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Within coastal communities, sea level rise (SLR) will result in widespread intermittent flooding and long-term inundation. Inundation effects will be evident, but isolation that arises from the loss of accessibility to critical services due to inundation of transportation networks may be less obvious. We examine who is most at risk of isolation due to SLR, which can inform community adaptation plans and help ensure that existing social vulnerabilities are not exacerbated. Combining socio-demographic data with an isolation metric, we identify social and economic disparities in risk of isolation under different SLR scenarios (1-10 ft) for the coastal U.S. We show that Black and Hispanic populations face a disproportionate risk of isolation at intermediate levels of SLR (4 ft and greater). Further, census tracts with higher rates of renters and older adults consistently face higher risk of isolation. These insights point to significant inequity in the burdens associated with SLR.

 

Climate change is expected to increase the frequency and intensity of natural hazards such as hurricanes. With a severe shortage of affordable housing in the United States, renters may be uniquely vulnerable to disaster‐related housing disruptions due to increased hazard exposure, physical vulnerability of structures, and socioeconomic disadvantage. In this work, we construct a panel dataset consisting of housing, socioeconomic, and hurricane disaster data from counties in 19 states across the East and Gulf Coasts of the United States from 2009 to 2018 to investigate how the frequency and intensity of a hurricane correspond to changes in median rent and housing affordability (the interaction between rent prices and income) over time. Using a two‐stage least square random‐effects regression model, we find that more intense prior‐year hurricanes correspond to increases in median rents via declines in housing availability. The relationship between hurricanes and rent affordability is more complex, though the occurrence of a hurricane in a given year or the previous year reduces affordable rental housing, especially for counties with higher percentages of renters and people of color. Our results highlight the multiple challenges that renters are likely to face following a hurricane, and we emphasize that disaster recovery in short‐ and medium‐term should focus on providing safe, stable, and affordable rental housing assistance.

 

The electrochemical CO2 reduction reaction (ECO2RR) driven by renewable electricity holds promise to store intermittent energy in chemical bonds, while producing value-added chemicals and fuels sustainably. Unfortunately, it remains a grand challenge to simultaneously achieve a high faradaic efficiency (FE), a low overpotential, and a high current density of the ECO2RR. Herein, we report the synthesis of heterostructured Bi–Cu2S nanocrystals via a one-pot solution-phase method. The epitaxial growth of Cu2S on Bi leads to abundant interfacial sites and the resultant heterostructured Bi–Cu2S nanocrystals enable highly efficient ECO2RR with a largely reduced overpotential (240 mV lower than that of Bi), a near-unity FE (>98%) for formate production, and a high partial current density (2.4- and 5.2-fold higher JHCOO− than Cu2S and Bi at −1.0 V vs. reversible hydrogen electrode, RHE). Density functional theory (DFT) calculations show that the electron transfer from Bi to Cu2S at the interface leads to the preferential stabilization of the formate-evolution intermediate (*OCHO). 

The electrochemical nitrate reduction reaction (NO₃RR) to ammonia is an essential step toward restoring the globally disrupted nitrogen cycle. In search of highly efficient electrocatalysts, tailoring catalytic sites with ligand and strain effects in random alloys is a common approach but remains limited due to the ubiquitous energy-scaling relations. With interpretable machine learning, we unravel a mechanism of breaking adsorption-energy scaling relations through the site-specific Pauli repulsion interactions of the metald-states with adsorbate frontier orbitals. The non-scaling behavior can be realized on (100)-type sites of ordered B2 intermetallics, in which the orbital overlap between the hollow *N and subsurface metal atoms is significant while the bridge-bidentate *NO₃is not directly affected. Among those intermetallics predicted, we synthesize monodisperse ordered B2 CuPd nanocubes that demonstrate high performance for NO₃RR to ammonia with a Faradaic efficiency of 92.5% at −0.5 V_RHEand a yield rate of 6.25 mol h⁻¹g⁻¹at −0.6 V_RHE. This study provides machine-learned design rules besides thed-band center metrics, paving the path toward data-driven discovery of catalytic materials beyond linear scaling limitations.

 

In this paper, we develop efficient methods for devising lower complexity receivers that can achieve performance close to the full complexity receivers for passive/active multiple-input multiple-output (MIMO) radar. The method employed eliminates some parts of the test statistic to lower either hardware or software complexity. For the case of spatially uncorrelated reflection coefficients and spatially white clutter-plus-noise, the test statistic requires the computation of a set of matched filters, each matched to a signal from a different transmitter. In this case, our method is equivalent to selecting a specific set of transmitters to provide optimum performance. In the more general case of correlated clutter-plus-noise and reflection coefficients, then the test statistic requires the computation of a larger set of matched filters. These matched filters correlate the clutter-plus-noise free signal received at one receive antenna due to the signal transmitted from some transmit antenna and the signal received at another receive antenna. In the more general case, our algorithm picks the best of these matched filters to implement when the total number of these matched filters one can implement is limited.