Taking stock of large-scale lithium-ion battery production using life cycle assessment

Battery electric vehicles are being increasingly favored as an alternative to internal combustion engine vehicles (ICEVs). This is mainly due to their lower environmental impact when compared to ICEVs over the vehicle’s lifetime. Life cycle assessment (LCA) studies focusing specifically on battery electric vehicles (BEVs) have identified battery cell production as an environmental hotspot in the BEV’s life cycle. However, lack of primary or industrial data, different technical scopes, and varying data quality, limit a thorough understanding of the environmental impacts of cell production. Further, with scaling-up of battery production (to meet the rising demand for BEVs), the source and level of impacts are expected to change. In response, the main aim of this thesis is to explore and understand the implications of upscaling in battery production. An example of such a change is provided at the mining sites where raw materials for lithium used in batteries are extracted and produced. As mining continues, over time, the ore grades at these sites decline. Thus, this thesis also aims to investigate the effect of declining ore grades on the overall impacts from cell production. A sub-goal is to understand the relevance of background data in LCA studies and its effect on overall results.The technical scope of this thesis is the production of a graphite-NMC:811 21700 type cylindrical cell. To assess the environmental impacts of upscaling, production in a small-scale facility is compared to production in a large-scale facility. Next, the impact of declining ore grades on overall cell production is estimated by analyzing the data from multiple mining sites for lithium, with varying ore grades and different types of sources – spodumene and brine. To assess the effect of background database on overall results, the LCA model for cell production was coupled with different versions of the Ecoinvent background database. Lastly, a physics-based model platform, developed in cross-disciplinary collaboration, is proposed with the objective of filling data gaps in LCA of lithium-ion batteries (LIBs). The model platform will help link the cell design aspects such as power or energy optimization to changes in the individual cell production processes. Further, the model platform will help expand the technical scope to broadened set of cell geometries and chemistries, and increase the precision in use phase modeling as well.The results show that the upscaling leads to a reduction in environmental impacts from cell production. This is due to higher energy and material efficiency of cell production at large scale. Further, when low-carbon intensive sources are used, then the impacts from cell production shift almost entirely to the raw material extraction and production phase. In the context of declining ore grades, the type of source and grade of lithium account for 5-15% of the global warming impacts from cell production. This implies that future environmental impacts from LIB production could increase, due to increased chemical and energy inputs, in response to declining ore grades at mining sites. The changes in the background data have a significant bearing on the overall results. These are due to evolving technical systems and an improved representation of these systems in terms of data quality and geo-spatial representativeness. Lastly, preliminary results from the physics-based model platform show that accounting for variations in cell design can further add variability in results

Why is order flow so persistent?

Order flow in equity markets is remarkably persistent in the sense that order signs (to buy or sell) are positively autocorrelated out to time lags of tens of thousands of orders, corresponding to many days. Two possible explanations are herding, corresponding to positive correlation in the behavior of different investors, or order splitting, corresponding to positive autocorrelation in the behavior of single investors. We investigate this using order flow data from the London Stock Exchange for which we have membership identifiers. By formulating models for herding and order splitting, as well as models for brokerage choice, we are able to overcome the distortion introduced by brokerage. On timescales of less than a few hours the persistence of order flow is overwhelmingly due to splitting rather than herding. We also study the properties of brokerage order flow and show that it is remarkably consistent both cross-sectionally and longitudinally.Comment: 42 pages, 15 figure

Directed evolution of artificial metalloenzyme – in vivo catalysis

There is a growing interest in implementing organometallic catalysis in the context of synthetic biology for sustainable production of chemicals. Some of the recent achievements in this field include development of bio-compatible cyclopropanation by Arnold and Balskus groups[1,2]. As a first step towards interfacing microbial metabolism we aim to utilize Artificial metalloenzymes (ArMs) to perform catalysis in the cell to augment cellular bio-synthesis. Integrating ArMs catalyzed reactions in cells also provides a springboard to apply Darwinian evolution to improve the performance of these primordial enzymes[3]. Please click Additional Files below to see the full abstract

Risk-Neutral Skewness, Informed Trading, and the Cross Section of Stock Returns

AbstractIn this article, we use volatility surface data from options contracts to document a strong, robust, and positive cross-sectional relation between risk-neutral skewness (RNS) and subsequent stock returns. The differential return between high- and low-RNS stocks amounts to 0.17% per week. Preannouncement RNS is positively related to earnings announcement returns, and the positive RNS&ndash;return relation is more pronounced for other nonscheduled news releases. This suggests that it is informed trading that drives the positive relation between RNS and subsequent stock returns. We also find that RNS contains incremental information beyond trading signals captured by option-implied volatility and volume.</jats:p