Overcoming barriers to improved decision-making for battery deployment in the clean energy transition.

Decarbonization plans depend on the rapid, large-scale deployment of batteries to sufficiently decarbonize the electricity system and on-road transport. This can take many forms, shaped by technology, materials, and supply chain selection, which will have local and global environmental and social impacts. Current knowledge gaps limit the ability of decision-makers to make choices in facilitating battery deployment that minimizes or avoids unintended environmental and social consequences. These gaps include a lack of harmonized, accessible, and up-to-date data on manufacturing and supply chains and shortcomings within sustainability and social impact assessment methods, resulting in uncertainty that limits incorporation of research into policy making. These gaps can lead to unintended detrimental effects of large-scale battery deployment. To support decarbonization goals while minimizing negative environmental and social impacts, we elucidate current barriers to tracking how decision-making for large-scale battery deployment translates to environmental and social impacts and recommend steps to overcome them.

Quantitative longitudinal mapping of radiation-treated prostate cancer using MR fingerprinting with radial acquisition and subspace reconstruction.

MR fingerprinting (MRF) enables fast multiparametric quantitative imaging with a single acquisition and has been shown to improve diagnosis of prostate cancer. However, most prostate MRF studies were performed with spiral acquisitions that are sensitive to B0 inhomogeneities and consequent blurring. In this work, a radial MRF acquisition with a novel subspace reconstruction technique was developed to enable fast T1/T2 mapping in the prostate in under 4 min. The subspace reconstruction exploits the extensive temporal correlations in the MRF dictionary to pre-compute a low dimensional space for the solution and thus reduce the number of radial spokes to accelerate the acquisition. Iterative reconstruction with the subspace model and additional regularization of the signal representation in the subspace is performed to minimize the number of spokes and maintain matching quality and SNR. Reconstruction accuracy was assessed using the ISMRM NIST phantom. In-vivo validation was performed on two healthy subjects and two prostate cancer patients undergoing radiation therapy. The longitudinal repeatability was quantified using the concordance correlation coefficient (CCC) in one of the healthy subjects by repeated scans over 1 year. One prostate cancer patient was scanned at three time points, before initiating therapy and following brachytherapy and external beam radiation. Changes in the T1/T2 maps obtained with the proposed method were quantified. The prostate, peripheral and transitional zones, and visible dominant lesion were delineated for each study, and the statistics and distribution of the quantitative mapping values were analyzed. Significant image quality improvements compared with standard reconstruction methods were obtained with the proposed subspace reconstruction method. A notable decrease in the spread of the T1/T2 values without biasing the estimated mean values was observed with the subspace reconstruction and agreed with reported literature values. The subspace reconstruction enabled visualization of small differences in T1/T2 values in the tumor region within the peripheral zone. Longitudinal imaging of a volunteer subject yielded CCC of 0.89 for MRF T1, and 0.81 for MRF T2 in the prostate gland. Longitudinal imaging of the prostate patient confirmed the feasibility of capturing radiation treatment related changes. This work is a proof-of-concept for a high resolution and fast quantitative mapping using golden-angle radial MRF combined with a subspace reconstruction technique for longitudinal treatment response assessment in subjects undergoing radiation treatment.

A feasibility study to assess Mediterranean Diet adherence using an AI-powered system.

Mediterranean diet (MD) can play a major role in decreasing the risks of non-communicable diseases and preventing overweight and obesity. In order for a person to follow the MD and assess their adherence to it, proper dietary assessment methods are required. We have developed an Artificial Intelligence-powered system that recognizes the food and drink items from a single meal photo and estimates their respective serving size, and integrated it into a smartphone application that automatically calculates MD adherence score and outputs a weekly feedback report. We compared the MD adherence score of four users as calculated by the system versus an expert dietitian, and the mean difference was 3.5% and statistically not significant. Afterwards, we conducted a feasibility study with 24 participants, to evaluate the system's performance and to gather the users' and dietitians' feedback. The image recognition system achieved 61.8% mean Average Precision for the testing set and 57.3% for the feasibility study images (where the ground truth was taken as the participants' annotations). The feedback from the participants of the feasibility study was also very positive.

Energy flow analysis of laboratory scale lithium-ion battery cell production.

Lithium-ion batteries (LIBs) have been proven as an enabling technology for consumer electronics, electro mobility, and stationary storage systems, and the steadily increasing demand for LIBs raises new challenges regarding their sustainability. The rising demand for comprehensive assessments of this technology's environmental impacts requires the identification of energy and materials consumed for its production, on lab to industrial scale. There are no studies available that provide a detailed picture of lab scale cell production, and only a few studies provide detailed analysis of the actual consumption, with large deviations. Thus, the present work provides an analysis of the energy flows for the production of an LIB cell. The analyzed energy requirements of individual production steps were determined by measurements conducted on a laboratory scale lithium-ion cell production and displayed in a transparent and traceable manner. For the comparison with literature values a distinction is made between the different production scales.

First report of Flavescence dorée-related Phytoplasma in Grapevine in Germany.

Flavescence dorée (FD) and Bois noir (BN) are the principal grapevine yellows diseases in Europe caused by distinct phytoplasmas: FD by 16SrV phytoplasmas (FDp), BN by Candidatus Phytoplasma solani. FDp is spread epidemically by the introduced Nearctic Deltocephalinae Scaphoideus titanus and is listed as a quarantine pest in the European Union (Regulation (EU) 2019/2072). Black Alder (Alnus glutinosa) is a common asymptotic host of 16SrV phytoplasmas in Europe and considered the original host of FDp (Malembic-Maher et al. 2020). Palatinate grapevine yellows (PGY) transmitted from alder to grapevine by the Macropsinae Oncopsis alni (Maixner et al. 2000) is not transmissible by S. titanus, unlike isolates transmitted by the autochthonous Deltocephalinae Allygus spp. and the invasive Orientus ishidae (Malembic-Maher et al. 2020). Germany is considered free from FD in grapevine and from its vector. A single case in a nursery in 2014 was eradicated (EPPO 2017). Since S. titanus was detected in 2016 in the neighboring French Region of Alsace, monitoring of FD was carried out in Germany. It was focused on vineyards within a distance of 100 m from stands of alder trees. A geodata-based risk map (Jalke 2020) was used to identify those plots. All symptomatic vines sampled until September 2020 proved to be infected by BN or, occasionally, by PGY. Eight vines with typical symptoms were sampled in vineyards adjacent to alder stands in the winegrowing region of Rheinhessen in September 2020. Symptoms comprised leaf rolling and discoloration, incomplete lignification, black pustules on shoots, dried inflorescences and shriveled berries. Diseased shoots were black and necrotic in December. Leaf midribs were sampled for total DNA extraction. The phytoplasma 16S rRNA gene was amplified by generic primers R16F2/R2-mod followed by a nested PCR using 16Sr(V) group-specific primers R16(V)F1/R1, and primers R16(I)F1/R1 (Lee et al. 1995) to detect 'Candidatus Phytoplasma solani', associated with BN. While BN was detected in seven vines, one sample tested positive for 16SrV phytoplasma. This result was confirmed by triplex real-time Taq-Man assay based on rpl14 gene sequences (IPADLAB), by multiplex real-time PCR of map locus as well as by Loop-mediated isothermal amplification (LAMP) according to the EPPO diagnostic standard PM 7/079(2) (EPPO 2016). PCR-products of the map- and the vmpA-Gene (Malembic-Maher et al., 2020) were sequenced and compared to reference sequences to distinguish between FD- and non-FD genotypes. The isolate from the diseased vine exhibited 100% identity with map-M38 (Accession No. LT221933), a genotype of the map-FD2 cluster. The same genotype was detected in A. glutinosa and Allygus spp. sampled at the infested site. A 234 bp sequence of the first repeat of the vmpA-gene showed 100% identity with the S. titanus transmitted isolate FD-92 (Accession No. LN680870) of the vmpA-II cluster. It can be concluded, that the 16SrV-isolate detected in a symptomatic grapevine is infected by FD and not PGY. This is the first report of FD in a vineyard in Germany. The infected vine of cv. Silvaner was 25 years old. While infected planting material is an unlikely source of the infection, a transmission of FDp from alder is highly probable. Finding a single FD-infection after several years of testing implies a low risk originating from the wild compartment, but the approach of the vector S. titanus justifies further monitoring activities. The infected vine was eradicated.

Assessing Mediterranean Diet Adherence with the Smartphone: The Medipiatto Project.

The Mediterranean diet (MD) is regarded as a healthy eating pattern with beneficial effects both for the decrease of the risk for non-communicable diseases and also for body weight reduction. In the current manuscript, we propose an automated smartphone application which monitors and evaluates the user's adherence to MD using images of the food and drinks that they consume. We define a set of rules for automatic adherence estimation, which focuses on the main MD food groups. We use a combination of a convolutional neural network (CNN) and a graph convolutional network to detect the types of foods and quantities from the users' food images and the defined set of rules to evaluate the adherence to MD. Our experiments show that our system outperforms a basic CNN in terms of recognizing food items and estimating quantity and yields comparable results as experienced dietitians when it comes to overall MD adherence estimation. As the system is novel, these results are promising; however, there is room for improvement of the accuracy by gathering and training with more data and certain refinements can be performed such as re-defining the set of rules to also be able to be used for sub-groups of MD (e.g., vegetarian type of MD).

Life Cycle Assessment of a Vanadium Redox Flow Battery.

Batteries are one of the key technologies for flexible energy systems in the future. In particular, vanadium redox flow batteries (VRFB) are well suited to provide modular and scalable energy storage due to favorable characteristics such as long cycle life, easy scale-up, and good recyclability. However, there is a lack of detailed original studies on the potential environmental impacts of their production and operation. The present study fills this gap by providing a comprehensive life cycle assessment of a representative VRFB. Transparent and comprehensive inventory data are disclosed as a basis for further environmental studies. VRFBs are found to be promising regarding the assessed impact categories, especially at high energy-to-power (E/P) ratios. On the other hand, significant impacts are associated with the vanadium pentoxide production, which is why the origin and processing of the vanadium bearing ores are a key for further reducing the environmental impacts associated with the VRFB manufacturing. While the lower efficiency of the VRFB is a disadvantage in comparison to e.g. lithium-ion batteries (LIB), its recyclability is significantly higher. In this sense, the importance of taking a cradle-to-cradle life cycle perspective when comparing very different battery systems can be highlighted for further research on this topic.

Prospective time-resolved LCA of fully electric supercap vehicles in Germany.

UNLABELLED: The ongoing transition of the German electricity supply toward a higher share of renewable and sustainable energy sources, called Energiewende in German, has led to dynamic changes in the environmental impact of electricity over the last few years. Prominent scenario studies predict that comparable dynamics will continue in the coming decades, which will further improve the environmental performance of Germany's electricity supply. Life cycle assessment (LCA) is the methodology commonly used to evaluate environmental performance. Previous LCA studies on electric vehicles have shown that the electricity supply for the vehicles' operation is responsible for the major part of their environmental impact. The core question of this study is how the prospective dynamic development of the German electricity mix will affect the impact of electric vehicles operated in Germany and how LCA can be adapted to analyze this impact in a more robust manner. The previously suggested approach of time-resolved LCA, which is located between static and dynamic LCA, is used in this study and compared with several static approaches. Furthermore, the uncertainty issue associated with scenario studies is addressed in general and in relation to time-resolved LCA. Two scenario studies relevant to policy making have been selected, but a moderate number of modifications have been necessary to adapt the data to the requirements of a life cycle inventory. A potential, fully electric vehicle powered by a supercapacitor energy storage system is used as a generic example. The results show that substantial improvements in the environmental repercussions of the electricity supply and, consequentially, of electric vehicles will be achieved between 2020 and 2031 on the basis of the energy mixes predicted in both studies. This study concludes that although scenarios might not be able to predict the future, they should nonetheless be used as data sources in prospective LCA studies, because in many cases historic data appears to be unsuitable for providing realistic information on the future. The time-resolved LCA approach improves the assessment's robustness substantially, especially when nonlinear developments are foreseen in the future scenarios. This allows for a reduction of bias in LCA-based decision making. However, a deeper integration of time-resolved data in the life cycle inventory and the implementation of a more suitable software framework are desirable. KEY POINTS: The study describes how life cycle assessment's (LCA) robustness can be improved by respecting prospective fluctuations, like the transition of the German electricity mix, in the modeling of the life cycle inventory. It presents a feasible and rather simple process to add time-resolved data to LCA. The study selects 2 different future scenarios from important German studies and processes their data systematically to make them compatible with the requirements of a life cycle inventory. The use of external scenarios as basis for future-oriented LCA is reflected critically. A case study on electric mobility is presented and used to compare historic, prospective static, and prospective time-resolved electricity mix modeling approaches. The case study emphasizes the benefits of time-resolved LCA in direct comparison with the currently used approaches.