258 Search Results

This study compares two battery modeling approaches for capacity expansion models: discrete-duration and continuous-duration formulations. In the discrete approach, battery duration is fixed, and power capacity is optimized. In the continuous approach, both power and energy capacities are decision variables, allowing storage duration to be optimized endogenously. Although both discrete-duration and continuous-duration battery formulations are used in long-term power system planning models, the literature has provided limited direct, systematic comparisons of their implications within a common modeling framework. To address this gap, this study implements both approaches in the Regional Energy Deployment System (ReEDS TM) capacity expansion model using twomore » resource adequacy methods, across a range of future system conditions, and with varying battery cost projections. Results show continuous-duration and high-resolution discrete approaches produce similar capacity expansion outcomes. The continuous formulation achieves faster runtimes compared to discrete-duration runs with many discrete-duration options. However, the discrete-duration approach allows users to choose to have limited fidelity for storage duration options, which in some cases can outperform the continuous formulation. The continuous formulation has the lowest overall system costs, indicating its ability to fine-tune storage duration to better meet specific system needs. This study's findings provide a side-by-side evaluation of discrete and continuous battery modeling approaches and offer guidance for improving the representation of real-world systems, flexibility, and computational efficiency for representing energy storage in long-term power system planning models.« less

Atomistic structures of materials offer valuable insights into their functionality. Determining these structures remains a fundamental challenge in materials science, especially for systems with defects. While both experimental and computational methods exist, each has limitations in resolving nanoscale structures. Core-level spectroscopies, such as X-ray absorption (XAS) or electron energy-loss spectroscopies (EELS), have been used to determine the local bonding environment and structure of materials. Recently, machine learning (ML) methods have been applied to extract structural and bonding information from XAS/EELS data. However, frameworks relying solely on a single data stream, defined as characterization data derived from a single element usingmore » one technique, are often insufficient because multiple local environments can yield similar spectral features, making it challenging to differentiate between competing structural hypotheses. Here, in this work, we address this challenge by integrating multimodal ab initio simulations, experimental data acquisition, and ML techniques for structure characterization. Our goal is to determine local structures and properties using EELS and XAS data from multiple elements and edges. To showcase our approach, we use various lithium nickel manganese cobalt (NMC) oxide compounds which are used for lithium ion batteries, including those with oxygen vacancies and antisite defects, as the sample material system. We successfully inferred local element content, ranging from lithium to transition metals, with quantitative agreement with experimental data. Beyond local element inference, we find that ML model based on multimodal spectroscopic data is able to determine whether local defects such as oxygen vacancy and antisites are present, a task which is impossible for single mode spectra or other experimental techniques. Furthermore, our framework is able to provide physical interpretability, bridging spectroscopy with the local atomic and electronic structures.« less

There is immense potential in crafting synthetic microbial communities for application in human health, agriculture, the environment, and even biomanufacturing where an appropriately constructed consortium can be assembled with tremendous biosynthetic or degradative capabilities. In many of these cases, bacterial signaling serves as a form of intercellular information transfer that guides the collective’s behavior. Such communication is complex, as many signals, signal disruptors, microbial species, physical barriers, and spatiotemporal constraints may be involved. Here, in this work, we demonstrate that a multisignal pathway for molecular information transfer within a consortium of several Pseudomonas spp. can be scrambled (genetically and organizationally)more » while the original message is still effectively conveyed. Assembled from the bottom up, we have employed two types of signaling molecules (i) a redox active secondary metabolite (rhizospheric signal, phloroglucinol), and (ii) a bacterial quorum sensing signal (3-oxo-C12 acylhomoserine lactone, AI-1). These signals can be intraconverted and acted upon by designated community members. We show how the order in which the signals are received, transduced, and subsequently transmitted can be rearranged with minimal impact on the intended outcome. In the consortial context, we found this messaging structure can be remarkably robust. Inspired by rhizospheric molecular signaling mechanisms, this work provides a conceptual framework for designing signaling and information transfer processes within assembled communities.« less

Redox-driven conductance changes are critical processes in molecular- and coordination-complex-based memristive thin films and devices that are envisioned for neuromorphic technologies, but fundamental mechanisms of conductance switching are not fully understood. Here, we explore charge disproportionation (CD) processes in [Ru^IIL₂](PF₆)₂ molecular systems that intrinsically involve interfragment charge transfer (IFCT). Using a combination of ab initio molecular dynamics simulation (AIMD), time-dependent density functional theory (TD-DFT), and density functional theory (DFT) calculations, we investigate the electron transfer mechanisms and the roles of temperature and cell volumetric expansion in facilitating the counterion movements and electronic transitions required for low-cost IFCT and charge redistribution.more » A detailed analysis of the density of states and TD-DFT calculations highlights that unpaired electrons play a crucial role in low-energy transitions, with the azo (N=N) groups of the ligand serving as the primary sites for electronic transport between molecular fragments, further stabilizing the asymmetric state. Localization of added electrons on azo ligands occurs with negligible change at the Ru centers, supported by atomic volume expansions up to +4.74 bohr³, and goes along with a progressive reduction of the HOMO−LUMO gap across redox states, suggesting enhanced conductivity. The TD-DFT analysis reveals a dominant IFCT excitation at 2082.76 nm in the doubly reduced (22) state, while a stabilization energy of 1.20 eV of the asymmetric (13) state relative to the symmetric (22) state is predicted by constrained DFT. Periodic DFT and AIMD simulations emulating a molecular film show that the stabilization of the asymmetric state, relative to a symmetric one, translates in net charge separation values (order of ∼0.33 e) that are strongly linked to increased counterion mobility (average counterion displacements exceeding 0.7 Å per atom during CD events) and the involvement of azo groups in electron redistribution. These findings, which align with previously reported experimental and computational data, provide key insights into the IFCT mechanisms and electronic transport facilitated by azo groups, with important implications for redox-driven memristive and neuromorphic technologies.« less

Rapid electrochemical diagnostics, like DC pulse sequences or electrochemical impedance spectroscopy, are known to be useful for capacity prediction. However, it is unclear how previous results will map to different cell types and use cases and whether rapid diagnostics are useful for remaining useful life prediction or for detecting potential safety issues. To that end, we have collected a data set with ∼50,000 DC pulse measurements from four types of commercial lithium-ion batteries to enable training of state-of-charge, health, and safety diagnostic models via machine-learning. We demonstrate that 120-second DC pulse sequences can be used to predict capacity with 2%–9%more » average error, which can separate high- from low-capacity cells with only a 0.3% false positive rate but is not accurate enough to estimate remaining useful life. We also find that no safety related targets can be accurately predicted, highlighting the critical need for other non-invasive methods to diagnose battery safety.« less

Here, the utility of the sterically bulky terphenylthiolate ligand, (SAr^iPr6)¹⁻ in expanding uranium reductive chemistry has been explored. Reduction of U(SAr^iPr6)₂I forms the U(II) complex, U(SAr^iPr6)₂, in which the metal is protected by the flanking arene rings of the ligand, but they move out of the way to accommodate the four electron reduction of PhN=NPh to form the U(VI) bis(imido) product U(SAr^iPr6)₂(=NPh)₂(THF)₂. Here, the KC₈ reduction of U(SAr^iPr6)₂ generates a more reduced complex, KU(μ-SAr^iPr6)₂, initially identified by a −2.55 V vs. Fc⁺/Fc electrochemical reduction event in THF.

Electrodepositing metal materials in large capacity, at low potential, and with high reversibility serves as the foundation for any aqueous rechargeable battery chemistry to realize the promises of high energy, low cost, and high safety. However, such a foundation is not solid because of the natural tendency of metals to form irregular, nonplanar, and often dendritic morphologies during electrochemical crystallization, which is further amplified in an acidic environment due to the faster kinetics of the coupled proton and mass-transport processes between hydrated metal ions and free metal atoms. As a typical representative, tin metal (Sn⁰) has potential to achieve highmore » energy in acidic batteries, but its nonuniform large-particle morphology, obtained from traditional electrodeposition, leads to dead Sn⁰ formation and deteriorating reversibility, accompanied by the sustained hydrogen evolution reaction (HER) and active Sn⁰ loss. Here, in this study, we report quaternary onium salts as effective interfacial cocations that, via selective adsorption, steadily texturize Sn⁰ deposition along the (211) plane, which is intrinsically inert to the HER, thus regulating the film deposition process by favoring the formation of planar Sn⁰ film. Such Sn⁰ film brings exceptional reversibility in acidic electrolytes, which translates into sustained cycling stability at applicable areal capacities in both anode-half cells (similar to 1500 deposition/dissolution cycles at 5 mAh cm^-2) and full cells (350 charge/discharge cycles at 5 mAh cm^-2). Textured electrodeposition with intrinsic HER-suppression capability provides a universal solution for diverse metal anode materials in rechargeable energy-dense aqueous batteries.« less

Early battery life prediction models are most useful for R&D if they help us understand the early changes in battery electrochemical response that correspond with long-term degradation and failure. Linear regression models such as Fused lasso and Partial Least Squares can fit coefficients directly to high-dimensional electrochemical data like capacity-voltage and ΔV–state-of-charge, i.e., Q(V) and ΔV(SOC) curves, learning coefficients that can be physically interpreted. We leverage the ISU-ILCC battery aging data set to learn high-dimensional coefficients for early battery life prediction from traditional slow-rate capacity check data, demonstrating learning on Q(V), dQ·dV⁻¹, and ΔV(SOC) curves. A thorough study on themore » dependence of coefficient values on train/test size and data preprocessing methods is made, demonstrating the reliability of high-dimensional regression approaches unless very small amounts of data are used for model training. For this data set, coefficients from Q(V) and dQ·dV⁻¹ models highlight changes in electrode stoichiometry due to lithium loss, while ΔV(SOC) coefficients highlight changes in positive electrode diffusivity due to particle cracking as well as electrode stoichiometry shifts. By directly interpreting the coefficients of a regression model, we make physical insights into battery degradation mechanisms without requiring the assumptions of traditional battery data analysis methods.« less

Iron oxide minerals regulate the flux of electrons in the environment and are important hosts for trace and minor, yet critical, elements. Here, we present the first evidence of a direct link between the local coordination environments of Ni and Zn and the redox properties of their host phase goethite (α-FeOOH), the most abundant Fe(III) (oxyhydr)oxide at Earth’s surface. Here, we used aqueous redox measurements to show that the redox potential E_H⁰, and hence the mineral’s stability, follows the order: pure goethite ≥ Zn-goethite > Ni-goethite. Parallel X-ray absorption and scattering measurements demonstrate, using quantum-informed analysis, that the local coordinationmore » environment of the smaller impurity, Ni, causes more bulk strain energy than Zn, which nearly accounts for the difference in E_H⁰ between Ni- and Zn-goethite. Our theory-informed, experimental study reveals how two common impurities affect the stability of goethite with implications for the biogeochemical reactivity of Fe(III) (oxyhydr)oxide in mediating elemental and electron fluxes in the environment.« less

Recycling Li-ion batteries from electric vehicles is critical for reducing costs and supporting the development of a domestic battery supply chain. Direct recycling of cathodes, like LiNixMnyCozO2 (NMC), is attractive due to its low cost, energy use, and emissions compared to traditional recycling techniques. However, a comprehensive understanding of the active material properties at end-of-life is needed to guide direct recycling processes and the performance-dependent reuse applications. Here, NMC material from an end-of-life commercial pouch cell is characterized and bench-marked against pristine non-cycled counterparts with respect to capacity, impedance, crystallography, morphology, and microstructure to identify major degradation modes and understandmore » variability in the end-of-life material. The spatial heterogeneity of each property throughout the cell is also quantified. While the degraded material demonstrated similar capacity as the pristine, its impedance and rate capability are severely diminished. Furthermore, samples from the periphery of the electrode layers showed more severe performance loss compared to samples extracted from central regions. The dominant culprit of performance loss is the material microstructure, where the magnitude of particle cracking showed the strongest correlation to the impedance components that are most unfavorably impacted. This work suggests severe cracks in cathode active materials are the primary challenge that direct recycling methods must overcome.« less