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V2C is trigonal omega structured and crystallizes in the trigonal P-3m1 space group. The structure is two-dimensional and consists of one V2C sheet oriented in the (0, 0, 1) direction. V2+ is bonded in a distorted T-shaped geometry to three equivalent C4- atoms. All V–C bond lengths are 2.03 Å. C4- is bonded to six equivalent V2+ atoms to form edge-sharing CV6 octahedra.
V2C crystallizes in the hexagonal P6_3/mmc space group. The structure is two-dimensional and consists of two V2C sheets oriented in the (0, 0, 1) direction. V2+ is bonded in a 3-coordinate geometry to three equivalent C4- atoms. All V–C bond lengths are 2.02 Å. C4- is bonded to six equivalent V2+ atoms to form edge-sharing CV6 octahedra.
V2C is beta Vanadium nitride-like structured and crystallizes in the orthorhombic Pbcn space group. The structure is three-dimensional. V2+ is bonded in a distorted trigonal planar geometry to three equivalent C4- atoms. There are a spread of V–C bond distances ranging from 2.01–2.04 Å. C4- is bonded to six equivalent V2+ atoms to form a mixture of corner and edge-sharing CV6 octahedra. The corner-sharing octahedra tilt angles range from 50–51°.
This project aimed to enhance the range and reduce the operating costs of battery electric Class 8 trucks traveling over 250 miles daily. This was achieved through the development and implementation of an intelligent-Energy Management System (i-EMS) that leverages vehicle and operations data, physics-aware machine learning algorithms, and vehicle-to-cloud (V2C) connectivity. The project hypothesized that advanced machine learning algorithms and real-time data analytics could significantly improve the energy efficiency and range of these trucks. Key objectives included developing a physics-aware machine learning algorithm, implementing an i-EMS with V2C connectivity and physics-aware spatial data analytics (PSDA), and validating the system’s effectiveness with fleet partners HEB Companies and Murphy Logistics. Extensive data collection from vehicle operations, including vehicle characteristics, road conditions, and payload, was conducted. A machine learning algorithm was developed to predict energy consumption and enable proactive decision-making. The i-EMS was implemented on two Volvo VNR BEVs, with operators receiving charging and routing recommendations. Charging stations were installed at depot locations in Texas and Minnesota, with an additional on-route charger in Minnesota. Significant findings included a 14% range improvement for Murphy Logistics on a highway-driving eco-route and a 22% range improvement for HEB Companies on a city-driving eco-route. The i-EMS utilized rule-based methods and physics-based algorithms to predict and reduce energy consumption, with real-time monitoring and analysis through V2C connectivity enabling proactive decision-making. The project demonstrated the feasibility and economic viability of battery electric Class 8 trucks for long-haul operations, showcasing the potential of physics-aware machine learning in optimizing energy management. The successful implementation of the i-EMS in real-world scenarios validates its practical application and effectiveness, paving the way for the widespread adoption of battery electric vehicles in the freight transportation industry.
The first concrete actions toward a next generation system for geodetic VLBI began in 2003 when the IVS initiated Working Group 3 to investigate requirements for a new system. The working group set out ambitious performance goals and sketched out initial recommendations for the system. Starting in 2006, developments continued under the leadership of the VLBI2010 Committee (V2C) in two main areas: Monte Carlo simulators were developed to evaluate proposed system changes according to their impact on IVS final products, and a proof-of-concept effort sponsored by NASA was initiated to develop next generation systems and verify the concepts behind VLBI2010. In 2009, the V2C produced a progress report that summarized the conclusions of the Monte Carlo work and outlined recommendations for the next generation system in terms of systems, analysis, operations, and network configuration. At the time of writing: two complete VLBI2010 signal paths have been completed and data is being produced; a number of VLBI2010 antenna projects are under way; and a VLBI2010 Project Executive Group (V2PEG) has been initiated to provide strategic leadership.
AGCMs w/ Specified SSTs (AMIPs) GEOS-5, ERA-20CM Ensembles Incorporate best historical estimates of SST, sea ice, radiative forcing Atmospheric "weather noise" is inconsistent with specified SST. Instantaneous Sfc fluxes can be wrong sign (e.g. Indian Ocean Monsoon, high latitude oceans). Averaging over ensemble members helps isolate SST-forced signal. Reduced Observational Reanalyses: NOAA 20CR V2C, ERA-20C, JRA-55C Incorporate observed Sfc Press (20CR), Marine Winds (ERA-20C) and rawinsondes (JRA-55C) to recover much of true synoptic or weather w/o shock of new sat obs. Comprehensive Reanalyses (MERRA-2) Full suite of observational constraints- both conventional and remote sensing. But... substantial uncertainties owing to evolving satellite observing system. Multi-source Statistically Blended OAFlux, LargeYeager Blend reanalysis, satellite, and ocean buoy information. While climatological biases are removed, non-physical trends or variations in components remain. Satellite Retrievals GSSTF3, SeaFlux, HOAPS3... Global coverage. Retrieved near sfc wind speed, & humidity used with SST to drive accurate bulk aerodynamic flux estimates. Satellite inter-calibration, spacecraft pointing variations crucial. Short record ( late 1987-present). In situ Measurements ICOADS, IVAD, Res Cruises VOS and buoys offer direct measurements. Sparse data coverage (esp south of 30S. Changes in measurement techniques (e.g. shipboard anemometer height).
MXenes offer high metallic conductivity and redox capacitance that are attractive for high-power, high-energy storage devices. However, they operate limitedly under high anodic potentials due to irreversible oxidation. Pairing them with oxides to design asymmetric supercapacitors may expand the voltage window and increase the energy storage capabilities. Hydrated lithium preintercalated bilayered V 2 O 5 ( δ-Li x V 2 O 5 ·nH 2 O) is attractive for aqueous energy storage due to its high Li capacity at high potentials; however, its poor cyclability remains a challenge. To overcome its limitations and achieve a wide voltage window and excellent cyclability, it is combined with V 2 C and Nb 4 C 3 MXenes. Asymmetric supercapacitors employing lithium intercalated V 2 C (Li-V 2 C) or tetramethylammonium intercalated Nb 4 C 3 (TMA-Nb 4 C 3 ) MXenes as the negative electrode, and a δ-Li x V 2 O 5 ·nH 2 O composite with carbon nanotubes as the positive electrode in 5 m LiCl electrolyte operate over wide voltage windows of 2 and 1.6 V, respectively. The latter shows remarkably high cyclability—capacitance retention of ≈95% after 10 000 cycles. Here, this work highlights the importance of selecting appropriate MXenes to achieve a wide voltage window and a long cycle life in combination with oxide anodes to demonstrate the potential of MXenes beyond Ti 3 C 2 in energy storage.