Caffeine determination at a carbon fiber ultramicroelectrodes by fast-scan cyclic voltammetry

Caffeine determination using a fast-scan voltammetric procedure at a carbon fiber ultramicroelectrode (CF-UME) is described. The CF-UME was submitted to electrochemical pretreatment. Parameters such as number of acquisition cycles, scan rate, potential window, and the electrochemical surface pretreatment were optimized. Using the optimized conditions, it was possible to achieve a LDR from 10.0 up to 200 μmol L-1, with a LOD of 3.33 μmol L-1. The method has been applied in the determination of caffeine in commercial samples, with errors of 1.0-3.5% in relation to the label values and recoveries of 97-114% within the linear range.

Optimization and control of a continuous polymerization reactor

This work studies the optimization and control of a styrene polymerization reactor. The proposed strategy deals with the case where, because of market conditions and equipment deterioration, the optimal operating point of the continuous reactor is modified significantly along the operation time and the control system has to search for this optimum point, besides keeping the reactor system stable at any possible point. The approach considered here consists of three layers: the Real Time Optimization (RTO), the Model Predictive Control (MPC) and a Target Calculation (TC) that coordinates the communication between the two other layers and guarantees the stability of the whole structure. The proposed algorithm is simulated with the phenomenological model of a styrene polymerization reactor, which has been widely used as a benchmark for process control. The complete optimization structure for the styrene process including disturbances rejection is developed. The simulation results show the robustness of the proposed strategy and the capability to deal with disturbances while the economic objective is optimized.

Characterization of immobilized biomass by amplified rDNA restriction analysis (ARDRA) in an anaerobic sequencing-batch biofilm reactor (ASBBR) for the treatment of industrial wastewater

The performance of an anaerobic sequencing-batch biofilm reactor (ASBBR- laboratory scale- 14L )containing biomass immobilized on coal was evaluated for the removal of elevated concentrations of sulfate (between 200 and 3,000 mg SO4-2·L-1) from industrial wastewater effluents. The ASBBR was shown to be efficient for removal of organic material (between 90% and 45%) and sulfate (between 95% and 85%). The microbiota adhering to the support medium was analyzed by amplified ribosomal DNA restriction analysis (ARDRA). The ARDRA profiles for the Bacteria and Archaea domains proved to be sensitive for the determination of microbial diversity and were consistent with the physical-chemical monitoring analysis of the reactor. At 3,000 mg SO4-2·L-1, there was a reduction in the microbial diversity of both domains and also in the removal efficiencies of organic material and sulfate.

QuMinS: fast and scalable querying, mining and summarizing multi-modal databases

Given a large image set, in which very few images have labels, how to guess labels for the remaining majority? How to spot images that need brand new labels different from the predefined ones? How to summarize these data to route the user’s attention to what really matters? Here we answer all these questions. Specifically, we propose QuMinS, a fast, scalable solution to two problems: (i) Low-labor labeling (LLL) – given an image set, very few images have labels, find the most appropriate labels for the rest; and (ii) Mining and attention routing – in the same setting, find clusters, the top-'N IND.O' outlier images, and the 'N IND.R' images that best represent the data. Experiments on satellite images spanning up to 2.25 GB show that, contrasting to the state-of-the-art labeling techniques, QuMinS scales linearly on the data size, being up to 40 times faster than top competitors (GCap), still achieving better or equal accuracy, it spots images that potentially require unpredicted labels, and it works even with tiny initial label sets, i.e., nearly five examples. We also report a case study of our method’s practical usage to show that QuMinS is a viable tool for automatic coffee crop detection from remote sensing images.

Reactive sputter magnetron reactor for preparation of thin films and simultaneous in-situ structural study by X-ray diffraction.

Reactive Sputter Magnetron (RSM) is a widely used technique to thin films growing of compounds both, in research laboratories and in industrial processes. The nature of the deposited compound will depend then on the nature of the magnetron target and the nature of the ions generated in the plasma. One important aspect of the problem is the knowledge of the evolution of the film during the process of growing itself. In this work, we present the design, construction of a chamber to be installed in the Huber goniometer in the XRD2 line of LNLS in Campinas, which allows in situ growing kinetic studies of thin films.