News Picture Generic

Fully integrated high-throughput methodology for the study of Ni- and Cu-supported catalysts for glucose hydrogenation

January 6, 2020

Catalysis Today Journal

A high-throughput (HT) methodology was applied for the synthesis, characterization and catalytic testing of silica and alumina supported Cu- and Ni-based catalysts for glucose hydrogenation. A design of Experiment (DoE) approach was also used in all steps. The deposition and reduction of both metals was performed using the chemical reduction with hydrazine method. In total, 36 catalysts were synthetized, characterized and tested in 5 days. The amount of metal deposited on the support was chosen as the discriminative and determining parameter. The catalysts were tested at low temperature (130 °C) in the hydrogenation of glucose to sorbitol. The results showed that the chemical reduction-precipitation method could be performed using fully automatized robots. The deposition of the metals strongly depended on the nature of the support, the temperature of the reduction and hydrazine/H2O ratio. The maximum metal precipitation occurred at higher temperature (70 °C) and lower N2H4/H2O ratio (0.04 mol/mol) in both cases. The results clearly showed that glucose conversion is higher for the catalysts synthesized at 70 °C compared to the catalysts synthesized at 50 °C, irrespective of the metal precursors, supports and hydrazine/water ratios employed during catalysts syntheses. With a total timespan of around 5 days we showed that HT methods applied to all the steps (synthesis, characterization and testing) can significantly reduce the time needed to develop a new catalytic process.

For details

Fully integrated high-throughput methodology for the study of Ni- and Cu-supported catalysts for glucose hydrogenation

L. Silvester, F. Ramos, J. Thuriot-Roukos, S. Heyte, M. Araque, S. Paul, R. Wojcieszak

Univ. Lille, CNRS, Centrale Lille, ENSCL, Univ. Artois, UMR 8181 - UCCS - Unité de Catalyse et Chimie du Solide, F-59000, Lille, France

For more information about Chemspeed solutions:

ISYNTH CATIMPREG

ISYNTH CATSCREEN

Catalysis Today Journal

https://doi.org/10.1016/j.cattod.2019.05.041

© 2019 Elsevier B.V. All rights reserved.

For details please contact [email protected]

Other Recent News

Discover more news articles you might be interested in

Read more about Automated synthesis and fragment descriptor-based machine learning for retention time prediction in supercritical fluid chromatography
News Picture 1 1 V2
Featured
Jan
6

Automated synthesis and fragment descriptor-based machine learning for retention time prediction in supercritical fluid chromatography

The integration of automated synthesis and machine learning (ML) is transforming analytical chemistry by enabling data-driven approaches to method development. Chromatographic column selection, a critical yet time-consuming step in separation science, stands to benefit substantially from such advances.

© Chemspeed Technologies 2026