Precise and controllable tandem strategy triggering boosted oxygen reduction activity

The development of efficient and cost‐effective metal‐free electrocatalysts for oxygen reduction reaction(ORR)has become crucial for electrochemical energy systems.However,reasonably validating and precisely regulating the active sites for designing optimized materials are still challenging.Herein,we report a precise and controllable tandem strategy to boost the ORR activity based on metal‐free covalent organic frameworks(MFCOFs)comprising imine‐N,thiophene‐S,or triazine‐N.Among these MFCOFs,post‐tandem BTT‐TAT‐COF structure displayed a more positive catalytic capability and excellent electrochemical stability,indicating that the synergistic catalysis of multiple active sites induced the ORR catalytic activity through the conjugated skeleton of the structure.Density‐functional theory calculations suggest that the series‐connected backbone contained highly effective electrocatalytic active centers and provided synergistic catalysis.More importantly,this strategy highlights new opportunities for the advancement of efficient COF‐based metal‐free ORR catalysts.

Interpreting Randomly Wired Graph Models for Chinese NER

Interpreting deep neural networks is of great importance to understand and verify deep models for natural language processing(NLP)tasks.However,most existing approaches only focus on improving the performance of models but ignore their interpretability.In this work,we propose a Randomly Wired Graph Neural Network(RWGNN)by using graph to model the structure of Neural Network,which could solve two major problems(word-boundary ambiguity and polysemy)of ChineseNER.Besides,we develop a pipeline to explain the RWGNNby using Saliency Map and Adversarial Attacks.Experimental results demonstrate that our approach can identify meaningful and reasonable interpretations for hidden states of RWGNN.

Predicting Simplified Thematic Progression Pattern for Discourse Analysis

The pattern of thematic progression,reflecting the semantic relationships between contextual two sentences,is an important subject in discourse analysis.We introduce a new corpus of Chinese news discourses annotated with thematic progression information and explore some computational methods to automatically extracting the discourse structural features of simplified thematic progression pattern(STPP)between contextual sentences in a text.Furthermore,these features are used in a hybrid approach to a major discourse analysis task,Chinese coreference resolution.This novel approach is built up via heuristic sieves and a machine learning method that comprehensively utilizes both the top-down STPP features and the bottom-up semantic features.Experimental results on the intersection of the CoNLL-2012 task shared dataset and the CDTC corpus demonstrate the effectiveness of our proposed approach.

A review of deep learningg methods for ligand based drug virtual screening

Drug discovery is costly and time consuming,and modern drug discovery endeavors are progressively reliant on computational methodologies,aiming to mitigate temporal and financial expenditures associated with the process.In particular,the time required for vaccine and drug discovery is prolonged during emergency situations such as the coronavirus 2019 pandemic.Recently,the performance of deep learning methods in drug virtual screening has been particularly prominent.It has become a concern for researchers how to summarize the existing deep learning in drug virtual screening,select different models for different drug screening problems,exploit the advantages of deep learning models,and further improve the capability of deep learning in drug virtual screening.This review first introduces the basic concepts of drug virtual screening,common datasets,and data representation methods.Then,large numbers of common deep learning methods for drug virtual screening are compared and analyzed.In addition,a dataset of different sizes is constructed independently to evaluate the performance of each deep learning model for the difficult problem of large-scale ligand virtual screening.Finally,the existing challenges and future directions in the field of virtual screening are presented.

Role of necroptosis in spinal cord injury and its therapeutic implications

Spinal cord injury(SCI),a complex neurological disorder,triggers a series of devastating neuropathological events such as ischemia,oxidative stress,inflammatory events,neuronal apoptosis,and motor dysfunction.However,the classical necrosome,which consists of receptor-interacting protein(RIP)1,RIP3,and mixed-lineage kinase domain-like protein,is believed to control a novel type of programmed cell death called necroptosis,through tumour necrosis factor-alpha/tumour necrosis factor receptor-1 signalling or other stimuli.Several studies reported that necroptosis plays an important role in neural cell damage,release of intracellular pro-inflammatory factors,lysosomal dysfunction and endoplasmic reticulum stress.Recent research indicates that necroptosis is crucial to the pathophysiology of a number of neurological disorders and SCIs.In our review,we summarize the potential role of programmed cell death regulated by necroptosis in SCI based on its molecular and pathophysiological mechanisms.We also summarize the targets of several necroptosis pathways,which provide a more reliable reference for the treatment of SCI.

Single-atomic Mn sites coupled with Fe_(3)C nanoparticles encapsulated in carbon matrixes derived from bimetallic Mn/Fe polyphthalocyanine conjugated polymer networks for accelerating electrocatalytic oxygen reduction

The construction of robust coupling catalysts for accelerating electrocatalytic oxygen reduction reaction(ORR)through the modulation of the electronic structure and local atomic configuration is critical but remains challenging.Herein,we report a facile and effective isolation-polymerization-pyrolysis(IPP)strategy for high-precision synthesis of single-atomic Mn sites coupled with Fe_(3)C nanoparticles encapsulated in N-doped porous carbon matrixes(Mn SAs/Fe_(3)C NPs@NPC)catalyst derived from predesigned bimetallic Fe/Mn polyphthalocyanine(FeMn-BPPc)conjugated polymer networks by solid-phase reaction approach.Benefiting from the synergistic effects between the single-atomic Mn-N_(4)sites and Fe_(3)C NPs as well as the confinement effect of NPC,the Mn SAs/Fe_(3)C NPs@NPC catalyst exhibited excellent electrocatalytic activity and stability for ORR.The assembled Znair battery displayed larger power density of 186 mW·cm^(−2)than that of Pt/C+Ir/C-based battery.It also exhibits excellent stability without obvious voltage change after 106 cycles with 36 h.Combing in-situ Raman spectra with in-situ attenuated total reflectance surface-enhanced infrared absorption spectroscopy(ATR-SEIRAS)characterization results indicated that the Mn-N_(4)site as an active site for the O_(2)adsorption-activation process,which effectively facilitates the generation of key*OOH intermediates and*OH desorption to promote the multielectron reaction kinetics.Theoretical calculation reveals that the excellent electrocatalytic performance originates from the charge redistribution and the d orbital shift resulting from Mn-Fe bond,which buffers the activity of ORR through the electron reservoir capable of electron donation or releasing.This work paves a novel IPP strategy for constructing high-performance coupling electrocatalyst towards the ORR for energy conversion devices.

Encapsulation of ultrafine Pd nanoparticles within the shallow layers of UiO-67 for highly efficient hydrogenation reactions

Metal-organic frameworks(MOFs)have been used to encapsulate active metal nanoparticles(MNPs)to fabricate MNPs@MOFs composites with high catalytic efficiencies.However,the diffusion of reactants and the accessibility of MNPs located in the center of MOFs may be hindered due to the inherent microporous structures of MOFs,which would affect the catalytic activities of MNPs.Herein,we report a solvent assisted ligand exchange-hydrogen reduction(SALE-HR)strategy to selectively encapsulate ultrafine MNPs(Pd or Pt)within the shallow layers of a MOF,i.e.,UiO-67.The particle sizes of the encapsulated MNPs and the thickness of the MNPs-embedded layers can be adjusted easily by controlling the SALE conditions(e.g.time and temperature).Crucially,the LE-Pd@UiO-80-0.5 composite with the thinnest Pd-embedded layers displays remarkable catalytic efficiency with a high turnover frequency(TOF)value of 600 h^-1towards hydrogenation of nitrobenzene under 1 atm H_2at room temperature.The results indicate that the catalytic efficiency and the utilization of MNPs can be enhanced by compactly encapsulating MNPs within the shallow layers of MOFs as close to their outer surfaces as possible,owing to the short masstransfer distance and enhanced accessibility of overall MNPs.

Active transfer learning of matching query results across multiple sources

Entity resolution （ER） is the problem of identi- fying and grouping different manifestations of the same real world object. Algorithmic approaches have been developed where most tasks offer superior performance under super- vised learning. However, the prohibitive cost of labeling training data is still a huge obstacle for detecting duplicate query records from online sources. Furthermore, the unique combinations of noisy data with missing elements make ER tasks more challenging. To address this, transfer learning has been adopted to adaptively share learned common structures of similarity scoring problems between multiple sources. Al- though such techniques reduce the labeling cost so that it is linear with respect to the number of sources, its random sam- piing strategy is not successful enough to handle the ordinary sample imbalance problem. In this paper, we present a novel multi-source active transfer learning framework to jointly select fewer data instances from all sources to train classi- fiers with constant precision/recall. The intuition behind our approach is to actively label the most informative samples while adaptively transferring collective knowledge between sources. In this way, the classifiers that are learned can be both label-economical and flexible even for imbalanced or quality diverse sources. We compare our method with the state-of-the-art approaches on real-word datasets. Our exper- imental results demonstrate that our active transfer learning algorithm can achieve impressive performance with far fewerlabeled samples for record matching with numerous and var- ied sources.

Realizing a“solid to solid”process via in situ cathode electrolyte interface(CEI)by solvent-in-salt electrolyte for Li-S batteries

Lithium-sulfur batteries(LSBs)are regarded as the most promising next-generation energy system due to their high theoretical energy density.However,LSBs suffer the“shuttle effect”if undergoing the solid-liquid-solid sulfur conversion process during cycling.Herein,we design a solvent-in-salt(SIS)electrolyte with co-solvent vinylene carbonate(VC)to synthesize an in situ dense cathode electrolyte interface(CEI)and successfully change sulfur conversion into a solid-solid way to avoid shuttle effect by separating the contact of sulfur and ether solvent.Dense CEI is formed at the beginning of first discharge by the combined action of SIS electrolyte and filmogen VC.Experiments and simulations show that SIS electrolyte controls the initial formed lithium polysulfides(LiPSs)to stay very closely on the cathode surface,and then converts them into a dense CEI film.As a result,Coulombic efficiency(above 99%)and cycling performance of LSBs are improved.Furthermore,the in situ dense CEI can nearly stop the self-discharge of LSBs,and enable the LSBs to work under a pretty lean electrolyte condition.

Efficient sampling methods for characterizing POIs on maps based on road networks

With the rapid development of location-based services, a particularly important aspect of start-up marketing research is to explore and characterize points of interest （PoIs） such as restaurants and hotels on maps. However, due to the lack of direct access to PoI databases, it is necessary to rely on existing APIs to query Pols within a region and calculate PoI statistics. Unfortunately, public APIs generally im- pose a limit on the maximum number of queries. Therefore, we propose effective and efficient sampling methods based on road networks to sample PoIs on maps and provide unbiased estimators for calculating PoI statistics. In general, the more intense the roads, the denser the distribution of PoIs is within a region. Experimental results show that compared with state-of-the-art methods, our sampling methods improve the efficiency of aggregate statistical estimations.