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Welcome to CatalysisRR!

We are a group of researchers in heterogeneous catalysis dedicated to implementing permanent, lasting mechanisms that improve the rigor and reproducibility of published data in our field, but we can’t do it alone!


Our goal is to initiate and support community efforts focused on improving R&R.


On this site, you’ll find information about several events we are planning, some “Big Ideas” we are thinking about, and useful references/resources describing best practices for many common techniques used in our field. You can also leave us your feedback or head over to our community forum to engage in conversations about R&R and best practices. Many more features will be coming soon, so check back for updates!


What is this effort?

Heterogeneous catalysis has long served as the bedrock of fuels and chemicals manufacturing. Complexity and variability spanning the entire breadth of catalyst materials properties, synthesis methods, characterization techniques, and evaluation procedures, has focused attention on the need to establish community-accepted practices for ensuring high-quality, benchmarked, and reproducible data. In addition, urgency around the transition to clean energy and greenhouse gas reduction has incentivized interdisciplinary, convergent, and translational approaches to catalysis research in recent years. Research engineers and scientists with expertise cutting broadly across materials, chemical synthesis, interfacial science, spectroscopic methods, and methods of data science and computational simulation, all bring welcome perspectives to catalysis research, but often with little awareness of the complexity of catalytic systems, especially in the working environment. Thus, mechanisms are needed to improve rigor and reproducibility (R&R) in experimental measurements to ensure alignment of the broader research community with a common core of practices specific to the realization of high-quality catalysis research.  Similarly, the field is moving rapidly toward computational and data-science driven catalyst design, but successful implementation of such predictive tools hinges on model training and validation rooted in rigorously obtained and reproducible experimental databases benchmarked to common specifications. The goal of this effort is to spur community-wide action that results in sustained mechanisms that enhance R&R in our field.

Below, you can listen to Neil Schweitzer describe this effort and our "Big Ideas" in more detail during our recent virtual  symposium.

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