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Light-driven self-assembly of spiropyran-functionalized covalent organic framework
Get PDFControlling the number of molecular switches and their relative positioning within porous materials is critical to their functionality and properties. The proximity of many molecular switches to one another can hinder or completely suppress their response. Herein, a synthetic strategy involving mixed linkers is used to control the distribution of spiropyran-functionalized linkers in a covalent organic framework (COF). The COF contains a spiropyran in each pore which exhibits excellent reversible photo switching behavior to its merocyanine form in the solid state in response to UV/Vis light. The spiro-COF possesses an urchin-shaped morphology and exhibits a morphological transition to 2D nanosheets and vesicles in solution upon UV light irradiation. The merocyanine-equipped COFs are extremely stable and possess a more ordered structure with enhanced photoluminescence. This approach to modulating structural isomerization in the solid state is used to develop inkless printing media, while the photomediated polarity change is used for water harvesting applications.This work was supported by New York University Abu Dhabi and the NYUAD Water Research Center, funded by Tamkeen under the NYUAD Research Institute Award (project CG007). We thank NYUAD for their generous support for the research program. We thank Sandooq Al Watan for funding (Grant No. SWARD-S22-014, Project ID: PRJ-SWARD-628). The research work was carried out by using the Core Technology Platform resources at NYUAD. Computer simulations were carried out on the High-Performance Computing resources at New York University Abu Dhabi. S.K. and N.A. are funded by the NYUAD research fund AD181. N.S. acknowledge the funding sponsored by the Zayed Center for Health Sciences at the UAE University (Grant #12R113)
Seven people enjoying their drinks
No full textSeven people sitting togeather and enjoying their drink
Men and Women in Uniform
No full textGroup of Fourteen Men and Eight Women in Uniform standing on step
Jimmy Dorsey and Rafael Galvan at Galvan's
No full textJimmy Dorsey and Rafael Galvan at Galvan's Smiling and shaking hand
Jake Stephens, Ralph and Eddie Galvan
No full textJake Stephens, Ralph and Eddie Galvan playing instrumen
The experience of working nurses attending graduate school during COVID-19: A hermeneutic phenomenology study
No full textIntroduction: There has been unprecedented uncertainty involved in the COVID-19 pandemic, especially for working nurses. Nurses working while attending graduate school faced additional unique challenges including working extended hours while also home-schooling young children, managing a family life while also navigating pandemic-related changes affecting students’ educational paths.
Objectives: The purpose of this study was to explore the lived experiences of working nurses attending graduate school during the COVID-19 pandemic. The central research question was: What is the lived experience of working nurses attending graduate school during COVID-19?
Methods: The exploration of the lived experience of working nurses attending graduate school during a pandemic required a research methodology delving into the meaning of lived experience as it has been lived, temporally, and contextually (during a pandemic). Qualitative hermeneutic phenomenology was used to explore the meaning of lived experience from an interpretational stance.
Results: The overall meaning of the experience was a paradigm shift of existence across the three realms of work, home, and school. The themes associated with the shift were rapid change, uncertainty, fear, and support persons. Stress was a resulting overarching theme.
Conclusions: To support working nurses further their education during times of crisis, nurse leaders and educators should put processes in place to mitigate change and stress through strategic communication and supportive work environments.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Spring 2021 Texas A&M University-Corpus Christi (TAMU-CC), College of Nursing and Health Sciences Research Enhancement Grant, and the TAMU-CC Division of Research and Innovation COVID-19 Just in Time funding program
Two women sitting in an automobile
No full textTwo women sitting in an 1920's automobile with the doors ope
Influences of the filling phases of the Grand Ethiopian Renaissance Dam on the Nile River's downstream reservoirs
No full textCoastal and Marine System Science Program, Department of Physical and Environmental SciencesWithin the Nile River Basin (NRB; area: 3.4 × 106 km2), the Nile River (length: 6.6 × 103 km) flows from south to north in Northeast Africa. The Blue Nile is the main source of surface water for Egypt, providing over 80% of the country's allocation and serving as the source of 90% of Egypt's freshwater resources, which are used to sustain a population of more than 104 million people. In April 2011, Ethiopia began the construction of the Grand Ethiopian Renaissance Dam (GERD) on the Blue Nile. The GERD is planned to generate more than 6 gigawatts of clean electricity (Gebreluel, 2014)
Aggregating XAI methods for insights into geoscience models with correlated and high-dimensional rasters
No full textGeoscience applications have been using sophisticated machine learning methods to model complex phenomena. These models are described as black boxes since it is unclear what relationships are learned. Models may exploit spurious associations that exist in the data. The lack of transparency may limit user’s trust, causing them to avoid high performance models since they cannot verify that it has learned realistic strategies. EXplainable Artificial Intelligence (XAI) is a developing research area for investigating how models make their decisions. However, XAI methods are sensitive to feature correlations. This makes XAI challenging for high-dimensional models whose input rasters may have extensive spatial-temporal autocorrelation. Since many geospatial applications rely on complex models for target performance, a recommendation is to combine raster elements into semantically meaningful feature groups. However, it is challenging to determine how best to combine raster elements. Here, we explore the explanation sensitivity to grouping scheme. Experiments are performed on FogNet, a complex deep learning model that uses 3D Convolutional Neural Networks (CNN) for coastal fog prediction. We demonstrate that explanations can be combined with domain knowledge to generate hypotheses about the model. Meteorological analysis of the XAI output reveal FogNet’s use of channels that capture relationships related to fog development, contributing to good overall model performance. However, analyses also reveal several deficiencies, including the reliance on channels and channel spatial patterns that correlate to the predominate fog type in the dataset, to make predictions of all fog types. Strategies to improve FogNet performance and trustworthiness are presented.This material is based upon work supported by the National Science Foundation under 1460 awards 2019758 and 1828380