The electrocatalysis of CO
2
reduction to formic acid (HCOOH) as a useful chemical fuel has attracted much attention, but is facing the dilemma of low conversion efficiency. Herein, a series of ...transition elements were intentionally implanted into layered Bi
2
SeO
2
surface to act as bimetallic reactive sites for decreasing the rate-limiting barriers. The calculations disclose that the doped 3d-metals can partially changes the electronic state distribution of 6 s lone pair electrons in Bi
3+
cation, in which this particular orbital hybridization leads to an intriguing bonding interaction between dopants and neighboring Se/O atomic layers. This interlayer electronic state coupling makes the half-filled dopants demonstrate the spin-resolved charge transfer and orbital interaction between bimetallic sites and reactants, because of the localized atomic distortion and electronic reconfiguration. This work provides a new insight into engineering surficial electronic structure and catalytic activity.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
The electrocatalysis of CO.sub.2 reduction to formic acid (HCOOH) as a useful chemical fuel has attracted much attention, but is facing the dilemma of low conversion efficiency. Herein, a series of ...transition elements were intentionally implanted into layered Bi.sub.2SeO.sub.2 surface to act as bimetallic reactive sites for decreasing the rate-limiting barriers. The calculations disclose that the doped 3d-metals can partially changes the electronic state distribution of 6 s lone pair electrons in Bi.sup.3+cation, in which this particular orbital hybridization leads to an intriguing bonding interaction between dopants and neighboring Se/O atomic layers. This interlayer electronic state coupling makes the half-filled dopants demonstrate the spin-resolved charge transfer and orbital interaction between bimetallic sites and reactants, because of the localized atomic distortion and electronic reconfiguration. This work provides a new insight into engineering surficial electronic structure and catalytic activity.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Searching for intriguing electronic structures at catalyst surface have attracted much attentions due to the potential applications in hydrogen evolution reaction (HER). Herein, the A3M2S2 (A = Fe, ...Co; M = In, Sn) materials with shandite phase are proposed to design surficial electronic structure due to the existence of an ideal kagome net, in which the ordered occupation of half of A-sites leads to a half-antiperowskites atomic configuration A3/2MS. Thanks for this particular crystal symmetry, the electronic reconfiguration induced by spontaneous super-exchange interaction can lead to an excellent HER activity when the surface is covered by non-bonding M (In, Sn) and S atoms. This orbital hybridization is different from the direct bonding interaction from In–S bonds or Sn–S bonds. Comprehensive density functional theory (DFT) calculations disclose this indirect orbital hybridization not only regulates the electronic occupation of active sites but also can affect their bonding interaction with reactants. These findings suggest a new strategy into enhancing reactive activity.
•Non-bonding interaction is proposed to regulate superficial electronic structure.•The catalytic activity can be enhanced by this surface engineering.•Electronic reconfiguration can be realized by non-bonding orbital hybridization.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Hydrogen evolution reaction (HER) plays a critical role in exploiting clean energy, but which remains a great challenge of low-cost catalysts and inadequate cognition about the spin-related catalytic ...activity. Distinguished from conventional microstructural strategy, this work suggests a super-exchange interaction between single-atom dimers (SADs) and metal clusters to increase the reactive activity in HER and simultaneously the ability of electron migration. Herein, a stronger electronic interaction is built between metallic clusters and satellite SADs because of unblocked electron transfer pathway and very short interacting distances. The introduced metallic clusters can optimise the adsorption ability of the reactants and weaken their bonding interaction as well, leading to a fast dissociation. As a result, both the Gibbs free energy and carrier transfer are optimised effectively. This work demonstrates a great potential of super-exchange interaction between multiphase metal structures for enhancing the HER performance in SADs.
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BFBNIB, GIS, IJS, KISLJ, NUK, PNG, UL, UM, UPUK
As a nanozyme, gold nanoparticles have some advantages compared with natural enzymes, such as stable structure, adjustable catalytic activity, multifunctionality, and recyclability. Due to their ...special dimension, they are easy to aggregate rapidly and lose their catalytic performance when exposed to normal saline or special pH environment. To avoid such a situation, Au@PNIPAm nanozymes with core–shell structure are constructed and their mimic peroxidase and glucose oxidase enzymatic activities are investigated. Kinetic examinations manifest that Au@PNIPAm nanozymes exhibited a high affinity for 3,3,5,5-tetramethylbenzidine (TMB), hydrogen peroxide (H2O2), and glucose. These predominant peroxidase-like and glucose-like oxidase Au@PNIPAm catalytic activities are successfully used in the detection of H2O2 or glucose (LOD is 2.43 mM or 5.07 mM). Otherwise, the potential Au@PNIPAm nanozymes are provided with a clear ability for decomposing the intracellular H2O2 in living cells. And it could protect cells from oxidative stress damage with inducing by H2O2. Therefore, it is easy to consider that Au@PNIPAm nanozymes show a certain possibility to retard cell senescence and increase the production of the hydroxyl radical which could prevent carcinogenesis of the cell.
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IJS, KILJ, NUK, PNG, UL, UM
Our work focuses on tackling large-scale fine-grained image retrieval as ranking the images depicting the concept of interests (i.e., the same sub-category labels) highest based on the fine-grained ...details in the query. It is desirable to alleviate the challenges of both fine-grained nature of small inter-class variations with large intra-class variations and explosive growth of fine-grained data for such a practical task. In this paper, we propose attribute-aware hashing networks with self-consistency for generating attribute-aware hash codes to not only make the retrieval process efficient, but also establish explicit correspondences between hash codes and visual attributes. Specifically, based on the captured visual representations by attention, we develop an encoder-decoder structure network of a reconstruction task to unsupervisedly distill high-level attribute-specific vectors from the appearance-specific visual representations without attribute annotations. Our models are also equipped with a feature decorrelation constraint upon these attribute vectors to strengthen their representative abilities. Then, driven by preserving original entities' similarity, the required hash codes can be generated from these attribute-specific vectors and thus become attribute-aware. Furthermore, to combat simplicity bias in deep hashing, we consider the model design from the perspective of the self-consistency principle and propose to further enhance models' self-consistency by equipping an additional image reconstruction path. Comprehensive quantitative experiments under diverse empirical settings on six fine-grained retrieval datasets and two generic retrieval datasets show the superiority of our models over competing methods. Moreover, qualitative results demonstrate that not only the obtained hash codes can strongly correspond to certain kinds of crucial properties of fine-grained objects, but also our self-consistency designs can effectively overcome simplicity bias in fine-grained hashing.
The existing methods to improve the charge balance of quantum dot light-emitting diodes (QLEDs) rely on energy-level matching, but these approaches have been limited by material availability and ...Fermi-level pinning. Here, we propose a solution that does not require changes to the materials’ electronic properties. By using nanoimprinting technology to texture the interface between the hole-transporting layer (HTL) and colloidal quantum dot (CQD) layer, we can increase the HTL–CQD contact area. This significantly enhances the hole injection rate while keeping the electron injection rate essentially unchanged. Compared with the conventional planar structure, QLEDs with textured HTL exhibit lower luminance threshold voltage, significantly higher external quantum efficiency at low bias voltages, improved operational stability, and a similar Lambertian factor. Comprehensive measurements confirm that the HTL–CQD interface texture allows more efficient hole injection into CQDs to occur under lower bias, resulting in less CQD charging and more efficient exciton recombination.
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We propose Equiangular Basis Vectors (EBVs) for classification tasks. In deep neural networks, models usually end with a k-way fully connected layer with softmax to handle different classification ...tasks. The learning objective of these methods can be summarized as mapping the learned feature representations to the samples' label space. While in metric learning approaches, the main objective is to learn a transformation function that maps training data points from the original space to a new space where similar points are closer while dissimilar points become farther apart. Different from previous methods, our EBVs generate normalized vector embeddings as "predefined classifiers" which are required to not only be with the equal status between each other, but also be as orthogonal as possible. By minimizing the spherical distance of the embedding of an input between its categorical EBV in training, the predictions can be obtained by identifying the categorical EBV with the smallest distance during inference. Various experiments on the ImageNet-1K dataset and other downstream tasks demonstrate that our method outperforms the general fully connected classifier while it does not introduce huge additional computation compared with classical metric learning methods. Our EBVs won the first place in the 2022 DIGIX Global AI Challenge, and our code is open-source and available at https://github.com/NJUST-VIPGroup/Equiangular-Basis-Vectors.
The oxygen evolution reaction (OER) plays an important role in efficient energy conversion, but needs excellent catalysts to overcome the sluggish reaction kinetics. The classic reaction kinetics ...implies that catalytic activity strongly depends on the electronic‐state occupation at surface active sites. Herein, a semimetallic orbital‐hybridization strategy is suggested to optimize the reaction activity of metal site at ternary cobalt silicide surface by particular interlayer electronic‐state coupling, which is completely different from the atomic configuration with double‐layered metal surface. The detailed analysis about electronic structure demonstrates that the semimetallic orbital hybridization is responsible for reducing OER activation barrier, due to a proper bonding strength. Instead, the stronger metallization cannot benefit a fast intermediate dissociation, which requires a higher activation barrier at OER. Herein, a new insight into accelerating OER by a particular surface engineering is provided.
Herein, the ternary cobalt silicide is proposed to realize a semimetallic orbital hybridization by surface engineering, in which the electronic‐state coupling between superficial reactive site and interlayer atoms can be used to regulate the surface metallization strength. In oxygen evolution reaction, the activation barrier at rate‐limiting step can be effectively reduced by this semimetallic orbital‐hybridization strategy.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
