Investigation of Silver Recovery from Thin Film CIGS Solar Cells by Selective Leaching
Examensarbete för masterexamen
Recycling and green energy has become increasingly hot topics in research and our everyday lives. One of the promising sources of green energy that is being developed is CIGS thin film solar cells, the photovoltaic cell contains an alloy of copper, indium, gallium and selenium where silver is used as a conducting material on the surface. However, innovative and sustainable strategies to recover silver from spent CIGS solar cells are still not readily available. Previous research has shown strategies including pyrolysis and electrodeposition, both which are energy consuming and require multiple steps prior to the separation stage. Reducing the number of pre-treatment steps will play an important role in realizing sustainable silver recovery. This study aims to investigate selective leaching as a feasible method of recovering metals, especially silver, from CIGS solar cells. This was achieved by combining thermodynamic investigation, analyzing old data combined with new leaching experiments to design a statistical model. A second aim is to evaluate different factors and their contribution to dissolution of silver. A model describing the system as a function of time, acid concentration and solid to liquid ratio was made using Design of Experiments (DOE) and Analysis of Variance (ANOVA). It was shown that silver can be selectively leached by nitric acid with a resulting purity above 90% after 1 h. The results show that the significant factors are time and nitric acid concentration. The major co-dissolved metal component is indium but low levels of tin is also found in the leachate. In the initial experiments of the study it was found that oxalic acid leaching may be an alternative route for simultaneous silver and indium recovery due to silver being precipitated while indium is found in the leachate, but further research should be conducted.
Silver , selective leaching , CIGS , recycling , solar cells , nitric acid , organic acids , factorial design , statistical modelling