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RR5: A dynamic method for measuring multicomponent adsorption equilibria at ultra-low concentration

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RR5: A dynamic method for measuring multicomponent adsorption equilibria at ultra-low concentration

Project Title: A dynamic method for measuring multicomponent adsorption equilibria at ultra-low concentration (WP3)

Host Institution: VUB

Country: Belgium

Supervisors: Prof. J. Denayer (VUB); Secondary Supervisors: Prof. M. Thommes (FAU), Dr. Michelle Mercer (Hiden)

Objectives:

  • The development of an experimental method that allows the study of competitive adsorption with complex mixtures at very low concentrations.
  • The use of this method to screen new adsorbent materials (zeolites and MOFs) for sensing applications.
  • The determination of adsorption parameters, required for the signal processing task and the validation of molecular modelling results.

Short Description of Work & Expected Results:

  • This RR project aims at the development and use of dynamic methods to determine multicomponent adsorption equilibria of mixtures of selected VOCs at very low concentrations. Inverse pulse chromatography will be used for a first screening of low coverage adsorption properties for pure components.

Planned secondment(s):

  • Host: FAU, Prof. M. Thommes, Timing: year 2, Length 6 months, Topic: Characterization using single-component adsorption methods.
  • Host: Hiden, Dr. Michelle Mercer, Timing: year 2, Length 2-3 months, Topic: Training in the Integral Mass Balance technique to study multicomponent adsorption. Working in a company environment.

Enrolment in Doctoral degree(s): VUB & FAU

Candidate requirements:

  • It is expected that the successful candidate has a master degree in chemical engineering, bio-engineering sciences, chemistry, or a related field
  • Excellent  knowledge in physical chemistry, mass and heat transfer and thermodynamics is expected
  • The successful candidate is interested in data analysis and computer modelling
  • The successful candidate has good experimental skills
  • The successful candidate has excellent communication skills and a strong motivation to collaborate with other researchers within the  SENNET  consortium
  • Excellent oral and written English language skills are mandatory.

Recruited researcher: Kavosh Karami