Sekretareva Group

protein on electrode

We aim to enhance the understanding of electron transfer processes within (bio)electrocatalysts and on the electrode/catalyst interface at the molecular level, and to design new (bio)electrocatalytic devices based on this new understanding. Our research bridge the disciplines of electrochemistry and (bio)inorganic chemistry and utilizes a range of electrochemical, spectroscopic and theoretical techniques, including single entity electrochemistry, protein film electrochemistry, electron paramagnetic resonance spectroscopy and quantum mechanical calculations, to address fundamental question of relevance to electrocatalysis.

Follow the link to find out more about our current projects.

Join us

If you are interested in joining our group, don’t hesitate to contact us. Applications from interested students (PhD, Master, etc.) and PostDocs are always welcome!

OPEN scholarship postdoc position

We have a two-year scholarship postdoc position on machine learning in catalysis. Please send your application no later than April 3rd, 2023.

Project description

The latest developments in artificial intelligence and data science have the ability to significantly speed up the progress of electrocatalysis  research. In particular, machine learning can be used to quickly explore vast chemical material spaces, and therefore, is considered as a promising alternative to the traditional trial and error method. ML methods are expected to initiate a paradign shift in the approach to creating and producing catalysts in the future.

In this project we will apply machine learning methods for plasmonic nanocatalysts.Several parameters, such as size, shape and composition of plasmonic nanostructures strongly affect the lifetime of hot carriers, and consequently the reaction yields of the target product. Moreover, we have recently found that inert support materials on which plasmonic nanoparticles are commonly immobilized in electrocatalytic systems significantly influence the mechanism (hot carriers vs thermal effects) and efficiency of catalysis. Optimization of plasmonic catalysts by a traditional trial and error experimental approach in this multiparameter space is time-consuming and not cost-effective.

In this project, we aim to further investigate the mechanism of plasmon-enhanced electrocatalysis and to develop machine learning algorithms enabling fast identification of key parameters determining catalysis efficiency and, therefore, accelerated optimization of the catalyst towards the target product. 

Further reading:

Sagar Ganguli, Alina Sekretareva. Role of an Inert Electrode Support in Plasmonic Electrocatalysis. ACS Catal. 2022, 12, 7, 4110–4118

Jean-Francois Masson et. al. Machine learning for nanoplasmonics. Nat. Nanotechol. 2023, 18, 121-123. 

Haoxin Mai et. al. Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery. Chem. Rev. 2022, 122, 16, 13478–13515.


The applicant must hold a Ph.D. degree or an international equivalent within the field of Machine learning, Electrochemistry, Physical Chemistry, or a related field, or must have submitted their doctoral thesis for assessment in a relevant field prior to the application deadline. Documented experience in using machine learning is required. Competence in plasmonic catalysis will be considered an advantage.

The assessment of applications is based primarily on the applicant's ability to conduct independent research based on scientific skills. Greater importance is attached to the quality of individual scientific work, than to the number of publications. The applicant must be sufficiently fluent in English to understand, discuss and communicate science on a high level.

Consideration will also be given to good collaborative skills, drive, creativity, and independence, how the applicant’s experience and skills complement and strengthen ongoing research within the group.

Further information about the position can be obtained from the project leader, Alina Sekretareva,

Please submit your application via email to before the deadline. Please include the following documents:

  • A letter of motivation as an account of the applicant’s research interests and motivation for applying for the position (max 1 page)
  • CV
  • Publication list
  • Copy of your Ph.D. degree certificate*
  • The names and contact information of three referees.

If you have not got a Ph.D. degree, please submit a certificate or relevant document stating your Ph.D. finishing time from your University or your main supervisor. 

Our research is financed by

Last modified: 2023-03-07