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With buildings accounting for 40% of global carbon emissions, cities striving to meet sustainability targets aligned with the Paris Agreement must retrofit their existing building stock within 30 years. Previous studies have shown that urban building energy models (UBEMs) can help cities identify technology pathways—combinations of energy efficiency retrofits and renewable energy deployment strategies—to meet emissions reduction goals. UBEMs are currently limited by cost to only the largest cities but must be expanded to all cities if society is going to meet scientifically-identified emissions reduction goals. This manuscript presents an eight-step framework to scale technology pathways analyses using UBEMs to all communities in a repeatable, affordable manner. The roles and responsibilities of three key personas, the sustainability champion, GIS manager, and an energy modeler, for each step are identified. The eight-step process is tested with a case study of 13 100 buildings in Oshkosh, WI, USA. The case study identified a technically-feasible path to nearly net zero emissions for Oshkosh’s buildings. Constraints in the workforce, supply chain, and retrofit adoption to attain this goal were identified to inform policymakers. The case study suggests that the eight-step process is a blueprint for action in communities around the world.

 

Nanograined metals have the merit of high strength, but usually suffer from low work hardening capacity and poor thermal stability, causing premature failure and limiting their practical utilities. Here we report a “nanodispersion-in-nanograins” strategy to simultaneously strengthen and stabilize nanocrystalline metals such as copper and nickel. Our strategy relies on a uniform dispersion of extremely fine sized carbon nanoparticles (2.6 ± 1.2 nm) inside nanograins. The intragranular dispersion of nanoparticles not only elevates the strength of already-strong nanograins by 35%, but also activates multiple hardening mechanisms via dislocation-nanoparticle interactions, leading to improved work hardening and large tensile ductility. In addition, these finely dispersed nanoparticles result in substantially enhanced thermal stability and electrical conductivity in metal nanocomposites. Our results demonstrate the concurrent improvement of several mutually exclusive properties in metals including strength-ductility, strength-thermal stability, and strength-electrical conductivity, and thus represent a promising route to engineering high-performance nanostructured materials.

 

Soil erosion in agricultural landscapes reduces crop yields, leads to loss of ecosystem services, and influences the global carbon cycle. Despite decades of soil erosion research, the magnitude of historical soil loss remains poorly quantified across large agricultural regions because preagricultural soil data are rare, and it is challenging to extrapolate local-scale erosion observations across time and space. Here we focus on the Corn Belt of the midwestern United States and use a remote-sensing method to map areas in agricultural fields that have no remaining organic carbon-rich A-horizon. We use satellite and LiDAR data to develop a relationship between A-horizon loss and topographic curvature and then use topographic data to scale-up soil loss predictions across 3.9 × 10⁵km²of the Corn Belt. Our results indicate that 35 ± 11% of the cultivated area has lost A-horizon soil and that prior estimates of soil degradation from soil survey-based methods have significantly underestimated A-horizon soil loss. Convex hilltops throughout the region are often completely denuded of A-horizon soil. The association between soil loss and convex topography indicates that tillage-induced erosion is an important driver of soil loss, yet tillage erosion is not simulated in models used to assess nationwide soil loss trends in the United States. We estimate that A-horizon loss decreases crop yields by 6 ± 2%, causing $2.8 ± $0.9 billion in annual economic losses. Regionally, we estimate 1.4 ± 0.5 Pg of carbon have been removed from hillslopes by erosion of the A-horizon, much of which likely remains buried in depositional areas within the fields.

 

The safety issue represents a long‐standing obstacle that retards large‐scale applications of high‐energy lithium batteries. Among different causes, thermal runaway is the most prominent one. To date, various approaches have been proposed to inhibit thermal runaway; however, they suffer from some intrinsic drawbacks, either being irreversible (one‐time protection), using volatile and flammable electrolytes, or delayed thermal protection (140–150 °C). Herein, this work exploits a non‐volatile, non‐flammable, and thermo‐reversible polymer/ionic liquid gel electrolyte as a built‐in safety switch, which provides highly precise and reversible thermal protection for lithium batteries. At high temperature, the gel electrolyte experiences phase separation and deposits polymer on the electrode surfaces/separators, which blocks Li⁺insertion reactions and thus prevents thermal runaway. When the temperature decreases, the gel electrolyte restores its original properties and battery performance resumes. Notably, the optimal protection effect is achieved at 110 °C, which is the critical temperature right before thermal runaway. More importantly, such a thermal‐protection process can repeat multiple times without compromising the battery performance, indicating extraordinary thermal reversibility. To the authors' knowledge, such a precise and reversible protection effect has never been reported in any electrolyte systems, and this work opens an exciting avenue for safe operation of high‐energy Li batteries.

 

We consider a scenario involving computations over a massive dataset stored distributedly across multiple workers, which is at the core of distributed learning algorithms. We propose Lagrange Coded Computing (LCC), a new framework to simultaneously provide (1) resiliency against stragglers that may prolong computations; (2) security against Byzantine (or malicious) workers that deliberately modify the computation for their benefit; and (3) (information-theoretic) privacy of the dataset amidst possible collusion of workers. LCC, which leverages the well-known Lagrange polynomial to create computation redundancy in a novel coded form across workers, can be applied to any computation scenario in which the function of interest is an arbitrary multivariate polynomial of the input dataset, hence covering many computations of interest in machine learning. LCC significantly generalizes prior works to go beyond linear computations. It also enables secure and private computing in distributed settings, improving the computation and communication efficiency of the state-of-the-art. Furthermore, we prove the optimality of LCC by showing that it achieves the optimal tradeoff between resiliency, security, and privacy, i.e., in terms of tolerating the maximum number of stragglers and adversaries, and providing data privacy against the maximum number of colluding workers. Finally, we show via experiments on Amazon EC2 that LCC speeds up the conventional uncoded implementation of distributed least-squares linear regression by up to 13.43×, and also achieves a 2.36×-12.65× speedup over the state-of-the-art straggler mitigation strategies.