging a rapid, metal-free approach to build sulfonyl fluoride compound libraries of considerable structural diversity. Further diversification of the SO2F-containing products is also demonstrated, which allows for access to a range of pharmaceutically important motifs such as sultam, sulfonate, and sulfonamide.

Natural adsorbent based on sawdust for removing impurities in waste lubricants.

Sawdust was expected to remove impurities in waste lubricant, and was modified with sodium hydroxide and triethanolamine, which can ameliorate its surface properties and improve its adsorption capacity. The increase of hydroxyl groups, the decrease of carbonyl groups and grafting new azyl after modification were beneficial for the adsorption of impurities. The surface area of modified sawdust is 0.969 m2 g-1, which is nearly 1.39 times as much as raw sawdust. The point of zero charge for modified sawdust decreased from 6.75 to 5.68 while the crystallinity of modified sawdust increased from 40.35 to 56.16. This research discovered that compared with raw sawdust and filter paper, modified sawdust possessed superior adsorption performances. The removal percentages for Si, Al, Fe, Cu was enhanced from 2.54%, 20.34%, 16.55%, 0.26% to 15.37%, 21.99%, 45.37%, 4.88%, respectively, while that for oxidation, aromatics, sulphation, soot and water was improved by 4.33, 4.69, 0.76, 1.20, 1.28 times at 80 °C with 1000 rpm for 12 h. The research has also explored the optimum adsorption conditions (adsorption temperature, adsorption time and rotation rate). The modified sawdust showed a stable adsorption capacity for impurities under different adsorption conditions.