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Calculated Reduction Potentials of Electrolyte Species in Lithium–Sulfur Batteries
In this work, reduction potentials of electrolyte molecules in the lithium–sulfur (Li/S) battery and their variations in several solvent environments are studied using the density functional theory method with Dunning’s triple-ζ correlation consistent basis set. Reliable reduction potential values are key for electrolyte additive design needed for suppressing polysulfide dissolution shuttle mechanism, resulting in poor cycle performance and severe self-discharge of the Li/S battery. Although isolated electrolyte molecules have reduction potentials outside the operating voltage range of the Li/S battery, complexation with other electrolyte species enables the electrolyte molecules to be reduced within the operating voltage range. Among the electrolyte species considered in this study, bis(fluorosulfonyl)imide (FSI – ) and fluoroethylene carbonate (FEC) yield reduction potentials within the expected range, suggesting the development of fluorine-containing additives as a promising line of research.
Techno-Economic Analysis and Market Potential of Geological Thermal Energy Storage (GeoTES) Charged With Solar Thermal and Heat Pumps into Depleted Oil/Gas Reservoirs and Shallow Reservoirs: A Technology Overview: Preprint
Depleted oil/gas reservoirs represent a waste of underground resources and investments of drilling, and also a potential risk to the earth's environment. Geologic thermal energy storage (GeoTES) is proposed as a solution to convert depleted oil/gas reservoirs into long-term seasonal energy storage. GeoTES can be hybridized with other techniques for viable commercial deployment, such as 1) concentrating solar power (CSP) collectors and 2) heat pumps with excess renewable energy. Here, a technology overview is given on the two GeoTES technologies, which includes system overview, techno-economic models, and case study. Both GeoTES technologies show great potential in the economics of individual project deployment and applicability across the US.
Southern Company Services: Project 2: Tall Tower Wind Technical Potential for the Southeast (Modifications 4, 5), Project 4: Offshore Wind Potential for Southern Company Services (Modifications 7, 8)
Project 2: The National Laboratory of the Rockies (NLR) and Southern Company Services (SCS) seek to establish an agreement to assess the technical potential for wind energy in the Southeastern U.S. SCS has requested that NLR conduct a detailed assessment of system performance, available capacity, transmission distance, and levelized cost of electricity (LCOE) at turbine hub heights of 100m to 160m within the SCS service territory. NLR will also provide an assessment of utility photovoltaic (PV) technical potential within the SCS service territory. NLR has unique modeling capabilities to assist SCS with high spatial and temporal resolution assessment of wind and PV technical potential, considering geospatial constraints. Project 4: NLR and SCS seek to develop a spatially explicit techno-economic assessment for offshore wind energy in the Southeastern U.S. The analysis aims to characterize the regional sensitivities of offshore wind plant costs and performance, underwater cabling, potential siting constraints, and landfall spur transmission costs.
An observational test of the quasi-geostrophic relation between eddy momentum flux convergence and eddy fluxes of potential vorticity and potential temperature
The quasi-geostrophic relation between the fluxes of momentum, potential vorticity and potential temperature is tested with atmospheric data by computing the convergence of momentum flux as a residual of the potential temperature and potential vorticity flux and comparing it to the momentum flux convergence computed directly. It is shown that in the troposphere between 18 and 74 deg N the observed momentum flux convergence differs from the quasi-geostrophic convergence by 25-60% depending on tropospheric level and season.
A combined vector potential-scalar potential method for FE computation of 3D magnetic fields in electrical devices with iron cores
A method of combined use of magnetic vector potential based finite-element (FE) formulations and magnetic scalar potential (MSP) based formulations for computation of three-dimensional magnetostatic fields is introduced. In this method, the curl-component of the magnetic field intensity is computed by a reduced magnetic vector potential. This field intensity forms the basic of a forcing function for a global magnetic scalar potential solution over the entire volume of the region. This method allows one to include iron portions sandwiched in between conductors within partitioned current-carrying subregions. The method is most suited for large-scale global-type 3-D magnetostatic field computations in electrical devices, and in particular rotating electric machinery.
Moving beyond the constraints of chemistry via crystal structure discovery with isotropic multiwell pair potentials
The rigid constraints of chemistry-dictated by quantum mechanics and the discrete nature of the atom-limit the set of observable atomic crystal structures. What structures are possible in the absence of these constraints? Here, we systematically crystallize one-component systems of particles interacting with isotropic multiwell pair potentials. We investigate two tunable families of pairwise interaction potentials. Our simulations self-assemble a multitude of crystal structures ranging from basic lattices to complex networks. Sixteen of the structures have natural analogs spanning all coordination numbers found in inorganic chemistry. Fifteen more are hitherto unknown and occupy the space between covalent and metallic coordination environments. The discovered crystal structures constitute targets for self-assembly and expand our understanding of what a crystal structure can look like.
Self-potential tomography preconditioned by particle swarm optimization—Self-potential monitoring and streamflow data acquired March 26–September 14, 2023 at East Fork Poplar Creek near Oak Ridge Tennessee
This data release contains self-potential (SP) monitoring data measured on the flood plain of East Fork Poplar Creek (East Fork) in Oak Ridge, Tennessee and streamflow data measured at streamgage EFK5.4 about 310 meters upstream from the SP monitoring site. Additionally, forward and inverse numerical modeling scripts used to model the electrical-potential field on the East Fork flood plain are provided. SP monitoring data included in this data release were measured at 39 different data-collection points on the east flood plain; 30 points were spaced 3-m apart along an 87-m profile parallel to the edge of the streambank, and 9 points were spaced 5-m apart along a 40-m profile approximately perpendicular to the streambank. The two profiles of SP data-collection points intersected at the approximate midpoint of the profile parallel to the streambank. Transient voltages were measured at each data-collection point every 60 seconds between 16:13 Eastern Standard Time (EST) on March 26, 2023, and 11:41 EST on September 14, 2023. Streamflow data included in this data release overlap the time-period of self-potential monitoring and were measured every 900 seconds between 16:23 on March 26, 2023, and 23:53 on September 14, 2023.
Solar for industrial process heat: A review of technologies, analysis approaches, and potential applications in the United States
Not Available
Hybrid Quantum Mechanical, Molecular Mechanical, and Machine Learning Potential for Computing Aqueous-Phase Adsorption Free Energies on Metal Surfaces
Performing reliable computer simulations of elementary processes occurring at metal–water interfaces is pivotal for novel catalyst design in sustainable energy applications. Computational catalyst design hinges on the ability to reliably and efficiently compute the potential energy surface (PES) of the system. Here, due to the large system sizes needed for studying processes at liquid water–metal interfaces, these systems can currently not be described using density functional theory (DFT). In this work, we used a hybrid quantum mechanical, molecular mechanical, and machine learning potential for studying the adsorption behavior of phenol, atomic hydrogen, 2-butanol, and 2-butanone on the (0001) facet of Ru under reducing conditions when Ru is not oxidized. Specifically, we describe the adsorbate and the surrounding metal atoms at the DFT level of theory. Here, we also considered the electrostatic field effect of the water molecules on adsorbate–metal interactions. Next, for the water–water and water–adsorbate interactions, we used established classical force fields. Finally, for the water–Ru surface interaction, for which no reliable force fields have been published, we used Behler–Parrinello high-dimensional neural network potentials (HDNNPs). Employing this setup, we used our explicit solvation for metal surface (eSMS) approach to compute the aqueous-phase effect on the low-coverage adsorption of selected molecules and atoms on the (0001) facet of Ru. In agreement with previous experimental and computational studies of oxygenated molecules over transition metal facets, we found that liquid water destabilizes the tested adsorbates on Ru(0001). Interestingly, our findings indicate that adsorbates on Ru are less affected by the presence of an aqueous phase than on other transition metals (e.g., Pt), highlighting the necessity of experimental investigations of Ru-based catalytic systems in liquid water.
Neural Network Water Model Based on the MB-Pol Many-Body Potential
The MB-pol many-body potential accurately predicts many properties of water, including cluster, liquid phase, and vapor–liquid equilibrium properties, but its high computational cost can make applying it in large-scale simulations quite challenging. Here, in order to address this limitation, we developed a “deep potential” neural network (DPMD) model based on the MB-pol potential for water. We find that a DPMD model trained on mostly liquid configurations yields a good description of the bulk liquid phase but severely underpredicts vapor–liquid coexistence densities. By contrast, adding cluster configurations to the neural network training set leads to a good agreement for the vapor coexistence densities. Liquid phase densities under supercooled conditions are also represented well, even though they were not included in the training set. These results confirm that neural network models can combine accuracy and transferability if sufficient attention is given to the construction of a representative training set for the target system.
Development of Lipid-Coated Semiconductor Nanosensors for Recording of Membrane Potential in Neurons
In the past decade, optical imaging methods have significantly improved our understanding of the information processing principles in the brain. Although many promising tools have been designed, sensors of membrane potential are lagging behind the rest. Semiconductor nanoparticles are an attractive alternative to classical voltage indicators, such as voltage-sensitive dyes and proteins. Such nanoparticles exhibit high sensitivity to external electric fields via the quantum-confine Stark effect. Here we report the development of semiconductor voltage-sensitive nanorods (vsNRs) that self-insert into the neuronal membrane. To facilitate interaction of the nanorods with the membrane, we functionalized their surface with the lipid mixture derived from brain extract. We describe a workflow to detect and process the photoluminescent signal of vsNRs after widefield time-lapse recordings. We also present data indicating that vsNRs are feasible for sensing membrane potential in neurons at a single-particle level. This shows the potential of vsNRs for the detection of neuronal activity with unprecedentedly high spatial and temporal resolution.
Mean Inner Potential of Liquid Water
Improving our experimental and theoretical knowledge of electric potentials at liquid-solid boundaries is essential to achieve a deeper understanding of the driving forces behind interfacial processes. Electron holography has proved successful in probing solid-solid interfaces but requires knowledge of the materials’ mean inner potential (MIP, V0), which is a fundamental bulk material property. Combining off-axis electron holography with liquid phase transmission electron microscopy (LPTEM), we provide the first quantitative MIP determination of liquid water V0=+4.48±0.19 V. This value is larger than most theoretical predictions, and to explain the disagreement we assess the dominant factors needed in quantum simulations of liquid water. A precise MIP lays the foundations for nanoscale holographic potential measurements in liquids and provides a benchmark to improve quantum mechanical descriptions of aqueous systems and their interfaces in e.g. electrochemistry, solvation processes and spectroscopy.
Errors in retarding potential analyzers caused by nonuniformity of the grid-plane potential.
One aspect of the degradation in performance of retarding potential analyzers caused by potential depressions in the retarding grid is quantitatively estimated from laboratory measurements and theoretical calculations. A simple expression is obtained that permits the use of laboratory measurements of grid properties to make first-order corrections to flight data. Systematic positive errors in ion temperature of approximately 16% for the Ogo 4 instrument and 3% for the Ogo 6 instrument are deduced. The effects of the transverse electric fields arising from the grid potential depressions are not treated.
An Investigation of Applications for Thermodynamic Work Potential Methods: Working Tables and Charts for Estimation of Thermodynamic Work Potential in Equilibrium Mixtures of Jet-A and Air
The objective of this report is to provide a tool to facilitate the application of thermodynamic work potential methods to aircraft and engine analysis. This starts with a discussion of the theoretical background underlying these methods, which is then used to derive various equations useful for thermodynamic analysis of aircraft engines. The work potential analysis method is implemented in the form of a set of working charts and tables that can be used to graphically evaluate work potential stored in high-enthalpy gas. The range of validity for these tables is 300 to 36,000 R, pressures between between 0.01 atm and 100 atm, and fuel-air ratios from zero to stoichiometric. The derivations and charts assume mixtures of Jet-A and air as the working fluid. The thermodynamic properties presented in these charts were calculated based upon standard thermodynamic curve fits.
Review of Potential–Induced Degradation in Bifacial Photovoltaic Modules
Bifacial modules are increasingly deployed in the field and are expected to represent half of the market share within 10 years. Their rear structure differs from monofacial modules to allow additional light absorption. However, it brings new reliability challenges to address. In particular, the risk of potential-induced degradation (PID) is increased as both module sides are impacted. Different PID processes have been identified in the literature: shunting type (PID-s), polarization type (PID-p), Na penetration type, and corrosion type (PID-c). Their occurrence depends on the photovoltaic system configuration as well as the module's materials. Apart from PID-s, PID processes are not well understood and extensive research is needed to elucidate the PID scenario and underlying mechanisms. Herein, current knowledge about PID processes and their impact on the main bifacial modules in the market are gathered with the aim to guide future research. Bifacial module technologies and leakage current paths leading to PID are described. Indoor and outdoor PID testing methods are detailed. For each bifacial module technology, the PID processes are investigated with their indicators, mechanism and recovery process. Furthermore, PID-impacting factors and limitation solutions are finally reported and a state of the art on PID modeling is presented.
Polarization Type Potential Induced Degradation under Positive Bias in a Commercial PERC Module: Preprint
Potential induced degradation of the polarization type (PID-p) can reduce module performance in a relatively short period of time. PID-p can occur at both voltage polarities, but most studies are focused on degradation under a negative bias. This paper uses commercial bifacial passivated emitter and rear contact (PERC) cells within a monofacial glass-backsheet module construction to evaluate the impact of PID-p under a positive bias on the front side. Using the aluminum-foil (Al-foil) method, the module was stressed for PID in an environmental chamber. After the stress, the maximum power (Pmax) showed a decline of 3.1% at 1000 W/m2 and 6.2% at 200 W/m2. Recovery under light was also investigated. Complete recovery was observed at high irradiance, while a partial recovery was seen at lower irradiance. The outcomes of this study can help in understanding PID-p degradation under a positive bias and its recovery under the light.
Finite Element Simulation of Potential-Induced Degradation Kinetics in p-Type Silicon Solar Modules
Not provided.