Carbon based double layer capacitors with aprotic electrolyte solutions: the possible role of intercalation/insertion processes

The extraordinary stability and cycle life performance of today's electrochemical double-layer capacitors (EDLCs) are generally ascribed to the fact that charge storage in activated carbon (AC) is based on pure double-layer charging. In contrast, Faradaic charge-transfer reactions like those occurring in batteries are often connected with dimensional changes, which can affect the cycle life of these storage devices. Here we report the charge-induced height change of an AC electrode in an aprotic electrolyte solution, 1mol/l (C2H5)4NBF4 (TEABF4) in acetonitrile. The results are compared with those obtained for a graphite electrode in the same electrolyte. For both electrodes, we observe an expansion/contraction of several percent for a potential window of ±2V vs. the immersion potential (ip). For the EDLC electrode, significant expansion starts at about 1V remote from the ip and hence is well within the normal EDLC operation range. For the graphite electrode, the height changes are unambiguously caused by intercalation/deintercalation of both anions and cations. The close analogies between the graphite and the EDLC electrode suggest that ion intercalation or insertion processes might play a major role for charge storage, self discharge, cyclability, and the voltage limitation of EDLC

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time, and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space. While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes, vast areas of the tropics remain understudied. In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity, but it remains among the least known forests in America and is often underrepresented in biodiversity databases. To worsen this situation, human-induced modifications may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge, it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost