Lawrence Berkeley National Laboratory

Abstract: The ATLAS Google Project was established as part of an ongoing evaluation of the use of commercial clouds by the ATLAS Collaboration, in anticipation of the potential future adoption of such resources by WLCG grid sites to fulfil or complement their computing pledges. Seamless integration of Google cloud resources into the worldwide ATLAS distributed computing infrastructure was achieved at large scale and for an extended period of time, and hence cloud resources are shown to be an effective mechanism to provide additional, flexible computing capacity to ATLAS. For the first time a total cost of ownership analysis has been performed, to identify the dominant cost drivers and explore effective mechanisms for cost control. Network usage significantly impacts the costs of certain ATLAS workflows, underscoring the importance of implementing such mechanisms. Resource bursting has been successfully demonstrated, whilst exposing the true cost of this type of activity. A follow-up to the project is underway to investigate methods for improving the integration of cloud resources in data-intensive distributed computing environments and reducing costs related to network connectivity, which represents the primary expense when extensively utilising cloud resources.

The discovery of ~80 K superconductivity in nickelate La3Ni2O7 under pressure has ignited intense interest. Here, we present a comprehensive first-principles study of the electron-phonon (e-ph) coupling in La3Ni2O7 and its implications on the observed superconductivity. Our results conclude that the e-ph coupling is too weak (with a coupling constant λ ≲ 0.5) to account for the high Tc, albeit interesting many-electron correlation effects exist. While Coulomb interactions (via GW self-energy and Hubbard U) enhance the e-ph coupling strength, electron doping (oxygen vacancies) introduces no major changes. Additionally, different structural phases display varying characteristics near the Fermi level, but do not alter the conclusion. The e-ph coupling landscape of La3Ni2O7 is intrinsically different from that of infinite-layer nickelates. These findings suggest that a phonon-mediated mechanism is unlikely to be responsible for the observed superconductivity in La3Ni2O7, pointing instead to an unconventional nature.

Halide solid electrolytes gain significant attention due to their high ionic conductivity, low processing temperature, dry air compatibility, and high-voltage stability. However, low cathode active material (CAM) loading in the composite cathode constrains the realization of high energy density for halide-based all-solid-state batteries. In this study, three halide materials, raw Li3YBrCl5 (LYBC-R, <30 μm), milled LYBC (LYBC-M, <5 μm) and freeze-dried Li3InCl6 (LIC, <500 nm), were used as catholytes, combined with LYBC-M as the electrolyte and LiIn alloy as the anode. The CAM:catholyte ratio was investigated as well as stack pressure and operating temperature. Our study demonstrates that particle size of the catholyte plays an important role only for high CAM loading or high C-rate cycling. At moderate CAM loading (65 and 70 wt% LiNi0.83Mn0.06Co0.11O2) and 0.1 C-rate, all the three catholytes perform well, providing initial discharge capacities >177 mAh/g. At high CAM loading (85 wt%) and 0.1 C-rate, a cathode with the nano-scale LIC catholyte provides discharge capacity of 175 mAh/g, while the larger particle size catholytes suffer significantly reduced capacity. Both LYBC and LIC catholytes provided capacity retention >80 % after 200 cycles at 0.5C. These results imply that cathode particle size is critically important for performance at high CAM loading. Furthermore, both electrolyte and cathode were tape cast to scale up size and prepare realistic layer thicknesses. A small amount of binder was used in both layers, to balance the electrochemical performance and mechanical properties. The discharge capacity of a tape cell was 152 mA h/g at 0.1C with a capacity retention of 81.8 % after 20 cycles at 0.5C. The results demonstrate the excellent performance of LYBC as an electrolyte, and provide guidance for halide-based cathode design.

Methods to sequester and store atmospheric CO2 are critical to combat climate change. Alkaline-rich bioashes are potential carbon fixing materials. This work investigates potential co-benefits from mineralizing carbon in biomass ashes and partially replacing high embodied greenhouse gas (GHG) Portland cement (PC) in cement-based materials with these ashes. Specifically, rice hull ash (RHA), wheat straw ash (WSA), and sugarcane bagasse ash (SBA) were treated to mineralize carbon, and their experimental carbon content was compared to modeled potential carbonation. To understand changes in the cement-based storage materials, mortars made with CO2-treated WSA and RHA were experimentally compared to PC-only mortars and mortars made with ashes without prior CO2 treatment. Life cycle assessment methodology was applied to understand potential reductions in GHG emissions. The modeled carbonation was ∼18 g-CO2/kg-RHA and ∼180 g-CO2/kg-WSA. Ashes oxidized at 500 °C had the largest measured carbon content (5.4 g-carbon/kg-RHA and 35.3 g-carbon/kg-WSA). This carbon appeared to be predominantly residual from the biomass. Isothermal calorimetry showed RHA-PC pastes had similar heat of hydration to PC-pastes, while WSA-PC pastes exhibited an early (at ∼1.5 min) endothermic dip. Mortars with 5 % and 15 % RHA replacement had 1–12 % higher compressive strength at 28 days than PC-only mortars, and milled WSA mortars with 5 % replacement had 3 % higher strength. A loss in strength was noted for the milled 15 % WSA, the CO2-treated 5 %, and the 15 % WSA mortars. Modeled reductions in GHG emissions from CO2-treated ashes were, however, marginal (<1 %) relative to the untreated ashes.

The persistence of bacteria in the root canal system is the primary cause of recurrent apical periodontitis. The adaptability of residual bacteria to changing environmental conditions is a key survival strategy of biofilms, often leading to endodontic treatment failure. DJK-5 is a protease-resistant, broad-spectrum D-enantiomeric peptide that degrades or prevents the accumulation of guanosine penta- and tetraphosphates, which are important for biofilm formation. We evaluated the effects of primary antimicrobial agents and nutrient conditions on the recovery, metabolism, diversity, and composition of oral biofilms, and investigated how these factors affect the efficacy of DJK-5 and chlorhexidine (CHX) during re-exposure. Primary irrigants and nutrient conditions significantly influenced biofilm recovery, metabolic activity, diversity, and composition. Biofilm recovery was slower in nutrient-poor groups compared to nutrient-rich ones, and nutrient availability had the greatest effect on shaping both the diversity and composition of the biofilms. Water and DJK-5 groups showed similar biofilm diversity trends, while CHX generally led to lower diversity. Results indicate that primary irrigants and nutrient conditions significantly impact biofilm composition, diversity, and recovery. However, these changes did not compromise DJK-5s effectiveness in killing of biofilm microbes during re-exposure of recovered biofilms.

The tuning of the Fermi level in tin telluride, a topological crystalline insulator, is essential for accessing its unique surface states and optimizing its electronic properties for applications such as spintronics and quantum computing. In this study, we demonstrate that the Fermi level in tin telluride can be effectively modulated by controlling the tin concentration during chemical vapor deposition synthesis. By introducing tin-rich conditions, we observed a blue shift in the x-ray photoelectron spectroscopy core-level peaks of both tin and tellurium, indicating an upward shift in the Fermi level. This shift is corroborated by a decrease in work function values measured via ultraviolet photoelectron spectroscopy, confirming the suppression of Sn vacancies. Our findings provide a low-cost, scalable method to achieve tunable Fermi levels in tin telluride, offering a significant advancement in the development of materials with tailored electronic properties for next-generation technological applications.

Effective policymaking to achieve net zero greenhouse gas emissions demands an understanding of the complex drivers of, and barriers to, consumer adoption behavior via behaviorally realistic energy system models. Existing models tend to oversimplify by focusing on homogenized financial factors while neglecting consumer heterogeneity and non-monetary influences. This study develops and applies a comprehensive framework for evaluating the behavioral realism of consumer adoption models, informed by the adoption literature. It introduces a typology for factors influencing low-carbon technology adoption decisions: monetary and nonmonetary factors relating to household characteristics, psychology, technological attributes, and contextual conditions. Next, reviews of the consumer adoption and decision-making literature identify the most influential adoption factor categories for distributed solar photovoltaics, electric vehicles, and air-source heat pumps. Finally, the extent to which a selection of energy system models accounts for these adoption factors is assessed. Existing models predominantly emphasize the economic aspects of technology, which are generally identified as the most important factors. Where the models fall short — in considering moderately important factor categories — sector-specific and agent-based models can offer more behaviorally realistic insights. This study sheds light on which types of factors are most important for consumer adoption decisions and investigates how well current models rise to the challenge of behavioral realism. The end-to-end analysis presented enables internally consistent comparisons across models and energy technologies. This research advances timely conversations on consumer adoption. It could inform more behaviorally realistic energy system modeling, and thereby more effective decarbonization policymaking.

Hydrogen is pivotal in the transition to sustainable energy systems, playing major roles in power generation and industrial applications. Metal–organic frameworks (MOFs) have emerged as promising mediums for efficient hydrogen storage. However, identifying potential candidates for deployment is challenging due to the vast number of currently available synthesized MOFs. This study integrates molecular simulations, machine learning, and techno-economic analysis to evaluate the performance of MOFs across broad operation conditions for hydrogen storage applications. While previous screenings of MOF databases have predominantly emphasized high hydrogen capacities under cryogenic conditions, this study reveals that optimal temperatures and pressures for cost minimization depend on the raw price of the MOF. Specifically, when MOFs are priced at $15/kg, among the 9720 MOFs tested, 9692 MOFs achieve the lowest cost at temperatures between 170 K and 250 K and a pressure of 150 bar. Under these optimal conditions, 362 MOFs deliver a lower levelized cost of storage than 350 bar compressed gas hydrogen storage. Furthermore, this study reveals key material properties that result in low system cost, such as high surface areas (>3000 m2/g), large void fractions (>0.78), and large pore volumes (>1.1 cm3/g).