Single atom catalysts (SACs) have been widely studied in the field of CO2 electroreduction, but industrial‐level current density and near‐unity product selectivity are still difficult to achieve. ...Herein, a diatomic site catalysts (DASCs) consisting of Co‐Cu hetero‐diatomic pairs is synthesized. The CoCu DASC exhibits excellent selectivity with the maximum CO Faradaic efficiency of 99.1 %. The CO selectivity can maintain above 95 % over a wide current density range from 100 mA cm−2 to 500 mA cm−2. The maximum CO partial current density can reach to 483 mA cm−2 in flow cell, far exceed industrial‐level current density requirements (>200 mA cm−2). Theoretical calculation reveals that the synergistic catalysis of the Co‐Cu bimetallic sites reduce the activation energy and promote the formation of intermediate *COOH. This work shows that the introduction of another metal atom into SACs can significantly affect the electronic structure and then enhance the catalytic activity of SACs.
A diatomic site catalyst consisting of Co‐Cu hetero‐diatomic pairs is designed via a general and facile method. Industrial‐level current density can be easily achieved in a flow cell system with the maximum CO partial current density up to 483 mA cm−2. The CO selectivity can be maintained above 95 % over a wide current density range from 100 mA cm−2 to 500 mA cm−2.
It is still a great challenge to achieve high selectivity of CH4 in CO2 electroreduction reactions (CO2RR) because of the similar reduction potentials of possible products and the sluggish kinetics ...for CO2 activation. Stabilizing key reaction intermediates by single type of active sites supported on porous conductive material is crucial to achieve high selectivity for single product such as CH4. Here, Cu2O(111) quantum dots with an average size of 3.5 nm are in situ synthesized on a porous conductive copper‐based metal–organic framework (CuHHTP), exhibiting high selectivity of 73 % towards CH4 with partial current density of 10.8 mA cm−2 at −1.4 V vs. RHE (reversible hydrogen electrode) in CO2RR. Operando infrared spectroscopy and DFT calculations reveal that the key intermediates (such as *CH2O and *OCH3) involved in the pathway of CH4 formation are stabilized by the single active Cu2O(111) and hydrogen bonding, thus generating CH4 instead of CO.
Cu2O(111) single‐type sites on a conductive metal–organic framework are successfully prepared by an in situ electrochemical method. The cooperative effect between the single active Cu2O(111) and hydrogen bonding contributes to the high selectivity of 73 % towards CH4 with large current density in CO2 electroreduction reduction for the obtained Cu2O(111)@CuHHTP.
We report the results of residue‐residue contact prediction of a new pipeline built purely on the learning of coevolutionary features in the CASP13 experiment. For a query sequence, the pipeline ...starts with the collection of multiple sequence alignments (MSAs) from multiple genome and metagenome sequence databases using two complementary Hidden Markov Model (HMM)‐based searching tools. Three profile matrices, built on covariance, precision, and pseudolikelihood maximization respectively, are then created from the MSAs, which are used as the input features of a deep residual convolutional neural network architecture for contact‐map training and prediction. Two ensembling strategies have been proposed to integrate the matrix features through end‐to‐end training and stacking, resulting in two complementary programs called TripletRes and ResTriplet, respectively. For the 31 free‐modeling domains that do not have homologous templates in the PDB, TripletRes and ResTriplet generated comparable results with an average accuracy of 0.640 and 0.646, respectively, for the top L/5 long‐range predictions, where 71% and 74% of the cases have an accuracy above 0.5. Detailed data analyses showed that the strength of the pipeline is due to the sensitive MSA construction and the advanced strategies for coevolutionary feature ensembling. Domain splitting was also found to help enhance the contact prediction performance. Nevertheless, contact models for tail regions, which often involve a high number of alignment gaps, and for targets with few homologous sequences are still suboptimal. Development of new approaches where the model is specifically trained on these regions and targets might help address these problems.
The electrocatalytic conversion of CO2 into value‐added chemicals is a promising approach to realize a carbon‐energy balance. However, low current density still limits the application of the CO2 ...electroreduction reaction (CO2RR). Metal–organic frameworks (MOFs) are one class of promising alternatives for the CO2RR due to their periodically arranged isolated metal active sites. However, the poor conductivity of traditional MOFs usually results in a low current density in CO2RR. We have prepared conductive two‐dimensional (2D) phthalocyanine‐based MOF (NiPc‐NiO4) nanosheets linked by nickel‐catecholate, which can be employed as highly efficient electrocatalysts for the CO2RR to CO. The obtained NiPc‐NiO4 has a good conductivity and exhibited a very high selectivity of 98.4 % toward CO production and a large CO partial current density of 34.5 mA cm−2, outperforming the reported MOF catalysts. This work highlights the potential of conductive crystalline frameworks in electrocatalysis.
Nickel phthalocyanine molecules as active sites were installed into nickel‐catecholate‐linked 2D conductive metal–organic framework nanosheets for efficient CO2 electroreduction with nearly 100 % CO selectivity.
The electroreduction of CO2 to value‐added chemicals such as CO is a promising approach to realize carbon‐neutral energy cycle, but still remains big challenge including low current density. Covalent ...organic frameworks (COFs) with abundant accessible active single‐sites can offer a bridge between homogeneous and heterogeneous electrocatalysis, but the low electrical conductivity limits their application for CO2 electroreduction reaction (CO2RR). Here, a 2D conductive Ni‐phthalocyanine‐based COF, named NiPc‐COF, is synthesized by condensation of 2,3,9,10,16,17,23,24‐octa‐aminophthalocyaninato Ni(II) and tert‐butylpyrene‐tetraone for highly efficient CO2RR. Due to its highly intrinsic conductivity and accessible active sites, the robust conductive 2D NiPc‐COF nanosheets exhibit very high CO selectivity (>93%) in a wide range of the applied potentials of −0.6 to −1.1 V versus the reversible hydrogen electrode (RHE) and large partial current density of 35 mA cm−2 at −1.1 V versus RHE in aqueous solution that surpasses all the conventional COF electrocatalysts. The robust NiPc‐COF that is bridged by covalent pyrazine linkage can maintain its CO2RR activity for 10 h. This work presents the implementation of the conductive COF nanosheets for CO2RR and provides a strategy to enhance energy conversion efficiency in electrocatalysis.
A conductive nickelophthalocyanine‐based 2D covalent organic framework is synthesized and employed as a robust and efficient electrocatalyst for CO2 electroreduction reaction, providing a new route to design highly efficient porous framework materials for the enhanced electrocatalysis via improving electrical conductivity.
Brain organoids derived from human pluripotent stem cells provide a highly valuable in vitro model to recapitulate human brain development and neurological diseases. However, the current systems for ...brain organoid culture require further improvement for the reliable production of high-quality organoids. Here, we demonstrate two engineering elements to improve human brain organoid culture, (1) a human brain extracellular matrix to provide brain-specific cues and (2) a microfluidic device with periodic flow to improve the survival and reduce the variability of organoids. A three-dimensional culture modified with brain extracellular matrix significantly enhanced neurogenesis in developing brain organoids from human induced pluripotent stem cells. Cortical layer development, volumetric augmentation, and electrophysiological function of human brain organoids were further improved in a reproducible manner by dynamic culture in microfluidic chamber devices. Our engineering concept of reconstituting brain-mimetic microenvironments facilitates the development of a reliable culture platform for brain organoids, enabling effective modeling and drug development for human brain diseases.
Charge redistribution on surface of Ru nanoparticle can significantly affect electrocatalytic HER activity. Herein, a double atomic‐tuned RuBi SAA/Bi@OG nanostructure that features RuBi single‐atom ...alloy nanoparticle supported by Bi−O single‐site‐doped graphene was successfully developed by one‐step pyrolysis method. The alloyed Bi single atom and adjacent Bi−O single site in RuBi SAA/Bi@OG can synergistically manipulate electron transfer on Ru surface leading to optimum charge redistribution. Thus, the resulting RuBi SAA/Bi@OG exhibits superior alkaline HER activity. Its mass activity is up to 65000 mA mg−1 at an overpotential of 150 mV, which is 72.2 times as much as that of commercial Pt/C. DFT calculations reveal that the RuBi SAA/Bi@OG possesses the optimum charge redistribution, which is most beneficial to strengthen adsorption of water and weaken hydrogen‐adsorption free energy in HER process. This double atomic‐tuned strategy on surface charge redistribution of Ru nanoparticle opens a new way to develop highly efficient electrocatalysts.
A double atomic‐tuned RuBi SAA/Bi@OG nanostructure was prepared by one‐step pyrolysis method. The electron density on surface of Ru nanoparticle can be synergistically modulated by alloyed Bi single atom and adjacent Bi−O single site leading to optimum charge redistribution. Thus, the resulting RuBi SAA/Bi@OG exhibits superior alkaline HER activity.
Abstract
Motivation
Contact-map of a protein sequence dictates the global topology of structural fold. Accurate prediction of the contact-map is thus essential to protein 3D structure prediction, ...which is particularly useful for the protein sequences that do not have close homology templates in the Protein Data Bank.
Results
We developed a new method, ResPRE, to predict residue-level protein contacts using inverse covariance matrix (or precision matrix) of multiple sequence alignments (MSAs) through deep residual convolutional neural network training. The approach was tested on a set of 158 non-homologous proteins collected from the CASP experiments and achieved an average accuracy of 50.6% in the top-L long-range contact prediction with L being the sequence length, which is 11.7% higher than the best of other state-of-the-art approaches ranging from coevolution coupling analysis to deep neural network training. Detailed data analyses show that the major advantage of ResPRE lies at the utilization of precision matrix that helps rule out transitional noises of contact-maps compared with the previously used covariance matrix. Meanwhile, the residual network with parallel shortcut layer connections increases the learning ability of deep neural network training. It was also found that appropriate collection of MSAs can further improve the accuracy of final contact-map predictions. The standalone package and online server of ResPRE are made freely available, which should bring important impact on protein structure and function modeling studies in particular for the distant- and non-homology protein targets.
Availability and implementation
https://zhanglab.ccmb.med.umich.edu/ResPRE and https://github.com/leeyang/ResPRE.
Supplementary information
Supplementary data are available at Bioinformatics online.
The diffusive wave equation, a simplified form of the Saint‐Venant equations, is extensively used in flood routing. To solve the equation, numerous methods have been developed over the years. Most of ...them are numerical and hence their application generally requires case‐specific modeling and analysis to ensure stable solution. For many practical routing applications, however, simpler yet accurate methods are highly desirable that do not require problem‐specific numerical modeling. This work extends the previous analytical solutions with more flexible boundary conditions, presents two quasianalytical methods for solving the 1‐D linear diffusive wave equation on finite domains, and applies them to different types of routing problems. Referred to as the Symbolic Diffusive Wave Solutions, the proposed methods yield explicit symbolic expressions for time‐continuous solutions at discrete nodes in space and provide solutions that are accurate and computationally efficient. The methods are easy to implement and may be used in a variety of routing applications in which accurate explicit symbolic solutions for linear advection‐diffusion are desired for a set of discrete locations such as known river forecast points. This study describes the solutions and their application in different types of real‐world and synthetic routing problems.
Key Points
Symbolic closed‐form solutions for 1‐D linear diffusive wave equation are developed
The solutions are valid at specific nodes and are compactly expressed as explicit functions of time and channel properties
The methods can handle various types of upstream inflow and downstream boundary conditions
NEDDylation, a post-translational modification through three-step enzymatic cascades, plays crucial roles in the regulation of diverse biological processes. NEDD8-activating enzyme (NAE) as the only ...activation enzyme in the NEDDylation modification has become an attractive target to develop anticancer drugs. To date, numerous inhibitors or agonists targeting NAE have been developed. Among them, covalent NAE inhibitors such as MLN4924 and TAS4464 currently entered into clinical trials for cancer therapy, particularly for hematological tumors. This review explains the relationships between NEDDylation and cancers, structural characteristics of NAE and multistep mechanisms of NEDD8 activation by NAE. In addition, the potential approaches to discover NAE inhibitors and detailed pharmacological mechanisms of NAE inhibitors in the clinical stage are explored in depth. Importantly, we reasonably investigate the challenges of NAE inhibitors for cancer therapy and possible development directions of NAE-targeting drugs in the future.
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