3D Interconnected Honeycomb-Like Multifunctional Catalyst for Zn–Air Batteries

Abstract Developing high-performance and low-cost electrocatalysts is key to achieve the clean-energy target. Herein, a dual regulation method is proposed to prepare a 3D honeycomb-like carbon-based catalyst with stable Fe/Co co-dopants. Fe atoms are highly dispersed and fixed to the polymer microsphere, followed by a high-temperature decomposition, for the generation of carbon-based catalyst with a honeycomb-like structure. The as-prepared catalyst contains a large number of Fe/Co nanoparticles (Fe/Co NPs), providing the excellent catalytic activity and durability in oxygen reduction reaction, oxygen evolution reaction and hydrogen evolution reaction. The Zn-air battery assembled by the as-prepared catalyst as air cathode shows a good charge and discharge capacity, and it exhibits an ultra-long service life by maintaining a stable charge and discharge platform for a 311-h cycle. Further X-ray absorption fine structure characterization and density functional theory calculation confirms that the Fe doping optimizes the intermediate adsorption process and electron transfer of Co

Medical supervised masked autoencoders: Crafting a better masking strategy and efficient fine-tuning schedule for medical image classification

Masked autoencoders (MAEs) have displayed significant potential in the classification and semantic segmentation of medical images in the last year. Due to the high similarity of human tissues, even slight changes in medical images may represent diseased tissues, necessitating fine-grained inspection to pinpoint diseased tissues. The random masking strategy of MAEs is likely to result in areas of lesions being overlooked by the model. At the same time, inconsistencies between the pre-training and fine-tuning phases impede the performance and efficiency of MAE in medical image classification. To address these issues, we propose a medical supervised masked autoencoder (MSMAE) in this paper. In the pre-training phase, MSMAE precisely masks medical images via the attention maps obtained from supervised training, contributing to the representation learning of human tissue in the lesion area. During the fine-tuning phase, MSMAE is also driven by attention to the accurate masking of medical images. This improves the computational efficiency of the MSMAE while increasing the difficulty of fine-tuning, which indirectly improves the quality of MSMAE medical diagnosis. Extensive experiments demonstrate that MSMAE achieves state-of-the-art performance in case with three official medical datasets for various diseases. Meanwhile, transfer learning for MSMAE also demonstrates the great potential of our approach for medical semantic segmentation tasks. Moreover, the MSMAE accelerates the inference time in the fine-tuning phase by 11.2% and reduces the number of floating-point operations (FLOPs) by 74.08% compared to a traditional MAE

Knowledge-aware Named Entity Recognition with Alleviating Heterogeneity

Named Entity Recognition (NER) is a fundamental and important research topic for many downstream NLP tasks, aiming at detecting and classifying named entities (NEs) mentioned in unstructured text into pre-defined categories. Learning from labeled data only is far from enough when it comes to domain-specific or temporally-evolving entities (medical terminologies or restaurant names). Luckily, open-source Knowledge Bases (KBs) (Wikidata and Freebase) contain NEs that are manually labeled with predefined types in different domains, which is potentially beneficial to identify entity boundaries and recognize entity types more accurately. However, the type system of a domain-specific NER task is typically independent of that of current KBs and thus exhibits heterogeneity issue inevitably, which makes matching between the original NER and KB types (Person in NER potentially matches President in KBs) less likely, or introduces unintended noises without considering domain-specific knowledge (Band in NER should be mapped to Out_of_Entity_Types in the restaurant-related task). To better incorporate and denoise the abundant knowledge in KBs, we propose a new KB-aware NER framework (KaNa), which utilizes type-heterogeneous knowledge to improve NER. Specifically, for an entity mention along with a set of candidate entities that are linked from KBs, KaNa first uses a type projection mechanism that maps the mention type and entity types into a shared space to homogenize the heterogeneous entity types. Then, based on projected types, a noise detector filters out certain less-confident candidate entities in an unsupervised manner. Finally, the filtered mention-entity pairs are injected into a NER model as a graph to predict answers. The experimental results demonstrate KaNa's state-of-the-art performance on five public benchmark datasets from different domains

Flexible, Porous, and Metal–Heteroatom-Doped Carbon Nanofibers as Efficient ORR Electrocatalysts for Zn–Air Battery

Abstract Developing an efficient and durable oxygen reduction electrocatalyst is critical for clean-energy technology, such as fuel cells and metal–air batteries. In this study, we developed a facile strategy for the preparation of flexible, porous, and well-dispersed metal–heteroatom-doped carbon nanofibers by direct carbonization of electrospun Zn/Co-ZIFs/PAN nanofibers (Zn/Co-ZIFs/PAN). The obtained Zn/Co and N co-doped porous carbon nanofibers carbonized at 800 °C (Zn/Co–N@PCNFs-800) presented a good flexibility, a continuous porous structure, and a superior oxygen reduction reaction (ORR) catalytic activity to that of commercial 20 wt% Pt/C, in terms of its onset potential (0.98 V vs. RHE), half-wave potential (0.89 V vs. RHE), and limiting current density (− 5.26 mA cm−2). In addition, we tested the suitability and durability of Zn/Co–N@PCNFs-800 as the oxygen cathode for a rechargeable Zn–air battery. The prepared Zn–air batteries exhibited a higher power density (83.5 mW cm−2), a higher specific capacity (640.3 mAh g−1), an excellent reversibility, and a better cycling life than the commercial 20 wt% Pt/C + RuO2 catalysts. This design strategy of flexible porous non-precious metal-doped ORR electrocatalysts obtained from electrospun ZIFs/polymer nanofibers could be extended to fabricate other novel, stable, and easy-to-use multi-functional electrocatalysts for clean-energy technology