The lab in the computer

By |Sep 28, 2020|

How are electrons “ticking”? And why is their behavior so important for efficient solar cells? Learn more about it in the research portrait of our new member Prof. Frank Ortmann.

A new grip on the Sabatier principle

By |Sep 9, 2020|

Even for experienced catalysts, matchmaking is not always easy. Some partners bind too strong, others leave too early. A team of scientists including members of e-conversion observed how a copper electrode solves this problem by itself.

New solar cells for space

By |Sep 2, 2020|

A payload module of perovskite and organic solar cells from the lab of e-conversion member Prof. Müller-Buschbaum proved successful on a rocket flight in space.

“Schee, dass do seids!”

By |Jul 10, 2020|

With this Bavarian expression for "welcome" we happily introduce two outstanding scientists as new members of our cluster: Prof. Alessio Gagliardi and Prof. Frank Ortmann.

Solve the mystery

By |Jul 8, 2020|

One picture - three possible answers. Find the right one and win a high-performance solar power bank.

Spanish-German Award

By |Jul 6, 2020|

The Spanish Royal Society of Chemistry and the German Chemical Society honored e-conversion coordinator Prof. Thomas Bein with the joint Elhuyar-Goldschmidt Award.

Fuel cell research

By |Jun 6, 2020|

In our series "e-conversion cosmos" we present key topics of energy research on the basis of current cluster publications and give an insight into the daily work of our members.

e-asy to understand

By |May 21, 2020|

Learn more about the idea behind e-conversion and why the research of our members is of such importance for the future of energy supply.

Supercomputers for the perfect material

By |May 21, 2020|

One year ago David Egger became professor for “Theory of Functional Energy Materials” at TU München and member of e-conversion. We asked him about his research, his ideas for the cluster and why he loves being a scientist.

Machine learning for improved fuel cell catalysts

By |May 1, 2020|

Platinum is the common catalyst material for the oxygen reduction in fuel cells and its main cost factor. With a machine learning technique, scientists from e-conversion now can forecast and optimize the performance of catalysts with less platinum.

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