Argonne National Laboratory

Large, ground-mounted photovoltaic solar energy projects (GPVs) are increasingly sited on former agricultural lands, creating opportunities to integrate ecological restoration as a scalable, nature-based solution to mitigate insect habitat loss and promote sustainable development. However, empirical evidence for the benefits of such efforts is nascent, particularly in water-limited regions where GPV development is rapidly expanding. We conducted an in-depth study at the UC Davis Experimental Ecovoltaic Park, a 16.3MW single-axis tracking GPV in the semi-arid Great Central Valley of California over two growing seasons following the sowing of a tailored seed mixture comprising 12 indigenous, pollinator-attractive wildflower species. Our objective was to evaluate how restoring native California prairie to replace poorly-managed, noxious-weed communities can interact with microclimate dynamics imposed by PV modules to influence floral resources and pollinator populations. Native wildflowers on restored plots exhibited 41% and 220% greater floral richness and cover, respectively, resulting in up to six times pollinator diversity than invasive broadleaves on unrestored controls where honey bees dominated visitations. Although perennials Phacelia californica and Grindelia camporum successfully established in the second year, the natural decline of annuals, particularly sunflower Helianthus bolanderi favored by many specialist bees, was associated with a 33% decrease of overall pollinator diversity. While panel-induced shading reduced flower unit densities while enlarging display areas of several native wildflowers, it did not significantly alter pollinator community composition. The findings demonstrate that ecological restoration at GPVs can potentially reconcile renewable energy production with biodiversity conservation in harsh, weed-prone environments.

Abstract Urban forest inventories are vital for understanding the ecological and social functions of neighborhood-scale arboreal systems, especially in communities experiencing climate-related stress. This study integrates field sampling, community questionnaires, and high-resolution (1m x 1m) LiDAR data to establish a comprehensive urban tree inventory for the Chatham neighborhood in Chicago, a community historically burdened by flooding, noise pollution, heat vulnerability, and limited green space. Findings demonstrate that trees in Chatham are predominantly tall, with an average height of 19 meters, and that canopy coverage constitutes approximately 17% of the neighborhood, higher than previous city estimates but still considerably below the recommended 40% urban canopy benchmark. Field assessments further indicate that crown vigor, as well as the health of leaves, trunks, twigs, and other woody components (including bark and branches), are adversely affected, suggesting substantial biotic and abiotic stress factors, which underscores the need for proactive urban forest management like regular tree inspections, inventories, removal or pruning of dead branches, and effective storm preparedness and emergency response. Additionally, it emphasizes the importance of targeted planting initiatives in areas with sparse tree cover and of implementing neighborhood-specific greening strategies to enhance climate resilience.

History (BH) has developed significantly since its formal naming in the early 1990s, yet its foundational ideas and research predate the label. Early influences include Herbert Spencer’s universal evolution, Carl Sagan’s public engagement with scientific history, and foundational works in physics, evolutionary biology, and anthropology. Integrating insights from history, systems theory, and various scientific disciplines, BH seeks to uncover large-scale patterns and transitions across cosmic, biological, and human history. Unlike traditional sciences, BH researchers rarely conduct direct experimentation; instead, it synthesizes information from multiple fields to construct a unified narrative of complexity, acceleration, and self-organization. While primary fundamental questions such as the origin of life, consciousness, and the universe continue to be explored in specialized fields, ongoing BH research explores fundamental questions, such as the mechanisms behind historical acceleration, the role of energy flow and information across disciplines, connections between fundamental physics and generalized evolution, and how to visualize abstract evolutionary processes. Emerging fields—such as evolutionary modeling, complexity science, and astrobiology—continue to enrich BH, while new communication strategies aim to engage broader audiences. However, BH faces challenges, including limited institutional support, debates over scope and inclusion, and uncertainties regarding acceleration trends in history and future scenarios. Looking ahead, BH must expand interdisciplinary collaboration, refine methodologies (e.g., for measuring complexity and uncertainty), and develop strategies to assess and understand future scenarios. By addressing these challenges, BH can continue to serve as a powerful framework for understanding history, scientific progress, and humanity’s evolving place in the cosmos.

Lithium-carbon dioxide batteries hold great promise for high-energy-density storage applications. However, advancing this technology as a sustainable alternative to Li-ion systems requires a deeper understanding of the underlying reaction mechanisms, which remain elusive. A key challenge stems from the added complexity introduced by the presence of oxygen in CO2 environment. In this study, we employ a stable Cu3(VBi)0.5Se4 mid-entropy catalyst and conduct comprehensive investigation to uncover the underlying reaction mechanisms in Li-CO2 batteries under varying CO2/O2 ratios. Under pure CO2 conditions, the battery shows extended rechargeability, sustaining up to 1200 cycles at a current density of 0.2 mA/cm2 and capacity of 0.1 mAh/cm2. However, at high current densities, the discharge potential drops significantly (below 2.0 V), primarily due to sluggish reaction kinetics caused by solid carbon formation. Interestingly, introducing O2 mitigates this limitation, leading to a 58% increase of the discharge potential (from 1.7 V to 2.7 V) at the current density of 0.8 mA/cm2, signifying a substantial boost in energy output. Our results reveal that the reactions follow distinct pathways, shifting from surface- to solution-based mechanism, and even exhibit coexistence of both mechanisms, depending on the CO2/O2 ratio. These findings offer useful insights for designing sustainable Li-gas batteries utilizing CO2 and O2 mixtures.

Pooled rideshare is considered an effective future travel mode for improving vehicle utilization and reducing congestion in urban transportation systems. However, its adoption remains limited due to insufficient passenger acceptance and uncertain economic benefits for transportation network companies (TNCs). The emergence of autonomous vehicles brings new momentum to pooled ridesharing services through centralized fleet management. Nevertheless, most existing studies examine traveler behavior and fleet operations separately, leaving the interaction between passenger preferences and operational strategies insufficiently represented. This study proposed an integrated behavioral–operational framework that jointly considers traveler choice behavior and fleet management decisions. An Integrated Choice and Latent Variable (ICLV) model is estimated using 8296 national survey responses collected in the United States in 2025 to capture post-pandemic traveler attitudes toward pooled rideshare. The behavioral model is embedded into a proactive assignment and repositioning strategy implemented on the POLARIS agent-based simulation platform. Simulation experiments are conducted in two urban networks, Greenville (SC) and Austin (TX), under multiple fleet size scenarios. Results show that the new pooling behavior model significantly increases pooling adoption compared with the previous mixed logit model, indicating that it better captures real-world traveler behavior. And the higher pooling adoption also reshapes the TNC trip structure in Greenville. Compared to the baseline in the POLARIS platform, the integrated framework increases pooling adoption and TNC profitability while reducing VMT, empty seat rates, and overall energy consumption. These findings provide insights for the sustainable deployment of pooled SAV services in heterogeneous urban environments.

A bstract Graph neural networks have recently shown strong promise for event reconstruction tasks in Liquid Argon Time Projection Chambers, yet their performance remains limited for underrepresented classes of particles, such as Michel electrons. In this work, we investigate physics-informed strategies to improve semantic segmentation within the NuGraph2 architecture. We explore three complementary approaches: (i) enriching the input representation with context-aware features derived from detector geometry and track continuity, (ii) introducing auxiliary decoders to capture class-level correlations, and (iii) incorporating energy-based regularization terms motivated by Michel electron energy distributions. Experiments on MicroBooNE public datasets show that physics-inspired feature augmentation yields the largest gains, particularly boosting Michel electron precision and recall by disentangling overlapping latent space regions. In contrast, auxiliary decoders and energy-regularization terms provided limited improvements, partly due to the hit-level nature of NuGraph2, which lacks explicit particle- or event-level representations. Our findings highlight that embedding physics context directly into node-level inputs is more effective than imposing task-specific auxiliary losses, and suggest that future hierarchical architectures such as NuGraph3, with explicit particle- and event-level reasoning, will provide a more natural setting for advanced decoders and physics-based regularization. The code for this work is publicly available on Github at https://github.com/vitorgrizzi/nugraph_phys/tree/main_phys .