Exploration of flow reaction conditions using machine-learning for enantioselective organocatalyzed Rauhut–Currier and [3+2] annulation sequence

2020 ◽  
Vol 56 (8) ◽  
pp. 1259-1262 ◽  
Author(s):  
Masaru Kondo ◽  
H. D. P. Wathsala ◽  
Makoto Sako ◽  
Yutaro Hanatani ◽  
Kazunori Ishikawa ◽  
...  
Keyword(s):  
Machine Learning ◽  
Flow System ◽  
Reaction Conditions ◽  
Flow Reaction

A highly atom-economical enantioselective Rauhut–Currier and [3+2] annulation has been established by flow system and machine-learning-assisted exploration of suitable conditions.

scholarly journals Correction: Exploration of flow reaction conditions using machine-learning for enantioselective organocatalyzed Rauhut–Currier and [3+2] annulation sequence

2020 ◽  
Vol 56 (81) ◽  
pp. 12256-12256
Author(s):  
Masaru Kondo ◽  
H. D. P. Wathsala ◽  
Makoto Sako ◽  
Yutaro Hanatani ◽  
Kazunori Ishikawa ◽  
...  
Keyword(s):  
Machine Learning ◽  
Reaction Conditions ◽  
Flow Reaction

Correction for ‘Exploration of flow reaction conditions using machine-learning for enantioselective organocatalyzed Rauhut–Currier and [3+2] annulation sequence’ by Masaru Kondo et al., Chem. Commun., 2020, 56, 1259–1262, DOI: 10.1039/C9CC08526B.

scholarly journals 5-Hydroxymethylfurfural Hydrodeoxygenation to 2,5-Dimethylfuran in Continuous-Flow System over Ni on Nitrogen-Doped Carbon

2020 ◽  
Vol 1 (2) ◽  
pp. 106-115 ◽  
Author(s):  
Francesco Brandi ◽  
Marius Bäumel ◽  
Irina Shekova ◽  
Valerio Molinari ◽  
Majd Al-Naji
Keyword(s):  
Continuous Flow ◽  
Noble Metals ◽  
Flow System ◽  
High Catalytic Activity ◽  
Reaction Conditions ◽  
Transportation Fuels ◽  
Nitrogen Doped ◽  
Continuous Flow System ◽  
Alternative Feedstock ◽  
Nitrogen Doped Carbon

Waste lignocellulosic biomass is sustainable and an alternative feedstock to fossil resources. Among the lignocellulosic derived compounds, 2,5-dimethylfuran (DMF) is a promising building block for chemicals, e.g., p-xylene, and a valuable biofuel. DMF can be obtained from 5-hydroxymethylfurfural (HMF) via catalytic deoxygenation using non-noble metals such as Ni in the presence of H2. Herein, we present the synthesis of DMF from HMF using 35 wt.% Ni on nitrogen-doped carbon pellets (35Ni/NDC) as a catalyst in a continuous flow system. The conversion of HMF to DMF was studied at different hydrogen pressures, reaction temperatures, and space times. At the best reaction conditions, i.e., 423 K, 8.0 MPa, and space time 6.4 kgNi h kgHMF−1, the 35Ni/NDC catalyst exhibited high catalytic activity with HMF conversion of 99 mol% and 80 mol% of DMF. These findings can potentially contribute to the transition toward the production of sustainable fine chemicals and liquid transportation fuels.

Highly Selective Hydrogenative Conversion to the Tertiary, Secondary, and Primary Amines of Nitriles under Flow Reaction Conditions

ChemSusChem ◽  
2021 ◽  
Author(s):  
Tsuyoshi Yamada ◽  
Kwihwan Park ◽  
Chikara Furugen ◽  
Jing Jiang ◽  
Eisho Shimizu ◽  
...  
Keyword(s):  
Primary Amines ◽  
Reaction Conditions ◽  
Flow Reaction

scholarly journals Reaction Condition Optimization for Non-Oxidative Conversion of Methane Using Artificial Intelligence

2020 ◽  
Author(s):  
Hyun Woo Kim ◽  
Sung Woo Lee ◽  
Gyoung S. Na ◽  
Seung Ju Han ◽  
Seok Ki Kim ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Chemical Reaction ◽  
Coke Formation ◽  
Oxidative Conversion ◽  
Reaction Conditions ◽  
Condition Optimization ◽  
Controllable Factors ◽  
Conversion Of Methane ◽  
Oxidative Conversion Of Methane

Chemical reactions typically have numerous controllable factors that need to be optimized to yield the desired products. Although traditional experimental methods are limited to explore possible combinations of these factors, artificial intelligence (AI) can provide the optimal solution based on chemical reaction data. In this study, we optimize the non-oxidative conversion of methane to C<sub>2</sub> compounds using AI, such as machine learning (ML) to predict experimental results and metaheuristics to optimize reaction conditions. A decision tree-based machine learning method can reasonably predict the reaction outcomes (CH<sub>4</sub> conversion, C<sub>2</sub> yield, and selectivities for C<sub>2</sub> and coke) with an error of < 5%. Trained ML models are applied to maximize the C<sub>2</sub> yield by optimizing the reaction parameters with metaheuristics. We can simultaneously enhance the C<sub>2</sub> yield and suppress the coke formation by improving the multi-objective function for the optimization. We believe that our method will be helpful to optimize the chemical reaction conditions with multiple targets.<br>

scholarly journals Reaction Condition Optimization for Non-Oxidative Conversion of Methane Using Artificial Intelligence

2020 ◽  
Author(s):  
Hyun Woo Kim ◽  
Sung Woo Lee ◽  
Gyoung S. Na ◽  
Seung Ju Han ◽  
Seok Ki Kim ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Chemical Reaction ◽  
Coke Formation ◽  
Oxidative Conversion ◽  
Reaction Conditions ◽  
Condition Optimization ◽  
Controllable Factors ◽  
Conversion Of Methane ◽  
Oxidative Conversion Of Methane

Chemical reactions typically have numerous controllable factors that need to be optimized to yield the desired products. Although traditional experimental methods are limited to explore possible combinations of these factors, artificial intelligence (AI) can provide the optimal solution based on chemical reaction data. In this study, we optimize the non-oxidative conversion of methane to C<sub>2</sub> compounds using AI, such as machine learning (ML) to predict experimental results and metaheuristics to optimize reaction conditions. A decision tree-based machine learning method can reasonably predict the reaction outcomes (CH<sub>4</sub> conversion, C<sub>2</sub> yield, and selectivities for C<sub>2</sub> and coke) with an error of < 5%. Trained ML models are applied to maximize the C<sub>2</sub> yield by optimizing the reaction parameters with metaheuristics. We can simultaneously enhance the C<sub>2</sub> yield and suppress the coke formation by improving the multi-objective function for the optimization. We believe that our method will be helpful to optimize the chemical reaction conditions with multiple targets.<br>

Biomimetic reductive amination under the continuous-flow reaction conditions

2010 ◽  
Vol 131 (2) ◽  
pp. 261-265 ◽  
Author(s):  
Vadim A. Soloshonok ◽  
Hector T. Catt ◽  
Taizo Ono
Keyword(s):  
Continuous Flow ◽  
Reductive Amination ◽  
Reaction Conditions ◽  
Flow Reaction

ChemInform Abstract: Biomimetic Reductive Amination under the Continuous-Flow Reaction Conditions.

ChemInform ◽  
2010 ◽  
Vol 41 (24) ◽  
pp. no-no
Author(s):  
Vadim A. Soloshonok ◽  
Hector T. Catt ◽  
Taizo Ono
Keyword(s):  
Continuous Flow ◽  
Reductive Amination ◽  
Reaction Conditions ◽  
Flow Reaction

scholarly journals Scaling up of continuous-flow, microwave-assisted, organic reactions by varying the size of Pd-functionalized catalytic monoliths

2011 ◽  
Vol 7 ◽  
pp. 1150-1157 ◽  
Author(s):  
Ping He ◽  
Stephen J Haswell ◽  
Paul D I Fletcher ◽  
Stephen M Kelly ◽  
Andrew Mansfield
Keyword(s):  
Flow Reactor ◽  
Flow System ◽  
Operating Conditions ◽  
Microwave Cavity ◽  
Heating Regime ◽  
Reaction Conditions ◽  
Silica Monoliths ◽  
Surface Areas ◽  
Silica Monolith ◽  
Optimal Reaction

A product-scalable, catalytically mediated flow system has been developed to perform Suzuki–Miyaura reactions under a microwave heating regime, in which the volumetric throughput of a Pd-supported silica monolith can be used to increase the quantity of the product without changing the optimal operating conditions. Two silica monoliths (both 3 cm long), with comparable pore diameters and surface areas, were fabricated with diameters of 3.2 and 6.4 mm to give volumetric capacities of 0.205 and 0.790 mL, respectively. The two monoliths were functionalized with a loading of 4.5 wt % Pd and then sealed in heat-shrinkable Teflon® tubing to form a monolithic flow reactor. The Pd-supported silica monolith flow reactor was then placed into the microwave cavity and connected to an HPLC pump and a backpressure regulator to minimize the formation of gas bubbles. The flow rate and microwave power were varied to optimize the reactant contact time and temperature, respectively. Under optimal reaction conditions the quantity of product could be increased from 31 mg per hour to 340 mg per hour simply by changing the volumetric capacity of the monolith.

scholarly journals Flow Giese reaction using cyanoborohydride as a radical mediator

2013 ◽  
Vol 9 ◽  
pp. 1791-1796 ◽  
Author(s):  
Takahide Fukuyama ◽  
Takuji Kawamoto ◽  
Mikako Kobayashi ◽  
Ilhyong Ryu
Keyword(s):  
Residence Time ◽  
Continuous Flow ◽  
Flow System ◽  
Ethyl Acrylate ◽  
Sodium Cyanoborohydride ◽  
Reaction Conditions ◽  
Radical Trap ◽  
Flow Microreactor ◽  
Alkyl Iodides ◽  
Tertiary Alkyl

Tin-free Giese reactions, employing primary, secondary, and tertiary alkyl iodides as radical precursors, ethyl acrylate as a radical trap, and sodium cyanoborohydride as a radical mediator, were examined in a continuous flow system. With the use of an automated flow microreactor, flow reaction conditions for the Giese reaction were quickly optimized, and it was found that a reaction temperature of 70 °C in combination with a residence time of 10–15 minutes gave good yields of the desired addition products.

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