Engineering porosity into trimetallic PtPdNi nanospheres for enhanced electrocatalytic oxygen reduction activity

Platinum(Pt)-based multi-metallic nanostructures show great promise as electrocatalysts for the oxygen reduction reaction(ORR) in fuel cell cathodes. Herein, we report a simple, one-step surfactant-directed synthetic strategy to directly synthesize tri-metallic PtPdNi mesoporous nanospheres(PtPdNi MNs) in a high yield. The synthesis could be accomplished in aqueous solution at mild reaction temperature(40C)without needing any organic solvent, yielding well-dispersed PtPdNi MNs with uniform shape and narrow size distribution. Benefitting from their unique mesoporous and highly open structure and tri-metallic composition, the as-synthesized PtPdNi MNs demonstrate superior catalytic activity and stability for ORR in acidic solution in comparison with PtPdNi nanodendrites(PtPdNi NDs), PtPd MNs and commercial Pt/C catalyst. The present approach may open a reliable path to the design of advanced electrocatalysts with desired performance.

Aggregate Point Cloud Geometric Features for Processing

As 3D acquisition technology develops and 3D sensors become increasingly affordable,large quantities of 3D point cloud data are emerging.How to effectively learn and extract the geometric features from these point clouds has become an urgent problem to be solved.The point cloud geometric information is hidden in disordered,unstructured points,making point cloud analysis a very challenging problem.To address this problem,we propose a novel network framework,called Tree Graph Network(TGNet),which can sample,group,and aggregate local geometric features.Specifically,we construct a Tree Graph by explicit rules,which consists of curves extending in all directions in point cloud feature space,and then aggregate the features of the graph through a cross-attention mechanism.In this way,we incorporate more point cloud geometric structure information into the representation of local geometric features,which makes our network perform better.Our model performs well on several basic point clouds processing tasks such as classification,segmentation,and normal estimation,demonstrating the effectiveness and superiority of our network.Furthermore,we provide ablation experiments and visualizations to better understand our network.

Intrinsic electronic structure and nodeless superconducting gap of YBa_(2)Cu_(3)O_(7)-σ observed by spatially-resolved laser-based angle resolved photoemission spectroscopy

The spatially-resolved laser-based high-resolution angle resolved photoemission spectroscopy(ARPES) measurements have been performed on the optimally-doped YBa_(2)Cu_(3)O_(7)-σ(Y123) superconductor. For the first time, we found the region from the cleaved surface that reveals clear bulk electronic properties. The intrinsic Fermi surface and band structures of Y123 were observed. The Fermi surface-dependent and momentum-dependent superconducting gap was determined which is nodeless and consistent with the d+is gap form.

7-硝基苯并噻吩-2-甲酸乙酯合成实验的优化与改进

有机合成实验是化学专业学生重要单列实验课程,目前选用的有机合成实验项目多为单步骤合成操作,对学生多步合成的衔接、化合物的检测和鉴定等方面的训练存在不足。7-硝基苯并噻吩-2-甲酸乙酯的合成是本校应用化学专业学生有机合成实验课程中的一个综合训练项目,但存在耗时较长、产率不稳定,后处理难度较大及使用有毒试剂等问题。本项目旨在改进苯并噻吩环系的合成方法和步骤,降低试剂用量,提高产率,优化后处理过程,减少有毒物使用和三废排放,使之更符合教学实验和绿色化学要求;同时,改进后的实验增加了有机波谱分析内容,训练学生通过红外光谱、核磁共振氢谱及质谱等方式对目标化合物进行表征,更有利于学生综合素质的培养。

Invasion Possibility and Potential Effects of Rhus typhina on Beijing Municipality

Rhus typhina, an alien species introduced from North America, was identified as a main afforestation species in Beijing municipality. However, its invasiveness is still at odds. To clarify this problem, we applied the North American Screening System and the Australian Screening System to preliminarily predict its invasion possibility. Both screening systems gave the same recommendation to ＂reject＂. The geographical distribution was surveyed, with the population features of R. typhina against the native plant communities being assessed. With anthropogenic assistance, R. typhina has been scattered on almost all habitats from downtown to mountains, including roadsides, farmlands and protected areas. As a clonal shrub, R. typhina possessed a high spreading rate, varying from 6.3 m/3 years at sterile habitats to 6.7 m/3 years at fertile ones. Significantly lower species richness, individual density and diversity were observed in the R. typhina community than those of the native Vitex negundo Linn.var. heterophylla （Franch.） Rehd. community at both sterile and fertile habitats. Continual wide plantation of R. typhina may further foster its population expansion, which helps the species to overcome spatial isolation, The fact that each root fragment can develop into a new individual makes R. typhina very difficult to be eradicated once established. From a biological point of view, we believe that R. typhina is a plant invader in Beijing. We therefore suggest the government should remove the name of R. typhina from the main tree species list in afforesUng Beijing.

Screen efficiency comparisons of decision tree and neural network algorithms in machine learning assisted drug design

In view of huge search space in drug design, machine learning has become a powerful method to predict the affinity between small molecular drug and targeting protein with the development of artificial intelligence technology. However, various machine learning algorithms including massive different parameters make the prediction framework choice to be quite difficult. In this work, we took a recent drug design competition(from XtalPi company on the DataCastle platform) as the typical case to find the optimized parameters for different machines learning algorithms and the most effective algorithm. After the parameter optimizations, we compared the typical machine learning methods as decision tree(XGBoost, LightGBM) and artificial neural network(MLP, CNN) with root-mean-square error(RMSE) and coefficient of determination(R^2) evaluation. As a result, decision tree is more effective than the neural network as LightGBM>XGBoost>CNN>MLP in the affinity prediction of the specific drug design problem with ～160000 samples. For a much larger screening task in a more complicated drug design study, the sophisticated neural network model may go beyond the decision tree algorithm after generalization enhancing and overfitting reducing. The advanced machine learning methods could extract more information of protein-ligand bindings than traditional ones and improve the screen efficiency of drug design up to 200–1000 times.

Engineering hiPSC-CM and hiPSC-EC laden 3D nanofibrous splenic hydrogel for improving cardiac function through revascularization and remuscularization in infarcted heart

Cell therapy has been a promising strategy for cardiac repair after myocardial infarction(MI),but a poor ischemic environment and low cell delivery efficiency remain significant challenges.The spleen serves as a hematopoietic stem cell niche and secretes cardioprotective factors after MI,but it is unclear whether it could be used for human pluripotent stem cell(hiPSC)cultivation and provide a proper microenvironment for cell grafts against the ischemic environment.Herein,we developed a splenic extracellular matrix derived thermoresponsive hydrogel(SpGel).Proteomics analysis indicated that SpGel is enriched with proteins known to modulate the Wnt signaling pathway,cell-substrate adhesion,cardiac muscle contraction and oxidation-reduction processes.In vitro studies demonstrated that hiPSCs could be efficiently induced into endothelial cells(iECs)and cardiomyocytes(iCMs)with enhanced function on SpGel.The cytoprotective effect of SpGel on iECs/iCMs against oxidative stress damage was also proven.Furthermore,in vivo studies revealed that iEC/iCM-laden SpGel improved cardiac function and inhibited cardiac fibrosis of infarcted hearts by improving cell survival,revascularization and remuscularization.In conclusion,we successfully established a novel platform for the efficient generation and delivery of autologous cell grafts,which could be a promising clinical therapeutic strategy for cardiac repair and regeneration after MI.