Chemical modification of electrode materials by heteroatom dopants is crucial for improving storage performance in rechargeable batteries. Electron configurations of different dopants significantly ...influence the chemical interactions inbetween and the chemical bonding with the host material, yet the underlying mechanism remains unclear. We revealed competitive doping chemistry of Group IIIA elements (boron and aluminum) taking nickel‐rich cathode materials as a model. A notable difference between the atomic radii of B and Al accounts for different spatial configurations of the hybridized orbital in bonding with lattice oxygen. Density functional theory calculations reveal, Al is preferentially bonded to oxygen and vice versa, and shows a much lower diffusion barrier than BIII. In the case of Al‐preoccupation, the bulk diffusion of BIII is hindered. In this way, a B‐rich surface and Al‐rich bulk is formed, which helps to synergistically stabilize the structural evolution and surface chemistry of the cathode.
A model study has been performed on Group IIIA element (boron and aluminum) co‐doped high‐nickel layered oxide cathode materials to understand competitive doping chemistry. A notable difference between the atomic radii of B and Al accounts for different spatial configurations of the hybridized orbital in bonding with lattice oxygen, resulting in the formation of a B‐rich surface and an Al‐rich bulk.
Single‐crystalline Ni‐rich cathodes are promising candidates for the next‐generation high‐energy Li‐ion batteries. However, they still suffer from poor rate capability and low specific capacity due ...to the severe kinetic hindrance at the nondilute state during Li+ intercalation. Herein, combining experiments with density functional theory (DFT) calculations, we demonstrate that this obstacle can be tackled by regulating the oxidation state of nickel via injecting high‐valence foreign Ta5+. The as‐obtained single‐crystalline LiNi0.8Co0.1Mn0.1O2 delivers a high specific capacity (211.2 mAh g−1 at 0.1 C), high initial Coulombic efficiency (93.8 %), excellent rate capability (157 mAh g−1 at 4 C), and good durability (90.4 % after 100 cycles under 0.5 C). This work provides a strategy to mitigate the Li+ kinetic hindrance of the appealing single‐crystalline Ni‐rich cathodes and will inspire peers to conduct an intensive study.
The Ta doping created some low‐valence Ni, decreasing the electrostatic repulsion between transition metal and Li+, thus the Li+ diffusion energy barrier has been decreased and the kinetic hindrance was mitigated.
In this paper, we introduce a new time-frequency (TF) analysis (TFA) method to study the trend and instantaneous frequency (IF) of nonlinear and nonstationary data. Our proposed method is termed the ...synchroextracting transform (SET), which belongs to a postprocessing procedure of the short-time Fourier transform (STFT). Compared with classical TFA methods, the proposed method can generate a more energy concentrated TF representation and allow for signal reconstruction. The proposed SET method is inspired by the recently proposed synchrosqueezing transform (SST) and the theory of the ideal TFA. To analyze a signal, it is important to obtain the time-varying information, such as the IF and instantaneous amplitude. The SST is to squeeze all TF coefficients into the IF trajectory. Differ from the squeezing manner of SST, the main idea of SET is to only retain the TF information of STFT results most related to time-varying features of the signal and to remove most smeared TF energy, such that the energy concentration of the novel TF representation can be enhanced greatly. Numerical and real-world signals are employed to validate the effectiveness of the SET method.
Item-to-item collaborative filtering ( aka. item-based CF) has been long used for building recommender systems in industrial settings, owing to its interpretability and efficiency in real-time ...personalization. It builds a user's profile as her historically interacted items, recommending new items that are similar to the user's profile. As such, the key to an item-based CF method is in the estimation of item similarities. Early approaches use statistical measures such as cosine similarity and Pearson coefficient to estimate item similarities, which are less accurate since they lack tailored optimization for the recommendation task. In recent years, several works attempt to learn item similarities from data, by expressing the similarity as an underlying model and estimating model parameters by optimizing a recommendation-aware objective function. While extensive efforts have been made to use shallow linear models for learning item similarities, there has been relatively less work exploring nonlinear neural network models for item-based CF. In this work, we propose a neural network model named Neural Attentive Item Similarity model (NAIS) for item-based CF. The key to our design of NAIS is an attention network, which is capable of distinguishing which historical items in a user profile are more important for a prediction. Compared to the state-of-the-art item-based CF method Factored Item Similarity Model (FISM) <xref ref-type="bibr" rid="ref1">1 , our NAIS has stronger representation power with only a few additional parameters brought by the attention network. Extensive experiments on two public benchmarks demonstrate the effectiveness of NAIS. This work is the first attempt that designs neural network models for item-based CF, opening up new research possibilities for future developments of neural recommender systems.
Antinuclei in heavy-ion collisions Chen, Jinhui; Keane, Declan; Ma, Yu-Gang ...
Physics reports,
10/2018, Letnik:
760, Številka:
C
Journal Article
Recenzirano
Odprti dostop
We review progress in the study of antinuclei, starting from Dirac’s equation and the discovery of the positron in cosmic-ray events. The development of proton accelerators led to the discovery of ...antiprotons, followed by the first antideuterons, demonstrating that antinucleons bind into antinuclei. With the development of heavy-ion programs at the Brookhaven AGS and CERN SPS, it was demonstrated that central collisions of heavy nuclei offer a fertile ground for research and discoveries in the area of antinuclei. In this review, we emphasize recent observations at Brookhaven’s Relativistic Heavy Ion Collider and at CERN’s Large Hadron Collider, namely, the antihypertriton and the antihelium-4, as well as measurements of the mass difference between light nuclei and antinuclei, and the interaction between antiprotons. Physics implications of the new observations and different production mechanisms are discussed. We also consider implications for related fields, such as hypernuclear physics and space-based cosmic-ray experiments.
The ability to quickly recognize and learn new visual concepts from limited samples enable humans to quickly adapt to new tasks and environments. This ability is enabled by the semantic association ...of novel concepts with those that have already been learned and stored in memory. Computers can start to ascertain similar abilities by utilizing a semantic concept space. A concept space is a high-dimensional semantic space in which similar abstract concepts appear close and dissimilar ones far apart. In this paper, we propose a novel approach to one-shot learning that builds on this core idea. Our approach learns to map a novel sample instance to a concept, relates that concept to the existing ones in the concept space and, using these relationships, generates new instances, by interpolating among the concepts, to help learning. Instead of synthesizing new image instance, we propose to directly synthesize instance features by leveraging semantics using a novel auto-encoder network called dual TriNet. The encoder part of the TriNet learns to map multi-layer visual features from CNN to a semantic vector. In semantic space, we search for related concepts, which are then projected back into the image feature spaces by the decoder portion of the TriNet. Two strategies in the semantic space are explored. Notably, this seemingly simple strategy results in complex augmented feature distributions in the image feature space, leading to substantially better performance.
Catalytic difunctionalization of alkenes has been an ideal strategy to generate structurally complex molecules with diverse substitution patterns. Although both phosphonyl and carboxyl groups are ...valuable functional groups, the simultaneous incorporation of them via catalytic difunctionalization of alkenes, ideally from abundant, inexpensive and easy-to-handle raw materials, has not been realized. Herein, we report the phosphonocarboxylation of alkenes with CO
via visible-light photoredox catalysis. This strategy is sustainable, general and practical, providing facile access to important β-phosphono carboxylic acids, including structurally complex unnatural α-amino acids. Diverse alkenes, including enamides, styrenes, enolsilanes and acrylates, undergo such reactions efficiently under mild reaction conditions. Moreover, this method represents a rare example of redox-neutral difunctionalization of alkenes with H-P(O) compounds, including diaryl- and dialkyl- phosphine oxides and phosphites. Importantly, these transition-metal-free reactions also feature low catalyst loading, high regio- and chemo-selectivities, good functional group tolerance, easy scalability and potential for product derivatization.
High‐nickel (Ni ≥ 90%) cathodes with high specific capacity hold great potential for next‐generation lithium‐ion batteries (LIBs). However, their practical application is restricted by the high ...interfacial reactivity under continuous air erosion and electrolyte assault. Herein, a stable high‐nickel cathode is rationally designed via in situ induction of a dense amorphous Li2CO3 on the particle surface by a preemptive atmosphere control. Among the residual lithium compounds, Li2CO3 is the most thermodynamically stable one, so a dense Li2CO3 coating layer can serve as a physical protection layer to isolate the cathode from contact with moist air. Furthermore, amorphous Li2CO3 can be transformed into a robust F‐rich cathode electrolyte interphase (CEI) during cycling, which reinforces the cathode's interfacial stability and improves the electrochemical performance. The assembled coin cell with this modified cathode delivers a high discharge capacity of 232.4 mAh g–1 with a superior initial Coulombic efficiency (CE) of 95.1%, and considerable capacity retention of 90.4% after 100 cycles. Furthermore, no slurry gelation occurs during the large‐scale electrode fabrication process. This work opens a valuable perspective on the evolution of amorphous Li2CO3 in LIBs and provides guidance on protecting unstable high‐capacity cathodes for energy‐storage devices.
A high‐nickel cathode (Ni ≥ 90%) is developed through controlling the surface residual lithium composition with a formed amorphous Li2CO3 protective layer. This cathode can resist corrosion by air, and will be converted into a stable cathode electrolyte interphase during the electrochemical process, resulting in enhanced storage performance and extended cycle life for high‐nickel cathodes.
As one of the most common human helminths, hookworm is a leading cause of maternal and child morbidity, which seriously threatens human health. Recently, wireless capsule endoscopy (WCE) has been ...applied to automatic hookworm detection. Unfortunately, it remains a challenging task. In recent years, deep convolutional neural network (CNN) has demonstrated impressive performance in various image and video analysis tasks. In this paper, a novel deep hookworm detection framework is proposed for WCE images, which simultaneously models visual appearances and tubular patterns of hookworms. This is the first deep learning framework specifically designed for hookworm detection in WCE images. Two CNN networks, namely edge extraction network and hookworm classification network, are seamlessly integrated in the proposed framework, which avoid the edge feature caching and speed up the classification. Two edge pooling layers are introduced to integrate the tubular regions induced from edge extraction network and the feature maps from hookworm classification network, leading to enhanced feature maps emphasizing the tubular regions. Experiments have been conducted on one of the largest WCE datasets with 440K WCE images, which demonstrate the effectiveness of the proposed hookworm detection framework. It significantly outperforms the state-of-the-art approaches. The high sensitivity and accuracy of the proposed method in detecting hookworms shows its potential for clinical application.
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