Prospective inventory modelling of emerging chemicals: The case of photonic materials

Other conference contribution, 2019

Prospective life cycle assessment (LCA), or ex-ante LCA, has been defined as an assessment of a product system modeled at a future time, before its commercialization. Such assessments bring the promise of altering emerging technologies in a more environmentally benefitial direction before they become difficult to change. Since the future cannot be known with certainty, prospective modeling need to rely on scenarios of various kinds. However, how to conduct such prospective scenario modeling in practice still has to be clarified. In this study, we have modeled two emerging chemicals that can be used for a technology called photon upconversion, which converts low-energy light into higher-energy light harvestable by solar photovoltaics, thereby increasing their efficiency. Two chemicals currently considered for this purpose are ruthenium bipyridine chloride (RBC) and diphenylanthracene (DPA). These novel, emerging chemicals have not been studied regarding environmental performance before and are consequently not present in any LCA databases. The aim of this study is to present a generic procedure for prospective inventory modeling of emerging chemicals and apply that to the cases of RBC and DPA by developing unit processes for these two chemicals. An industrial synthesis scenario was adopted as our main scenario, reflecting a possible future time when RBC and DPA are produced at an industrial scale. The modeling was conducted in six steps: (1) Identify likely chemical syntheses. (2) Calculate inputs stoichiometrically based on the chemical synthesis reactions. (3) Modify inputs based on available yields for reactants and solvents (e.g. obtained from patents or estimated). (4) Categorize outputs as by-products or waste depending on their likely subsequent fate. (5)Calculate process emissions. (6) Model energy flows. Unit processes for the two emerging chemicals are thusly developed. The procedure is considered particularly strong for estimating inputs and output materials related to the stoichiometric reaction, but weaker regarding the estimation of emissions and energy requrement. Further research into the modeling of energy flows for high-temperature processes is therefore recommended, as well as estimation procedures for emissions from emerging chemicals production.