Experimental methods in chemical engineering: High throughput catalyst testing — HTCT

Abstract

The conventional one‐at‐a‐time strategy to evaluate catalysts is inefficient and resource intensive. Even a fractional factorial design takes weeks to control for temperature, pressure, composition, and stability. Furthermore, quantifying day‐to‐day variability and data quality exacerbates the time sink. High‐throughput catalyst testing (HTCT) with as many as 64 parallel reactors reduces experimental time by two orders of magnitude and decreases the variance, as it is capable of quantifying random errors. This approach to heterogeneous catalyst development requires dosing each reactor precisely with the same flow and composition, controlling the temperature and identifying the isothermal zone, on‐line analysis of the gas‐phase, and a common back pressure regulator to maintain constant pressure. Silica capillary or microfluidic distribution chip manifolds split a common feed stream precisely (standard deviation <0.5%), thus guaranteeing reproducibility. With these systems, experimenters shift their focus from operating a single reactor to careful catalyst synthesis, sometimes delicate catalytic reactor loadings, and the handling, processing, and analysis of massive amounts of data. This review presents an overview of the main elements of HTCT, its applications, potential sources of uncertainty, and a set of best practises derived from scientific literature and research experience. A bibliometric analysis of articles Web of Science indexed from 2015–2020 grouped research into five clusters: (1) discovery, directed evolution, enzymes, and complexes; (2) electrocatalysis, reduction, adsorption, and nanoparticles; (3) hydrogen, oxidation, and stability; (4) DFT, combinatorial chemistry, and NH3; and (5) graphene and carbon.

Experimental and Computational Synergistic Design of Cu and Fe Catalysts for the Reverse Water–Gas Shift: A Review

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Catalytic Materials: Concepts to Understand the Pathway to Implementation

“…Catalyst activity can nowadays be quite well-predicted by design using theoretical models . In practice, to develop a catalyst of a desirable activity, high-throughput preparation and testing techniques have been employed. − These tools accelerate the discovery and implementation of new catalysts. Next to the discovery phase, stability tests, , as well as in situ and operando − studies, are important.…”

High-throughput screening and literature data-driven machine learning-assisted investigation of multi-component La2O3-based catalysts for the oxidative coupling of methane

“…Indeed, within the last a few years, the combined use of tailor-made experimental datasets obtained with a high throughput screening (HTS) machine and data analytic techniques such as multi-output ML and network profiling have presented new avenues for the design and elucidation of the nature and catalytic activity of catalysts, in particular OCM. [22][23][24][25][26][27][28][29][30][31][32] Several parallel reactor systems for effective data collection in a fixed bed catalytic reaction have become commercially available 33,34 and others have been proposed in the literature. 22,[35][36][37] Nevertheless, these systems still require special skills and entail high costs for operation and construction similar to the conventional modes of catalyst investigation.…”

“…Several parallel reactor systems for effective data collection in a fixed bed catalytic reaction have become commercially available 33,34 and others have been proposed in the literature. 22,35–37 Nevertheless, these systems still require special skills and entail high costs for operation and construction similar to the conventional modes of catalyst investigation.…”