Interfacial engineering of Cu2Se/Co3Se4 multivalent hetero-nanocrystals for energy-efficient electrocatalytic co-generation of value-added chemicals and hydrogen

Zhao, Bin; Liu, Jianwen; Xu, Chenyu; Feng, Renfei; Sui, Peng‐Fei; Luo, Junxuan; Wang, Lei; Zhang, Jiujun; Luo, Jing‐Li; Fu, Xian‐Zhu

doi:10.1016/j.apcatb.2020.119800

Cited by 72 publications

(42 citation statements)

References 57 publications

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“…[33] The peaks at 59.2 eV are assigned to the surface oxidation of the selenides caused by solvothermal treatment or atmospheric exposure. [34] However, the intensity of the characteristic peaks at 59.2 eV are getting weaker with an increase in annealing temperature, which is consistent with the experimental observation from FTIR spectra. It results from the thermal decomposition of selenium-containing oxides.…”

Section: Resultssupporting

confidence: 88%

Orbital Hybridization Induced by Double‐Anion Coordination to Enhance Room‐Temperature Ferromagnetic Response

Liu

Guo

et al. 2022

Physica Rapid Research Ltrs

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The oxygen introduction is expected to enable manipulation of material properties, however, the coordination mechanism between oxygen and other nonmetal dopants remains inadequate. Herein, the oxygen introduction into cobalt selenides has been achieved through annealing in a controlled gas environment to disclose the anion defects formation process. Superconducting quantum interference device (SQUID) measurements indicate that the cobalt selenides annealed exhibit significantly enhanced room‐temperature ferromagnetism. Calculations reveal that the magnetic improvement is mainly attributed to orbital hybridization induced by double‐anion coordination (selenium and oxygen). This work can provide new insight into the role of oxygen incorporation in magnetic structure transformation.

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Section: Resultssupporting

confidence: 88%

Orbital Hybridization Induced by Double‐Anion Coordination to Enhance Room‐Temperature Ferromagnetic Response

Liu

Guo

et al. 2022

Physica Rapid Research Ltrs

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“…Furthermore, the selective oxidation of methanol is a known alternative to the sluggish OER reaction for water‐splitting in anodes, which would significantly lower the overpotential for H 2 generation. [ 110 ] For these reactions, non‐precious metals, such as, a nickel‐based electrocatalysts, would achieve high activity. [ 111 ] However, long‐term durability remains a challenge for large‐scale implementation.…”

Section: Perspectivesmentioning

confidence: 99%

“…[ 111 ] However, long‐term durability remains a challenge for large‐scale implementation. [ 110,112 ] ML‐based electrocatalyst design might enhance the activity and stability of electrocatalysts for commercial applications.…”

Section: Perspectivesmentioning

confidence: 99%

Toward Excellence of Electrocatalyst Design by Emerging Descriptor‐Oriented Machine Learning

Liu

Luo

Wang

et al. 2022

Adv Funct Materials

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(1 of 25)development, it is still far from meeting the increased demand. [3] The emergence of artificial intelligence (AI) has fundamentally changed the situation, which has significantly accelerated the discovery process, owing to greatly improved algorithms and developments in data science. [4] Machine learning (ML), a simple and practical AI framework based on computer and statistical science, is used to develop algorithms to learn from historic data without being explicitly programmed to obtain specific results. [5] It can investigate relationships that are hard to clearly and definitely model mathematically, providing insights for new scientific advancements related to highly complex with many uncertain twisted together factors. [6] There are usually three factors that govern the learning and prediction process of an ML: algorithms, data/database, and descriptors. [4] The algorithms involve data extraction, data filing, and propagation from mathematical derivation. [5,7] The data can be derived not only from experiments but also from theoretical calculations. [4] A number of databases based on experiments [8] and calculations [9] have already been established. The descriptors depend to a large extent on the predicted material or properties. Based on the algorithm, databases, and descriptors, the ML applications have been successfully implemented to support various energy materials with analysis tools (e.g., Python-based SciKit-Learn [10] and TensorFlow [11] ) in combination with workflow management tools (e.g., ASE [12] and Atomate [13] ). However, the prediction accuracy depends highly on the descriptors, as descriptors have a certain uniqueness for various materials and properties as long as the algorithm is selected correctly and the data set is complete. [14] For catalysis, the descriptors contain the essence from the physicochemical nature. Based on effective descriptors, ML can uncover the relationship bridging structure and its activity, selectivity, and stability. [5,15] Thus, suitable descriptors must be established to understand the structure-activity relationship. Although many efforts have been made to accelerate the rational design of homogeneous catalysts, [7b,16] heterogeneous catalysts, [7b,16b,17] and electrocatalysis, [3,18] the development of ML-assisted real catalysts is still in its infancy. Despite these considerable research efforts, the lack of universal selection tactics for descriptors bridging the gap between activity and structures impedes the application

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“…Although remarkable advances have been achieved in the research on TMDs, there is still significant scope for the further design and synthesis of HER electrocatalysts with high catalytic activity and stability via interfacial engineering, and a similar strategy has been applied for other materials in the same field. [23][24][25][26][27][28][29][30][31][32][33][34][35][36] All the above-mentioned TMD combinations provide useful insights and point to the possibility to maximize the potential capability of the heterostructures of TMDs as electrocatalysts. Accordingly, in this study, an ultrathin MoS 2 nanosheet was vertically grown on CoSe 2 hollow nanotube arrays supported by carbon cloth as an earth-abundant, highly efficient, and robust catalyst for the HER in alkaline media.…”

Section: Introductionmentioning

confidence: 99%

MoS₂ nanosheets vertically grown on CoSe₂ hollow nanotube arrays as an efficient catalyst for the hydrogen evolution reaction

et al. 2022

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Interfacial engineering of Cu2Se/Co3Se4 multivalent hetero-nanocrystals for energy-efficient electrocatalytic co-generation of value-added chemicals and hydrogen

Cited by 72 publications

References 57 publications

Orbital Hybridization Induced by Double‐Anion Coordination to Enhance Room‐Temperature Ferromagnetic Response

Orbital Hybridization Induced by Double‐Anion Coordination to Enhance Room‐Temperature Ferromagnetic Response

Toward Excellence of Electrocatalyst Design by Emerging Descriptor‐Oriented Machine Learning

MoS₂ nanosheets vertically grown on CoSe₂ hollow nanotube arrays as an efficient catalyst for the hydrogen evolution reaction

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Interfacial engineering of Cu2Se/Co3Se4 multivalent hetero-nanocrystals for energy-efficient electrocatalytic co-generation of value-added chemicals and hydrogen

Cited by 72 publications

References 57 publications

Orbital Hybridization Induced by Double‐Anion Coordination to Enhance Room‐Temperature Ferromagnetic Response

Orbital Hybridization Induced by Double‐Anion Coordination to Enhance Room‐Temperature Ferromagnetic Response

Toward Excellence of Electrocatalyst Design by Emerging Descriptor‐Oriented Machine Learning

MoS2 nanosheets vertically grown on CoSe2 hollow nanotube arrays as an efficient catalyst for the hydrogen evolution reaction

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Product

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About

MoS₂ nanosheets vertically grown on CoSe₂ hollow nanotube arrays as an efficient catalyst for the hydrogen evolution reaction