A New Electrolyte Formulation for Securing High Temperature Cycling and Storage Performances of Na‐Ion Batteries

The Na‐ion battery is recognized as a possible alternative to the Li‐ion battery for applications where power and cost override energy density performance. However, the increasing instability of their electrolyte with temperature is still problematic. Thus, a central question remains how to design Na‐based electrolytes. Here, the discovery of a Na‐based electrolyte formulation is reported which enlists four additives (vinylene carbonate, succinonitrile, 1,3‐propane sultone, and sodium difluoro(oxalate)borate) in proper quantities that synergistically combine their positive attributes to enable a stable solid electrolyte interphase at both negative and positive electrodes surface at 55 °C. Moreover, the role of each additive that consists in producing specific NaF coatings, thin elastomers, sulfate‐based deposits, and so on via combined impedance and X‐ray photoelectron spectroscopy is rationalized. It is demonstrated that empirical electrolyte design rules previously established for Li‐ion technology together with theoretical guidance is vital in the quest for better Na‐based electrolytes that can be extended to other chemistries. Overall, this finding, which is implemented to 18 650 cells, widens the route to the rapid development of the Na‐ion technology based on Na₃V₂(PO₄)₂F₃/C chemistry.     

Selective Oxidation of 2′-Deoxyguanosine to Imidazolone by the Chemical Nuclease MnTMPyP Associated to KHSO5 or Sulfite/O2.

Abstract

Mn TMPyP in the presence of sulfite/O₂ catalyses the oxidation of dG into dIz as selectively but slower and less efficiently than in the presence of KHSO_5.     

Phenotype Classification of Zebrafish Embryos by Supervised Learning

Zebrafish is increasingly used to assess biological properties of chemical substances and thus is becoming a specific tool for toxicological and pharmacological studies. The effects of chemical substances on embryo survival and development are generally evaluated manually through microscopic observation by an expert and documented by several typical photographs. Here, we present a methodology to automatically classify brightfield images of wildtype zebrafish embryos according to their defects by using an image analysis approach based on supervised machine learning. We show that, compared to manual classification, automatic classification results in 90 to 100% agreement with consensus voting of biological experts in nine out of eleven considered defects in 3 days old zebrafish larvae. Automation of the analysis and classification of zebrafish embryo pictures reduces the workload and time required for the biological expert and increases the reproducibility and objectivity of this classification.