Materials for optical telecommunications
The increasing burden of the internet on data communications and data storage has prompted a need for new materials in the telecommunications industry which possess much faster optical response times than those which are currently available. Organic non-linear optical (NLO) materials provide a possible solution to this materials-bottleneck since their optical response times are much faster than those of their inorganic counterparts that have been used traditionally. This superiority of organics lies in the electronic origins of their NLO phenomenon, being primarily motivated by intramolecular charge transfer and supporting intermolecular forces. This contrasts with the much slower process of ionic displacement that belies the functional mechanism for inorganic NLO compounds.
Accordingly, our research in this area focuses on trying to understand these electronic origins of organic NLO materials, by unravelling structure-property relationships that then serve as the knowledge base for the rational molecular design of more advanced materials.
On the experimental side, we perform high-resolution X-ray and neutron diffraction to afford charge-density maps of NLO molecules. Much like an electronic form of a cartographer's map, these charge-density maps enable us to quantify the nature of intramolecular charge transfer and surrounding intermolecular forces within these organic NLO materials. The data analysis uses electronic moments to model electrons around each atom. A few years ago, our group demonstrated that one could employ these moments to derive solid-state NLO property coefficients exclusively from diffraction, via the molecular hyperpolarisability, β. We also work in collaboration with KUL, Leuven, Belgium to determine β via optical experiments. This work enables us to rationalize the molecular origins of organic NLO materials, thereby building up a knowledge base of the structure-property relationships that are needed to design better NLO materials.
To this end, we employ data science methods to predict new organic NLO materials via large-scale data-mining strategies and mathematical algorithms.
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