scholarly journals Data-science driven autonomous process optimization

2020 ◽  
Author(s):  
Melodie Christensen ◽  
Lars Yunker ◽  
Folarin Adedeji ◽  
Florian Häse ◽  
Loic Roch ◽  
...  
Keyword(s):  
Process Optimization ◽  
Process Parameters ◽  
Data Science ◽  
Chemical Space ◽  
Molecular Descriptor ◽  
Phosphine Ligands ◽  
High Yield ◽  
Reaction Yield ◽  
Arylboronic Acid ◽  
Autonomous Optimization

<p>Autonomous process optimization involves the human intervention-free exploration of a range of pre-defined process parameters in order to improve responses such as reaction yield and product selectivity. Utilizing off-the-shelf components, we developed a closed-loop system capable of carrying out parallel autonomous process optimization experiments in batch with significantly reduced cycle times. Upon implementation of our system in the autonomous optimization of a palladium-catalyzed stereoselective Suzuki-Miyaura coupling, we found that the definition of a set of meaningful, broad, and unbiased process parameters was the most critical aspect of a successful optimization. In addition, we found that categorical parameters such as phosphine ligand were vital to determining the reaction outcome. To date, categorical parameter selection has relied on chemical intuition, potentially introducing an element of bias into the experimental design. In seeking a systematic method for the selection of a diverse set of phosphine ligands fully representative of the chemical space, we developed a strategy that leveraged computed molecular descriptor clustering analysis. This strategy allowed for the successful autonomous optimization of a stereoselective Suzuki-Miyaura coupling between a vinyl sulfonate and an arylboronic acid to selectively generate the <i>E</i>-product isomer in high yield. </p>

scholarly journals Data-Science Driven Autonomous Process Optimization

2020 ◽  
Author(s):  
Melodie Christensen ◽  
Lars Yunker ◽  
Folarin Adedeji ◽  
Florian Häse ◽  
Loïc Roch ◽  
...  
Keyword(s):  
Process Optimization ◽  
Process Parameters ◽  
Data Science ◽  
Chemical Space ◽  
Molecular Descriptor ◽  
Phosphine Ligands ◽  
High Yield ◽  
Reaction Yield ◽  
Arylboronic Acid ◽  
Autonomous Optimization

Abstract Autonomous process optimization involves the human intervention-free exploration of a range of pre-defined process parameters in order to improve responses such as reaction yield and product selectivity. Utilizing off-the-shelf components, we developed a closed-loop system capable of carrying out parallel autonomous process optimization experiments in batch with significantly reduced cycle times. Upon implementation of our system in the autonomous optimization of a palladium-catalyzed stereoselective Suzuki-Miyaura coupling, we found that the definition of a set of meaningful, broad, and unbiased process parameters was the most critical aspect of a successful optimization. In addition, we found that categorical parameters such as phosphine ligand were vital to determining the reaction outcome. To date, categorical parameter selection has relied on chemical intuition, potentially introducing an element of bias into the experimental design. In seeking a systematic method for the selection of a diverse set of phosphine ligands fully representative of the chemical space, we developed a strategy that leveraged computed molecular descriptor clustering analysis. This strategy allowed for the successful autonomous optimization of a stereoselective Suzuki-Miyaura coupling between a vinyl sulfonate and an arylboronic acid to selectively generate the E-product isomer in high yield.

scholarly journals Data-science driven autonomous process optimization

2020 ◽  
Author(s):  
Melodie Christensen ◽  
Lars Yunker ◽  
Folarin Adedeji ◽  
Florian Häse ◽  
Loic Roch ◽  
...  
Keyword(s):  
Process Optimization ◽  
Process Parameters ◽  
Data Science ◽  
Chemical Space ◽  
Molecular Descriptor ◽  
Phosphine Ligands ◽  
High Yield ◽  
Reaction Yield ◽  
Arylboronic Acid ◽  
Autonomous Optimization

<p>Autonomous process optimization involves the human intervention-free exploration of a range of pre-defined process parameters in order to improve responses such as reaction yield and product selectivity. Utilizing off-the-shelf components, we developed a closed-loop system capable of carrying out parallel autonomous process optimization experiments in batch with significantly reduced cycle times. Upon implementation of our system in the autonomous optimization of a palladium-catalyzed stereoselective Suzuki-Miyaura coupling, we found that the definition of a set of meaningful, broad, and unbiased process parameters was the most critical aspect of a successful optimization. In addition, we found that categorical parameters such as phosphine ligand were vital to determining the reaction outcome. To date, categorical parameter selection has relied on chemical intuition, potentially introducing an element of bias into the experimental design. In seeking a systematic method for the selection of a diverse set of phosphine ligands fully representative of the chemical space, we developed a strategy that leveraged computed molecular descriptor clustering analysis. This strategy allowed for the successful autonomous optimization of a stereoselective Suzuki-Miyaura coupling between a vinyl sulfonate and an arylboronic acid to selectively generate the <i>E</i>-product isomer in high yield. </p>

scholarly journals Data-science driven autonomous process optimization

2020 ◽  
Author(s):  
Melodie Christensen ◽  
Lars Yunker ◽  
Folarin Adedeji ◽  
Florian Häse ◽  
Loic Roch ◽  
...  
Keyword(s):  
Process Optimization ◽  
Process Parameters ◽  
Data Science ◽  
Chemical Space ◽  
Molecular Descriptor ◽  
Phosphine Ligands ◽  
High Yield ◽  
Reaction Yield ◽  
Arylboronic Acid ◽  
Autonomous Optimization

<p>Autonomous process optimization involves the human intervention-free exploration of a range of pre-defined process parameters in order to improve responses such as reaction yield and product selectivity. Utilizing off-the-shelf components, we developed a closed-loop system capable of carrying out parallel autonomous process optimization experiments in batch with significantly reduced cycle times. Upon implementation of our system in the autonomous optimization of a palladium-catalyzed stereoselective Suzuki-Miyaura coupling, we found that the definition of a set of meaningful, broad, and unbiased process parameters was the most critical aspect of a successful optimization. In addition, we found that categorical parameters such as phosphine ligand were vital to determining the reaction outcome. To date, categorical parameter selection has relied on chemical intuition, potentially introducing an element of bias into the experimental design. In seeking a systematic method for the selection of a diverse set of phosphine ligands fully representative of the chemical space, we developed a strategy that leveraged computed molecular descriptor clustering analysis. This strategy allowed for the successful autonomous optimization of a stereoselective Suzuki-Miyaura coupling between a vinyl sulfonate and an arylboronic acid to selectively generate the <i>E</i>-product isomer in high yield. </p>

scholarly journals Data-science driven autonomous process optimization

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Melodie Christensen ◽  
Lars P. E. Yunker ◽  
Folarin Adedeji ◽  
Florian Häse ◽  
Loïc M. Roch ◽  
...  
Keyword(s):  
Process Optimization ◽  
Process Parameters ◽  
Data Science ◽  
Product Yield ◽  
Phosphine Ligands ◽  
High Yield ◽  
Molecular Feature ◽  
Free Exploration ◽  
Definition Of

AbstractAutonomous process optimization involves the human intervention-free exploration of a range process parameters to improve responses such as product yield and selectivity. Utilizing off-the-shelf components, we develop a closed-loop system for carrying out parallel autonomous process optimization experiments in batch. Upon implementation of our system in the optimization of a stereoselective Suzuki-Miyaura coupling, we find that the definition of a set of meaningful, broad, and unbiased process parameters is the most critical aspect of successful optimization. Importantly, we discern that phosphine ligand, a categorical parameter, is vital to determination of the reaction outcome. To date, categorical parameter selection has relied on chemical intuition, potentially introducing bias into the experimental design. In seeking a systematic method for selecting a diverse set of phosphine ligands, we develop a strategy that leverages computed molecular feature clustering. The resulting optimization uncovers conditions to selectively access the desired product isomer in high yield.

High-yield synthesis of CsPbBr3 nanoparticles: Diphenylphosphine as a reducing agent and its effect in Pb-seeding nucleation and growth

2021 ◽  
Author(s):  
Eder Antonio Castillo-Ruiz ◽  
Diana Fabiola Garcia-Gutierrez ◽  
Domingo Ixcóatl Garcia-Gutierrez
Keyword(s):  
Nucleation And Growth ◽  
Reducing Agent ◽  
High Yield ◽  
Reaction Yield ◽  
High Quality ◽  
Stabilizing Agents ◽  
Nucleation Sites ◽  
Nucleation Mechanisms ◽  
Attractive Option ◽  
Mass Increment

Abstract Based on the reported nucleation mechanisms for CsPbX3 and II-VI/IV-VI quantum dots, CsPbBr3 nanoparticles with a high reaction-yield, up to 393% mass-increment, were synthesized by the hot-injection method. The introduction of diphenylphosphine (DPP) as a reducing agent improved nanoparticle nucleation and growth, giving out evidence for Pb-seeding in CsPbBr3 nanoparticles formation. Additionally, a clear influence of the DPP in a CsPbBr3-Cs4PbBr6 incomplete phase transformation was observed, marked by the appearance of several PbBr2 nanoparticles, indicating the need for an improved ratio between the stabilizing agents and the precursors, due to the increased number of nucleation sites produced by the DPP. The resulting CsPbBr3 nanoparticles showed high quality, as they displayed 70%-90% photoluminescence quantum yield (PLQY), narrow size distribution with an average nanoparticle size of ~10 nm and the characteristic cubic morphology reported in previous works. This increment in CsPbBr3 nanoparticles’ reaction yield will contribute to making them a more attractive option for different optoelectronic applications.

scholarly journals Welding Process Optimization of WELDOX960 High Strength Steel

2018 ◽  
Vol 207 ◽  
pp. 04005
Author(s):  
Min Hu
Keyword(s):  
Penetration Depth ◽  
Process Optimization ◽  
Process Parameters ◽  
Welding Speed ◽  
High Strength Steel ◽  
Welding Process ◽  
Welding Current ◽  
High Strength ◽  
Influence Of Process Parameters ◽  
Steel Analysis

This paper studies WELDOX960 high strength steel, analysis of the welding ability of WELDOX960 high strength steel. Analyze the weld ability of WELDOX960 high-strength steel materials, and study the influence of process parameters such as welding current, welding voltage, and welding speed on penetration depth and weld width in the automated welding process. Through this test, the welding process is optimized to ensure the weld quality. The results show that WELDOX960 high-strength steel adopts multi-layer and multi-pass welding to form better welds.

Visible Light-Promoted C–C Bond Formation from Hydroxyaryls in Water

2019 ◽  
Vol 72 (12) ◽  
pp. 978 ◽  
Author(s):  
Dafne Saporito ◽  
Sergio A. Rodriguez ◽  
Maria T. Baumgartner
Keyword(s):  
Visible Light ◽  
Bond Formation ◽  
High Yield ◽  
Reaction Yield ◽  
Halogen Lamp ◽  
One Pot ◽  
Direct Arylation ◽  
Photoinduced Reactions ◽  
Optical Applications ◽  
Very High

An eco-friendly and direct arylation of hydroxyaryls in water using photoinduced reactions with different substrates (1-bromo-2-naphthol, 1-iodo-2-naphthol, N-(2-iodophenyl)acetamide, 5-bromouracil, 2-iodo-N-methylbenzamide, and 2-iodobenzamide) was studied. For example, π-expanded coumarins, compounds with potential optical applications, were synthesized in very high yield, without the use of toxic reagents, in a one-pot reaction. In addition, we demonstrate that the irradiation source (halogen lamp) can be efficiently replaced by an LED without altering the reaction yield.

Optimization of physical parameters for phycobiliprotein extracted from Oscillatoria agardhii and Synechococcus nidulans / Oscillatoria agardhii ve Synechococcus nidulans türlerinden fikobiliprotein ekstraksiyonu için fiziksel parametrelerin optimizasyonu

2015 ◽  
Vol 40 (6) ◽  
Author(s):  
İrem Deniz ◽  
Esra İmamoğlu ◽  
Meltem Conk Dalay
Keyword(s):  
Response Surface Methodology ◽  
Experimental Design ◽  
Light Intensity ◽  
Response Surface ◽  
Process Parameters ◽  
High Yield ◽  
Physical Parameters ◽  
Agitation Rate ◽  
Orbital Shaking ◽  
Oscillatoria Agardhii

AbstractObjective: Physical process parameters play a major role in the cultivation of cyanobacteria to provide high yield. The aim of this study was to optimize physical parameters such as light intensity and agitation rate which might affect the phycobiliprotein formations for cyanobacterial strains of Oscillatoria agardhii and Synechococcus nidulans using response surface methodology.Methods: The cyanobacterial strains were cultured in 250 mL flasks containing 100 mL of EM medium in orbital shaking incubator under the temperature of 22±2°C at different light intensities and agitation rates for 10 days. The experimental design was carried out using 2Results: The optimization solution of O. agardhii (approximately at 156 rpm under the light intensity of 65 μmol photons mConclusion: High agitation rate stimulated the faster growth than increased the light intensity for the growths of cyanobacterial strains.

Reproducible Reliable AuSi Eutectic Wafer Bond Process With High Yield

2007 ◽  
Author(s):  
R. Schwerdtner ◽  
M. Wiemer ◽  
J. Froemel ◽  
Th. Gessner
Keyword(s):  
Process Parameters ◽  
Low Cost ◽  
Surface Condition ◽  
Applied Pressure ◽  
High Yield ◽  
Single Chip ◽  
Sensors And Actuators ◽  
Bond Yield ◽  
Wafer Pair ◽  
Low Temperature Process

The AuSi eutectic bond process is a well known and important technique in the field of single chip packaging. When it comes to low-cost and hermetic sealed packages for MEMS/NEMS sensors and actuators this technology has its decisive merits. The AuSi bonding is a low-temperature process with an electric conductive alloy. To achieve a reliable bonding with 100% yield is quite difficult, especially for large areas. In our institute we made several analyses with different process parameters and surface properties variations. The results show that the surface condition of the silicon side of the wafer pair as well as the process parameters are very important factors in relation to the yield of the eutectic bond. We also did investigations on the thickness of the gold layer. Unlike conventional AuSi wafer bonding technologies [1] our technique does not need several μm thick gold layers. We were able to achieve 100% bond yield with 1500nm and even 150nm thin gold layers. Another result we found was that a good bonding process is not only depending on the value of applied temperature and time, there is also an important influence because of the heat flow and applied pressure. In the presentation we would like to introduce our results and experience, plus we will present the coherences of parameter variations for achieving 100% yield.

Temperature Measurements Using In Thermocouple In The Mocvd Rotating Disk Reactors

1995 ◽  
Vol 406 ◽  
Author(s):  
A. I. Gurary ◽  
R. A. Stall
Keyword(s):  
Process Parameters ◽  
Chemical Vapor ◽  
Rotating Disk ◽  
Temperature Measurements ◽  
High Yield ◽  
Wafer Temperature ◽  
Rotating Disk Reactors ◽  
And Control ◽  
Measurement And Control ◽  
Accuracy And Repeatability

AbstractRotating Disk Reactors used for Metalorganic Chemical Vapor Deposition have evolved into a leading manufacturing technology for several materials, including nitrides, compound semiconductors, metals, and oxides. One of the major issues to be resolved in bringing this technology into routine high yield manufacturing has been precise and repeatable wafer temperature measurement and control. The conventional approach to the rotating wafer temperature measurements by a stationary thermocouple located near the rotating wafer carrier suffers from low accuracy and repeatability. We have implemented a rotating thermocouple with a junction located close to the wafer for the temperature measurements in the MOCVD Rotating Disk Reactor. This approach allowed us to obtain reliable and accurate wafer temperature measurements with minimum dependence upon variable process parameters and to protect the thermocouple from degradation in the aggressive reactor environment. The temperature difference between wafer and thermocouple for the rotating and stationary thermocouple designs as a function of process parameters will be discussed.

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