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Physiological cellular responses and adaptations of Rhodococcus erythropolis IBB_(Po1) to toxic organic solvents

Journal of Environmental Sciences 2014年第10期26卷 2065-2075页

作者： mihaela marilena stancu Institute of Biology Bucharest of Romanian Academy Bucharest 060031 P.O. Box 56-53 Romania

A new Gram-positive bacterium, Rhodococcus erythropolis IBBPo1（KF059972.1） was isolated from a crude oil-contaminated soil sample by enrichment culture method. R. erythropolis IBBPo1 was able to tolerate a wide range of toxic compounds, such as antibiotics（800–1000 μg/mL）,synthetic surfactants（50–200 μg/mL）, and organic solvents（40%–100%）. R. erythropolis IBBPo1 showed good tolerance to both alkanes（cyclohexane, n-hexane, n-decane） and aromatics（toluene, styrene, ethylbenzene） with logPOW（logarithm of the partition coefficient of the solvent in octanol–water mixture） values between 2.64 and 5.98. However, alkanes were less toxic for R. erythropolis IBBPo1 cells, compared with aromatics. The high organic solvent tolerance of R. erythropolis IBBPo1 could be due to the presence in their large genome of some catabolic（alkB, alkB1, todC1, todM, xylM）, transporter（HAE1） and trehalose-6-phosphate synthase（otsA1, KF059973.1） genes. Numerous and complex physiological cellular responses and adaptations involved in organic solvent tolerance were revealed in R. erythropolis IBBPo1 cells exposed 1 and 24 hr to 1% organic solvents. R. erythropolis IBBPo1 cells adapt to 1% organic solvents by changing surface hydrophobicity, morphology and their metabolic *** modifications in otsA1 gene sequence were also observed in cells exposed to organic solvents（except ethylbenzene）.

关键词： Rhodococcus erythropolis Adaptation Organic solvents

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Prognostics for Lithium-ion batteries for electric Vertical Take-off andLanding aircraft using data-driven machine learning

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能源与人工智能(英文) 2023年第2期12卷 145-162页

作者： mihaela Mitici Birgitte Hennink marilena Pavel Jianning Dong Faculty of Science Utrecht UniversityHeidelberglaan 83584 CSUtrechtThe Netherlands Faculty of Aerospace Engineering Delft University of TechnologyKluyverweg 1HS 2926DelftThe Netherlands Electrical Engineering Mathematics and Computer Science FacultyTU Delft2628 CDDelftThe Netherlands

The health management of batteries is a key enabler for the adoption of Electric Vertical Take-off and Landingvehicles (eVTOLs). Currently, few studies consider the health management of eVTOL batteries. One distinctcharacteristic of batteries for eVTOLs is that the discharge rates are significantly larger during take-off andlanding, compared with the battery discharge rates needed for automotives. Such discharge protocols areexpected to impact the long-run health of batteries. This paper proposes a data-driven machine learningframework to estimate the state-of-health and remaining-useful-lifetime of eVTOL batteries under varying flightconditions and taking into account the entire flight profile of the eVTOLs. Three main features are consideredfor the assessment of the health of the batteries: charge, discharge and temperature. The importance of thesefeatures is also quantified. Considering battery charging before flight, a selection of missions for state-ofhealth and remaining-useful-lifetime prediction is performed. The results show that indeed, discharge-relatedfeatures have the highest importance when predicting battery state-of-health and *** several machine learning algorithms, it is shown that the battery state-of-health and remaining-useful-lifeare well estimated using Random Forest regression and Extreme Gradient Boosting, respectively.

关键词： Electric Vertical Take-off and Landing vehicles Lithium-ion battery State-of-health Machine learning Remaining-useful-life

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