S-Doped hierarchical graphene decorated with Co-porphyrins as an efficient electrocatalyst for zinc–air batteries

Peng, Zhi-Jie; Zhao, Jiao; Gu, Lin; Sun, Xuan-Long; Jia, Hai-Lang; Guan, Mingyun; Ma, Shuaishuai

doi:10.1039/d0nj03443f

Cited by 8 publications

(5 citation statements)

References 51 publications

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“…FlexPose represents a protein–ligand pair as a graph of nodes (residue/atom-level for protein/ligand) and edges (Figure b), where each node and edge is initialized as scalars (biochemical properties, e.g., residue/atom type) and a collection of Euclidean vectors (geometric features, e.g., relative positions between protein atoms) (see Methods section for details). Different from commonly used methods containing only scalar features (typically, they only use distances or torsion angles to represent geometric features), ,, such scalar–vector dual feature representation allows FlexPose to manage dynamic changes of a node explicitly rather than simply leaning on relative position with all its adjacent nodes. ,− Furthermore, the dual feature allows FlexPose to cover more protein residues while having a sufficient geometric representation without directly accessing their atomic structures.…”

Section: Resultsmentioning

confidence: 99%

Equivariant Flexible Modeling of the Protein–Ligand Binding Pose with Geometric Deep Learning

Dong,

Yang,

Zhou

et al. 2023

J. Chem. Theory Comput.

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Flexible modeling of the protein−ligand complex structure is a fundamental challenge for in silico drug development.Recent studies have improved commonly used docking tools by incorporating extra-deep learning-based steps. However, such strategies limit their accuracy and efficiency because they retain massive sampling pressure and lack consideration for flexible biomolecular changes. In this study, we propose FlexPose, a geometric graph network capable of direct flexible modeling of complex structures in Euclidean space without the following conventional sampling and scoring strategies. Our model adopts two key designs: scalar-vector dual feature representation and SE(3)-equivariant network, to manage dynamic structural changes, as well as two strategies: conformation-aware pretraining and weakly supervised learning, to boost model generalizability in unseen chemical space. Benefiting from these paradigms, our model dramatically outperforms all tested popular docking tools and recently advanced deep learning methods, especially in tasks involving protein conformation changes. We further investigate the impact of protein and ligand similarity on the model performance with two conformation-aware strategies. Moreover, FlexPose provides an affinity estimation and model confidence for postanalysis.

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Section: Resultsmentioning

confidence: 99%

Equivariant Flexible Modeling of the Protein–Ligand Binding Pose with Geometric Deep Learning

Dong,

Yang,

Zhou

et al. 2023

J. Chem. Theory Comput.

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“…This sulfur doping method has been tried in our previous work and proved to be feasible. 32 Sulfur doping is a widely used method in the design of catalytic materials. Its electronegativity is obviously different from that of carbon atoms, so it will change the spin density, charge distribution and valence orbital energy level of nearby carbon atoms, so as to activate carbon atoms to become active centers.…”

Section: Resultsmentioning

confidence: 99%

Hyperdispersed ruthenium nanoparticles anchored on S/N co-doped carbon nanotubes as an efficient HER electrocatalyst

Jia

Zhao

et al. 2022

New J. Chem.

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“…Owing to the excellent physical and chemical properties of graphene, much effort has been devoted to loading transition MMC on graphene as ORR catalysts to improve the MMC catalytic performance. [ 55–60 ]…”

Section: Graphene/mmc‐based Orr Catalystsmentioning

confidence: 99%

Molecular Control of Carbon‐Based Oxygen Reduction Electrocatalysts through Metal Macrocyclic Complexes Functionalization

Hong

Huang

et al. 2021

Advanced Energy Materials

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Owing to their zero pollution and high efficiency, fuel cells and metal–air batteries show great potential for broad application to sustainable energy technologies. However, the use of expensive and scarce Pt‐based materials as cathode catalysts to overcome the slow kinetics of oxygen‐reduction reaction (ORR) limits the scalability of such devices. Recently, considerable progress has been made in the development of nonprecious‐metal ORR catalysts. Although metal macrocyclic complexes (MMC) exhibiting a well‐defined M‐N4 (M = Fe, Co, Mn, Cu, etc.) structure (which can provide open sites to combine with oxygen and catalyze ORR) have attracted widespread attention, the MMC ORR performance is usually unsatisfactory because MCCs exhibit poor conductivity, symmetric electron distribution, inferior O2 adsorption, and low activation. However, MMC‐modified conductive‐carbon materials effectively solve such problems and simultaneously boost the ORR catalytic activity. In this review, the recent achievements in MMC‐functionalized carbon materials as ORR catalysts are summarized, and the current challenges and prospects of MMC‐functionalized carbon‐based ORR catalysts are discussed based on recent experimental and theoretical studies.

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S-Doped hierarchical graphene decorated with Co-porphyrins as an efficient electrocatalyst for zinc–air batteries

Abstract: Oxygen reduction reaction is an important cathode reaction of fuel cell, metal-air battery, water-splitting and other devices, however, the reaction kinetics is very slow, so the development of high-performance electrocatalysts...

Cited by 8 publications

References 51 publications

Equivariant Flexible Modeling of the Protein–Ligand Binding Pose with Geometric Deep Learning

Equivariant Flexible Modeling of the Protein–Ligand Binding Pose with Geometric Deep Learning

Hyperdispersed ruthenium nanoparticles anchored on S/N co-doped carbon nanotubes as an efficient HER electrocatalyst

Molecular Control of Carbon‐Based Oxygen Reduction Electrocatalysts through Metal Macrocyclic Complexes Functionalization

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