Abstract The improvement of CO2 absorption by CNT nanofluids with deionized water as the base fluid was studied experimentally. The reactor used was a stirred thermostatic reactor, operated batchwise. Pure CO2 was employed in all the experiments. The content of CNTs in the nanofluids ranged from 0 to 0.2% (wt). The acidification treatment of CNTs was employed to improve the stability of the nanofluids. The parameters, such as the concentration of CNT nanoparticles in the nanofluids, the stirring speed, the ultrasonic time for CNT nanofluid preparation, the nitration time and the amount of nitric acid for CNT acidification were varied. The results show that, with the increase of CNT concentration, the enhancement factor first increased and then leveled off after a certain value of the CNT concentration. With increasing stirring speed, the enhancement factor in stable functional CNT nanofluids declines monotonously, while in poorly dispersed raw CNT suspensions, it first increased and then reduced. The ultrasonic and nitration times and the amount of nitric acid have optimum values for the CO2 absorption enhancement. The mechanism of the CNT nanofluid enhancement of CO2 absorption is discussed accordingly. The absorption enhancement by the CNT nanofluid should be mainly attributable to convective motion induced by the Brownian motion and the shuttle effect.

Trophic Level Index (TLI) is often used to assess the general eutrophication state of inland lakes in water science, technology, and engineering. In this paper, a data-driven inland-lake eutrophication assessment method was proposed by using an artificial neural network (ANN) to build relationships from remote sensing data and in-situ TLI sampling. In order to train the net, Moderate Resolution Imaging Spectroradiometer (MODIS, which has a revisit cycle of 4 times per day) data were combined with in-situ observations. Results demonstrate that the TLI obtained directly from remote-sensing images using the data-driven method is more accurate than the TLI calculated from the water quality factors retrieved from remote-sensing images using a multivariate regression method. Spatially continuous and quasi-real time results were retrieved by using MODIS data. This method provides an efficient way to map the TLI spatial distribution in inland lakes, and provides a scheme for increased automation in TLI estimation.