High‐Throughput Experimentation

Rodemerck, Uwe; Linke, David

doi:10.1002/9783527626854.ch11

Cited by 4 publications

(3 citation statements)

References 109 publications

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“…Currently, mixed oxides can be obtained by several synthesis methods such as co-precipitation (Kolthoff, 1932), solid-state (Menon and Delmon, 1999), sol-gel (Ko, 1999), freeze-dried (Vie et al, 2004), and hydrothermal (Rodemerck and Linke, 2009). Despite the recognized effectiveness of these synthesis methods, preparation itself of layered materials as precursors of mixed oxides has been also recognized as a promising method (Braterman et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

X-ray powder diffraction data for a new compound (NH₄)H₂Co₂O(OH)(MoO₄)_1.6(WO₄)_0.4•H₂O type ϕ_y

Echavarrı́a

Amaya

2018

Powder Diffr.

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A new trimetallic compound with formula (NH4)H2Co2O(OH)(MoO4)1.6(WO4)0.4•H2O and lamellar structure was prepared by hydrothermal synthesis. The solid was characterized using X-ray diffraction (XRD), thermogravimetric (TGA) and differential thermal (DTA) analyses, Fourier-transform infrared spectroscopy (FT–IR), laser Raman spectroscopy (LRS), and atomic absorption spectroscopy (AA). Crystallographic studies showed that the solid crystallizes with hexagonal symmetry in space group R-3 m with a = 6.0807 and c = 21.7591 Å.

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Section: Introductionmentioning

confidence: 99%

X-ray powder diffraction data for a new compound (NH₄)H₂Co₂O(OH)(MoO₄)_1.6(WO₄)_0.4•H₂O type ϕ_y

Echavarrı́a

Amaya

2018

Powder Diffr.

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“…The concept originated from attempts to model the functioning of the human brain. Subsequently, neural networks have been successfully applied in various fields of chemistry, such as structure-activity relationships, structure-spectrum correlations, catalyst activity predictions [21][22][23] , chemistry kinetics, 24,25 and controls 26 . Like QSPR, the ANN approach is data driven and ANNs can be trained to predict an output (ignition delay) from a given set of inputs (descriptors).…”

Section: Ann Prediction Of Ignition Delaymentioning

confidence: 99%

QSPR and Artificial Neural Network Predictions of Hypergolic Ignition Delays for Energetic Ionic Liquids

Sengupta

Pindera

Cole

et al. 2023

Preprint

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Due to their negligible volatility, energetic ionic liquids are being considered as next generation hypergolic fuels for replacing toxic monomethylhydrazine. One design challenge for energetic ionic liquids is to maintain their ignition delays as close to that of monomethylhydrazine. The ignition process of ionic liquids with an oxidizer, such as nitric acid, is a complex process and, to date, there are no theoretical methods for predicting the ignition delay. The present work examines two correlation methods, Quantitative Structure Property Relationship (QSPR) and Artificial Neural Networks (ANNs), for their ability to predict this quantity. A set of five descriptors were chosen from a pool of more than 160 to establish these correlations. A good QSPR correlation was obtained using these descriptors. We then trained an artificial neural network and examined the predictive ability of the network using an extensive 5-fold cross validation process with the same set of descriptors. A number of data normalization techniques were examined for network training and validation. The results show that ANNs exhibit excellent prediction capabilities for this application.

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“…Dazu gehören die Oxidation von Isobuten zu Methacrolein bzw. Methacrylsäure [2,3], von n-Butan oder n-Butenen zu Maleinsäureanhydrid [4,5], die selektive Oxidation von Propan zu Acrylsäure [6] und die Ammoxidation von Propen zu Acrolein bzw. Acrylnitril [7,8].…”

Section: Introductionunclassified

Untersuchung der Sorption und Reaktion von 1‐Buten an einem technischen VO_x‐TiO₂‐Katalysator

Suprun

Kießling

Staudt

et al. 2006

Chemie Ingenieur Technik

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High‐Throughput Experimentation

Cited by 4 publications

References 109 publications

X-ray powder diffraction data for a new compound (NH₄)H₂Co₂O(OH)(MoO₄)_1.6(WO₄)_0.4•H₂O type ϕ_y

X-ray powder diffraction data for a new compound (NH₄)H₂Co₂O(OH)(MoO₄)_1.6(WO₄)_0.4•H₂O type ϕ_y

QSPR and Artificial Neural Network Predictions of Hypergolic Ignition Delays for Energetic Ionic Liquids

Untersuchung der Sorption und Reaktion von 1‐Buten an einem technischen VO_x‐TiO₂‐Katalysator

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High‐Throughput Experimentation

Cited by 4 publications

References 109 publications

X-ray powder diffraction data for a new compound (NH4)H2Co2O(OH)(MoO4)1.6(WO4)0.4•H2O type ϕy

X-ray powder diffraction data for a new compound (NH4)H2Co2O(OH)(MoO4)1.6(WO4)0.4•H2O type ϕy

QSPR and Artificial Neural Network Predictions of Hypergolic Ignition Delays for Energetic Ionic Liquids

Untersuchung der Sorption und Reaktion von 1‐Buten an einem technischen VOx‐TiO2‐Katalysator

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X-ray powder diffraction data for a new compound (NH₄)H₂Co₂O(OH)(MoO₄)_1.6(WO₄)_0.4•H₂O type ϕ_y

X-ray powder diffraction data for a new compound (NH₄)H₂Co₂O(OH)(MoO₄)_1.6(WO₄)_0.4•H₂O type ϕ_y

Untersuchung der Sorption und Reaktion von 1‐Buten an einem technischen VO_x‐TiO₂‐Katalysator