King Abdullah University of Science and Technology

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: Membrane & Catalysis. The organization has 6221 authors who have published 22019 publications receiving 625706 citations. The organization is also known as: KAUST.

Developing thin-film-composite forward osmosis membranes on the PES/SPSf substrate through interfacial polymerization

Abstract: The authors thank financial support from King Abdullah University of Science and Technology (KAUST) by Award No SA-C0005/UK-C0002, and National University of Singapore (NUS) for funding this research project with the grant number of and R-279-000-265-598. Special thanks are due to Dr. Youchang Xiao, Dr. Jincai Su, Mr. Shipeng Sun, and Ms. Rui Chin Ong for their valuable suggestions.

Bright high-colour-purity deep-blue carbon dot light-emitting diodes via efficient edge amination

Abstract: Deep-blue light-emitting diodes (LEDs) (emitting at wavelengths of less than 450 nm) are important for solid-state lighting, vivid displays and high-density information storage. Colloidal quantum dots, typically based on heavy metals such as cadmium and lead, are promising candidates for deep-blue LEDs, but these have so far had external quantum efficiencies lower than 1.7%. Here we present deep-blue light-emitting materials and devices based on carbon dots. The carbon dots produce emission with a narrow full-width at half-maximum (about 35 nm) with high photoluminescence quantum yield (70% ± 10%) and a colour coordinate (0.15, 0.05) closely approaching the standard colour Rec. 2020 (0.131, 0.046) specification. Structural and optical characterization, together with computational studies, reveal that amine-based passivation accounts for the efficient and high-colour-purity emission. Deep-blue LEDs based on these carbon dots display high performance with a maximum luminance of 5,240 cd m−2 and an external quantum efficiency of 4%, notably exceeding that of previously reported quantum-tuned solution-processed deep-blue LEDs. Deep-blue high-colour-purity light-emitting materials are developed by using amine-based edge passivation. The light-emitting diodes based on the carbon dots exhibit a maximum luminance of 5,240 cd m–2 and an external quantum efficiency of 4%.

Gold Doping of Silver Nanoclusters: A 26‐Fold Enhancement in the Luminescence Quantum Yield

Abstract: A high quantum yield (QY) of photoluminescence (PL) in nanomaterials is necessary for a wide range of applications. Unfortunately, the weak PL and moderate stability of atomically precise silver nanoclusters (NCs) suppress their utility. Herein, we accomplished a ≥26-fold PL QY enhancement of the Ag29(BDT)12(TPP)4 cluster (BDT: 1,3-benzenedithiol; TPP: triphenylphosphine) by doping with a discrete number of Au atoms, producing Ag29−xAux(BDT)12(TPP)4, x=1–5. The Au-doped clusters exhibit an enhanced stability and an intense red emission around 660 nm. Single-crystal XRD, mass spectrometry, optical, and NMR spectroscopy shed light on the PL enhancement mechanism and the probable locations of the Au dopants within the cluster.

Multi-Task Learning as Multi-Objective Optimization

Abstract: In multi-task learning, multiple tasks are solved jointly, sharing inductive bias between them Multi-task learning is inherently a multi-objective problem because different tasks may conflict, necessitating a trade-off A common compromise is to optimize a proxy objective that minimizes a weighted linear combination of per-task losses However, this workaround is only valid when the tasks do not compete, which is rarely the case In this paper, we explicitly cast multi-task learning as multi-objective optimization, with the overall objective of finding a Pareto optimal solution To this end, we use algorithms developed in the gradient-based multi-objective optimization literature These algorithms are not directly applicable to large-scale learning problems since they scale poorly with the dimensionality of the gradients and the number of tasks We therefore propose an upper bound for the multi-objective loss and show that it can be optimized efficiently We further prove that optimizing this upper bound yields a Pareto optimal solution under realistic assumptions We apply our method to a variety of multi-task deep learning problems including digit classification, scene understanding (joint semantic segmentation, instance segmentation, and depth estimation), and multi-label classification Our method produces higher-performing models than recent multi-task learning formulations or per-task training

An Air‐Stable Copper Reagent for Nucleophilic Trifluoromethylthiolation of Aryl Halides

Abstract: We thank Prof. John Hartwig for useful discussions. The financial support from National Natural Science Foundation of China (21072030) and Fuzhou University (022318) to Z.W. and KAUST to K.-W.H. are acknowledged.