This work presents an electroanalytical method for the determination of moxifloxacin (MOXI) in tablets by its interaction with Cu(II) ion and subsequent electrochemical reduction at hanging mercury drop electrode (HMDE). A well-defined reduction peak at -0.21 V vs. Ag/AgCl in Phosphate buffer 0.04 mol L-1 pH 8.0 was observed for the complex reduction MOXI-Cu(II), using square-wave voltammetry (SWV). Using a 10 s of accumulation time at -0.40 V was found a limit detection of 3.60x10-8 mol l-1. The obtained results have shown good agreement with those obtained by spectrophotometric method.
We present SHEEP, a new machine learning approach to the classic problem of astronomical source classification, which combines the outputs from the XGBoost, LightGBM, and CatBoost learning algorithms to create stronger classifiers. A novel step in our pipeline is that prior to performing the classification, SHEEP first estimates photometric redshifts, which are then placed into the dataset as an additional feature for classification model training; this results in significant improvement in the subsequent classification performance. SHEEP contains two distinct classification methodologies: (i) Multi-class; (ii) one vs all with correction by a metalearner. We demonstrate the performance of SHEEP for the classification of stars, galaxies and quasars using a dataset composed of SDSS and WISE photometry of 3.5 million astronomical sources. The resulting F1-scores are as follows: (i) 0.992 for galaxies; (ii) 0.967 for quasars; (iii) and 0.985 for stars. In terms of the F1-scores for the three classes, SHEEP is found to outperform the recent RandomForest-based classification approach of Clarke et al. ( 2020) using an essentially identical dataset. Our methodology also facilitates model and dataset explainability via feature importances; it also allows the selection of sources whose uncertain classifications may make them interesting sources for follow-up observations.
Raceways ponds are the microalgal production systems most commonly used at industrial scale. In this work, two different raceway configurations were tested under the same processing conditions to compare their performance on the production of Nannochloropsis oceanica. Biomass productivity, biochemical composition of the produced biomass, and power requirements to operate those reactors were evaluated. Water depths of 0.20 and 0.13 m, and culture circulation velocities of 0.30 and 0.15 m s−1 were tested. A standard configuration, which had a full channel width paddlewheel, proved to be the most energy efficient, consuming less than half of the energy required by a modified configuration (had a half channel width paddlewheel). The later showed to have slightly higher productivity, not enough to offset the large difference in energetic consumption. Higher flow velocity (0.30 m s−1) led to a 1.7 g m−2 d−1 improvement of biomass productivity of the system, but it increased the energy consumption twice as compared to the 0.15 m s−1 flow velocity. The latter velocity showed to be the most productive in lipids. A water depth of 0.20 m was the most suitable option tested to cultivate microalgae, since it allowed a 54% energy saving. Therefore, a standard raceway pond using a flow velocity of 0.3 m s−1 with a 0.20 m water depth was the most efficient system for microalgal cultivation. Conversely, a flow velocity of 0.15 m s−1 was the most suitable to produce lipids.
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