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Register a new userPerformance Metrics for the Objective Assessment of Capacitive Deionization Systems
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In the growing field of capacitive deionization (CDI), a number of performance metrics have emerged to describe the desalination process. Unfortunately, the separation conditions under which these metrics are measured are often not specified, resulting in optimal performance at minimal removal. Here we outline a system of performance metrics and reporting conditions that resolves this issue. Our proposed system is based on volumetric energy consumption (Wh/m$^3$) and throughput productivity (L/h/m$^2$) reported for a specific average concentration reduction, water recovery, and feed salinity. To facilitate and rationalize comparisons between devices, materials, and operation modes, we propose a nominal standard testing condition of removing 5 mM from a 20 mM NaCl feed solution at 50% water recovery for CDI research. Using this separation, we compare the desalination performance of a flow-through electrode (fte-CDI) cell and a flow between membrane (fb-MCDI) device, showing how significantly different systems can be compared in terms of generally desirable desalination characteristics. In general, we find that performance analysis must be considered carefully so to not allow for ambiguous separation conditions or the maximization of one metric at the expense of another. Additionally, for context we discuss a number of important underlying performance indicators and cell characteristics that are not performance measures in and of themselves but can be examined to better understand differences in performance.
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Over the past decade, capacitive deionization (CDI) has realized a surge in attention in the field of water desalination and can now be considered as an important technology class, along with reverse osmosis and electrodialysis. While many of the recently developed technologies no longer use a mechanism that follows the strict definition of the term capacitive, these methods nevertheless share many common elements that encourage treating them with similar metrics and analyses. Specifically, they all involve electrically driven removal of ions from a feed stream, storage in an electrode (i.e., ion electrosorption) and release, in charge/discharge cycles. Grouping all these methods in the technology class of CDI makes it possible to treat evolving new technologies in standardized terms and compare them to other technologies in the same class.
We demonstrate the phenomenon of induced-charge capacitive deionization (ICCDI) that occurs around a porous and conducting particle immersed in an electrolyte, under the action of an external electric field. The external electric field induces an electric dipole in the porous particle, leading to its capacitive charging by both cations and anions at opposite poles. This regime is characterized by a long charging time which results in significant changes in salt concentration in the electrically neutral bulk, on the scale of the particle. We qualitatively demonstrate the effect of advection on the spatio-temporal concentration field which, through diffusiophoresis, may introduce corrections to the electrophoretic mobility of such particles.
Any simulation of the r-process is affected by uncertainties in our present knowledge of nuclear physics quantities and astrophysical conditions. It is common to quantify the impact of these uncertainties through a global sensitivity metric, which is then used to identify specific nuclides that would be most worthwhile to measure experimentally. Using descriptive statistics, we assess a set of metrics used in previous sensitivity studies, as well as a new logarithmic measure. For certain neutron-rich nuclides lying near the r-process path for the typical hot-wind scenario, we find opposing conclusions on their relative sensitivity implied by different metrics, although they all generally agree which ones are the most sensitive nuclei. The underlying reason is that sensitivity metrics which simply sum over variations in the r-process distribution depend on the scaling used in the baseline, which often varies between simulations. We show that normalization of the abundances causes changes in the reported sensitivity factors and recommend reporting a minimized F statistic in addition to a scale estimation for rough calibration to be used when comparing tables of sensitivity factors from different studies.
Recommender systems operate in an inherently dynamical setting. Past recommendations influence future behavior, including which data points are observed and how user preferences change. However, experimenting in production systems with real user dynamics is often infeasible, and existing simulation-based approaches have limited scale. As a result, many state-of-the-art algorithms are designed to solve supervised learning problems, and progress is judged only by offline metrics. In this work we investigate the extent to which offline metrics predict online performance by evaluating eleven recommenders across six controlled simulated environments. We observe that offline metrics are correlated with online performance over a range of environments. However, improvements in offline metrics lead to diminishing returns in online performance. Furthermore, we observe that the ranking of recommenders varies depending on the amount of initial offline data available. We study the impact of adding exploration strategies, and observe that their effectiveness, when compared to greedy recommendation, is highly dependent on the recommendation algorithm. We provide the environments and recommenders described in this paper as Reclab: an extensible ready-to-use simulation framework at https://github.com/berkeley-reclab/RecLab.
We use molecular dynamics simulations in a constant potential ensemble to study the effects of solution composition on the electrochemical response of a double layer capacitor. We find that the capacitance first increases with ion concentration following its expected ideal solution behavior, but decreases upon approaching a pure ionic liquid in agreement with recent experimental observations. The non-monotonic behavior of the capacitance as a function of ion concentration results from the competition between the independent motion of solvated ions in the dilute regime and solvation fluctuations in the concentrated regime. Mirroring the capacitance, we find that the characteristic decay length of charge density correlations away from the electrode is also non-monotonic. The correlation length first decreases with ion concentration as a result of better electrostatic screening but increases with ion concentration as a result of enhanced steric interactions. When charge fluctuations induced by correlated ion-solvent fluctuations are large relative to those induced by the pure ionic liquid, such capacitive behavior is expected to be generic.
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