Data-driven nonparametric Li-ion battery ageing model aiming at learningfrom real operation data - Part B: Cycling operation

Conventional Li-ion battery ageing models, such as electrochemical, semi-empirical and empirical models, require a significant amount of time and experimental resources to provide accurate predictions under realistic operating conditions. At the same time, there is significant interest from industry in the introduction of new data collection telemetry technology. This implies the forthcoming availability of a significant amount of real-world battery operation data. In this context, the development of ageing models able to learn from in-field battery operation data is an interesting solution to mitigate the need for exhaustive laboratory testing. In a series of two papers, a data-driven ageing model is developed for Li-ion batteries under the Gaussian Process framework. A special emphasis is placed on illustrating the ability of the Gaussian Process model to learn from new data observations, providing more accurate and confident predictions, and extending the operating window of the model. The first paper of the series focussed on the systematic modelling and experimental verification of cell degradation through calendar ageing. Conversantly, this second paper addresses the same research challenge when the cell is electrically cycled. A specific covariance function is composed, tailored for use in a battery ageing application. Over an extensive dataset involving 124 cells tested during more than three years, different training possibilities are contemplated in order to quantify the minimal number of laboratory tests required for the design of an accurate ageing model. A model trained with only 26 tested cells achieves an overall mean-absolute-error of 1.04% in the capacity curve prediction, after being validated under a broad window of both dynamic and static cycling temperatures, Depth-of-Discharge, middle-SOC, charging and discharging C-rates.This investigation work was financially supported by ELKARTEK (CICe2018 - Desarrollo de actividades de investigacion fundamental estrategica en almacenamiento de energia electroquimica y termica para sistemas de almacenamiento hibridos, KK-2018/00098) and EMAITEK Strategic Programs of the Basque Government. In addition, the research was undertaken as a part of ELEVATE project (EP/M009394/1) funded by the Engineering and Physical Sciences Research Council (EPSRC) and partnership with the WMG High Value Manufacturing (HVM) Catapult. Authors would like to thank the FP7 European project Batteries 2020 consortium (grant agreement No. 608936) for the valuable battery ageing data provided during the project

Data-driven nonparametric Li-ion battery ageing model aiming at learningfrom real operation data – Part A: Storage operation

Conventional Li-ion battery ageing models, such as electrochemical, semi-empirical and empirical models, require a significant amount of time and experimental resources to provide accurate predictions under realistic operating conditions. At the same time, there is significant interest from industry in the introduction of new data collection telemetry technology. This implies the forthcoming availability of a significant amount of real-world battery operation data. In this context, the development of ageing models able to learn from in-field battery operation data is an interesting solution to mitigate the need for exhaustive laboratory testing. In a series of two papers, a data-driven ageing model is developed for Li-ion batteries under the Gaussian Process framework. A special emphasis is placed on illustrating the ability of the Gaussian Process model to learn from new data observations, providing more accurate and confident predictions, and extending the operating window of the model. This first paper focusses on the systematic modelling and experimental verification of cell degradation through calendar ageing. A specific covariance function is composed, tailored for use in a battery ageing application. Over an extensive dataset involving 32 cells tested during more than three years, different training possibilities are contemplated in order to quantify the minimal number of laboratory tests required for the design of an accurate ageing model. A model trained with only 18 tested cells achieves an overall mean-absolute-error of 0.53% in the capacity curves prediction, after being validated under a broad window of both dynamic and static temperature and SOC storage conditions.This investigation work was financially supported by ELKARTEK (CICe2018 -Desarrollo de actividades de investigacion fundamental estrategica en almacenamiento de energia electroquimica y termica para sistemas de almacenamiento hibridos, KK-2018/00098) and EMAITEK Strategic Programs of the Basque Government. In addition, the research was undertaken as a part of ELEVATE project (EP/M009394/1) funded by the Engineering and Physical Sciences Research Council (EPSRC) and partnership with the WMG High Value Manufacturing (HVM) Catapult. Authors would like to thank the FP7 European project Batteries 2020 consortium (grant agreement No. 608936) for the valuable battery ageing data provided during the course of the project

Perfil químico y biológico de aceites esenciales de plantas aromáticas de interés agro-industrial en Castilla-La Mancha (España)

The chemical composition and biological activities of essential oils of <i>Salvia officinalis</i> L., <i>Salvia lavandulifolia</i> Vahl., <i>Lavandula x intermedia</i> Emeric ex Loisel., <i>Lavandula latifolia</i> Medik., <i>Lavandula angustifolia</i> Mill. and <i>Thymus vulgaris</i> L. are presented. The essential oils have been analysed by Gas Chromatography Mass Spectrometry and 61 compounds were identified, 23 of which represented more than 1% of the essential oil. The 1,8 cineole (16-23%) appeared as the main compound of <i>Salvia</i> sp. essential oils. The high content of α-thujone was characteristic in <i>S. officinalis</i> oil. Remarkable concentrations of linalool (30-33%), camphor (5-17%) and linalyl acetate (9-28%) were detected in <i>Lavandula</i> sp. oils while carvacrol (21.6%) and p-cimene (23.7%) were the most abundant compounds in <i>T. vulgaris</i> oil. Biological characterization was based on their bioplaguicide activity. The essential oils studied had strong antifeedant effects against <i>Leptinotarsa decemlineata</i> Say, <i>Spodoptera littoralis</i> Boisd., <i>Myzus persicae</i> Sulzer and <i>Rhopalosiphum padi</i> L., phytotoxic activity against <i>Lactuca sativa</i> L. and <i>Lolium perenne</i> L. and also exhibited high antifungal activity against <i>Fusarium</i> sp. Oils from <i>T. vulgaris</i> and <i>L. latifolia</i> showed the highest levels of bioactivity against all target species. These results provide an added-value to the essential oils of aromatic plants of agro-industrial interest for its potential use in the development of natural agrochemicals.<br><br>En este trabajo se presenta el estudio químico y biológico de los aceites esenciales de <i>Salvia officinalis</i> L., <i>Salvia lavandulifolia</i> Vahl., <i>Lavandula</i> x intermedia Emeric ex Loisel., <i>Lavandula latifolia</i> Medik., <i>Lavandula angustifolia</i> Mill. y <i>Thymus vulgaris</i> L. El estudio químico por cromatografía de gases acoplada a espectrometría de masas de los aceites esenciales permitió la identificación de 61 compuestos, de los cuales 23 presentaron un porcentaje mayor o igual al 1 %. Los aceites esenciales de <i>Salvia</i> sp. se caracterizaron por presentar un alto contenido de 1,8 cineol (16-23%) y, en el caso específico de <i>S. officinalis</i>, una elevada proporción de α-tuyona (15.7%). En <i>Lavandula</i> sp., los compuestos mayoritarios del aceite fueron linalol (30-33%), alcanfor (5-17%) y acetato de linalilo (9-28%); mientras que en <i>T. vulgaris</i> lo fueron carvacrol (21.6%) y p-cimeno (23.7%). La caracterización biológica, desde el punto de vista de la actividad bioplaguicida, mostró que los aceites ensayados disminuyeron significativamente la alimentación de <i>Leptinotarsa decemlineata</i> Say, <i>Spodoptera littoralis</i> Boisd., <i>Myzus persicae</i> Sulzer y <i>Rhopalosiphum padi</i> L., mostraron actividad fitotóxica frente a <i>Lactuca sativa</i> L. y <i>Lolium perenne</i> L. y disminuyeron el crecimiento del micelio del hongo de Fusarium sp. Los aceites de <i>T. vulgaris</i> y <i>L. latifolia</i> fueron los más activos frente a todas las especies empleadas como dianas biológicas. Los resultados obtenidos potencian el valor añadido de los aceites de plantas aromáticas de interés agro-industrial en Castilla- La Mancha como una alternativa interesante en programas de desarrollo de agroquímicos naturales

SuperCam on the Perseverance Rover for Exploration of Jezero Crater: Remote LIBS, VISIR, Raman, and Time-Resolved Luminescence Spectroscopies Plus Micro-Imaging and Acoustics

International audienc

Circular Economy and the Fate of Lithium Batteries: Second Life and Recycling

There is a growing demand of electrochemical energy storage, driven by automotive and stationary requirements. Lithium‐ion batteries (LIBs) are expected to dominate the market from the current 0.5 TWh to about 2.5 TWh in 2030. This will lead to great difficulties in the procurement of critical raw materials and in the management of end‐of‐life systems. From a circular economy perspective, it is necessary to identify reuse and recycling strategies that can make the demand fully sustainable. However, second life and recycling are not mutually excluding, while the final fate of the battery, or at least of its noblest components, should be recycling instead of disposal. In this context, to allow new strategies such as direct recycling of cathode powders, an accurate redesign of the battery system, from the single cell to the modules, which allows ease of separation of the compartments, should be considered. The correct evaluation of the best strategies cannot be separated from an accurate and transparent life cycle assessment (LCA), which would take into account both economic and environmental aspects. Herein, the most advanced recycling methods are analyzed and the issues underlying the efficient reuse and recycling of battery packs from electric vehicles are critically discussed

Prevalence and risk factors for Enterobacteriaceae in patients hospitalized with community-acquired pneumonia

Background and objective: Enterobacteriaceae (EB) spp. family is known to include potentially multidrug-resistant (MDR) microorganisms, and remains as an important cause of community-acquired pneumonia (CAP) associated with high mortality. The aim of this study was to determine the prevalence and specific risk factors associated with EB and MDR-EB in a cohort of hospitalized adults with CAP. Methods: We performed a multinational, point-prevalence study of adult patients hospitalized with CAP. MDR-EB was defined when 653 antimicrobial classes were identified as non-susceptible. Risk factors assessment was also performed for patients with EB and MDR-EB infection. Results: Of the 3193 patients enrolled with CAP, 197 (6%) had a positive culture with EB. Fifty-one percent (n = 100) of EB were resistant to at least one antibiotic and 19% (n = 38) had MDR-EB. The most commonly EB identified were Klebsiella pneumoniae (n = 111, 56%) and Escherichia coli (n = 56, 28%). The risk factors that were independently associated with EB CAP were male gender, severe CAP, underweight (body mass index (BMI) < 18.5) and prior extended-spectrum beta-lactamase (ESBL) infection. Additionally, prior ESBL infection, being underweight, cardiovascular diseases and hospitalization in the last 12 months were independently associated with MDR-EB CAP. Conclusion: This study of adults hospitalized with CAP found a prevalence of EB of 6% and MDR-EB of 1.2%, respectively. The presence of specific risk factors, such as prior ESBL infection and being underweight, should raise the clinical suspicion for EB and MDR-EB in patients hospitalized with CAP