Dioxyde de carbone

Le dioxyde de carbone, aussi appelé gaz carbonique ou anhydride carbonique, est un composé inorganique dont la formule chimique est , la molécule ayant une structure linéaire de la forme O=C=O. Il se présente, sous les conditions normales de température et de pression, comme un gaz incolore, inodore, à la saveur piquante. Le est utilisé par l'anabolisme des végétaux pour produire de la biomasse à travers la photosynthèse, processus qui consiste à réduire le dioxyde de carbone par l'eau, grâce à l'énergie lumineuse reçue du Soleil et captée par la chlorophylle, en libérant de l'oxygène pour produire des oses, et en premier lieu du glucose par le cycle de Calvin. Le est libéré, à travers le cycle de Krebs, par le catabolisme des plantes, des animaux, des fungi (mycètes, ou champignons) et des micro-organismes. Ce catabolisme consiste notamment à oxyder les lipides et les glucides en eau et en dioxyde de carbone grâce à l'oxygène de l'air pour produire de l'énergie et du pouvoir réduc

Comparison of two carbon-nitrogen regulatory models calibrated with mesocosm data

Shubham Krishna

Marine phytoplankton can regulate their stoichiometric composition in response to variations in the availability of nutrients, light and the pH of seawater. Varying elemental composition of photoautotrophs affects several important ecological and biogeochemical processes, e.g., primary and export production, nutrient cycling, calcification, and grazing. Here we compare two plankton ecosystem models that consider regulatory mechanisms of cellular carbon and nitrogen, driving the physiological acclimation of photoautotrophs. The Carbon:Nitrogen Regulated Ecosystem Model (CN-REcoM) and the optimality-based model (OBM) differ in their representation of phytoplankton dynamics, i.e. nutrient acquisition, synthesis of chlorophyll a, and growth. All other model compartments (zooplankton, detritus, dissolved inorganic and organic matter) and processes (grazing, aggregation, remineralisation) remain identical in both models. We assess the skills of the two models against data from an ocean acidification mesocosm experiment with three CO2 treatments. Neither model accounts for any carbon dioxide (CO2) effects explicitly. Instead, we assimilate data of the different CO2 treatments separately into the models. Thereby we aim at identifying optimal model parameter values that might correlate with differences in CO2 conditions. For the OBM, optimal parameter estimates of Q(min) (subsistence N:C ratio) and V-C(0) (maximum potential photosynthesis rate of photoautotrophs) turned out to be higher for mesocosms exposed to high CO2 compared to those with low CO2 concentrations. By contrast, a similar correlation is not observed for the CN-REcoM. A possible physiological interpretation of higher estimates of Q(min), and V-C(0) according to the OBM is that phytoplankton may experience environmental stress under more acidic conditions, and hence must invest more energy/resources for maintaining basic cellular functions. Our data assimilation reveals that the parameters of the OBM are better constrained by the data than those of the CN-REcoM. Furthermore, the OBM is better able than CN-REcoM to reproduce data that were not used for parameter optimization.

2019

Comparison of two carbon-nitrogen regulatory models calibrated with mesocosm data

Shubham Krishna

2019

Electrochemical reduction of carbon dioxide using pyridinium as catalyst

Electrochemical reduction of carbon dioxide on gas diffusion electrodes : optimization of electrode preparation and reaction conditions

Comparison of two carbon-nitrogen regulatory models calibrated with mesocosm data

Comparison of two carbon-nitrogen regulatory models calibrated with mesocosm data

Electrochemical reduction of carbon dioxide on gas diffusion electrodes : optimization of electrode preparation and reaction conditions

Electrochemical reduction of carbon dioxide using pyridinium as catalyst