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Lithium-ion batteries (LIBs) are currently one of the most important electrochemical energy storage devices, powering electronic mobile devices and electric vehicles alike. However, there is a remarkable difference between their rate of production and rate of recycling. At the end of their lifecycle, only a limited number of LIBs undergo any recycling treatment, with the majority go to landfills or being hoarded in households. Further losses of LIB components occur because the the state-of-the-art LIB recycling processes are limited to components with high economic value, e.g., Co, Cu, Fe, and Al. With the increasing popularity of concepts such as “circular economy” (CE), new LIB recycling systems have been proposed that target a wider spectrum of compounds, thus reducing the environmental impact associated with LIB production. This review work presents a discussion of the current practices and some of the most promising emerging technologies for recycling LIBs. While other authoritative reviews have focused on the description of recycling processes, the aim of the present was is to offer an analysis of recycling technologies from a CE perspective. Consequently, the discussion is based on the ability of each technology to recover every component in LIBs. The gathered data depicted a direct relationship between process complexity and the variety and usability of the recovered fractions. Indeed, only processes employing a combination of mechanical processing, and hydro- and pyrometallurgical steps seemed able to obtain materials suitable for LIB (re)manufacture. On the other hand, processes relying on pyrometallurgical steps are robust, but only capable of recovering metallic components.

The mass production of graphene oxide (GO) unavoidably elevates the chance of human exposure, as well as the possibility of release into the environment with high stability, raising public concern as to its potential toxicological risks and the implications for humans and ecosystems. Therefore, a thorough assessment of GO toxicity, including its potential reliance on key physicochemical factors, which is lacking in the literature, is of high significance and importance. In this study, GO toxicity, and its dependence on oxidation level, elemental composition, and size, were comprehensively assessed. A newly established quantitative toxicogenomic-based toxicity testing approach, combined with conventional phenotypic bioassays, were employed. The toxicogenomic assay utilized a GFP-fused yeast reporter library covering key cellular toxicity pathways. The results reveal that, indeed, the elemental composition and size do exert impacts on GO toxicity, while the oxidation level exhibits no significant effects. The UV-treated GO, with significantly higher carbon-carbon groups and carboxyl groups, showed a higher toxicity level, especially in the protein and chemical stress categories. With the decrease in size, the toxicity level of the sonicated GOs tended to increase. It is proposed that the covering and subsequent internalization of GO sheets might be the main mode of action in yeast cells.

Data-Driven approaches for State of Charge (SOC) prediction have been developed considerably in recent years. However, determining the appropriate training dataset is still a challenge for model development and validation due to the considerably varieties of lithium-ion batteries in terms of material, types of battery cells, and operation conditions. This work focuses on optimization of the training data set by using simple measurable data sets, which is important for the accuracy of predictions, reduction of training time, and application to online estimation. It is found that a randomly generated data set can be effectively used for the training data set, which is not necessarily the same format as conventional predefined battery testing protocols, such as constant current cycling, Highway Fuel Economy Cycle, and Urban Dynamometer Driving Schedule. The randomly generated data can be successfully applied to various dynamic battery operating conditions. For the ML algorithm, XGBoost is used, along with Random Forest, Artificial Neural Network, and a reduced-order physical battery model for comparison. The XGBoost method with the optimal training data set shows excellent performance for SOC prediction with the fastest learning time within 1 s, a short running time of 0.03 s, and accurate results with a 0.358% Mean Absolute Percentage Error, which is outstanding compared to other Data-Driven approaches and the physics-based model.

Anthropogenic stressors to marine ecosystems from climate change and human activities increase extinction risk of species, disrupt ecosystem integrity, and threaten important ecosystem services. Addressing these stressors requires understanding where and to what extent they are impacting marine biological and functional diversity. We model cumulative risk of human impact upon 21,159 marine animal species by combining information on species-level vulnerability and spatial exposure to a range of anthropogenic stressors. We apply this species-level assessment of human impacts to examine patterns of species-stressor interactions within taxonomic groups. We then spatially map impacts across the global ocean, identifying locations where climate-driven impacts overlap with fishing, shipping, and land-based stressors to help inform conservation needs and opportunities. Comparing species-level modeled impacts to those based on marine habitats that represent important marine ecosystems, we find that even relatively untouched habitats may still be home to species at elevated risk, and that many species-rich coastal regions may be at greater risk than indicated from habitat-based methods alone. Finally, we incorporate a trait-based metric of functional diversity to identify where impacts to functionally unique species might pose greater risk to community structure and ecosystem integrity. These complementary lenses of species, function, and habitat provide a richer understanding of threats to marine biodiversity to help inform efforts to meet conservation targets and ensure sustainability of nature’s contributions to people.

AbstractThe long‐term stability of coastal wetlands is determined by interactions among sea level, plant primary production, sediment supply, and wetland vertical accretion. Human activities in watersheds have significantly altered sediment delivery from the landscape to the coastal ocean, with declines along much of the U.S. East Coast. Tidal wetlands in coastal systems with low sediment supply may have limited ability to keep pace with accelerating rates of sea‐level rise (SLR). Here, we show that rates of vertical accretion and carbon accumulation in nine tidal wetland systems along the U.S. East Coast from Maine to Georgia can be explained by differences in the rate of relative SLR (RSLR), the concentration of suspended sediments in the rivers draining to the coast, and temperature in the coastal region. Further, we show that rates of vertical accretion have accelerated over the past century by between 0.010 and 0.083 mm yr−2, at roughly the same pace as the acceleration of global SLR. We estimate that rates of carbon sequestration in these wetland soils have accelerated (more than doubling at several sites) along with accelerating accretion. Wetland accretion and carbon accumulation have accelerated more rapidly in coastal systems with greater relative RSLR, higher watershed sediment availability, and lower temperatures. These findings suggest that the biogeomorphic feedback processes that control accretion and carbon accumulation in these tidal wetlands have responded to accelerating RSLR, and that changes to RSLR, watershed sediment supply, and temperature interact to determine wetland vulnerability across broad geographic scales.

SummaryDespite the importance of nonstructural carbohydrates (NSC) for growth and survival in woody plants, we know little about whole‐treeNSCstorage. The conventional theory suggests thatNSCreserves will increase over the growing season and decrease over the dormant season. Here, we compare storage in five temperate tree species to determine the size and seasonal fluctuation of whole‐tree totalNSCpools as well as the contribution of individual organs.NSCconcentrations in the branches, stemwood, and roots of 24 trees were measured across 12 months. We then scaled up concentrations to the whole‐tree and ecosystem levels using allometric equations and forest stand inventory data.While whole‐tree totalNSCpools followed the conventional theory, sugar pools peaked in the dormant season and starch pools in the growing season. Seasonal depletion of totalNSCs was minimal at the whole‐tree level, but substantial at the organ level, particularly in branches. Surprisingly, roots were not the major storage organ as branches stored comparable amounts of starch throughout the year, and root reserves were not used to support springtime growth.Scaling upNSCconcentrations to the ecosystem level, we find that commonly used, process‐based ecosystem and land surface models all overpredictNSCstorage.

Wide-area dynamic studies are of paramount importance to ensure the stability and reliability of power grids. The rising deployment synchrophasor and other sensing technologies has made data-driven modeling and analysis possible using the synchronized fast-rate dynamic measurements. This paper presents a general model-free framework of inferring the grid dynamic responses using the ubiquitous ambient data collected during normal grid operations. Building upon the second-order dynamic model, we have established the connection from the cross-correlation of various types of angle, frequency, and line flow data at any two locations, to their corresponding dynamic responses. The theoretical results enabled a fully data-driven framework for estimating the latter using real-time ambient data. Numerical results using the WSCC 9-bus system and a synthetic 2000-bus Texas system have demonstrated the effectiveness of proposed approaches for dynamic modeling of realistic power systems.

AbstractClimate change is a major threat to global biodiversity. Antarctic ecosystems are no exception. Investigating past species responses to climatic events can distinguish natural from anthropogenic impacts. Climate change produces ‘winners’, species that benefit from these events and ‘losers’, species that decline or become extinct. Using molecular techniques, we assess the demographic history and population structure of Pygoscelis penguins in the Scotia Arc related to climate warming after the Last Glacial Maximum (LGM). All three pygoscelid penguins responded positively to post-LGM warming by expanding from glacial refugia, with those breeding at higher latitudes expanding most. Northern (Pygoscelis papua papua) and Southern (Pygoscelis papua ellsworthii) gentoo sub-species likely diverged during the LGM. Comparing historical responses with the literature on current trends, we see Southern gentoo penguins are responding to current warming as they did during post-LGM warming, expanding their range southwards. Conversely, Adélie and chinstrap penguins are experiencing a ‘reversal of fortunes’ as they are now declining in the Antarctic Peninsula, the opposite of their response to post-LGM warming. This suggests current climate warming has decoupled historic population responses in the Antarctic Peninsula, favoring generalist gentoo penguins as climate change ‘winners’, while Adélie and chinstrap penguins have become climate change ‘losers’.