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Institution

King Abdullah University of Science and Technology

EducationJeddah, Saudi Arabia
About: King Abdullah University of Science and Technology is a education organization based out in Jeddah, Saudi Arabia. It is known for research contribution in the topics: Catalysis & Membrane. The organization has 6221 authors who have published 22019 publications receiving 625706 citations. The organization is also known as: KAUST.


Papers
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Journal ArticleDOI
TL;DR: This work demonstrates that the polymer:fullerene arrangement and resulting intermolecular interactions may be key factors in determining the performance of OPV material systems.
Abstract: The performance of organic photovoltaic (OPV) material systems are hypothesized to depend strongly on the intermolecular arrangements at the donor:fullerene interfaces. A review of some of the most efficient polymers utilized in polymer:fullerene PV devices, combined with an analysis of reported polymer donor materials wherein the same conjugated backbone was used with varying alkyl substituents, supports this hypothesis. Specifically, the literature shows that higher-performing donor–acceptor type polymers generally have acceptor moieties that are sterically accessible for interactions with the fullerene derivative, whereas the corresponding donor moieties tend to have branched alkyl substituents that sterically hinder interactions with the fullerene. To further explore the idea that the most beneficial polymer:fullerene arrangement involves the fullerene docking with the acceptor moiety, a family of benzo[1,2-b:4,5-b′]dithiophene–thieno[3,4-c]pyrrole-4,6-dione polymers (PBDTTPD derivatives) was synthesi...

302 citations

Journal ArticleDOI
TL;DR: It is shown that the performance of H2O2 treated MXene as an anode material in Li ion batteries (LIBs) was significantly improved as compared to as-prepared MXenes.
Abstract: Herein we demonstrate that a prominent member of the MXene family, Ti2C, undergoes surface oxidation at room temperature when treated with hydrogen peroxide (H2O2). The H2O2 treatment results in opening up of MXene sheets and formation of TiO2 nanocrystals on their surface, which is evidenced by the high surface area of H2O2 treated MXene and X-ray diffraction (XRD) analysis. We show that the reaction time and the amount of hydrogen peroxide used are the limiting factors, which determine the morphology and composition of the final product. Furthermore, it is shown that the performance of H2O2 treated MXene as an anode material in Li ion batteries (LIBs) was significantly improved as compared to as-prepared MXenes. For instance, after 50 charge/discharge cycles, specific discharge capacities of 389 mA h g−1, 337 mA h g−1 and 297 mA h g−1 were obtained for H2O2 treated MXene at current densities of 100 mA g−1, 500 mA g−1 and 1000 mA g−1, respectively. In addition, when tested at a very high current density, such as 5000 mA g−1, the H2O2 treated MXene showed a specific capacity of 150 mA h g−1 and excellent rate capability. These results clearly demonstrate that H2O2 treatment of Ti2C MXene improves MXene properties in energy storage applications, such as Li ion batteries or capacitors.

302 citations

Journal ArticleDOI
05 Aug 2019
TL;DR: In this paper, a planar polydimethylsiloxane (PDMS)/metal thermal emitter thin film structure was fabricated using a fast solution coating process that is scalable for large-area manufacturing.
Abstract: Radiative cooling is a passive cooling strategy with zero consumption of electricity that can be used to radiate heat from buildings to reduce air-conditioning requirements. Although this technology can work well during optimal atmospheric conditions at night, it is essential to achieve efficient cooling during the daytime when peak cooling demand actually occurs. Here we report an inexpensive planar polydimethylsiloxane (PDMS)/metal thermal emitter thin film structure, which was fabricated using a fast solution coating process that is scalable for large-area manufacturing. By performing tests under different environmental conditions, temperature reductions of 9.5 °C and 11.0 °C were demonstrated in the laboratory and an outside environment, respectively, with an average cooling power of ~120 W m–2 for the thin film thermal emitter. In addition, a spectral-selective structure was designed and implemented to suppress the solar input and control the divergence of the thermal emission beam. This enhanced the directionality of the thermal emissions, so the emitter’s cooling performance was less dependent on the surrounding environment. Outside experiments were performed in Buffalo, New York, realizing continuous all-day cooling of ~2–9 °C on a typical clear sunny day at Northern United States latitudes. This practical strategy that cools without electricity input could have a significant impact on global energy consumption. Radiative cooling can reduce air-conditioning requirements. In this study, the authors demonstrated an inexpensive thermal emitter that provided continuous daytime cooling up to 9 °C outdoors on a clear, sunny New York day.

302 citations

Proceedings ArticleDOI
14 Jun 2020
TL;DR: Semantic Region Adaptive Normalization (SEAN) as mentioned in this paper is a simple but effective building block for Generative Adversarial Networks conditioned on segmentation masks that describe the semantic regions in the desired output image.
Abstract: We propose semantic region-adaptive normalization (SEAN), a simple but effective building block for Generative Adversarial Networks conditioned on segmentation masks that describe the semantic regions in the desired output image. Using SEAN normalization, we can build a network architecture that can control the style of each semantic region individually, e.g., we can specify one style reference image per region. SEAN is better suited to encode, transfer, and synthesize style than the best previous method in terms of reconstruction quality, variability, and visual quality. We evaluate SEAN on multiple datasets and report better quantitative metrics (e.g. FID, PSNR) than the current state of the art. SEAN also pushes the frontier of interactive image editing. We can interactively edit images by changing segmentation masks or the style for any given region. We can also interpolate styles from two reference images per region.

302 citations

Journal ArticleDOI
TL;DR: In this paper, a comprehensive meta-analysis of organic zooplankton values at the base of the food web, dissolved inorganic carbon δ13C values, and seawater δ18O values to create, for the first time, robust isoscapes for the Atlantic Ocean is presented.
Abstract: Ecogeochemistry—the application of geochemical techniques to fundamental questions in population and community ecology—has been used in animal migration studies in terrestrial environments for several decades; however, the approach has received far less attention in marine systems. This review includes comprehensive meta-analyses of organic zooplankton δ13C and δ15N values at the base of the food web, dissolved inorganic carbon δ13C values, and seawater δ18O values to create, for the first time, robust isoscapes for the Atlantic Ocean. These isoscapes present far greater geographic variability in multiple geochemical tracers than was previously thought, thus forming the foundation for reconstructions of habitat use and migration patterns of marine organisms. We review several additional tracers, including trace-element-to-calcium ratios and heavy element stable isotopes, to examine anadromous migrations. We highlight the value of the ecogeochemistry approach by examining case studies on three components of connectivity: dispersal and natal homing, functional connectivity, and migratory connectivity. We also discuss recent advances in compound-specific stable carbon and nitrogen isotope analyses for tracking animal movement. A better understanding of isotopic routing and fractionation factors, particularly of individual compound classes, is necessary to realize the full potential of ecogeochemistry.

301 citations


Authors

Showing all 6430 results

NameH-indexPapersCitations
Jian-Kang Zhu161550105551
Jean M. J. Fréchet15472690295
Kevin Murphy146728120475
Jean-Luc Brédas134102685803
Carlos M. Duarte132117386672
Kazunari Domen13090877964
Jian Zhou128300791402
Tai-Shung Chung11987954067
Donal D. C. Bradley11565265837
Lain-Jong Li11362758035
Hong Wang110163351811
Peng Wang108167254529
Juan Bisquert10745046267
Jian Zhang107306469715
Karl Leo10483242575
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023141
2022371
20212,836
20202,809
20192,544
20182,251