The long-range delocalization of electrons in macrocyclic compounds, such as porphyrins and phthalocyanines, allows for tuning the electronic properties of domains that utilize these chemicals as building blocks. This approach has rapidly become a powerful platform for preparing diverse electroactive materials for electrochemical applications. To that end, in this review, we outline the most recent advancements in design, synthesis, and computational modeling of porphyrin-based frameworks. Herein, we highlight innovative synthetic approaches that are utilized to both enhance the structural stability and porosity of the materials and control the microenvironment of their catalytic sites. These approaches include post-synthetic modification, metal coordination, and hierarchical assembly. Key focus areas of research, including the use of these materials as electrocatalysts and capacitors, are highlighted. The expansion in the utility of these materials in environmental remediation applications, such as carbon dioxide capture, photocatalytic degradation of pollutants, and selective adsorption of heavy metals, underscores the versatility of the porphyrin-based materials across many disciplines.
Abstract Transportation planning requires anticipating how built environments, population characteristics, and aggregate travel indicators evolve over time. Conventional forecasting approaches often treat these dimensions separately, limiting their ability to represent spatial-demographic change. This study introduces the Demographics-Informed Neural Network (DINN), a deep learning framework that forecasts next-year county-level satellite imagery from historical image sequences while enforcing demographic consistency through a frozen demographic predictor. DINN combines an encoder-decoder image predictor, gated skip connections, demographic bottleneck fusion, and demographic consistency regularization. The frozen demographic encoder is then reused as a fixed feature extractor to estimate aggregate county-level commute-mode and vehicle-availability indicators. Experiments using US county data from 2012 to 2023 show that DINN achieves an MSE of 0.00379 and SSIM of 0.8342 for satellite image prediction. Across repeated grouped spatial splits, DINN provides modest but consistent improvements over ConvLSTM, transformer-based, and encoder-decoder baselines, with stronger performance on both image reconstruction and change-detection metrics. The demographic predictor explains approximately 80% of the variance in population characteristics from satellite imagery. Transfer learning results show higher accuracy for absolute travel-indicator counts than for scale-controlled shares, indicating that part of the signal reflects county population scale. Overall, the findings suggest that satellite-derived spatial representations capture built-environment and demographic correlates of aggregate travel indicators; however, the results should be interpreted as county-level predictive associations rather than causal behavioral inference.
Abstract Polymer clay nanocomposites (PCNs) have garnered considerable attention in both research and industrial applications due to their superior properties, ecological features, wide availability, and cost-effectiveness. However, key challenges remain in managing the interfacial interactions between nanoclays (NCs) and polymers and achieving uniform dispersion of the nanoclays within the polymer matrix. To address these challenges, nanoclay modifications are often employed to enhance the compatibility between polar nanoclays and nonpolar polymers while promoting the dispersion quality of nanoclays. An appropriate modification of montmorillonite (MMT) is expected to expand the d-spacing from 14.5 Å to around 30 Å in an intercalated state, and even up to 80 Å toward an exfoliated morphology. This review discusses the role of nanoclay modification, fabrication techniques for PCNs, and the influence of nanoclay concentration on the dispersion quality and properties of PCNs. In addition, it covers various techniques used for PCN characterization and highlights the application of PCNs in infrastructure. By carefully optimizing the fabrication parameters, the mechanical, thermal, and barrier properties of PCNs can be enhanced by up to 270%, 15%, and 80%, respectively. This review highlights the effects of the morphological characteristics of nanoclays on the macroscale performance of PCNs, emphasizing their great potential in developing more durable, efficient, and sustainable materials in infrastructure applications.
Abstract This study investigates the effects of adverse weather conditions on fuel consumption, CO 2 emissions, and traffic flow at an urban intersection in Saratoga Springs, Utah. Given that approximately 70% of the US population resides in areas frequently impacted by snow and rain, the findings of this study are crucial for shaping future urban transportation planning and traffic management strategies. Using the PTV Vision Verkehr In Städten—SIMulationsmodell (VISSIM) microsimulation tool, traffic flow and driving behavior were modeled under four weather conditions: clear sky, dense fog, rain, and snow. This study is the first to incorporate the Bosch Environmentally Sensitive Traffic Management service as an add-on module to the VISSIM within a US urban context, providing high-resolution, microscopic-level assessments of fuel consumption and vehicular emissions under adverse weather conditions for Saratoga Springs, Utah. To analyze the relationship between weather impacts, traffic flow, fuel consumption, and emissions, the Wiedemann 99 car-following model and speed distributions were adjusted to reflect the conditions of each weather scenario. This paper offers a comprehensive framework for evaluating how adverse weather influences urban mobility and fuel consumption, emphasizing the implications for urban planning and policy. The results underscore the significant negative effects of weather on mobility and environmental performance, highlighting the need for urban planners to account for these conditions in the design of transportation systems, particularly near sensitive locations such as hospitals, schools, and residential areas with vulnerable populations.
Global populations of Pyrenophora teres f. maculata have evolved to target multiple dominant barley susceptibility loci, highlighting the risk of widespread disease under local selection pressures. The necrotrophic fungal pathogen Pyrenophora teres f. maculata causes spot form net blotch (SFNB), a global disease of barley. This fungus uses effector proteins to promote infection, which act in an inverse gene-for-gene manner by targeting dominant host susceptibility genes. Currently, there is a general understanding of the genetics of resistance/susceptibility in the host; however, there are still gaps in our understanding of global pathogen virulence and how it has evolved to target the host and cause disease. Because the P. teres f. maculata-barley interaction conforms to an inverse gene-for-gene model, we crossed three different susceptible barley lines (Hockett, TR 326, and PI 392501) with the resistant line PI 67381 and mapped recombinant inbred populations to characterize the susceptibility in these lines based on their response to ten pathogen isolates collected from diverse barley growing regions on five continents. Four independent quantitative trait loci (QTL) showed associations with susceptibility and mapped to barley chromosomes (Chr) 2HS, 4HS, 4HL, and 7HL. In all three populations, the same genomic position on Chr7HL was associated with the highest susceptibility levels and was targeted by seven of the ten fungal isolates. The QTL identified on Chr2HS mapped to the same position in two populations and was also targeted by seven of the ten isolates. However, the Chr4HS and Chr4HL susceptibilities were targeted by only three and two of the global isolates, respectively. This work shows that pathogen populations under different host selection pressures can evolve to target different barley susceptibility loci.
