Toyota

About: Toyota is a company organization based out in Safenwil, Switzerland. It is known for research contribution in the topics: Internal combustion engine & Battery (electricity). The organization has 40032 authors who have published 55003 publications receiving 735317 citations. The organization is also known as: Toyota Motor Corporation & Toyota Jidosha KK.

Funktionale poröse Koordinationspolymere

Abstract: Die Chemie der Koordinationspolymere hat sich in den vergangenen Jahren rasant entwickelt. Strukturen aus einer Vielzahl molekularer Bausteine mit unterschiedlichen Wechselwirkungen sind mittlerweile zuganglich. Die nachste Stufe ist die chemische und physikalische Funktionalisierung dieser Architekturen durch Einstellung ihrer Porositaten. Dieser Aufsatz konzentriert sich auf drei Aspekte von Koordinationspolymeren: 1) Anwendung von Kristall-Engineering zum Aufbau poroser Geruste aus Konnektoren und Linkern (“Nanospace-Engineering”), 2) Charakterisierung und Katalogisierung poros-struktureller Funktionalitat fur Anwendungen in Speicherungs-, Austausch-, Trennprozessen etc. und 3) poros-strukturelle Funktionalitat auf der Basis dynamischer Kristallumwandlungen durch Gastmolekule oder physikalische Reize. Ziel ist es, den aktuellen Stand der Forschung zur Chemie und Physik von und in den Mikroporen poroser Koordinationspolymere vorzustellen.

Data-driven prediction of battery cycle life before capacity degradation

Abstract: Accurately predicting the lifetime of complex, nonlinear systems such as lithium-ion batteries is critical for accelerating technology development. However, diverse aging mechanisms, significant device variability and dynamic operating conditions have remained major challenges. We generate a comprehensive dataset consisting of 124 commercial lithium iron phosphate/graphite cells cycled under fast-charging conditions, with widely varying cycle lives ranging from 150 to 2,300 cycles. Using discharge voltage curves from early cycles yet to exhibit capacity degradation, we apply machine-learning tools to both predict and classify cells by cycle life. Our best models achieve 9.1% test error for quantitatively predicting cycle life using the first 100 cycles (exhibiting a median increase of 0.2% from initial capacity) and 4.9% test error using the first 5 cycles for classifying cycle life into two groups. This work highlights the promise of combining deliberate data generation with data-driven modelling to predict the behaviour of complex dynamical systems. Accurately predicting battery lifetime is difficult, and a prediction often cannot be made unless a battery has already degraded significantly. Here the authors report a machine-learning method to predict battery life before the onset of capacity degradation with high accuracy.

The Lithium–Oxygen Battery with Ether-Based Electrolytes

Abstract: The rechargeable Li–air (O2) battery is receiving a great deal of interest because theoretically it can store significantly more energy than lithium ion batteries, thus potentially transforming energy storage. Since it was first described, a number of aspects of the Li–O2 battery with a non-aqueous electrolyte have been investigated. The electrolyte is recognized as one of the greatest challenges. To date, organic carbonate-based electrolytes (e.g. LiPF6 in propylene carbonate) have been widely used. However, recently, it has been shown that instead of O2 being reduced in the porous cathode to form Li2O2, as desired, discharge in organic carbonate electrolytes is associated with severe electrolyte decomposition. As a result it is very important to investigate other solvents in the search for a suitable electrolyte. In this regard much attention is now focused on electrolytes based on ethers (e.g. tetraglyme (tetraethylene glycol dimethyl ether)). Ethers are attractive for the Li–O2 battery because they are one of the few solvents that combine the following attributes: capable of operating with a lithium metal anode, stable to oxidation potentials in excess of 4.5 V versus Li/Li, safe, of low cost and, in the case of higher molecular weights, such as tetraglyme, they are of low volatility. Crucially, they are also anticipated to show greater stability towards reduced O2 species compared with organic carbonates. Herein we show that although the ethers are more stable than the organic carbonates, the Li2O2 that forms on the first discharge is accompanied by electrolyte decomposition, to give a mixture of Li2CO3, HCO2Li, CH3CO2Li, polyethers/ esters, CO2, and H2O. The extent of electrolyte degradation compared with Li2O2 formation on discharge appears to increase rapidly with cycling (that is, charging and discharging), such that after only 5 cycles there is little or no evidence of Li2O2 from powder X-ray diffraction. We show that the same decomposition products occur for linear chain lengths other than tetraglyme. In the case of cyclic ethers, such as 1,3dioxolane and 2-methyltetrahydrofuran (2-Me-THF), decomposition also occurs. For 1,3-dioxolane, decomposition forms polyethers/esters, Li2CO3, HCO2Li, and C2H4(OCO2Li)2, and for 2-Me-THF the main products are HCO2Li, CH3CO2Li; in both cases CO2 and H2O evolve. The results presented herein demonstrate that ether-based electrolytes are not suitable for rechargeable Li–O2 cells. A Li–O2 cell consisting of a lithium metal anode, an electrolyte, comprising 1m LiPF6 in tetraglyme, and a porous cathode (Super P/Kynar) was constructed as described in the Experimental Section. The cell was discharged in 1 atm O2 to 2 V. The porous cathode was then removed, washed with CH3CN, and examined by powder X-ray diffraction (PXRD) and FTIR. The results are presented in Figure 1 and Figure 2. The PXRD data demonstrate the presence of Li2O2, consistent with previous PXRD data for a Li–O2 cell with a tetraglyme electrolyte at the end of the first discharge. However, examination of the FTIR spectra, Figure 2, reveals that, in addition to Li2O2, other products form. Although the FTIR spectra provide clear evidence of electrolyte decom-