In Operando Acoustic Detection of Lithium Metal Plating in Commercial LiCoO2/Graphite Pouch Cells
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Due to complex degradation mechanisms, disparities between the theoretical and practical capacities of lithium-ion battery cathode materials persist. Specifically, Ni-rich chemistries such as LiNi 0.8 Mn 0.1 Co 0.1 O 2 (or NMC811) are one of the most promising choices for automotive applications; however, they continue to suffer severe degradation during operation that is poorly understood, thus challenging to mitigate. Here in this paper we use operando Bragg coherent diffraction imaging for 4D analysis of these mechanisms by inspecting the individual crystals within primary particles at various states of charge (SoC). Although some crystals were relatively homogeneous, we consistently observed non-uniform distributions of inter- and intracrystal strain at all measured SoC. Pristine structures may already possess heterogeneities capable of triggering crystal splitting and subsequently particle cracking. During low-voltage charging (2.7–3.5 V), crystal splitting may still occur even during minimal bulk deintercalation activity; and during discharging, rotational effects within parallel domains appear to be the precursor for the nucleation of screw dislocations at the crystal core. Ultimately, this discovery of the central role of crystal grain splitting in the charge/discharge dynamics may have ramifications across length scales that affect macroscopic performance loss during real-world battery operation.
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Artificial intelligence has emerged as a technology to enhance productivity and improve life quality. However, its role in building energy efficiency and carbon emission reduction has not been systematically studied. This study evaluated artificial intelligence’s potential in the building sector, focusing on medium office buildings in the United States. A methodology was developed to assess and quantify potential emissions reductions. Key areas identified were equipment, occupancy influence, control and operation, and design and construction. Six scenarios were used to estimate energy and emissions savings across representative climate zones. Here we show that artificial intelligence could reduce cost premiums, enhancing high energy efficiency and net zero building penetration. Adopting artificial intelligence could reduce energy consumption and carbon emissions by approximately 8% to 19% in 2050. Combining with energy policy and low-carbon power generation could approximately reduce energy consumption by 40% and carbon emissions by 90% compared to business-as-usual scenarios in 2050.
The high melting temperature of tungsten (W) makes it an attractive candidate for energy generation applications; however, its use is limited by its poor ductility at low temperatures. This limitation affects even melt-based additive manufacturing (AM) processes such as laser powder bed fusion (PBF-LB), with as-fabricated pure W exhibiting both longitudinal and transverse cracks. Metallurgical and processing factors that affect these cracks are still being explored. This work utilizes powderless single-tracks on pure W plates made over a range of power and velocity combinations in three unique PBF-LB setups: one with flowing argon, one with continuous vacuum, and an in situ high-speed synchrotron X-ray radiography setup. Each had different oxygen activities in their build environments. Longitudinal cracks with oxides appearing to exude from the crack were found for tracks deposited in a build environment with argon shield gas, while no such cracks were observed for builds conducted in vacuum. A calculation showed that direct oxygen ingress into the melt pool from the build environment is negligible when argon shield gas is present. Thus, incorporation of spatter is a likely mechanism for oxygen ingress into the melt pool. Radiography showed extensive keyholing, spatter generation, and crack formation at keyhole porosity. A heat transfer calculation showed the crack formation time was consistent with cooling below its ductile-to-brittle transition (DBT) temperature. This work identifies solidification cracking as a feasible mechanism in pure W beyond the well-known DBT related cracking.
Production and purification of vanillin from lignin using ion exchange, and subsequent conversion to aminophenol ionic liquid precursors.
First-of-its-kind effort to understand full-scale multi-product biorefineries established upon co-solvent enhanced lignocellulosic fractionation (CELF) from an economic and environmental standpoint.
The CERN ATLAS Experiment successfully uses a worldwide dis-tributed computing Grid infrastructure to support its physics programme at the Large Hadron Collider (LHC). The Grid workflow system PanDA routinely manages up to 700,000 concurrently running production and analysis jobs to process simulation and detector data. In total more than 500 PB of data are distributed over more than 150 sites in the WLCG and handled by the ATLAS data management system Rucio. To prepare for the ever growing data rate in future LHC runs new developments are underway to embrace industry accepted protocols and technologies, and utilize opportunistic resources in a standard way. This paper reviews how the Google and Amazon Cloud computing ser-vices have been seamlessly integrated as a Grid site within PanDA and Rucio. Performance and brief cost evaluations will be discussed. Such setups could offer advanced Cloud tool-sets and provide added value for analysis facilities that are under discussions for LHC Run-4.
For the next generation of concentrating solar power (CSP) plants, K-Mg-Na chloride salts are being explored as a potential higher temperature thermal storage medium. However, there are concerns about compatibility of structural alloys with the salt. To move beyond simple static pot compatibility experiments while a pumped salt loop is being constructed, thermal convection loops were constructed from ~16wt.%Cr Ni-based alloy 600 and operated for 100-1000 h with peak temperatures of 700° and 750°C. These experiments indicated that alloy 600 was compatible up to 700°C with purified (i.e. low O2-) or dried (low H2O) industrial-sourced Mg-K-Na chloride salt with <10 µm/yr loss. Mass transfer was observed from the hot leg to the cold leg in each experiment and more surface deposits were observed with the dried salt. Post-exposure room temperature tensile tests showed minimal degradation of alloy 600 under these conditions.
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Monitoring of energy infrastructure through robust yet economical sensing platforms is becoming an area of increased importance, with ubiquitous applications including the electrical grid, natural gas and oil transportation pipelines, H2 infrastructure (storage and transportation), carbon storage, power generation, and subsurface environments. Plasmonic and functional nanomaterial enabled fiber optic sensors show excellent promise for a wide range of sensing applications due to their versatility to be engineered for specific analytes of interest while retaining inherent advantages of the optical fiber sensor platform. Through the design of novel sensing layers, the optical transduction mechanism and wavelength dependence can also be tailored for ease of integration with low-cost interrogation systems enabling an inexpensive yet highly functional optical fiber sensing platform. In addition, recent advances in artificial intelligence and machine learning theoretical methods have been leveraged to simultaneously extract multiple parameters through multi-wavelength interrogation such that unique wavelengths can also serve as unique sensing elements, analogous to electronic nose sensor technologies. The concept of an optical fiber based “photonic nose” via multiple interrogation wavelengths and/or sensor nodes offers a compelling platform technology to realize multiparameter speciation of chemical analytes within complex gas mixtures. In this Perspective, we further generalize the notion of multiparameter sensing through the novel “photonic nervous system” concept based upon low-cost, functionalized optical fiber sensor probes monitoring a variety of distinct analyte classes (physical, chemical, electromagnetic, etc.) simultaneously to provide broad situational awareness via integrated sensors.
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