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This paper proposes a deep leaning technique for accurate detection and reliable classification of organic pollutants in water. The pollutants are detected by means of cyclic voltammetry characterizations made by using low-cost disposable screen-printed electrodes. The paper demonstrates the possibility of strongly improving the detection of such platforms by modifying them with nanomaterials. The classification is addressed by using a deep learning approach with convolutional neural networks. To this end, the results of the voltammetry analysis are transformed into equivalent RGB images by means of Gramian angular field transformations. The proposed technique is applied to the detection and classification of hydroquinone and benzoquinone, which are particularly challenging since these two pollutants have a similar electroactivity and thus the voltammetry curves exhibit overlapping peaks. The modification of electrodes by carbon nanotubes improves the sensitivity of a factor of about ×25, whereas the convolution neural network after Gramian transformation correctly classifies 100% of the experiments.

Water pollution is nowadays a global problem and the effective detection of pollutants is of fundamental importance. Herein, a facile, efficient, robust, and rapid (response time < 2 min) method for the determination of important quinone-based industrial pollutants such as hydroquinone and benzoquinone is reported. The recognition method is based on the use of screen-printed electrodes as sensing platforms, enhanced with carbon-based nanomaterials. The enhancement is achieved by modifying the working electrode of such platforms through highly sensitive membranes made of Single- or Multi-Walled Carbon Nanotubes (SWNTs and MWNTs) or by graphene nanoplatelets. The modified sensing platforms are first carefully morphologically and electrochemically characterized, whereupon they are tested in the detection of different pollutants (i.e., hydroquinone and benzoquinone) in water solution, by using both cyclic and square-wave voltammetry. In particular, the sensors based on film-deposited nanomaterials show good sensitivity with a limit of detection in the nanomolar range (0.04 and 0.07 μM for SWNT- and MWNT-modified SPEs, respectively) and a linear working range of 10 to 1000 ppb under optimal conditions. The results highlight the improved performance of these novel sensing platforms and the large-scale applicability of this method for other analytes (i.e., toxins, pollutants).

Lattice structures with lattice parameters in the mm range are routinely fabricated by additive manufacturing. Combining light weight and mechanical strength, these structures have plenty of potential applications. When composed of conducting elements, a 3D lattice has interesting electrical and electromagnetic properties. In this work, the electrokinetic properties of a conducting lattice are described by mixing the theory of resistor networks and continuous-medium electrodynamics. Due to the length scale provided by the lattice parameter, the effective continuous medium that mimics the electrokinetic response of a resistor lattice is characterized by a non-local Ohm’s law.

Electrical transport properties and magnetoresistance of single-wall carbon nanotubes (SWCNT) films were investigated within temperature range (2 – 300) K and in magnetic fields up to 8 T. A crossover between metallic (dR/dT > 0) and non-metallic (dR/dT < 0) temperature dependence of the resistance as well as low-temperature saturation of the resistance in high bias regime indicated on the diminishing of role of the contact barriers between individual nanotubes essential for the charge transport in SWCNT arrays. The magnetoresistance (MR) data demonstrated influence of weak localization and electron-electron interactions on charge transport properties in SWCNT films. The low-field negative MR with positive upturn was observed at low temperatures. At T > 10 K only negative MR was observed in the whole range of available magnetic fields. The negative MR can be approximated using 1D weak localization (WL) model. The low temperature positive MR is induced by contribution from electron-electron interactions. DOI: http://dx.doi.org/10.5755/j01.ms.20.2.6311