scholarly journals Organocatalytic Michael Addition to (D)-Mannitol-Derived Enantiopure Nitroalkenes: A Valuable Strategy for the Synthesis of Densely Functionalized Chiral Molecules

Molecules ◽  
2019 ◽  
Vol 24 (24) ◽  
pp. 4588 ◽  
Author(s):  
Lucia Caruso ◽  
Alessandra Puglisi ◽  
Emmerance Gillon ◽  
Maurizio Benaglia
Keyword(s):  
Michael Addition ◽  
Renewable Resources ◽  
Large Scale ◽  
Chiral Molecules ◽  
Flow Conditions ◽  
Dimethyl Malonate ◽  
Enantiomerically Pure ◽  
Keto Esters ◽  
Bicyclic Lactams ◽  
High Stereoselectivity

Carbohydrates are abundant renewable resources and are a feedstock for green chemistry and sustainable synthesis of the future. Among the hexoses and the pentoses present in biomass, mannitol was selected in the present project as a valuable platform, directly available from the chiral pool, to build highly functionalized molecules. Starting from (R)-2,3-O-cyclohexylidene glyceraldehyde, which is easily prepared in a large scale from D-mannitol, an enantiopure chiral nitro alkene was prepared by reaction with nitromethane, and its reactivity studied. Organocatalytic Michael addition of dimethyl malonate, β-keto esters, and other nucleophiles on the nitro alkene afforded high stereoselectivity and densely functionalized chiral molecules, which were further synthetically developed, leading to five-membered lactones and bicyclic lactams. Preliminary studies showed that the metal-free catalytic reaction on the chiral nitro alkene can be performed under continuous flow conditions, thus enabling the use of (micro)mesofluidic systems for the preparation of enantiomerically pure organic molecules from the chiral pool.

Highly Enantioselective Michael Addition of Cyclic Diketones to β,γ-Unsaturated α-Keto Esters Catalyzed by Squaramide Organocatalyst

Synthesis ◽  
2022 ◽  
Author(s):  
Zhi-Wei Ma ◽  
Chuan-Chuan Wang ◽  
Quan-Jian Lv ◽  
Xiao-Pei Chen ◽  
Ai-Qin Li ◽  
...  
Keyword(s):  
Michael Addition ◽  
Large Scale ◽  
Catalyst System ◽  
Catalyst Loading ◽  
Reaction Conditions ◽  
Keto Esters ◽  
Bicyclic Compounds ◽  
Asymmetric Michael Addition ◽  
High Yields ◽  
Low Catalyst Loading

AbstractA new tertiary amine-squaramide organocatalyst has been developed and applied to the asymmetric Michael addition of cyclic diketones to β,γ-unsaturated α-keto esters. The catalyst system performed well with a low catalyst loading of 1 mol% under mild reaction conditions. A series of synthetically and pharmaceutically useful chiral bicyclic compounds were obtained in high yields (up to 97%) with excellent enantioselectivities (up to 99 % ee). Furthermore, this catalytic system can be used efficiently in large-scale reactions with the yields and enantioselectivities being maintained.

P-Chiral 2-{1'-[Butyl(phenyl)phosphanyl]ferrocen-1-yl}-4-isopropyl-4,5-dihydrooxazoles: A Second Chirality Center in Catalytic System

2005 ◽  
Vol 70 (3) ◽  
pp. 361-369 ◽  
Author(s):  
Dušan Drahoňovský ◽  
Petr Štěpnička ◽  
Dalimil Dvořák
Keyword(s):  
Catalytic System ◽  
Asymmetric Induction ◽  
Dimethyl Malonate ◽  
Enantiomerically Pure

P-Chiral (S,RP)-2-{1'-[butyl(phenyl)phosphanyl]ferrocen-1-yl}-4-isopropyl-4,5-dihydrooxazole (6) and (S,SP)-2-{1'-[butyl(phenyl)phosphanyl]ferrocen-1-yl}-4-isopropyl-4,5-dihydrooxazole (7) were prepared by the procedure developed by Jugé, starting from enantiomerically pure (-)- or (+)-ephedrine and dichloro(phenyl)phosphine. Compounds 6 and 7 were examined for asymmetric induction in the Pd-catalyzed reaction of rac-1,3-diphenylallyl acetate with dimethyl malonate. The best results were obtained with 7 (98% ee), while 6 gave 82% ee.

Synthesis of Enantiomerically Pure Bicyclic Lactams

Synlett ◽  
1999 ◽  
Vol 1999 (7) ◽  
pp. 1127-1129 ◽  
Author(s):  
Nicole Diedrichs ◽  
Bernhard Westermann
Keyword(s):  
Enantiomerically Pure ◽  
Bicyclic Lactams

Investigation on hydrodynamic forces leading to coarse particle entrainment using Large-Eddy Simulations

2021 ◽  
Author(s):  
Gaston Latessa ◽  
Angela Busse ◽  
Manousos Valyrakis
Keyword(s):  
Experimental Data ◽  
Flow Field ◽  
Large Scale ◽  
Large Eddy Simulations ◽  
Flow Conditions ◽  
Mean Flow ◽  
Protection Measures ◽  
Practical Applications ◽  
Particle Entrainment ◽  
Large Eddy

<p>The prediction of particle motion in a fluid flow environment presents several challenges from the quantification of the forces exerted by the fluid onto the solids -normally with fluctuating behaviour due to turbulence- and the definition of the potential particle entrainment from these actions. An accurate description of these phenomena has many practical applications in local scour definition and to the design of protection measures.</p><p>In the present work, the actions of different flow conditions on sediment particles is investigated with the aim to translate these effects into particle entrainment identification through analytical solid dynamic equations.</p><p>Large Eddy Simulations (LES) are an increasingly practical tool that provide an accurate representation of both the mean flow field and the large-scale turbulent fluctuations. For the present case, the forces exerted by the flow are integrated over the surface of a stationary particle in the streamwise (drag) and vertical (lift) directions, together with the torques around the particle’s centre of mass. These forces are validated against experimental data under the same bed and flow conditions.</p><p>The forces are then compared against threshold values, obtained through theoretical equations of simple motions such as rolling without sliding. Thus, the frequency of entrainment is related to the different flow conditions in good agreement with results from experimental sediment entrainment research.</p><p>A thorough monitoring of the velocity flow field on several locations is carried out to determine the relationships between velocity time series at several locations around the particle and the forces acting on its surface. These results a relevant to determine ideal locations for flow investigation both in numerical and physical experiments.</p><p>Through numerical experiments, a large number of flow conditions were simulated obtaining a full set of actions over a fixed particle sitting on a smooth bed. These actions were translated into potential particle entrainment events and validated against experimental data. Future work will present the coupling of these LES models with Discrete Element Method (DEM) models to verify the entrainment phenomena entirely from a numerical perspective.</p>

scholarly journals Large-eddy simulation of airborne wind energy farms

2021 ◽  
Author(s):  
Thomas Haas ◽  
Jochem De Schutter ◽  
Moritz Diehl ◽  
Johan Meyers
Keyword(s):  
Power Generation ◽  
Wind Energy ◽  
Large Scale ◽  
Flow Conditions ◽  
Model Predictive Controller ◽  
Large Eddy ◽  
Power Yield ◽  
Predictive Controller ◽  
Utility Scale ◽  
Turbulent Flow Conditions

Abstract. The future utility-scale deployment of airborne wind energy technologies requires the development of large-scale multi-megawatt systems. This study aims at quantifying the interaction between the atmospheric boundary layer (ABL) and large-scale airborne wind energy systems operating in a farm. To that end, we present a virtual flight simulator combining large-eddy simulations to simulate turbulent flow conditions and optimal control techniques for flight-path generation and tracking. The two-way coupling between flow and system dynamics is achieved by implementing an actuator sector method that we pair to a model predictive controller. In this study, we consider ground-based power generation pumping-mode AWE systems (lift-mode AWES) and on-board power generation AWE systems (drag-mode AWES). For the lift-mode AWES, we additionally investigate different reel-out strategies to reduce the interaction between the tethered wing and its own wake. Further, we investigate AWE parks consisting of 25 systems organized in 5 rows of 5 systems. For both lift- and drag-mode archetypes, we consider a moderate park layout with a power density of 10 MW km−2 achieved at a rated wind speed of 12 m s−1. For the drag-mode AWES, an additional park with denser layout and power density of 28 MW km−2 is also considered. The model predictive controller achieves very satisfactory flight-path tracking despite the AWE systems operating in fully waked, turbulent flow conditions. Furthermore, we observe significant wake effects for the utility-scale AWE systems considered in the study. Wake-induced performance losses increase gradually through the downstream rows of systems and reach in the last row of the parks up to 17 % for the lift-mode AWE park and up to 25 % and 45 % for the moderate and dense drag-mode AWE parks, respectively. For an operation period of 60 minutes at a below-rated reference wind speed of 10 m s−1, the lift-mode AWE park generates about 84.4 MW of power, corresponding to 82.5 % of the power yield expected when AWE systems operate ideally and interaction with the ABL is negligible. For the drag-mode AWE parks, the moderate and dense layouts generate about 86.0 MW and 72.9 MW of power, respectively, corresponding to 89.2 % and 75.6 % of the ideal power yield.

scholarly journals Day-ahead renewable scenario forecasts based on generative adversarial networks

2020 ◽  
Author(s):  
Congmei Jiang ◽  
Yongfang Mao ◽  
Yi Chai ◽  
Mingbiao Yu
Keyword(s):  
Power Systems ◽  
Renewable Resources ◽  
Large Scale ◽  
Generative Adversarial Networks ◽  
Power Sources ◽  
Renewable Power ◽  
Adversarial Networks ◽  
Future Behavior ◽  
Large Scale Integration ◽  
Scale Integration

<p>With the increasing penetration of renewable resources such as wind and solar, the operation and planning of power systems, especially in terms of large-scale integration, are faced with great risks due to the inherent stochasticity of natural resources. Although this uncertainty can be anticipated, the timing, magnitude, and duration of fluctuations cannot be predicted accurately. In addition, the outputs of renewable power sources are correlated in space and time, and this brings further challenges for predicting the characteristics of their future behavior. To address these issues, this paper describes an unsupervised method for renewable scenario forecasts that considers spatiotemporal correlations based on generative adversarial networks (GANs), which have been shown to generate high-quality samples. We first utilized an improved GAN to learn unknown data distributions and model the dynamic processes of renewable resources. We then generated a large number of forecasted scenarios using stochastic constrained optimization. For validation, we used power-generation data from the National Renewable Energy Laboratory wind and solar integration datasets. The experimental results validated the effectiveness of our proposed method and indicated that it has significant potential in renewable scenario analysis.</p>

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