About the author
The dream of living in a clean, green, pollution-free and sustainable environment cannot be realised without renewable energy. The challenge is indeed daunting! Science and technology play a crucial role in tackling global energy demand, which is rising as fast as the population increases. Yet the fossil fuels on which we still depend are a limited resource and also generate carbon dioxide, which adds to global warming. The next step is the urgent need to create viable alternatives in an environmentally- friendly manner.
Water, sunlight, wind and plants are abundant in nature and so we must harness these renewable resources to the fullest.
The world is facing challenges in developing an affordable, sustainable and plentiful supply of energy for the coming years. Tackling the energy crisis issue requires multidisciplinary input from across the scientific landscape; however, the role of chemistry as a central science is important in seeking innovative solutions.
We are in an exciting era where computers are becoming more powerful and are widely available.
This has led to the development of influential quantum methods that are aiding computational chemists in their quest to develop solutions to the energy challenge.
Computational chemistry is playing a significant role in accelerating the required research.
Solar cells, one of the most favourable ways to convert solar energy into electricity, were demonstrated for the first time by Bell Laboratory in 1954 with an efficiency of 6%.1 Since then, major advances have been made in solar cell technology. In particular, dye-sensitised solar cells, invented by Grätzel and O’Regan2, are the most promising alternative to conventional silicon-based solar cells, owing to their low cost of manufacture, easy fabrication and tunable optical properties.
It is quite complex to explain how dye-sensitised solar cells work using only words, so let’s take a look at this video:
One of the crucial concerns for the improvement of photovoltaic performance is to enhance light-absorbing capability and to slow the degradation process. Up till now 13% efficiency has been achieved through molecular engineering of sensitisers.3 However, in order to design a more efficient solar cell, there is a critical need to understand the different steps in solar cell operation and experimentally it may take years to realise one particular solar cell design. This is where computational techniques come in. Different computational strategies have been used to study the optical and electronic properties of sensitisers, but the ab initio method – that is from first principle – has become the method of choice.
Computations are currently being used to simulate UV spectra and to predict absorption wavelength and oscillator strength, which aid in determining organic molecules that are active material in the solar spectrum. These theoretical results can also eliminate unfit candidates and provide information for promising structural molecules. Consequently, these data are helpful to experimentalists in their attempts to synthesise these molecules. The HOMO-LUMO gap, an important parameter, can be evaluated so that an efficient charge separated state can be tuned by substituting electron donating and withdrawing groups to[HM1] the dye-sensitizers. The electron coupling with the semiconductor, such as TiO2, and the light harvesting efficiency can also be evaluated before the dyes are synthesised. In addition, to overcome the degradation process induced by sunlight, it is essential to gain insight into the chemical reactions in the excited state. This is difficult to study by experiment but the mechanism can be elucidated with the help of quantum chemical computations.
From a synthetic point of view, the geometric and electronic structures have a central role in the optimisation of dye-sensitized solar cells.
Thanks to Computational Chemistry, these parameters can be determined with well-established methods and, therefore, are helpful in the design and improvement of dye-sensitizers that satisfy the needs of solar cell technology.
- D. M. Chapin, C. S. Fuller and G. L. Pearson, J. Appl. Phys., 25, 1954, 676.
- B. O’regan and M. Grätzel, Nature, 353, 1991, 737.
- S. Mathew, A. Yella, P. Gao, R. Humphry-Baker, B. F. E. Curchod, N. Ashari-Astani, I. Tavernelli, U. Rothlisberger, Md. K. Nazeeruddin and M. Grätzel, Nature, 6, 2014, 242.