Hype Cycle Assessment Of Emerging Technologies For Battery Production

The demand for battery-powered electric vehicles is growing rapidly as more and more OEMs are shifting their strategy towards an all-electric vehicle fleet. The lithium-ion battery cell is considered as the core component in terms of performance, range and price of electric vehicles. Since the development of the functional principle of the lithium-ion battery, both the product and the associated production technology have evolved significantly. OEMs, start-ups, equipment suppliers and other players in the automotive industry are investing heavily in research and development of various technologies to improve both the battery as a product and its production. An essential aspect is to enable sustainable battery production. While breakthroughs in battery technology are regularly announced, the actual merits of the technologies and the potential remain uncertain until commercial deployment. The aim of this paper is to systematically identify upcoming breakthroughs and announced innovations to provide an overview of promising battery technologies that companies should focus on to enable the planning of resilient and sustainable production systems. Hence, a hype cycle assessment following Gartner was adopted as the underlying approach to evaluate battery technologies for deployment in electromobility and mass production. First, various technologies, innovations, research activities and announcements in the field of battery technologies were screened, recorded and classified in order to obtain an overview of the current state of developments on both product and production levels. This includes an overview of innovations in battery design and configuration as well as process technologies and production systems. Subsequently, these technologies are evaluated according to predefined evaluation criteria in order to enable a systematic classification of the individual technologies in the hype cycle. The result is a consolidated overview of emerging battery technologies for sustainable battery production and a display for further recommendations for relevant companies and stakeholders

Investigation Of Laser-Based Drying Of Electrodes For Lithium-Ion-Battery Production Using Vertical-Cavity Surface-Emitting Lasers (VCSEL)

Demand for lithium-ion batteries is expected to increase significantly within the upcoming decade. This trend is already evident today. Accordingly, an increasing number of new production lines as well as extensions of existing production lines can be observed. To reduce global CO2 emissions and maintain competitiveness, the development of new, more efficient production technologies is essential. At the same time, innovative production methods can make a decisive contribution to significantly improve the quality and performance of lithium-ion batteries. Currently, the convection drying technology marks the state of the art in drying the wet-coated electrode foils. Accounting for approximately one quarter of the total energy consumption in battery production, this technology represents one of the most cost-intensive process steps along the value chain. A promising approach to increase quality and efficiency of electrode drying is the use of vertical-cavity surface-emitting lasers (VCSEL). In addition to the improved controllability compared to conventional drying processes, these also offer the advantage of a direct energy contribution that is tailored to the material. Furthermore, the exceptionally high-power density enables a reduction in machine footprint while simultaneously increasing production throughput. In this study, the general experimental setup for vertical drying of electrodes using VCSEL technology is described. Furthermore, the main results are presented and discussed in order to derive subsequent conclusions for further improvements in quality and energy efficiency in the drying process of electrodes for lithium-ion batteries

Predicting Survival for Veno-Arterial ECMO Using Conditional Inference Trees-A Multicenter Study

BACKGROUND Despite increasing use and understanding of the process, veno-arterial extracorporeal membrane oxygenation (VA-ECMO) therapy is still associated with considerable mortality. Personalized and quick survival predictions using machine learning methods can assist in clinical decision making before ECMO insertion. METHODS This is a multicenter study to develop and validate an easy-to-use prognostic model to predict in-hospital mortality of VA-ECMO therapy, using unbiased recursive partitioning with conditional inference trees. We compared two sets with different numbers of variables (small and comprehensive), all of which were available just before ECMO initiation. The area under the curve (AUC), the cross-validated Brier score, and the error rate were applied to assess model performance. Data were collected retrospectively between 2007 and 2019. RESULTS 837 patients were eligible for this study; 679 patients in the derivation cohort (median (IQR) age 60 (49 to 69) years; 187 (28%) female patients) and a total of 158 patients in two external validation cohorts (median (IQR) age 57 (49 to 65) and 70 (63 to 76) years). For the small data set, the model showed a cross-validated error rate of 35.79% and an AUC of 0.70 (95% confidence interval from 0.66 to 0.74). In the comprehensive data set, the error rate was the same with a value of 35.35%, with an AUC of 0.71 (95% confidence interval from 0.67 to 0.75). The mean Brier scores of the two models were 0.210 (small data set) and 0.211 (comprehensive data set). External validation showed an error rate of 43% and AUC of 0.60 (95% confidence interval from 0.52 to 0.69) using the small tree and an error rate of 35% with an AUC of 0.63 (95% confidence interval from 0.54 to 0.72) using the comprehensive tree. There were large differences between the two validation sets. CONCLUSIONS Conditional inference trees are able to augment prognostic clinical decision making for patients undergoing ECMO treatment. They may provide a degree of accuracy in mortality prediction and prognostic stratification using readily available variables

Laser Drying Of Graphite Anodes For The Production Of Lithium-Ion Batteries - A Process- And Material-Side Analysis For Sustainable Battery Production

In many industries, such as the automotive industry or consumer electronics, the demand for lithium-ion batteries is increasing significantly. The state of the art in battery production is energy-consuming and cost-intensive. The drying process of the viscous active material applied to the conductor foils, together with the coating process, is responsible for more than half of the production costs of an electrode. The high energy consumption of conventional drying processes, such as convection drying, must be reduced. Therefore, lasers are used to dry the active material of the electrodes. Further advantages are the low footprint and the increased process flexibility. Moreover, the controlled energy deposition and the spatially selective heat input increase the energy efficiency of the process innovation laser drying. In this review, the results of experiments on drying anodes by laser are compared with the results of convection drying. For this purpose, different production process parameter combinations and material compositions for anodes are chosen in order to be able to derive the process and material influences on the electrode quality

COVID-19 symptoms at hospital admission vary with age and sex: results from the ISARIC prospective multinational observational study

Background: The ISARIC prospective multinational observational study is the largest cohort of hospitalized patients with COVID-19. We present relationships of age, sex, and nationality to presenting symptoms. Methods: International, prospective observational study of 60 109 hospitalized symptomatic patients with laboratory-confirmed COVID-19 recruited from 43 countries between 30 January and 3 August 2020. Logistic regression was performed to evaluate relationships of age and sex to published COVID-19 case definitions and the most commonly reported symptoms. Results: ‘Typical’ symptoms of fever (69%), cough (68%) and shortness of breath (66%) were the most commonly reported. 92% of patients experienced at least one of these. Prevalence of typical symptoms was greatest in 30- to 60-year-olds (respectively 80, 79, 69%; at least one 95%). They were reported less frequently in children (≤ 18 years: 69, 48, 23; 85%), older adults (≥ 70 years: 61, 62, 65; 90%), and women (66, 66, 64; 90%; vs. men 71, 70, 67; 93%, each P < 0.001). The most common atypical presentations under 60 years of age were nausea and vomiting and abdominal pain, and over 60 years was confusion. Regression models showed significant differences in symptoms with sex, age and country. Interpretation: This international collaboration has allowed us to report reliable symptom data from the largest cohort of patients admitted to hospital with COVID-19. Adults over 60 and children admitted to hospital with COVID-19 are less likely to present with typical symptoms. Nausea and vomiting are common atypical presentations under 30 years. Confusion is a frequent atypical presentation of COVID-19 in adults over 60 years. Women are less likely to experience typical symptoms than men

Lung ultrasound findings in patients with COVID-19

The current SARS-CoV-2 outbreak leads to a growing need of point-of-care thoracic imaging that is compatible with isolation settings and infection prevention precautions. We retrospectively reviewed 17 COVID-19 patients who received point-of-care lung ultrasound imaging in our isolation unit. Lung ultrasound was able to detect interstitial lung disease effectively; severe cases showed bilaterally distributed B-Lines with or without consolidations; one case showed bilateral pleural plaques. Corresponding to CT scans, interstitial involvement is accurately depicted as B-Lines on lung ultrasound. Lung ultrasound might be suitable for detecting interstitial involvement in a bedside setting under high security isolation precautions

Process and Material Analysis of Laser- and Convection-Dried Silicon–Graphite Anodes for Lithium-Ion Batteries

Drying electrodes is very cost-intensive as it is characterized by high energy and space consumption. Laser drying is considered a promising alternative process due to direct energy input and lower operating costs. However, it is unclear whether the same product and process quality can be achieved with laser drying. Silicon–graphite anodes with different silicon contents were processed using either a high-power diode laser or a convection oven. The laser-drying process was investigated using thermography, and the effect of laser drying on the electrode quality was examined using adhesion and residual moisture measurements. Furthermore, thermogravimetric analysis, SEM images and electrical conductivity were used to analyse the laser- and convection-dried anodes. It was shown that silicon–graphite anodes can also be manufactured using laser drying, with a significant reduction in drying time of over 80%