Escape of lattice water in potassium iron hexacyanoferrate for cyclic optimization in potassium‐ion batteries

Abstract Potassium iron hexacyanoferrate (Prussian blue [PB]) is a very competitive cathode for potassium‐ion batteries due to its 3D robust open framework. However, [Fe(CN)6]4− vacancies and lattice water existed in PB lattices aggravate electrochemical performances. Herein, PBs with different content of vacancies and lattice water are obtained under two synthesis temperatures of 0°C and 25°C. Although K1.36Fe[Fe(CN)6]0.74·0.48H2O (PB0) exhibits an outstanding rate capability compared with K1.43Fe[Fe(CN)6]0.94·0.42H2O (PB25), PB25 with less defects shows a lower polarization and superior stability than PB0 during the cycle. Fourier transform infrared (FTIR) spectra results show that lattice water can escape from PB lattices during the cycle, which enhances the diffusion of K+ kinetically in the PB structure. Benefited from this phenomenon, the diffusion coefficient of K+ in vacancy‐less PB25 reaches 10−8 in two reaction platforms. As potassium‐ion battery cathodes, PB25 displays higher capacity retention of 86.5% over 1000 cycles at 5 C than PB0 with 20.1% capacity retention over 600 cycles. This study provides a new understanding of [Fe(CN)6]4− vacancy and lattice water behavior in K‐containing PB structure

Research on Modeling of Microgrid Based on Data Testing and Parameter Identification

The model parameter identification based on real operation data is a means to accurately determine the simulation parameters of the microgrid, but the real operation data cannot guarantee the exact agreement with the required data for parameter identification, which has become an important restriction factor in the accurate simulation and analysis of the dynamics of the microgrid. This paper provides a method of modeling of microgrid based on data testing and parameter identification. In this paper, the method of parameter trajectory sensitivity is first introduced. Then, the data testing scheme for parameter identification is presented, and the parameter identification flow chart is given. Thirdly, a microgrid demonstration system in China is taken as an example, the important parameters of the distributed photovoltaic, direct-drive wind turbine and energy storage unit in the system are obtained by data testing and parameter identification, and in the end, the accuracy of the model is verified through the comparison of the simulation data and the test data of the microgrid during grid-connection/island switching process. The obtained microgrid model provides a base model for the analysis of the overall characteristics, such as the transient stability, as well as power quality of the microgrid

Universal Information Extraction as Unified Semantic Matching

The challenge of information extraction (IE) lies in the diversity of label schemas and the heterogeneity of structures. Traditional methods require task-specific model design and rely heavily on expensive supervision, making them difficult to generalize to new schemas. In this paper, we decouple IE into two basic abilities, structuring and conceptualizing, which are shared by different tasks and schemas. Based on this paradigm, we propose to universally model various IE tasks with Unified Semantic Matching (USM) framework, which introduces three unified token linking operations to model the abilities of structuring and conceptualizing. In this way, USM can jointly encode schema and input text, uniformly extract substructures in parallel, and controllably decode target structures on demand. Empirical evaluation on 4 IE tasks shows that the proposed method achieves state-of-the-art performance under the supervised experiments and shows strong generalization ability in zero/few-shot transfer settings

A two-step algorithm for rapid diagnosis of active pulmonary tuberculosis in entry applicants using the T-SPOT.TB and Xpert MTB/RIF assays in Shanghai, China

Emerging Microbes & Infections (2017) 6, e67; doi:10.1038/emi.2017.52; published online 26 July 201

A novel experimental method for in situ strain measurement during selective laser melting

Selective laser Melting (SLM), a powder bed-based additive manufacturing technology, has been developed and applied in multiple industrial fields in the last decade. However, the distortion and swelling in the SLM process resulting from thermal stress cannot be predicted subject to measurement. In this work, an in situ distortion measurement system applied to the SLM process is presented. The distortion behaviour of component under laser scanning can be precisely recorded in real-time by this system. The detailed evolution and driving force of specimen distortion in the SLM process are discussed based on the experimental results. The distortion in single laser scanning presents a strong instantaneous upward motion of the central section during laser heating and a relatively slow downward recovery motion of the central section during cooling. The distortion behaviour of the sample with and without a layer of metal powder are compared, and laser scanning on the bare sample surface leads to a significantly higher residual distortion. The influence of SLM parameter variables (such as scanning speed, laser power, scanning width, layer thickness and scanning times) on SLM distortion is also analysed. At last, the stress distribution of laser melting is verified by the high-resolution EBSD analysis

Highly stable potassium metal batteries enabled by regulating surface chemistry in ether electrolyte

Rechargeable potassium (K) metal batteries (PMBs) remain deeply challenged by the lack of suitable electrolytes that are stable against both highly reactive K anodes and 4 V-class cathodes. Despite their good reductive stability with K metal, classic potassium bis(fluorosulfonyl)amide (KFSI)-based ether electrolytes are typically used only in \u3c4.0 V PMBs due to their limited oxidation stability. Herein, a potassium nitrate (KNO3)-containing ether electrolyte, at a moderate KFSI concentration (2.3 M) rather than a high concentration (normally, \u3e3 M), is reported for the first time to be used in 4 V-class PMBs. A stable N/F-rich solid electrolyte interphase (SEI) is formed, enabling dense and uniform K deposition, especially under high current density. Remarkably, the PMBs with Prussian blue cathode exhibits an unprecedented cycle life (1000 cycles, 122 days). This work provides new perspectives of electrolyte design for 4 V-class PMBs

Learning In-context Learning for Named Entity Recognition

Named entity recognition in real-world applications suffers from the diversity of entity types, the emergence of new entity types, and the lack of high-quality annotations. To address the above problems, this paper proposes an in-context learning-based NER approach, which can effectively inject in-context NER ability into PLMs and recognize entities of novel types on-the-fly using only a few demonstrative instances. Specifically, we model PLMs as a meta-function $\mathcal{ \lambda_ {\text{instruction, demonstrations, text}}. M}$, and a new entity extractor can be implicitly constructed by applying new instruction and demonstrations to PLMs, i.e., $\mathcal{ (\lambda . M) }$(instruction, demonstrations) $\to$ $\mathcal{F}$ where $\mathcal{F}$ will be a new entity extractor, i.e., $\mathcal{F}$: text $\to$ entities. To inject the above in-context NER ability into PLMs, we propose a meta-function pre-training algorithm, which pre-trains PLMs by comparing the (instruction, demonstration)-initialized extractor with a surrogate golden extractor. Experimental results on 4 few-shot NER datasets show that our method can effectively inject in-context NER ability into PLMs and significantly outperforms the PLMs+fine-tuning counterparts.Comment: Accepted to ACL 2023 Main Conferenc

Cobalt-induced highly-electroactive Li2S heterostructured cathode for Li-S batteries

Lithium sulfide (Li2S) is a promising cathode material with a high theoretical capacity (1166 mA h g−1) that can be paired with nonlithium-metal anodes, which can eliminate the safety issue related with lithium anode. Nevertheless, its poor electronic conductivity and low Li ion diffusion lead to the high activation barrier of Li2S and sluggish kinetic conversion to polysulfides, hindering its commercialization. Herein, Li2S particles coated by Co nanomaterial-decorated porous carbon shells (Li2S/Co@C) are catalytically synthesized in-situ as the Li2S-Co heterostructures to enhance Li2S reactivity and its kinetic conversion via a carbothermic reduction. This Li2S/Co@C shows an ultra-low activation potential of 3.12 V, smaller by 0.74 V compared with commercial Li2S. Significantly, it presents an initial reversible capacity of 1006 mA h g−1 and maintains a high reversible capacity of 335 mA h g−1 at 0.1 C (1 C = 1166 mA g−1) after 500 cycles. An outstanding rate capacity is also achieved with a reversible capacity of 148 mA h g−1 at 3 C. More importantly, in-/ex-situ characterizations underscore that Co nanomaterials can serve as an Li2S-Co heterostructure catalyst to enhance the reactivity of Li2S, lithium polysulfides, and sulfur, thereby achieving high performance in Li-S batteries