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Abstract Capturing and sequestering carbon dioxide (CO₂) from the atmosphere via large‐scale direct air capture (DAC) deployment is critical for achieving net‐zero emissions. Large‐scale DAC deployment, though, will require significant cost reductions in part through policy and investment support. This study evaluates the impact of policy interventions on DAC cost reduction by integrating energy system optimization and learning curve models. We examine how three policy instruments—incremental deployment, accelerated deployment, and R&D‐driven innovation—impact DAC learning investment, which is the total investment required until the technology achieves cost parity with conventional alternatives or target cost. Our findings show that while incremental deployment demands significant learning investment, R&D‐driven innovation is considerably cheaper at cost reduction. Under a baseline 8% learning rate, incremental deployment may require up to $998 billion to reduce costs from $1,154 to $400/tCO₂, while accelerated deployment support could save approximately $7 billion on that investment. In contrast, R&D support achieves equivalent cost reductions at less than half the investment of incremental deployment. However, the effectiveness of R&D intervention varies with learning rates and R&D breakthroughs. R&D yields net benefits in all cases except at extremely low breakthroughs (5%) and very high learning rates (20%), where they are slightly more expensive. For learning rates below 20%, R&D provides net benefits even at minimal breakthroughs. These findings underscore the need for comprehensive public policy strategies that balance near‐term deployment incentives with long‐term innovation investments if we are to ensure DACS becomes a viable technology for mitigating climate change. 

Abstract Climate change threatens the resource adequacy of future power systems. Existing research and practice lack frameworks for identifying decarbonization pathways that are robust to climate‐related uncertainty. We create such an analytical framework, then use it to assess the robustness of alternative pathways to achieving 60% emissions reductions from 2022 levels by 2040 for the Western U.S. power system. Our framework integrates power system planning and resource adequacy models with 100 climate realizations from a large climate ensemble. Climate realizations drive electricity demand; thermal plant availability; and wind, solar, and hydropower generation. Among five initial decarbonization pathways, all exhibit modest to significant resource adequacy failures under climate realizations in 2040, but certain pathways experience significantly less resource adequacy failures at little additional cost relative to other pathways. By identifying and planning for an extreme climate realization that drives the largest resource adequacy failures across our pathways, we produce a new decarbonization pathway that has no resource adequacy failures under any climate realizations. This new pathway is roughly 5% more expensive than other pathways due to greater capacity investment, and shifts investment from wind to solar and natural gas generators. Our analysis suggests modest increases in investment costs can add significant robustness against climate change in decarbonizing power systems. Our framework can help power system planners adapt to climate change by stress testing future plans to potential climate realizations, and offers a unique bridge between energy system and climate modeling. 

Abstract Power system resource adequacy (RA), or its ability to continually balance energy supply and demand, underpins human and economic health. How meteorology affects RA and RA failures, particularly with increasing penetrations of renewables, is poorly understood. We characterize large-scale circulation patterns that drive RA failures in the Western U.S. at increasing wind and solar penetrations by integrating power system and synoptic meteorology methods. At up to 60% renewable penetration and across analyzed weather years, three high pressure patterns drive nearly all RA failures. The highest pressure anomaly is the dominant driver, accounting for 20-100% of risk hours and 43-100% of cumulative risk at 60% renewable penetration. The three high pressure patterns exhibit positive surface temperature anomalies, mixed surface solar radiation anomalies, and negative wind speed anomalies across our region, which collectively increase demand and decrease supply. Our characterized meteorological drivers align with meteorology during the California 2020 rolling blackouts, indicating continued vulnerability of power systems to these impactful weather patterns as renewables grow. 

This research studied the effect of channel roughness on micro-droplet distributions in internal minimum quantity lubrication for effective machining. Mixtures of different oils and air were flown though internal channels with simulated different roughness: as fabricated, partially threaded, and fully threaded. The airborne droplets were collected, analyzed, and compared with simulated results by computational fluid dynamics. For low-viscous lubricant, the rough channel surface helped to break large droplets in the boundary layer into smaller droplets and reintroduce them into the main downstream flow. The opposite trend was found for the higher viscous lubricant. The study also performed chemical etching to roughen selected surfaces of carbide cutting tools. The synergy of hand and ultrasonic agitation successfully roughened a carbide surface within twelve minutes. Scanning electron microscopy examination showed deep etching that removed all grinding marks on a WC–Co cutting tool surface.