Quantum Dot-Sensitized Solar Cells Based on Directly Adsorbed Zinc Copper Indium Sulfide Colloids

Heavy metal-based quantum dots (QDs) have demonstrated to behave as efficient sensitizers in QD-sensitized solar cells (QDSSCs), as attested by the countless works and encouraging efficiencies reported so far. However, their intrinsic toxicity has arisen as a major issue for the prospects of commercialization. Here, we examine the potential of environmentally friendly zinc copper indium sulfide (ZCIS) QDs for the fabrication of liquid-junction QDSSCs by means of photoelectrochemical measurements. A straightforward approach to directly adsorb ZCIS QDs on TiO2 from a colloidal dispersion is presented. Incident photon-to-current efficiency (IPCE) spectra of sensitized photoanodes show a marked dependence on the adsorption time, with longer times leading to poorer performances. Cyclic voltammograms point to a blockage of the channels of the mesoporous TiO2 film by the agglomeration of QDs as the main reason for the decrease in efficiency. Photoanodes were also submitted to the ZnS treatment. Its effects on electron recombination with the electrolyte are analyzed through electrochemical impedance spectroscopy and photopotential measurements. The corresponding results bring out the role of the ZnS coating as a barrier layer preventing electron leakage toward the electrolyte, as argued in other QD-sensitized systems. The beneficial effect of the ZnS coating is ultimately reflected on the power conversion efficiency of complete devices, reaching values of 2 %. In a more general vein, through these findings, we aim to call the attention to the potentiality of this quaternary alloy, virtually unexplored as a light harvester for sensitized devices.

Screen-printed electrode-based electrochemical detector coupled with in-situ ionic-liquid-assisted dispersive liquid–liquid microextraction for determination of 2,4,6-trinitrotoluene

A novel method is reported, whereby screen-printed electrodes (SPELs) are combined with dispersive liquid–liquid microextraction. In-situ ionic liquid (IL) formation was used as an extractant phase in the microextraction technique and proved to be a simple, fast and inexpensive analytical method. This approach uses miniaturized systems both in sample preparation and in the detection stage, helping to develop environmentally friendly analytical methods and portable devices to enable rapid and onsite measurement. The microextraction method is based on a simple metathesis reaction, in which a water-immiscible IL (1-hexyl-3-methylimidazolium bis[(trifluoromethyl)sulfonyl]imide, [Hmim][NTf2]) is formed from a water-miscible IL (1-hexyl-3-methylimidazolium chloride, [Hmim][Cl]) and an ion-exchange reagent (lithium bis[(trifluoromethyl)sulfonyl]imide, LiNTf2) in sample solutions. The explosive 2,4,6-trinitrotoluene (TNT) was used as a model analyte to develop the method. The electrochemical behavior of TNT in [Hmim][NTf2] has been studied in SPELs. The extraction method was first optimized by use of a two-step multivariate optimization strategy, using Plackett–Burman and central composite designs. The method was then evaluated under optimum conditions and a good level of linearity was obtained, with a correlation coefficient of 0.9990. Limits of detection and quantification were 7 μg L−1 and 9 μg L−1, respectively. The repeatability of the proposed method was evaluated at two different spiking levels (20 and 50 μg L−1), and coefficients of variation of 7 % and 5 % (n = 5) were obtained. Tap water and industrial wastewater were selected as real-world water samples to assess the applicability of the method.

An Electronic Reconfigurable Neural Architecture for Intrusion Detection

The explosive growth of the traffic in computer systems has made it clear that traditional control techniques are not adequate to provide the system users fast access to network resources and prevent unfair uses. In this paper, we present a reconfigurable digital hardware implementation of a specific neural model for intrusion detection. It uses a specific vector of characterization of the network packages (intrusion vector) which is starting from information obtained during the access intent. This vector will be treated by the system. Our approach is adaptative and to detecting these intrusions by using a complex artificial intelligence method known as multilayer perceptron. The implementation have been developed and tested into a reconfigurable hardware (FPGA) for embedded systems. Finally, the Intrusion detection system was tested in a real-world simulation to gauge its effectiveness and real-time response.