Converting CO2 into carbon‐based fuels is promising for relieving the greenhouse gas
effect and the energy crisis. However, the selectivity and efficiency of current electrocatalysts …

Designing catalysts is a challenging matter as catalysts are involved with various factors that
impact synthesis, catalysts, reactor and reaction. In order to overcome these difficulties …

The development of machine learning models for electrocatalysts requires a broad set of
training data to enable their use across a wide variety of materials. One class of materials …

Data science and machine learning have the potential to accelerate the discovery of
effective catalysts; however, these approaches are currently held back by the issue of …

Modern research methods produce large amounts of scientifically valuable data. Tools to
process and analyze such data have advanced rapidly. Yet, access to large amounts of high …

Heterogeneous catalysis is an important area of research that generates data as intricate as
the phenomenon itself. Complexity is inherently coupled to the function of the catalyst and …

The coupling of high-throughput calculations with catalyst informatics is proposed as an
alternative way to design heterogeneous catalysts. High-throughput first-principles …

Most of the products on which our welfare state is based are composed of chemicals. The
growth of the world's population, its ageing and the continuous improvement of welfare state …

Combinatorial catalyst design is hardly generalizable, and the empirical aspect of the
research has biased the literature data toward accidentally found combinations. Here, 300 …

Miniaturization of mixers is a hot topic in process intensification, but efficient mixing at
operational conditions with relatively large viscosity μ and large flow rate ratio R between …