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1. A METHOD TO GENERATE FLOOD MAPS IN 3D USING DEM AND DEEP LEARNING

   https://doi.org/10.5194/isprs-archives-XLIV-M-2-2020-25-2020  

   Gebrehiwot, A.; Hashemi-Beni, L. (January 2020, ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences)

   null (Ed.)

   Abstract. High-resolution remote sensing imagery has been increasingly used for flood applications. Different methods have been proposed for flood extent mapping from creating water index to image classification from high-resolution data. Among these methods, deep learning methods have shown promising results for flood extent extraction; however, these two-dimensional (2D) image classification methods cannot directly provide water level measurements. This paper presents an integrated approach to extract the flood extent in three-dimensional (3D) from UAV data by integrating 2D deep learning-based flood map and 3D cloud point extracted from a Structure from Motion (SFM) method. We fine-tuned a pretrained Visual Geometry Group 16 (VGG-16) based fully convolutional model to create a 2D inundation map. The 2D classified map was overlaid on the SfM-based 3D point cloud to create a 3D flood map. The floodwater depth was estimated by subtracting a pre-flood Digital Elevation Model (DEM) from the SfM-based DEM. The results show that the proposed method is efficient in creating a 3D flood extent map to support emergency response and recovery activates during a flood event. 

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2. Addressing Urgent Questions for PFAS in the 21st Century

   https://doi.org/10.1021/acs.est.1c03386  

   Ng, Carla; Cousins, Ian T.; DeWitt, Jamie C.; Glüge, Juliane; Goldenman, Gretta; Herzke, Dorte; Lohmann, Rainer; Miller, Mark; Patton, Sharyle; Scheringer, Martin; et al (September 2021, Environmental Science & Technology)

   Despite decades of research on per- and polyfluoroalkyl substances (PFAS), fundamental obstacles remain to addressing worldwide contamination by these chemicals and their associated impacts on environmental quality and health. Here, we propose six urgent questions relevant to science, technology, and policy that must be tackled to address the “PFAS problem”: (1) What are the global production volumes of PFAS, and where are PFAS used? (2) Where are the unknown PFAS hotspots in the environment? (3) How can we make measuring PFAS globally accessible? (4) How can we safely manage PFAS-containing waste? (5) How do we understand and describe the health effects of PFAS exposure? (6) Who pays the costs of PFAS contamination? The importance of each question and barriers to progress are briefly described, and several potential paths forward are proposed. Given the diversity of PFAS and their uses, the extreme persistence of most PFAS, the striking ongoing lack of fundamental information, and the inequity of the health and environmental impacts from PFAS contamination, there is a need for scientific and regulatory communities to work together, with cooperation from PFAS-related industries, to fill in critical data gaps and protect human health and the environment. 

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3. Classification of apatite structures via topological data analysis: a framework for a ‘Materials Barcode’ representation of structure maps

   https://doi.org/10.1038/s41598-021-90070-4  

   Broderick, Scott; Dongol, Ruhil; Zhang, Tianmu; Rajan, Krishna (December 2021, Scientific Reports)

   null (Ed.)

   Abstract This paper introduces the use of topological data analysis (TDA) as an unsupervised machine learning tool to uncover classification criteria in complex inorganic crystal chemistries. Using the apatite chemistry as a template, we track through the use of persistent homology the topological connectivity of input crystal chemistry descriptors on defining similarity between different stoichiometries of apatites. It is shown that TDA automatically identifies a hierarchical classification scheme within apatites based on the commonality of the number of discrete coordination polyhedra that constitute the structural building units common among the compounds. This information is presented in the form of a visualization scheme of a barcode of homology classifications, where the persistence of similarity between compounds is tracked. Unlike traditional perspectives of structure maps, this new “Materials Barcode” schema serves as an automated exploratory machine learning tool that can uncover structural associations from crystal chemistry databases, as well as to achieve a more nuanced insight into what defines similarity among homologous compounds. 

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4. Deep Learning Based Prediction of Perovskite Lattice Parameters from Hirshfeld Surface Fingerprints

   https://doi.org/10.1021/acs.jpclett.0c02201  

   Williams, Logan; Mukherjee, Arpan; Rajan, Krishna (August 2020, The Journal of Physical Chemistry Letters)

   This Letter describes the use of deep learning methods on Hirshfeld surface representations of crystal structure, as an automated means of predicting lattice parameters in cubic inorganic perovskites. While Hirshfeld Surface Analysis is a well-established tool in organic crystallography, we also introduce modified computational protocols for Hirshfeld Surface Analysis tailored specifically to account for nuanced but important differences dealing with inorganic crystals. We demonstrate how two-dimensional Hirshfeld surface fingerprints can serve as a rich “database” of information encoding the complexity of relationships between chemical bonding and bond geometry characteristics of perovskites. Our results are compared with other studies on lattice parameter prediction involving both experimental and computationally derived data, and it is shown that our approach is an improvement over other reported methods. The paper concludes by discussing how this work opens new avenues for data-driven high throughput computational predictions of structure–property relationships involving complex crystal chemistries. 

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5. Rapid Aminations of Functionalized Aryl Fluorosulfates in Water

   https://doi.org/10.1002/anie.202411295  

   Iyer, Karthik S; Dismuke_Rodriguez, Kylee B; Lammert, Robert M; Yirak, Jordan R; Saunders, John M; Kavthe, Rahul D; Aue, Donald H; Lipshutz, Bruce H (September 2024, Angewandte Chemie International Edition)

   Abstract Aryl fluorosulfates of varying complexities have been used in amination reactions in water using a new Pd oxidative addition complex (OAC‐1) developed specifically to match the needs of the fine chemicals industry, not only in terms of functional group tolerance, but also reflecting time considerations associated with these important C−N couplings. Also especially noteworthy is that they replace both PFAS‐related triflates and nonaflates, which are today out of favor due to recent government regulations. The new complex based on the BippyPhos ligand is used at low loadings and under aqueous micellar conditions. Moreover, it is easily prepared and stable to long term storage. DFT calculations on the OAC precatalyst compare well with the X‐ray structure of the crystals with π‐complexation to the aromatic system of the ligand and also confirm the NMR data showing a mixture of conformers in solution that differ from the X‐ray structure in rotation of the phenyl andt‐butyl ligand substituents. An extensive variety of coupling partners, including pharmaceutically relevant APIs, readily participate under mild and environmentally responsible reaction conditions. 

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