Background

Nada Benhaddou was awarded a PhD in thin-film solar cells with a chemistry background. She is currently a Research Associate working in PV Materials and Devices group at CREST (Centre for Renewable Energy Systems Technology). Her work includes various aspects of photovoltaic device optimisation and characterisation, with an emphasis on the development of solution-processed CIGS solar cells. She works on solar cells with a trade-off of improved conversion efficiencies and process reproducibility.

Main research interests

  • Development of photovoltaic solar cells using solution processing methods.
  • Interface engineering of the absorber/buffer interface.
  • Cd free solar cells architectures.
  • structural, composition and opto-electronic measurements of device performance.
  • Optical and electrical simulation of solar cells.
  • Fabrication of mini-module sized devices.

 

Selected publications

  • Benhaddou, N., Aazou, S., Fonoll-Rubio, R., Sánchez, Y., Giraldo, S., Guc, M., Calvo-Barrio, L., Izquierdo-Roca, V., Abd-Lefdil, M., Sekkat, Z. and Saucedo, E., 2020. Uncovering details behind the formation mechanisms of Cu2ZnGeSe4 photovoltaic absorbers. Journal of Materials Chemistry C8(12), pp.4003-4011.
  • Benhaddou, N., Aazou, S., Sánchez, Y., Andrade-Arvizu, J., Becerril-Romero, I., Guc, M., Giraldo, S., Izquierdo-Roca, V., Saucedo, E. and Sekkat, Z., 2020. Investigation on limiting factors affecting Cu2ZnGeSe4 efficiency: Effect of annealing conditions and surface treatment. Solar Energy Materials and Solar Cells, 216, p.110701.
  • Benhaddou, N., Sánchez, Y., Rubio, R.F., Aazou, S., Guc, M., Izquierdo-Roca, V., Giraldo, S., Sekkat, Z. and Saucedo, E., 2019, June. An insight into pure ge based kesterite synthesis. In 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) (pp. 0971-0974). IEEE.
  • Grau-Luque, E., Anefnaf, I., Benhaddou, N., Fonoll-Rubio, R., Becerril-Romero, I., Aazou, S., Saucedo, E., Sekkat, Z., Perez-Rodriguez, A., Izquierdo-Roca, V. and Guc, M., 2021. Combinatorial and machine learning approaches for the analysis of Cu₂ZnGeSe₄: influence of the off stoichiometry on defect formation and solar cell performance.