Now showing items 1-20 of 24271

• #### Expansion Planning for Renewable Integration in Power System of Regions with Very High Solar Irradiation

(Journal of Modern Power Systems and Clean Energy, Journal of Modern Power Systems and Clean Energy, 2021-09-15) [Article]
In this paper, we address the long-term generation and transmission expansion planning for power systems of regions with very high solar irradiation. We target the power systems that currently rely mainly on thermal generators and that aim to adopt high shares of renewable sources. We propose a stochastic programming model with expansion alternatives including transmission lines, solar power plants (photovoltaic and concentrated solar), wind farms, energy storage, and flexible combined cycle gas turbines. The model represents the longterm uncertainty to characterize the demand growth, and the short-term uncertainty to characterize daily solar, wind, and demand patterns. We use the Saudi Arabian power system to illustrate the functioning of the proposed model for several cases with different renewable integration targets. The results show that a strong dependence on solar power for high shares of renewable sources requires high generation capacity and storage to meet the night demand.
• #### Influence of the anionic ligands on properties and reactivity of Hoveyda-Grubbs catalysts

(Molecular Catalysis, Elsevier BV, 2021-06-26) [Article]
Ruthenium based catalysts remain among the more successful complexes used in the catalysis of metathesis processes for the synthesis of new carbon-carbon bonds. The investigation of the influence of the different system moieties on its catalytic performance has led to important improvements in the field. To this extent, density functional theory (DFT) calculations have contributed significantly providing fundamental understandings to develop new catalysts. With this aim, we presented here a detailed computational study of how the nature of the anion ligand binding to the metal affects the global properties and reactivity of the catalyst. Geometric, energetic and electronic analysis have been performed to reach the key insights necessary to build structure-performance correlations.
• #### Synthesis of Chitosan-La2O3 Nanocomposite and Its Utility as a Powerful Catalyst in the Synthesis of Pyridines and Pyrazoles

(Molecules, MDPI AG, 2021-06-17) [Article]
Recently, the development of nanocatalysts based on naturally occurring polysaccharides has received a lot of attention. Chitosan (CS), as a biodegradable and biocompatible polysaccharide, is considered to be an excellent template for the design of a hybrid biopolymer-based metal oxide nanocomposite. In this case, lanthanum oxide nanoparticles doped with chitosan at different weight percentages (5, 10, 15, and 20 wt% CS/La2O3) were prepared via a simple solution casting method. The prepared CS/La2O3 nanocomposite solutions were cast in a Petri dish in order to produce the developed catalyst, which was shaped as a thin film. The structural features of the hybrid nanocomposite film were studied by FTIR, SEM, and XRD analytical tools. FTIR spectra confirmed the presence of the major characteristic peaks of chitosan, which were modified by interaction with La2O3 nanoparticles. Additionally, SEM graphs showed dramatic morphological changes on the surface of chitosan, which is attributed to surface adsorption with La2O3 molecules. The prepared CS/La2O3 nanocomposite film (15% by weight) was investigated as an effective, recyclable, and heterogeneous base catalyst in the synthesis of pyridines and pyrazoles. The nanocomposite used was sufficiently stable and was collected and reused more than three times without loss of catalytic activity.
• #### Uncertainty Quantification and Bayesian Inference of Cloud Parameterization in the NCAR Single Column Community Atmosphere Model (SCAM6)

(Frontiers in Climate, Frontiers, 2021-06-16) [Article]
Uncertainty quantification (UQ) in weather and climate models is required to assess the sensitivity of their outputs to various parameterization schemes and thereby improve their consistency with observations. Herein, we present an efficient UQ and Bayesian inference for the cloud parameters of the NCAR Single Column Atmosphere Model (SCAM6) using surrogate models based on a polynomial chaos expansion. The use of a surrogate model enables to efficiently propagate uncertainties in parameters into uncertainties in model outputs. We investigated eight uncertain parameters: the auto-conversion size threshold for ice to snow (dcs), the fall speed parameter for stratiform cloud ice (ai), the fall speed parameter for stratiform snow (as), the fall speed parameter for cloud water (ac), the collection efficiency of aggregation ice (eii), the efficiency factor of the Bergeron effect (berg_eff), the threshold maximum relative humidity for ice clouds (rhmaxi), and the threshold minimum relative humidity for ice clouds (rhmini). We built two surrogate models using two non-intrusive methods: spectral projection (SP) and basis pursuit denoising (BPDN). Our results suggest that BPDN performs better than SP as it enables to filter out internal noise during the process of fitting the surrogate model. Five out of the eight parameters (namely dcs, ai, rhmaxi, rhmini, and eii) account for most of the variance in predicted climate variables (e.g., total precipitation, cloud distribution, shortwave and longwave cloud forcing, ice and liquid water path). A first-order sensitivity analysis reveals that dcs contributes approximately 40–80% of the total variance of the climate variables, ai around 15–30%, and rhmaxi, rhmini, and eii around 5–15%. The second- and higher-order effects contribute approximately 20% and 11%, respectively. The sensitivity of the model to these parameters was further explored using response curves. A Markov chain Monte Carlo (MCMC) sampling algorithm was also implemented for the Bayesian inference of dcs, ai, as, rhmini, and berg_eff using cloud distribution data collected at the Southern Great Plains (USA). Our study has implications for enhancing our understanding of the physical mechanisms associated with cloud processes leading to uncertainty in model simulations and further helps to improve the models used for their assessment.
• #### Quality Evaluation of Epoxy Pore Casts Using Silicon Micromodels: Application to Confocal Imaging of Carbonate Samples

(Applied Sciences, MDPI AG, 2021-06-16) [Article]
Pore casting refers to filling the void spaces of porous materials with an extraneous fluid, usually epoxy resin, to obtain a high-strength composite material, stabilize a fragile porous structure, produce a three-dimensional replica of the pore space, or provide imaging contrast. Epoxy pore casting may be accompanied by additional procedures, such as etching, in which the material matrix is dissolved, leaving a clean cast. Moreover, an epoxy resin may be mixed with fluorophore substances to allow fluorescence imaging. Our work aims to investigate and optimize the epoxy pore casting procedure parameters, for example, impregnation pressure. We use silicon micromodels as a reference to validate the key parameters of high-pressure resin impregnation. We demonstrate possible artifacts and defects that might develop during impregnation with resin, e.g., resin shrinkage and gas trapping. In the end, we developed an optimized protocol to produce high-quality resin pore casts for high-resolution 3D imaging and the description of microporosity in micritic carbonates. In our applications, the high-quality pore casts were acid-etched to remove the non-transparent carbonate material, making the pore casts suitable for imaging with Confocal Laser Scanning Microscopy (CLSM). In addition, we evaluate the quality of our etching procedure using micro-computed tomography (micro-CT) scans of the pre- and post-etched samples and demonstrate that the etched epoxy pore casts represent the pore space of microporous carbonate rock samples with high fidelity.
• #### Synthesis, Structural Studies, and Anticancer Properties of [CuBr(PPh3)2(4,6-Dimethyl-2-Thiopyrimidine-κS]

(Crystals, MDPI AG, 2021-06-16) [Article]
CuBr(PPh3)2(4,6-dimethylpyrimidine-2-thione) (Cu-L) was synthesized by stirring CuBr(PPh3)3 and 4,6-dimethylpyrimidine-2-thione in dichloromethane. The crystal structure of Cu-L was obtained, and indicated that the complex adopts a distorted tetrahedral structure with several intramolecular hydrogen bonds. Moreover, a centrosymmetric dimer is formed by the intermolecular hydrogen bonding of the bromine acceptor created by symmetry operation 1−x, 1−y, 1−z to the methyl group (D3 = C42) of the pyrimidine–thione ligand. HSA-binding of Cu-L and its ligand were evaluated, revealing that Cu-L binds to HSA differently than its ligand. The HSA-bindings were modeled by molecular docking, which suggested that Cu-L binds to the II A domain while L binds between the I B and II A domains. Anticancer activities toward OVCAR-3 and HeLa cell lines were tested and indicated the significance of the copper center in enhancing the cytotoxic effect; negligible toxicities for L and Cu-L were observed towards a non-cancer cell line. The current study highlights the potential of copper(I)-phosphine complexes containing thione ligands as therapeutic agents.
• #### Integrated solar-driven PV cooling and seawater desalination with zero liquid discharge

(Joule, Elsevier BV, 2021-06-16) [Article]
Utilizing the ‘‘waste heat’’ of solar cells for desalination enables the simultaneous production of freshwater and electricity and represents low barrier-of-entry electricity and freshwater supplies to off-grid communities for point of consumption. Herein, guided by theoretical modeling, this project demonstrated that a higher freshwater production rate and a lower solar cell temperature could be achieved simultaneously. With a five-stage photovoltaics-membrane distillation-evaporative crystallizer (PME), we experimentally demonstrated a high and stable freshwater production rate of 2.45 kg m2 h1 and a reduced solar cell temperature of 47 C under 1 sun irradiation, as compared to 62 C of the same solar cell working alone. The reduced solar cell temperature led to an 8% increase in its electricity production. Moreover, the concentrated brine produced in the process was fully evaporated by the underlying evaporative crystallizer, achieving zero liquid discharge. We expect that our work will have important implications for the understanding and advancement of solar distillation.
• #### Molecular Doping Directed by a Neutral Radical

(ACS Applied Materials & Interfaces, American Chemical Society (ACS), 2021-06-16) [Article]
Molecular doping makes possible tunable electronic properties of organic semiconductors, yet a lack of control of the doping process narrows its scope for advancing organic electronics. Here, we demonstrate that the molecular doping process can be improved by introducing a neutral radical molecule, namely nitroxyl radical (2,2,6,6-teramethylpiperidin-i-yl) oxyl (TEMPO). Fullerene derivatives are used as the host and 1,3-dimethyl-2-phenyl-2,3-dihydro-1H-benzo[d]imidazoles (DMBI-H) as the n-type dopant. TEMPO can abstract a hydrogen atom from DMBI-H and transform the latter into a much stronger reducing agent DMBI•, which efficiently dopes the fullerene derivative to yield an electrical conductivity of 4.4 S cm–1. However, without TEMPO, the fullerene derivative is only weakly doped likely by a hydride transfer following by an inefficient electron transfer. This work unambiguously identifies the doping pathway in fullerene derivative/DMBI-H systems in the presence of TEMPO as the transfer of a hydrogen atom accompanied by electron transfer. In the absence of TEMPO, the doping process inevitably leads to the formation of less symmetrical hydrogenated fullerene derivative anions or radicals, which adversely affect the molecular packing. By adding TEMPO we can exclude the formation of such species and, thus, improve charge transport. In addition, a lower temperature is sufficient to meet an efficient doping process in the presence of TEMPO. Thereby, we provide an extra control of the doping process, enabling enhanced thermoelectric performance at a low processing temperature.
• #### Concurrent cationic and anionic perovskite defect passivation enables 27.4% perovskite/silicon tandems with suppression of halide segregation

(Joule, Elsevier BV, 2021-06-16) [Article]
Stable and efficient perovskite/silicon tandem solar cells require defect passivation and suppression of light-induced phase segregation of the wide-band-gap perovskite. Here, we report how molecules containing both electron-rich and electron-poor moieties, such as phenformin hydrochloride (PhenHCl), can satisfy both requirements, independent of the perovskite’s surface chemical composition and its grain boundaries and interfaces. PhenHClpassivated wide-band-gap ( 1.68 eV) perovskite p-i-n single-junction solar cells deliver an open-circuit voltage (VOC) 100 mV higher than control devices, resulting in power conversion efficiencies (PCEs) up to 20.5%. These devices do not show any VOC losses after more than 3,000 h of thermal stress at 85C in a nitrogen ambient. Moreover, PhenHCl passivation improves the PCE of textured perovskite/silicon tandem solar cells from 25.4% to 27.4%. Our findings provide critical insights for improved passivation of metal halide perovskite surfaces and the fabrication of highly efficient and stable perovskite-based single-junction and tandem solar cells.
• #### Tumor-Associated-Macrophage-Membrane-Coated Nanoparticles for Improved Photodynamic Immunotherapy

(Nano Letters, American Chemical Society (ACS), 2021-06-16) [Article]
Cell-membrane-coated nanoparticles have emerged as a promising antitumor therapeutic strategy. However, the immunologic mechanism remains elusive, and there are still crucial issues to be addressed including tumor-homing capacity, immune incompatibility, and immunogenicity. Here, we reported a tumor-associated macrophage membrane (TAMM) derived from the primary tumor with unique antigen-homing affinity capacity and immune compatibility. TAMM could deplete the CSF1 secreted by tumor cells in the tumor microenvironment (TME), blocking the interaction between TAM and cancer cells. Especially, after coating TAMM to upconversion nanoparticle with conjugated photosensitizer (NPR@TAMM), NPR@TAMM-mediated photodynamic immunotherapy switched the activation of macrophages from an immunosuppressive M2-like phenotype to a more inflammatory M1-like state, induced immunogenic cell death, and consequently enhanced the antitumor immunity efficiency via activation of antigen-presenting cells to stimulate the production of tumor-specific effector T cells in metastatic tumors. This TAM-membrane-based photodynamic immunotherapy approach offers a new strategy for personalized tumor therapy.
• #### Latitudinal variation in monthly-scale reproductive synchrony among Acropora coral assemblages in the Indo-Pacific

(Coral Reefs, Springer Science and Business Media LLC, 2021-06-15) [Article]
Early research into coral reproductive biology suggested that spawning synchrony was driven by variations in the amplitude of environmental variables that are correlated with latitude, with synchrony predicted to break down at lower latitudes. More recent research has revealed that synchronous spawning, both within and among species, is a feature of all speciose coral assemblages, including equatorial reefs. Nonetheless, considerable variation in reproductive synchrony exists among locations and the hypothesis that the extent of spawning synchrony is correlated with latitude has not been formally tested on a large scale. Here, we use data from 90 sites throughout the Indo-Pacific and a quantitative index of reproductive synchrony applied at a monthly scale to demonstrate that, despite considerable spatial and temporal variation, there is no correlation between latitude and reproductive synchrony. Considering the critical role that successful reproduction plays in the persistence and recovery of coral reefs, research is urgently needed to understand the drivers underpinning variation in reproductive synchrony
• #### Latitudinal variation in monthly-scale reproductive synchrony among Acropora coral assemblages in the Indo-Pacific

(Coral Reefs, Springer Science and Business Media LLC, 2021-06-15) [Article]
Early research into coral reproductive biology suggested that spawning synchrony was driven by variations in the amplitude of environmental variables that are correlated with latitude, with synchrony predicted to break down at lower latitudes. More recent research has revealed that synchronous spawning, both within and among species, is a feature of all speciose coral assemblages, including equatorial reefs. Nonetheless, considerable variation in reproductive synchrony exists among locations and the hypothesis that the extent of spawning synchrony is correlated with latitude has not been formally tested on a large scale. Here, we use data from 90 sites throughout the Indo-Pacific and a quantitative index of reproductive synchrony applied at a monthly scale to demonstrate that, despite considerable spatial and temporal variation, there is no correlation between latitude and reproductive synchrony. Considering the critical role that successful reproduction plays in the persistence and recovery of coral reefs, research is urgently needed to understand the drivers underpinning variation in reproductive synchrony
• #### Evaluation of minerals being deposited in the Red Sea using gravimetric, size distribution, and mineralogical analysis of dust deposition samples collected along the Red Sea coastal plain

(Aeolian Research, Elsevier BV, 2021-06-15) [Article]
The effect of atmospheric dust on the Earth's climate and air quality is especially severe in the major dust-source regions of the globe, such as the Arabian Peninsula. To better quantify the impact of dust over this region, we established the dust deposition measurement sites at King Abdullah University of Science and Technology (KAUST) and an AErosol RObotic NETwork (AERONET) station. We measured and analyzed dust deposition for 61 months from 2014 to 2019, totaling 442 samples, in 6 different locations on the KAUST campus (22.3 N; 39.1E). The analyses include gravimetric measurements, X-Ray Diffraction (XRD) mineral analyses, and particle size distribution measurements. The intercomparisons of the samples collected from different sampling sites show that the dust deposition rates on campus are spatially uniform. Particle size and mass measurements of deposition dust samples are found to be uncorrelated with the concurrent AERONET measurements. Deposition sample sieving (D < 56 µm), applied since May 2019, make the measurements more consistent but do not significantly affect particles' size distribution with diameters D < 20 μm. Large particles with D > 20 µm are typically of local origin, since they deposit quickly. The annual mean deposition rate is about 11 g m-2 mo-1, with higher spring and fall rates and reduced rates in summer. The mineralogical analysis shows an abundance of quartz and feldspar with lesser amounts of micas, gypsum, clays, carbonate, halite, and iron oxides. Gypsum traces are probably produced either in the atmosphere or in the deposited sample by the reaction between carbonates and sulfur dioxide. The deposition of dust particles with D < 20 µm in the Red Sea totals 8.6 Mt annually. This comprises 1.05 Mt of quartz, 0.88 Mt of feldspars, 0.22 Mt of carbonates, 1.39 Mt of clays, and 0.06 Mt of hematite, which plays a vital role in maintaining the Red Sea nutrient balance.
• #### A self-adaptive deep learning algorithm for intelligent natural gas pipeline control

(Energy Reports, Elsevier BV, 2021-06-15) [Article]
Natural gas has been recognized as a promising energy supply for modern society due to its relatively less air pollution in consumption, while pipeline transportation is preferred especially for long-distance transmissions. A simplified pipeline control scenario is proposed in this paper to deeply accelerate the management and decision process in pipeline dispatch, in which a direct relevance between compressor operations and the inlet flux at certain stations is established as the main dispatch logic. A deep neural network is designed with specific input and output features for this scenario and the hyper-parameters are carefully tuned for a better adaptability of this problem. The realistic operation data of two pipelines have been obtained and prepared for learning and testing. The proposed algorithm with the optimized network structure is proved to be effective and reliable in predicting the pipeline operation status, under both the normal operation conditions and abnormal situations. The successful definition of "ghost compressors" make this algorithm to be the first self-adaptive deep learning algorithm to assist natural gas pipeline intelligent control.
• #### Klarigi: Explanations for Semantic Groupings

(Cold Spring Harbor Laboratory, 2021-06-15) [Preprint]
Semantic annotation facilitates the use of background knowledge in analysis. This includes approaches that sort entities into groups, clusters, or assign labels or outcomes that are typically difficult to derive semantic explanations for. We introduce Klarigi, a tool that creates semantic explanations for groups of entities described by ontology terms implemented in a manner that balances multiple scoring heuristics. We demonstrate Klarigi by using it to identify characteristic terms for text-derived phenotypes of emergency admissions for two frequently conflated diagnoses, pulmonary embolism and pneumonia. Klarigi provides a universal method by which entity groups or labels can be explained semantically, and thus contributes to improved explainability of analysis methods.
• #### Sustained Solar-Powered Electrocatalytic H2 Production by Seawater Splitting Using Two-Dimensional Vanadium Disulfide

(ACS Sustainable Chemistry & Engineering, American Chemical Society (ACS), 2021-06-15) [Article]
Robust and stable electrodes made from earth-abundant materials have gained widespread interest in large-scale electrocatalytic water splitting toward hydrogen energy technologies. In this study, the vanadium disulfide (VS2)/amorphous carbon (AC) heterostructure was employed as an electrode for direct seawater splitting. Two-dimensional VS2 nanoparticles were deposited on AC with a high degree of uniformity via a well-optimized one-step chemical vapor deposition approach. The VS2/AC heterostructure electrode was found to possess rich active sulfur sites, near-zero Gibbs free energy, a large surface area, and exceptional charge transfer toward the electrolyte, resulting in enhanced hydrogen evolution reaction (HER) performance with a low onset potential and low overpotential of 11 and 61 mV (vs reversible hydrogen electrode (RHE)), respectively. The electrode also sustained robust stability throughout the 50 h of chronoamperometry studies under acidic electrolyte conditions. Interestingly, the VS2/AC electrocatalyst accomplished an exceptional HER performance under natural seawater conditions in the absence of an external electrolyte with an onset potential of 56 mV vs RHE and attained η200 at an overpotential of 0.53 V vs RHE. In spite of this, the heterostructure exhibited superior stability over 21 days at a high current density of 250 mA/cm2 under both indoor and solar-powered outdoor conditions. Overall, this VS2/AC heterostructure may open a new pathway toward direct seawater splitting for long-term, stable, large-scale hydrogen generation.
• #### n-Type organic semiconducting polymers: stability limitations, design considerations and applications

(Journal of Materials Chemistry C, Royal Society of Chemistry (RSC), 2021-06-15) [Article]
This review outlines the design strategies which aim to develop high performing n-type materials in the fields of organic thin film transistors (OTFT), organic electrochemical transistors (OECT) and organic thermoelectrics (OTE). Figures of merit for each application and the limitations in obtaining these are set out, and the challenges with achieving consistent and comparable measurements are addressed. We present a thorough discussion of the limitations of n-type materials, particularly their ambient operational instability, and suggest synthetic methods to overcome these. This instability originates from the oxidation of the negative polaron of the organic semiconductor (OSC) by water and oxygen, the potentials of which commonly fall within the electrochemical window of n-type OSCs, and consequently require a LUMO level deeper than ∼−4 eV for a material with ambient stability. Recent high performing n-type materials are detailed for each application and their design principles are discussed to explain how synthetic modifications can enhance performance. This can be achieved through a number of strategies, including utilising an electron deficient acceptor–acceptor backbone repeat unit motif, introducing electron-withdrawing groups or heteroatoms, rigidification and planarisation of the polymer backbone and through increasing the conjugation length. By studying the fundamental synthetic design principles which have been employed to date, this review highlights a path to the development of promising polymers for n-type OSC applications in the future.
• #### Redox-Neutral Cross-Coupling Amination with Weak N-Nucleophiles: Arylation of Anilines, Sulfonamides, Sulfoximines, Carbamates, and Imines via Nickelaelectrocatalysis

(JACS Au, American Chemical Society (ACS), 2021-06-15) [Article]
A nickel-catalyzed cross-coupling amination with weak nitrogen nucleophiles is described. Aryl halides as well as aryl tosylates can be efficiently coupled with a series of weak N-nucleophiles, including anilines, sulfonamides, sulfoximines, carbamates, and imines via concerted paired electrolysis. Notably, electron-deficient anilines and sulfonamides are also suitable substrates. Interestingly, when benzophenone imine is applied in the arylation, the product selectivity toward the formation of amine and imine product can be addressed by a base switch. In addition, the alternating current mode can be successfully applied. DFT calculations support a facilitated reductive elimination pathway.
• #### Machine learning-based conditional mean filter: a generalization of the ensemble Kalman filter for nonlinear data assimilation

(arXiv, 2021-06-15) [Preprint]
Filtering is a data assimilation technique that performs the sequential inference of dynamical systems states from noisy observations. Herein, we propose a machine learning-based ensemble conditional mean filter (ML-EnCMF) for tracking possibly high-dimensional non-Gaussian state models with nonlinear dynamics based on sparse observations. The proposed filtering method is developed based on the conditional expectation and numerically implemented using machine learning (ML) techniques combined with the ensemble method. The contribution of this work is twofold. First, we demonstrate that the ensembles assimilated using the ensemble conditional mean filter (EnCMF) provide an unbiased estimator of the Bayesian posterior mean, and their variance matches the expected conditional variance. Second, we implement the EnCMF using artificial neural networks, which have a significant advantage in representing nonlinear functions over high-dimensional domains such as the conditional mean. Finally, we demonstrate the effectiveness of the ML-EnCMF for tracking the states of Lorenz-63 and Lorenz-96 systems under the chaotic regime. Numerical results show that the ML-EnCMF outperforms the ensemble Kalman filter.
• #### Training Graph Neural Networks with 1000 Layers

(arXiv, 2021-06-14) [Preprint]
Deep graph neural networks (GNNs) have achieved excellent results on various tasks on increasingly large graph datasets with millions of nodes and edges. However, memory complexity has become a major obstacle when training deep GNNs for practical applications due to the immense number of nodes, edges, and intermediate activations. To improve the scalability of GNNs, prior works propose smart graph sampling or partitioning strategies to train GNNs with a smaller set of nodes or sub-graphs. In this work, we study reversible connections, group convolutions, weight tying, and equilibrium models to advance the memory and parameter efficiency of GNNs. We find that reversible connections in combination with deep network architectures enable the training of overparameterized GNNs that significantly outperform existing methods on multiple datasets. Our models RevGNN-Deep (1001 layers with 80 channels each) and RevGNN-Wide (448 layers with 224 channels each) were both trained on a single commodity GPU and achieve an ROC-AUC of $87.74 \pm 0.13$ and $88.14 \pm 0.15$ on the ogbn-proteins dataset. To the best of our knowledge, RevGNN-Deep is the deepest GNN in the literature by one order of magnitude. Please visit our project website https://www.deepgcns.org/arch/gnn1000 for more information.