Interphases in the electrodes of potassium ion batteries

Rechargeable potassium-ion batteries (PIBs) are of great interest as a sustainable, environmentally friendly, and cost-effective energy storage technology. The electrochemical performance of a PIB is closely related to the reaction kinetics of active materials, ionic/electronic transport, and the structural/electrochemical stability of cell components. Alongside the great effort devoted in discovering and optimising electrode materials, recent research unambiguously demonstrates the decisive role of the interphases that interconnect adjacent components in a PIB. Knowledge of interphases is currently less comprehensive and satisfactory compared to that of electrode materials, and therefore, understanding the interphases is crucial to facilitating electrode materials design and advancing battery performance. The present review aims to summarise the critical interphases that dominate the overall battery performance of PIBs, which includes solid-electrolyte interphase, cathode-electrolyte interphase, and solid–solid interphases within composite electrodes, via exploring their formation principles, chemical compositions, and determination of reaction kinetics. State-of-the-art design strategies of robust interphases are discussed and analysed. Finally, perspectives are given to stimulate new ideas and open questions to further the understanding of interphases and the development of PIBs

Landmark Tracking in Liver US images Using Cascade Convolutional Neural Networks with Long Short-Term Memory

This study proposed a deep learning-based tracking method for ultrasound (US) image-guided radiation therapy. The proposed cascade deep learning model is composed of an attention network, a mask region-based convolutional neural network (mask R-CNN), and a long short-term memory (LSTM) network. The attention network learns a mapping from a US image to a suspected area of landmark motion in order to reduce the search region. The mask R-CNN then produces multiple region-of-interest (ROI) proposals in the reduced region and identifies the proposed landmark via three network heads: bounding box regression, proposal classification, and landmark segmentation. The LSTM network models the temporal relationship among the successive image frames for bounding box regression and proposal classification. To consolidate the final proposal, a selection method is designed according to the similarities between sequential frames. The proposed method was tested on the liver US tracking datasets used in the Medical Image Computing and Computer Assisted Interventions (MICCAI) 2015 challenges, where the landmarks were annotated by three experienced observers to obtain their mean positions. Five-fold cross-validation on the 24 given US sequences with ground truths shows that the mean tracking error for all landmarks is 0.65+/-0.56 mm, and the errors of all landmarks are within 2 mm. We further tested the proposed model on 69 landmarks from the testing dataset that has a similar image pattern to the training pattern, resulting in a mean tracking error of 0.94+/-0.83 mm. Our experimental results have demonstrated the feasibility and accuracy of our proposed method in tracking liver anatomic landmarks using US images, providing a potential solution for real-time liver tracking for active motion management during radiation therapy

Layered Potassium Titanium Niobate/Reduced Graphene Oxide Nanocomposite as a Potassium-Ion Battery Anode

With graphite currently leading as the most viable anode for potassium-ion batteries (KIBs), other materials have been left relatively under-examined. Transition metal oxides are among these, with many positive attributes such as synthetic maturity, long-term cycling stability and fast redox kinetics. Therefore, to address this research deficiency we report herein a layered potassium titanium niobate KTiNbO5 (KTNO) and its rGO nanocomposite (KTNO/rGO) synthesised via solvothermal methods as a high-performance anode for KIBs. Through effective distribution across the electrically conductive rGO, the electrochemical performance of the KTNO nanoparticles was enhanced. The potassium storage performance of the KTNO/rGO was demonstrated by its first charge capacity of 128.1 mAh g-1 and reversible capacity of 97.5 mAh g-1 after 500 cycles at 20 mA g-1, retaining 76.1% of the initial capacity, with an exceptional rate performance of 54.2 mAh g-1 at 1 A g-1. Furthermore, to investigate the attributes of KTNO in-situ XRD was performed, indicating a low-strain material. Ex-situ X-ray photoelectron spectra further investigated the mechanism of charge storage, with the titanium showing greater redox reversibility than the niobium. This work suggests this low-strain nature is a highly advantageous property and well worth regarding KTNO as a promising anode for future high-performance KIBs

Multi-Layer Feature Boosting Framework for Pipeline Inspection using an Intelligent Pig System

As pipelines take an increasingly important role in energy transportation, their health management is necessary. In-pipe inspection is a common pipeline life maintenance method. The signal obtained through internal inspection contains strong noise and interference where the internal environment of the pipeline is extremely complicated. Thus, it is challenging to accurately identify the defect signal. In this paper, a defect detection framework based on feature boosting is proposed by using the multi sensing pipeline pig as the detection signals. Through boosting construction of features and hierarchical classification, the framework can not only correctly classify various signals in the internal detection signals but also realize the accurate identification of defect signals. Concurrently, in order to demonstrate the high flexibility and robustness of the detection framework, experiments and verifications have been carried out on specimens in three different environments i.e., laboratory environment, simulated environment and actual environment. In the classification of actual environmental detection signals, quantitative evaluation with different algorithms have been undertaken using the F-score to demonstrate the effectiveness of the proposed framework

Effects of S. cerevisiae strains on the sensory characteristics and flavor profile of kiwi wine based on E-tongue, GC-IMS and 1H-NMR

The fermentation of kiwifruit into kiwi wine (KW) can represent a strategy to reduce the economic losses linked to fruits imperfections, spoilage, over production and seasonality. In the study, Pujiang kiwifruit, a China National Geographical Indication Product, was used as raw material to produce KW fermented by four commercial S. cerevisiae strains, namely Drop Acid Yeast, DV10, SY and RW. The sensory characteristics and flavor profile of KW were assessed by means of sensory evaluation, E-tongue, GC-IMS and 1H-NMR. KW fermented by RW strain obtained the higher sensory evaluation score. E-tongue could clearly distinguish the taste differences of KW fermented by distinct S. cerevisiae strains. A total of 128 molecules were characterized by GC-IMS and 1H-NMR, indicating that the combinations of multiple technologies could provide a comprehensive flavor profile of KW. The main flavor compounds in KW pertained to the classes of esters and alcohols. Several pathways were found to be differently altered by the fermentation with the different yeast strains, namely butanoate metabolism, glycerolipid metabolism, alanine, aspartate and glutamate metabolism, arginine biosynthesis, arginine and proline metabolism. The present study will facilitate screening suitable S. cerevisiae strains for KW production and provide a theoretical basis for large-scale production of KW

Toxicity of kadsura coccinea (Lem.) A. C. Sm. essential oil to the bed bug, cimex lectularius L. (hemiptera: Cimicidae)

Copyright © 2019 American Society for Microbiology. All Rights Reserved. We sought to define trends in and predictors of carbapenem consumption across community, teaching, and university-affiliated hospitals in the United States and Canada. We conducted a retrospective multicenter survey of carbapenem and broad-spectrum noncarbapenem beta-lactam consumption between January 2011 and December 2013. Consumption was tabulated as defined daily doses (DDD) or as days of therapy (DOT) per 1,000 patient days (PD). Multivariate mixed-effects models were explored, and final model goodness of fit was assessed by regressions of observed versus predicted values and residual distributions. A total of 20 acute-care hospitals responded. The centers treated adult patients (n 19/20) and pediatric/neonatal patients (n 17/20). The majority of the centers were nonprofit (n 17/20) and not affiliated with medical/teaching institutions (n 11/20). The median (interquartile range [IQR]) carbapenem consumption rates were 38.8 (17.4 to 95.7) DDD/1,000 PD and 29.7 (19.2 to 40.1) DOT/1,000 PD overall. Carbapenem consumption was well described by a multivariate linear mixed-effects model (fixed effects, R2 0.792; fixed plus random effects, R2 0.974). Carbapenem consumption increased by 1.91-fold/quarter from 48.6 DDD/1,000 PD (P 0.004) and by 0.056-fold/quarter from 45.7 DOT/ 1,000 PD (P 0.93) over the study period. Noncarbapenem consumption was independently related to increasing carbapenem consumption (beta 0.31 for increasing noncarbapenem beta-lactam consumption; P 0.001). Regular antibiogram publication and promotion of conversion from intravenous (i.v.) to oral (p.o.) administration independently affected carbapenem consumption rates. In the final model, 58.5% of the observed variance in consumption was attributable to between-hospital differences. Rates of carbapenem consumption across 20 North American hospitals differed greatly, and the observed differences were correlated with hospital-specific demographics. Additional studies focusing on the drivers of hospital-specific carbapenem consumption are needed to determine whether these rates are justifiable

Electromagnetic Pigging System Based on Sandwich Differential Planar Coil

In-pipeline inspection is an important precontrol method to ensure the safety of oil and gas pipeline transportation. This article proposes an electromagnetic in-pipe detector based on passive resonance-enhanced differential planar coils to detect defects on the inner surface of pipes. Both qualitative and quantitative analyses of pipeline defects and damage are developed. The introduction of passive resonant coils is shown to significantly improve the detection capability of the sensor. This is coupled with the establishment of a theoretical derivation model of the proposed structure. The hardware platform of the laboratory system has been built, and an eddy current internal detector suitable for 8-in-diameter pipes is developed and integrated into the system. Numerical simulations and experimental verifications on flat defects and pipe defects have been undertaken. The obtained results have shown that the real defects have been correctly detected, and the system is effective, reliable, and efficient