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Communities are increasingly harnessing the coastal protection functions of marshes and other coastal ecosystems within built infrastructure, developing nature-based designs to stabilize coastlines. These “living shorelines” often include planting ecosystem-engineering plants, which have traits that attenuate waves and facilitate sediment accretion while limiting erosion. However, failure is common during plant establishment, requiring interdisciplinary approaches to inform planting designs that enhance short-term sediment stability. Here we combine hydrodynamic modelling with mesocosm experiments to assess different planting approaches for the marsh grass Spartina alterniflora. The model, parameterized with traits measured in the experiments, showed that random arrangement of plants outperformed regular arrangements, reducing areas of high flow velocities and increasing tortuosity, facilitating sediment stability. Furthermore, wide-diameter Spartina clumps with increased biomass reduced flow better than small-diameter clumps, even when the area occupied by the vegetation site-wide is identical. Our experiments revealed multiple factors that influence the diameter and biomass of Spartina clumps, including plant source, sediment characteristics, and spatial arrangement of propagules. While some sources performed better than others, their relative performance varied with time and environment, suggesting that practitioners plant multiple sources to ensure incorporating high-performers in variable and often unexamined planting environments. Furthermore, clumping propagules during planting best generated the large, dense clumps that facilitate sediment stability. 

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) has many promising features in studying materials including high spatial resolution and high mass accuracy of elements, molecules, and isotopes. Its ability to resolve isotopes is especially attractive in studying transmutation products of single crystal tungsten (SCW) post neutron irradiation. Tungsten (W) is a contender of plasma facing materials (PFMs) due to its high thermal and radiological stability. PFMs to be used in the construction of fusion vessels are subject to high temperature and neutron irradiation, resulting in changes to materials including transmutation, which ultimately impact material mechanical and thermal properties. We used IONTOF TOF.SIMS V instrument equipped with a 30 keV Bi3+ primary ion beam to study pristine SCW and irradiated SCW speciemens. Scanning electron microscope coupled with focused ion beam (SEM-FIB) was used to reduce the dosage of neutron irradiated tungsten and prepare for specimens for SIMS analysis. Static ToF-SIMS spectra were obtained, and transmutation product peak identification was presented in this work. Identified molecules and molecular fragments were compared against isotope theoretical mass to charge ratios of tungsten, rhenium, osmium, and other relevant products. Our results show that ToF-SIMS provides a viable means to study transmutation products of W post neutron irradiation. Such applications are suitable to investigate transmutation effects on materials that are being considered and developed for fusion pilot plants. 

Topological materials hold immense promise for exhibiting exotic quantum phenomena, yet achieving controllable topological phase transitions remains challenging. Here, we demonstrate a structurally driven, reversible topological phase transition in the distorted square net material GdPS, induced via in situ potassium dosing. Using angle-resolved photoemission spectroscopy and first principles calculations, we demonstrate a cascade of topological phases in the subsurface P layer: from a large, topologically trivial band gap to a gapless Dirac cone state with a 2 eV dispersion, and finally to a two-dimensional topological insulator as inferred from theory. This evolution is driven by subtle structural distortions in the first P layer caused by potassium adsorption, which in turn contribute to the band gap closure and topological phase transition. Furthermore, the ability to manipulate the topology of a subsurface layer in GdPS offers a unique route for exploring and controlling topological states in bulk materials. 

On February 6, 2023, a major earthquake of 7.8 magnitude and its aftershocks caused widespread destruction in Turkey and Syria, causing more than 55,000 deaths, displacing 3 million people in Turkey and 2.9 million in Syria, and destroying or damaging at least 230,000 buildings. Our research presents detailed city-scale maps of landslides, liquefaction, and building damage from this earthquake, utilizing a novel variational causal Bayesian network. This network integrates InSAR-derived change detection with new empirical ground failure models and building footprints, enabling us to (1) rapidly estimate large-scale building damage, landslides, and liquefaction from remote sensing data, (2) jointly attribute building damage to landslides, liquefaction, and shaking, (3) improve regional landslide and liquefaction predictions impacting infrastructure, and (4) simultaneously identify damage degrees in thousands of buildings. For city-scale, building-by-building damage assessments, we use building footprints and satellite imagery with a spatial resolution of approximately 30 meters. This allows us to achieve a high resolution in damage assessment, both in timeliness and scale, enabling damage classification at the individual building level within days of the earthquake. Our findings detail the extent of building damage, including collapses, in Hatay, Osmaniye, Adıyaman, Gaziantep, and Kahramanmaras. We classified building damages into five categories: no damage, slight, moderate, partial collapse, and collapse. We evaluated damage estimates against preliminary ground-truth data reported by the civil authorities. Our results demonstrate the accuracy of our classification system, as evidenced by the area under the curve (AUC) scores on the receiver operating characteristic (ROC) curve, which ranged from 0.9588 to 0.9931 across different damage categories and regions. Specifically, our model achieved an AUC of 0.9931 for collapsed buildings in the Hatay/Osmaniye area, indicating a 99.31% probability that the model will rank a randomly chosen collapsed building higher than a randomly chosen non-collapsed building. These accurate, building-specific damage estimates, with greater than 95% classification accuracy across all categories, are crucial for disaster response and can aid agencies in effectively allocating resources and coordinating efforts during disaster recovery. 

Abstract A catastrophic Mw7.8 earthquake hit southeast Turkey and northwest Syria on February 6th, 2023, leading to more than 44 k deaths and 160 k building collapses. The interpretation of earthquake-triggered building damage is usually subjective, labor intensive, and limited by accessibility to the sites and the availability of instant, high-resolution images. Here we propose a multi-class damage detection (MCDD) model enlightened by artificial intelligence to synergize four variables, i.e., amplitude dispersion index (ADI) and damage proxy (DP) map derived from Synthetic Aperture Radar (SAR) images, the change of the normalized difference built-up index (NDBI) derived from optical remote sensing images, as well as peak ground acceleration (PGA). This approach allows us to characterize damage on a large, tectonic scale and a small, individual-building scale. The integration of multiple variables in classifying damage levels into no damage, slight damage, and serious damage (including partial or complete collapses) excels the traditional practice of solely use of DP by 11.25% in performance. Our proposed approach can quantitatively and automatically sort out different building damage levels from publicly available satellite observations, which helps prioritize the rescue mission in response to emergent disasters.