Illustrating anticipatory life cycle assessment for emerging photovoltaic technologies.

Current research policy and strategy documents recommend applying life cycle assessment (LCA) early in research and development (R&D) to guide emerging technologies toward decreased environmental burden. However, existing LCA practices are ill-suited to support these recommendations. Barriers related to data availability, rapid technology change, and isolation of environmental from technical research inhibit application of LCA to developing technologies. Overcoming these challenges requires methodological advances that help identify environmental opportunities prior to large R&D investments. Such an anticipatory approach to LCA requires synthesis of social, environmental, and technical knowledge beyond the capabilities of current practices. This paper introduces a novel framework for anticipatory LCA that incorporates technology forecasting, risk research, social engagement, and comparative impact assessment, then applies this framework to photovoltaic (PV) technologies. These examples illustrate the potential for anticipatory LCA to prioritize research questions and help guide environmentally responsible innovation of emerging technologies.

Fluorescence spectroscopy and multivariate spectral descriptor analysis for high-throughput multiparameter optimization of polymerization conditions of combinatorial 96-microreactor arrays.

Selection of optimum process conditions in combinatorial microreactors is essential if the combinatorial synthesis process is to be correlated with the synthesis process on a more conventional scale and the materials are to have the desired chemical properties. We have developed a new methodology for the high-throughput multiparameter optimization of polymerization reaction conditions in arrays of microreactors. Our strategy is based on the application of nondestructive spectroscopic techniques to measure chemical properties of polymers directly in individual microreactors followed by the multivariate spectral descriptor analysis for rapid determination of the optimal process conditions. We have demonstrated our strategy in the high-throughput multiparameter optimization of process conditions in thin-film melt polymerization reactions performed in 96-microreactor arrays for combinatorial screening of new polymerization catalysts. The combinatorial polymerization system was optimized for the best processing parameters using a set of input variables that included reactant parameters (relative amounts of starting components and catalyst loading) and processing variables (reaction time, reaction temperature, and inert gas flow rate). The measured output parameters were the chemical properties of materials and reproducibility of the material formation in replicate polymerizations in microreactors. Spatially resolved nondestructive evaluation of polymer formation was performed directly in individual microreactors and provided information about the spatial homogeneity of polymers in microreactors. It showed to be another powerful indicator of the reproducible polymerization process on the combinatorial scale. Although the methodology described here was implemented for high-throughput optimization of polymerization conditions, it is more general and can be further implemented for a variety of applications in which optimization of process parameters can be studied in situ or off-line using spectroscopic and other tools.

Development of combinatorial chemistry methods for coatings: high-throughput adhesion evaluation and scale-up of combinatorial leads.

Coupling of combinatorial chemistry methods with high-throughput (HT) performance testing and measurements of resulting properties has provided a powerful set of tools for the 10-fold accelerated discovery of new high-performance coating materials for automotive applications. Our approach replaces labor-intensive steps with automated systems for evaluation of adhesion of 8 x 6 arrays of coating elements that are discretely deposited on a single 9 x 12 cm plastic substrate. Performance of coatings is evaluated with respect to their resistance to adhesion loss, because this parameter is one of the primary considerations in end-use automotive applications. Our HT adhesion evaluation provides previously unavailable capabilities of high speed and reproducibility of testing by using a robotic automation, an expanded range of types of tested coatings by using the coating tagging strategy, and an improved quantitation by using high signal-to-noise automatic imaging. Upon testing, the coatings undergo changes that are impossible to quantitatively predict using existing knowledge. Using our HT methodology, we have developed several coatings leads. These HT screening results for the best coating compositions have been validated on the traditional scales of coating formulation and adhesion loss testing. These validation results have confirmed the superb performance of combinatorially developed coatings over conventional coatings on the traditional scale.

Reaction of silicate minerals to form tetramethoxysilane.

Several silicon dioxide sources were used as reagents in the base-mediated reaction with dimethyl carbonate (DMC) to make tetramethoxysilane (Q'). Several commercially available diatomaceous earth materials were investigated. High throughput screening was employed to explore over 200 silicate rocks and minerals as alternative silicon dioxide sources for formation of Q' from DMC and base. Amorphous silicon dioxide materials are effective reagents for the Q' forming reaction. Effective silicon dioxide sources in addition to the diatomaceous earth materials include opal and various synthetic silicates (Li, Co, and Ca).