(DarkAI)Mapping the large-scale density field of dark matter using artificial intelligence

Herein,we present a deep-learning technique for reconstructing the dark-matter density field from the redshift-space distribution of dark-matter halos.We built a UNet-architecture neural network and trained it using the COmoving Lagrangian Acceleration fast simulation,which is an approximation of the N-body simulation with 5123 particles in a box size of 500 h^(-1)Mpc.Further,we tested the resulting UNet model not only with training-like test samples but also with standard N-body simulations,such as the Jiutian simulation with 61443particles in a box size of 1000 h^(-1)Mpc and the ELUCID simulation,which has a different cosmology.The real-space dark-matter density fields in the three simulations can be reconstructed reliably with only a small reduction of the cross-correlation power spectrum at 1%and 10%levels at k=0.1 and 0.3 h Mpc-1,respectively.The reconstruction clearly helps to correct for redshift-space distortions and is unaffected by the different cosmologies between the training(Planck2018)and test samples(WMAP5).Furthermore,we tested the application of the UNet-reconstructed density field to obtain the velocity&tidal field and found that this approach provides better results compared with the traditional approach based on the linear bias model,showing a 12.2%improvement in the correlation slope and a 21.1%reduction in the scatter between the predicted and true velocities.Thus,our method is highly efficient and has excellent extrapolation reliability beyond the training set.This provides an ideal solution for determining the three-dimensional underlying density field from the plentiful galaxy survey data.

Human-machine interactive streaming anomaly detection by online self-adaptive forest

At nomaly detectors are used to distinguish differences between normal and abnormal data,which are usually implemented by evaluating and ranking the anomaly scores of each instance.A static unsupervised streaming anomaly detector is difficult to dynamically adjust anomaly score calculation.In real scenarios,anomaly detection often needs to be regulated by human feedback,which benefits adjusting anomaly detectors.In this paper,we propose a human-machine interactive streaming anomaly detection method,named ISPForest,which can be adaptively updated online under the guidance of human feedback.In particular,the feedback will be used to adjust the anomaly score calculation and structure of the detector,ideally attaining more accurate anomaly scores in the future.Our main contribution is to improve the tree-based streaming anomaly detection model that can be updated online from perspectives of anomaly score calculation and model structure.Our approach is instantiated for the powerful class of tree-based streaming anomaly detectors,and we conduct experiments on a range of benchmark datasets.The results demonstrate that the utility of incorporating feedback can improve the performance of anomaly detectors with a few human efforts.

Visualization of enantioselective recognition and separation of chiral acids by aggregation-induced emission chiral diamine

Enantioselective recognition and separation are the most important issues in the fields of chemistry,pharmacy,agrochemical,and food science.Here,we developed two optically active diamines showing aggregation-induced emission(AIE)that can discriminate 5 kinds of chiral acids with high enantioselectivity.Especially,a very high fluorescence intensity ratio(IL/ID)of 281 for(±)-Dibenzoyl-D/L-tartaric acid was obtained through the collection of fluorescence change after interaction with chiral AIE-active diamine.By virtue of AIE property and intermolecular acidbase interaction,enantioselective separation was facilely realized by simple filtration of the precipitates formed by chiral AIE luminogen(AIEgen)and one enantiomer in the racemic solution.The chiral HPLC data indicated that the precipitates of AIEgen/chiral acid possessed 82%L-analyte(the enantiomeric excess value was assessed to be 64%ee).Therefore,this method can serve as a simple,convenient,and low-cost tool for chiral detection and separation.

“Mutually Doped”Conjugated Polyelectrolyte-Polyoxometalate Complex as Hole Transporting Material for Efficient Organic Solar Cells

Comprehensive Summary Compared to electron transporting layer materials,the species and numbers of hole transporting layer(HTL)materials for organic solar cells(OSCs)are rare.The development of HTL materials with excellent hole collection ability and non-corrosive nature is a long-standing issue in the field of OSCs.Herein,we designed and synthesized a series of conjugated polyelectrolytes(CPEs)with continuously varied energy levels toward HTL materials for efficient OSCs.Through a“mutual doping”treatment,we obtained a CPE composite PCT-F:POM with a WF of 5.48 eV and a conductivity of 1.56х10^(-3)S/m,meaning that a good hole collection ability can be expected for PCT-F:POM.The OSC modified by PCT-F:POM showed a high PCE of 18.0%,which was superior to the reference device with PEDOT:PSS.Moreover,the PCT-F:POM-based OSC could maintain 91%of the initial PCE value after storage of 20 d,meaning that the long-term stability of OSCs is improved by incorporating the PCT-F:POM HTL.In addition,PCT-F:POM possesses good compatibility with large-area processing technique;i.e.,a PCT-F:POM HTL was processed by the blade-coating method for fabricating 1 cm^(2)OSC,and a PCE of 15.1%could be achieved.The results suggest the promising perspective of PCT-F:POM in practical applications.

Catalytic transformation of 4-nitrophenol into 4-aminophenol over ZnO nanowire array-decorated Cu nanoparticles

To realize economical and effective removal of hazardous 4-nitrophenol from the environment,we developed an easily recyclable ZnO nanowire array decorated with Cu nanoparticles.Its salix argyracea-shaped structure not only provides a platform to achieve stable and good dispersion of Cu nanoparticles,but also offers a great deal of catalytically active sites.The density functional theory calculations reveal that ZnO and Cu have a very beneficial synergistic effect on their catalytic capability.This synergy is ascribed to the electronic localization occurring at ZnO/Cu interface,which helps improve Cu nanoparticle’s ability to adsorb electro-negatively 4-nitrophenolate ions and to capture hydrogen radicals,thereby accelerating the hydrogen transfer from metal hydride complex to 4-nitrophenol.Benefiting from these characteristics,it exhibits high efficiency and reusability towards the catalytic reduction of waste 4-nitrophenol to valuable 4-aminophenol with a rate constant of 43.02×10^(-3)s^(-1)and an average conversion of 96.5%in 90 s during 10 cycles.This activity is superior to that of most reported noble-or non-noble-metal powder,bulk,coating,and array catalysts,indicating its competitive advantages in cost and efficiency,as well as enticing application prospects.

多相双功能催化剂催化乙酰丙酸制备γ-戊内酯

乙酰丙酸是重要的生物质衍生物,通过多相双功能催化剂催化转化其制备γ-戊内酯(GVL)成为生物精炼领域的研究热点。本文综述了近年来贵金属以及非贵金属双功能催化剂催化乙酰丙酸及其酯直接加氢制备GVL,以及金属负载型、改性分子筛和混合金属氧化物等双功能催化剂催化乙酰丙酸及其酯转移加氢制备GVL。在双功能催化剂作用下,乙酰丙酸及其酯通过羰基加氢和后续内酯化反应两个过程生成GVL。本文详细研究了不同双功能催化剂中活性位点在反应路径中的重要性,讨论了不同双功能催化剂在乙酰丙酸加氢转化过程中存在的优势和问题,并对未来双功能催化剂的开发和GVL的合成进行展望。

Plane-Wave Least-Squares Reverse Time Migration for Rugged Topography

We present a method based on least-squares reverse time migration with plane-wave encoding （P-LSRTM） for rugged topography. Instead of modifying the wave field before migration, we modify the plane-wave encoding function and fill constant velocity to the area above rugged topography in the model so that P-LSRTM can be directly performed from rugged surface in the way same to shot domain reverse time migration. In order to improve efficiency and reduce I/O （input/output） cost, the dynamic en- coding strategy and hybrid encoding strategy are implemented. Numerical test on SEG rugged topography model show that P-LSRTM can suppress migration artifacts in the migration image, and compensate am- plitude in the middle-deep part efficiently. Without data correction, P-LSRTM can produce a satisfying image of near-surface if we could get an accurate near-surface velocity model. Moreover, the pre-stack P- LSRTM is more robust than conventional RTM in the presence of migration velocity errors.