An experimental assessment of model-based solvent selection for enhancing chemical reactions

Scientific advances in the field of chemistry have established that solvents have a critical impact on the rate of a wide array of chemical reactions. This has triggered the interest of academia and industry for the search of solvents that optimize reaction kinetics. Yet, there are only a few systematic approaches to guide solvent selection in this direction and to date, a systematic model-based approach that considers direct applicability to industrial reaction problems has not been reported. In this study, a hybrid experimental/model-based solvent selection methodology built around the solvatochromic equation is established for the prediction of the best solvent(s) for an industrial chemical process at minimum experimental effort. For the rapid data generation and quantification, a modular continuous reactor coupled with real-time analytics has been set up. The solvatochromic equation was used to model the solvent effects on the reaction rates. The solvents that were experimentally investigated were selected from a solvent database consisting of known organic solvents and structures generated with the aid of CAMD techniques which was further refined to meet the specific reaction requirements, environmental and health constraints and equipment limitations. The selection was diversity-oriented, aiming at the acquisition of the maximum possible information at the least experimental effort. The methodology was applied on the amination of ethyl trichloroacetate with liquefied ammonia, a reaction of industrial interest. Two of the predicted promising solvents were verified experimentally, demonstrating the predictive ability of the methodology. The established methodology can be used as a starting point for further improvement. Inclusion of ionic liquids, supercritical fluids and structures generated with the aid of Computer Aided Molecular Design (CAMD) techniques in the solvent database, may reveal new promising solvent candidates, opening new windows in chemical synthesis. Coupling with model identification and discrimination techniques can further minimize the experimental effort involved. The current approach deals with the enhancement of the rate of the reaction leading to the desired products and the reduction of the rate of the side reactions as two independent objectives. Future research could be dedicated into treating the two objectives as a unified one

Photoprotection is regulated by light-independent CO 2 availability

Abstract Photosynthetic algae cope with suboptimal levels of light and CO 2 . In low CO 2 and excess light, the green alga Chlamydomonas reinhardtii activates a CO 2 Concentrating Mechanism (CCM) and photoprotection; the latter is mediated by LHCSR1/3 and PSBS. How light and CO 2 signals converge to regulate photoprotective responses remains unclear. Here we show that excess light activates expression of photoprotection- and CCM-related genes and that depletion of CO 2 drives these responses, even in total darkness. High CO 2 levels, derived from respiration or impaired photosynthetic fixation, repress LHCSR3 and CCM genes while stabilizing the LHCSR1 protein. We also show that CIA5, which controls CCM genes, is a major regulator of photoprotection, elevating LHCSR3 and PSBS transcript accumulation while inhibiting LHCSR1 accumulation. Our work emphasizes the importance of CO 2 in regulating photoprotection and the CCM, demonstrating that the impact of light on photoprotection is often indirect and reflects intracellular CO 2 levels

Light-independent regulation of algal photoprotection by CO2 availability

Photosynthetic algae have evolved to survive in suboptimal light and CO2 conditions. Here, the authors show that depletion of CO2 can drive photoprotection and carbon acquisition even in the absence of light, that was previously believed to be indispensable for the activation of these processes

Phototropin connects blue light perception to starch metabolism in green algae

Abstract In photosynthetic organisms light acts as an environmental signal to control their development and physiology, and as energy source to drive the conversion of CO 2 into carbohydrates used for growth or storage. The main storage carbohydrate in green algae is starch, which accumulates during the day and is broken down at night to meet cellular energy demands. The signalling role of light quality in the regulation of starch accumulation remains unexplored. Here, we report that in the model green alga Chlamydomonas reinhardtii blue light perceived by the photoreceptor PHOTOTROPIN causes dephosphorylation of the PHOTOTROPIN-MEDIATED SIGNALLING KINASE 1 that then suppresses starch accumulation by inhibiting the expression of GLYCERALDEHYDE-3-PHOSPHATE DEHYDROGENASE. Our results provide an in-depth view of how photoreceptor-mediated signalling controls microalgal carbon metabolism. One-Sentence Summary Blue light perception by PHOTOTROPIN triggers kinase-mediated signaling to inhibit starch accumulation in the green alga Chlamydomonas