Abstract
Aqueous zinc (Zn) chemistry features intrinsic safety, but suffers from severe irreversibility, as exemplified by low Coulombic efficiency, sustained water consumption and dendrite growth, ...which hampers practical applications of rechargeable Zn batteries. Herein, we report a highly reversible aqueous Zn battery in which the graphitic carbon nitride quantum dots additive serves as fast colloid ion carriers and assists the construction of a dynamic & self-repairing protective interphase. This real-time assembled interphase enables an ion-sieving effect and is found actively regenerate in each battery cycle, in effect endowing the system with single Zn
2+
conduction and constant conformal integrality, executing timely adaption of Zn deposition, thus retaining sustainable long-term protective effect. In consequence, dendrite-free Zn plating/stripping at ~99.6% Coulombic efficiency for 200 cycles, steady charge-discharge for 1200 h, and impressive cyclability (61.2% retention for 500 cycles in a Zn | |MnO
2
full battery, 73.2% retention for 500 cycles in a Zn | |V
2
O
5
full battery and 93.5% retention for 3000 cycles in a Zn | |VOPO
4
full battery) are achieved, which defines a general pathway to challenge Lithium in all low-cost, large-scale applications.
Soft porous organic crystals with stimuli‐responsive single‐crystal‐to‐single‐crystal (SCSC) transformations are important tools for unraveling their structural transformations at the molecular ...level, which is of crucial importance for the rapid development of stimuli‐responsive systems. Carefully balancing the crystallinity and flexibility of materials is the prerequisite to construct advanced organic crystals with SCSC, which remains challenging. Herein, a squaraine‐based soft porous organic crystal (SPOC‐SQ) with multiple gas‐induced SCSC transformations and temperature‐regulated gate‐opening adsorption of various C1‐C3 hydrocarbons is reported. SPOC‐SQ is featured with both crystallinity and flexibility, which enable pertaining the single crystallinity of the purely organic framework during accommodating gas molecules and directly unveiling gas‐framework interplays by SCXRD technique. Thanks to the excellent softness of SPOC‐SQ crystals, multiple metastable single crystals are obtained after gas removals, which demonstrates a molecular‐scale shape‐memory effect. Benefiting from the single crystallinity, the molecule‐level structural evolutions of the SPOC‐SQ crystal framework during gas departure are uncovered. With the unique temperature‐dependent gate‐opening structural transformations, SPOC‐SQ exhibits distinctly different absorption behaviors towards C3H6 and C3H8, and highly efficient and selective separation of C3H6/C3H8 (v/v, 50/50) is achieved at 273 K. Such advanced soft porous organic crystals are of both theoretical values and practical implications.
By balancing the crystallinity and flexibility, multiple gases‐induced single‐crystal‐to‐single‐crystal (SCSC) transformations of a novel soft porous organic crystal (SPOC) are discovered for not only elucidation of gas‐framework interplays and molecular‐scale shape‐memory effect at molecular level but also highly selective separation of propylene/propane, demonstrating the crucial significance of stimuli‐responsive SCSC transformations in guiding development of stimuli‐responsive systems.
Threat modeling has emerged as a key process for understanding relevant threats within businesses. However, understanding the importance of threat events is rarely driven by the business ...incorporating the system. Furthermore, prioritization of threat events often occurs based on abstract and qualitative scoring. While such scores enable prioritization, they do not allow the results to be easily interpreted by decision-makers. This can hinder downstream activities, such as discussing security investments and a security control's economic applicability. This article introduces QuantTM, an approach that incorporates views from operational and strategic business representatives to collect threat information during the threat modeling process to measure potential financial loss incurred by a specific threat event. It empowers the analysis of threats' impacts and the applicability of security controls, thus supporting the threat analysis and prioritization from an economic perspective. QuantTM comprises an overarching process for data collection and aggregation and a method for business impact analysis. The performance and feasibility of the QuantTM approach are demonstrated in a real-world case study conducted in a Swiss SME to analyze the impacts of threats and economic benefits of security controls. Secondly, it is shown that employing business impact analysis is feasible and that the supporting prototype exhibits great usability.
As a new type of jamming, the interrupted sampling repeater jamming (ISRJ) derived from the digital radio frequency memory (DRFM) technology, can generate coherent multiple false targets after pulse ...compression. At present, the traditional interference suppression method and its improved methods have insufficient characteristics and poor detection performance under the condition of low signal-to-noise ratio (SNR). Aiming at addressing this defect, this paper proposes an interference suppression method for ISRJ based on singular spectrum entropy function (SSEF) from the aspects of singular value decomposition (SVD) and information entropy theories. In this method, firstly, considering the local fine characteristics and extraction efficiency, an adaptive multi-scale segmentation (AMS) method is proposed. The purpose of this processing is to extend the salient characteristics while to smooth the similar ones. In AMS, the segmentation criterion based on average energy of segments and the constraint of minimum segmentation is also proposed, then the improved delay embedded matrix is established from the improved trajectory matrix by AMS and delay embedded mapping. Secondly, the singular spectrum of the improved delay embedded matrix is extracted by SVD. Thirdly, because the recognition algorithms based on singular spectrum analysis (SSA), classical SSE and other characteristics fail at low SNR, this paper proposes a characteristic named as SSEF retrieved from the Shannon entropy model. The following proposed entropy-based threshold detection is carried out on the echo signal to realize the band-pass filtering and interference suppression. Finally, experiment results show that in comparison with other interference suppression approaches, SSEF can increase the probability of target detection and the peak-to-side-lobe ratio (PSR) after pulse compression, which validates its stability to noise and jamming especially in the condition of low SNRs.
•The molecular structure characterization of Luling10 coal was realized.•The macromolecular model of Luling10 coal was constructed.•The micro-wetting mechanism of Luling10 coal is revealed.
The ...physicochemical structure and energy composition of coal macromolecules are the major factors affecting coal wettability. This study thus employed 13C NMR, FTIR, and XPS to determine the chemical structure of coal in the No. 10 coal seam of the Luling coal mine and gain insights into the molecular mechanisms of coal wetting. The results showed that the molecular formula of the Luling 10 model was C290H223O25N5S. Through molecular dynamics simulation, upon the stable adsorption of sodium dodecyl sulfate (SDS) on the coal surface, both the mobility of water molecules and the water molecule content in the interfacial adsorption layer increased. After SDS was added, except for the hydrogen bond energy, the increase rate of each potential energy increased, and van der Waals energy and bond length energy changed the most. The reason for the decrease of hydrogen bond energy is that more atoms in coal macromolecules form hydrogen bonds with polar water molecules. The increase of SDS promoted the significant changes of bond length energy, bond angle energy and van der Waals force of coal, thus increasing the wettability of coal. Taken together, our findings provide important insights into the molecular mechanisms through which surfactants improve chemical wetting.
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•The wetting law of surfactant on coal under different pH was characterized.•The surface morphology and pore structure of treated coal samples are described.•The micro-wetting ...mechanism of coal was revealed.
The effect of acidity and alkalinity of mine water on the ability of surfactants to wet coal was studied with contact angle measurements to determine the optimal aqueous surfactant concentration, quantitative osmosis experiments to characterize the wettability of coal, and atomic force microscopy to analyze the surface microstructure of the coal. The contact angle measurements showed that the optimum concentrations of both selected surfactants, sodium dodecyl sulfate (SDS) and coconut oil diethanolamide (CDEA), were 1.0 wt%, with contact angles on the coal samples of 25.8° and 36.1°, respectively, which were lower than from other concentrations of the surfactants. The use of quantitative osmosis experiments revealed that the acid-base environment of the surfactant solutions impacted the effect of the surfactants to different extents depending on the type of surfactant. SDS at pH = 9 had the best wetting performance and its osmosis height was 17.2 × greater than the treatment with water at pH = 7. Analyses with atomic force microscopy determined the effect of the wetting mechanism of the surfactant in different acid-base environments on the microstructural characteristics of the coal. The experiments showed that the surface morphology and pore structure of the coal changed after surfactant treatment in different acid-base environments, with more pore structures appearing on the surface of the coal samples. Both acidic and alkaline conditions could enhance the surface roughness of coal. Coal treated with SDS at a pH of 9 had the highest surface porosity of 13.8%. These studies clarify the microscopic wetting mechanism of the surfactants SDS and CDEA at different pH levels of mine water in terms of the microscopic surface morphology and pore structure of the coal, and provide an experimental basis for improving the wetting of coal and the water injection process of a coal seam.
Microproteins, prevalent across all kingdoms of life, play a crucial role in cell physiology and human health. Although global gene transcription is widely explored and abundantly available, our ...understanding of microprotein functions using transcriptome data is still limited. To mitigate this problem, we present a database, Mip-mining ( https://weilab.sjtu.edu.cn/mipmining/ ), underpinned by high-quality RNA-sequencing data exclusively aimed at analyzing microprotein functions. The Mip-mining hosts 336 sets of high-quality transcriptome data from 8626 samples and nine representative living organisms, including microorganisms, plants, animals, and humans, in our Mip-mining database. Our database specifically provides a focus on a range of diseases and environmental stress conditions, taking into account chemical, physical, biological, and diseases-related stresses. Comparatively, our platform enables customized analysis by inputting desired data sets with self-determined cutoff values. The practicality of Mip-mining is demonstrated by identifying essential microproteins in different species and revealing the importance of ATP15 in the acetic acid stress tolerance of budding yeast. We believe that Mip-mining will facilitate a greater understanding and application of microproteins in biotechnology. Moreover, it will be beneficial for designing therapeutic strategies under various biological conditions.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Traditional deep convolutional neural networks have achieved excellent performance on various machine learning tasks. Still, they perform poorly in continuous data stream environments, where models ...trained on new datasets often suffer from a significant drop in performance on old datasets, a phenomenon known as "catastrophic forgetting." Incremental learning can help solve the "catastrophic forgetting" problem in deep learning by learning new knowledge while retaining what has already been learned. In practice, incremental learning algorithms usually need to be deployed on edge devices with limited memory and restricted access to training data, facing the problems of high model complexity and imbalance between old and new categories of data. We propose an Adaptive Threshold Hierarchical Incremental Learning (ATHIL) method to address the above problems. Our proposed method does not require additional data and model storage space during the training process, combines local weight discrete coefficient thresholding and the mean nearest neighbor principle, uses a sparse matrix hierarchical masking network, and flexibly adjusts the network structure according to different tasks to achieve learning multiple image classification tasks in a single network. The experimental results show that the performance of the proposed method significantly outperforms existing methods on fine-grained classification datasets under three evaluation metrics.
Introduction:
Adipose tissue-derived extracellular vesicles (EVs-AT) are recognized as critical mediators of metabolic alterations in obesity-related diseases. However, few studies have focused on ...the role of lipids within EVs-AT in the development of obesity-related diseases.
Methods:
In this study, we performed a targeted lipidomic analysis to compare the lipidome of EVs secreted by inguinal white adipose tissue (EVs-iWAT), epididymal white adipose tissue (EVs-eWAT), and interscapular brown adipose tissue (EVs-BAT) in lean and obese mice.
Results:
We uncovered a comprehensive lipidomic map, revealing the diversity and specific lipid sorting in EVs-iWAT, EVs-eWAT, and EVs-BAT in obesity. Biological function analyses suggested that lipids encapsulated within EVs-AT of obese individuals might correlate with metabolism, pro-inflammatory response, and insulin resistance. These effects were particularly pronounced in EVs-eWAT and EVs-BAT.
Conclusion:
Our findings indicated that EVs-AT serves as novel carriers for lipokines, thereby mediating the biological functions of EVs-AT. This study holds promise for the identification of new biomarkers for obesity-related diseases and the development of new strategies to combat metabolic diseases.
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