Electrospinning as a route to advanced carbon fibre materials for selected low-temperature electrochemical devices: a review

Electrospinning has been proven as a highly versatile fabrication method for producing nano-structured fibres with controllable morphology, of both the fibres themselves and the void structure of the mats. Additionally, it is possible to use heteroatom doped polymers or to include catalytic precursors in the electrospinning solution to control the surface properties of the fibres. These factors make it an ideal method for the production of electrodes and flow media for a variety of electrochemical devices, enabling reduction in mass transport and activation overpotentials and therefore increasing efficiency. Moreover, the use of biomass as a polymer source has recently gained attention for the ability to embed sustainable principles in the materials of electrochemical devices, complementing their ability to allow an increase in the use of renewable electricity via their application. In this review, the historical and recent developments of electrospun materials for application in redox flow batteries, fuel cells, metal air batteries and supercapacitors are thoroughly reviewed, including an overview of the electrospinning process and a guide to best practice. Finally, we provide an outlook for the emerging use of this process in the field of electrochemical energy devices with the hope that the combination of tailored microstructure, surface functionality and computer modelling will herald a new era of bespoke functional materials that can significantly improve the performance of the devices in which they are used

Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)1.

In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field

Outlier-Robust Truncated Maximum Likelihood Parameter Estimation of Compound-Gaussian Clutter with Inverse Gaussian Texture

Compound-Gaussian distributions with inverse Gaussian textures, referred to as the IGCG distributions, are often used to model moderate/high-resolution sea clutter in amplitude. In moderate/high-resolution maritime radars, parameter estimation of the IGCG distributions from radar returns data plays an important role in adaptive target detection. Due to the inevitable existence of outliers of high amplitude in radar returns data from targets and reefs, parameter estimation must be outlier robust. In this paper, an outlier-robust truncated maximum likelihood (TML) estimation method is proposed to mitigate the effect of outliers of high amplitude in data. The data are first transferred into the truncated data by removing a given percentage of the largest samples in amplitude. From the truncated data, the truncated likelihood function is constructed, and its maximum corresponds to the TML estimates of the scale and inverse shape parameters. Further, an iterative algorithm is presented to obtain the TML estimates from data with outliers, which is an extension of the ML estimation method in the case that data contain outliers. In comparison with outlier-sensitive estimation methods and outlier-robust bipercentile estimation methods, the performance of the TML estimation method is close to that of the best ML estimation method in the case that data are without outlier, and it is better in the case that data are with outliers