Tailoring the surface chemistry of zeolite templated carbon by electrochemical methods

One option to optimize carbon materials for supercapacitor applications is the generation of surface functional groups that contribute to the pseudocapacitance without losing the designed physical properties. This requires suitable functionalization techniques able to selectively introduce a given amount of electroactive oxygen groups. In this work, the influence of the chemical and electrochemical oxidation methods, on the chemical and physical properties of a zeolite templated carbon (ZTC), as a model carbon material, have been studied and compared. Although both oxidation methods generally produce a loss of the original ZTC physical properties with increasing amount of oxidation, the electrochemical method shows much better controllability and, unlike chemical treatments, enables the generation of a large number of oxygen groups (O = 11000- 3300 μmol/g), with a higher proportion of active functionalities, while retaining a high surface area (ranging between 1900-3500 m2/g), a high microporosity and an ordered 3-D structure.

Tailoring the Surface Chemistry of Activated Carbon Cloth by Electrochemical Methods

This paper presents a systematic study of the effect of the electrochemical treatment (galvanostatic electrolysis in a filter-press electrochemical cell) on the surface chemistry and porous texture of commercial activated carbon cloth. The same treatments have been conducted over a granular activated carbon in order to clarify the effect of morphology. The influence of different electrochemical variables, such as the electrode polarity (anodic or cathodic), the applied current (between 0.2 and 1.0 A) and the type of electrolyte (HNO3 and NaCl) have also been analyzed. The anodic treatment of both activated carbons causes an increase in the amount of surface oxygen groups, whereas the cathodic treatment does not produce any relevant modification of the surface chemistry. The HNO3 electrolyte produced a lower generation of oxygen groups than the NaCl one, but differences in the achieved distribution of surface groups can be benefitial to selectively tune the surface chemistry. The porous texture seems to be unaltered after the electro-oxidation treatment. The validity of this method to introduce surface oxygen groups with a pseudocapacitive behavior has been corroborated by cyclic voltammetry. As a conclusion, the electrochemical treatment can be easily implemented to selectively and quantitatively modify the surface chemistry of activated carbons with different shapes and morphologies.

Electrooxidation methods to produce pseudocapacitance-containing porous carbons

Surface oxygen groups play a key role on the performance of porous carbon electrodes for electrochemical capacitors in aqueous media. The electrooxidation method in NaCl electrolyte using a filter press cell and dimensionally stable anodes is proposed as a viable process for the generation of oxygen groups on porous carbon materials. The experimental set-up is so flexible that allows the easy modification of carbon materials with different configurations, i.e. cloths and granular, obtaining different degrees of oxidation for both conformations without the requirement of binders and conductivity promoters. After the electrooxidation method, the attained porosity is maintained between 90 and 75% of the initial values. The surface oxygen groups generated can increase the capacitance up to a 30% when compared to the pristine material. However, a severe oxidation is detrimental since it may decrease the conductivity and increase the resistance for ion mobility.

Real-time 3D semi-local surface patch extraction using GPGPU

Feature vectors can be anything from simple surface normals to more complex feature descriptors. Feature extraction is important to solve various computer vision problems: e.g. registration, object recognition and scene understanding. Most of these techniques cannot be computed online due to their complexity and the context where they are applied. Therefore, computing these features in real-time for many points in the scene is impossible. In this work, a hardware-based implementation of 3D feature extraction and 3D object recognition is proposed to accelerate these methods and therefore the entire pipeline of RGBD based computer vision systems where such features are typically used. The use of a GPU as a general purpose processor can achieve considerable speed-ups compared with a CPU implementation. In this work, advantageous results are obtained using the GPU to accelerate the computation of a 3D descriptor based on the calculation of 3D semi-local surface patches of partial views. This allows descriptor computation at several points of a scene in real-time. Benefits of the accelerated descriptor have been demonstrated in object recognition tasks. Source code will be made publicly available as contribution to the Open Source Point Cloud Library.

Power line interference filtering on surface electromyography based on the stationary wavelet packet transform

Power line interference is one of the main problems in surface electromyogram signals (EMG) analysis. In this work, a new method based on the stationary wavelet packet transform is proposed to estimate and remove this kind of noise from EMG data records. The performance has been quantitatively evaluated with synthetic noisy signals, obtaining good results independently from the signal to noise ratio (SNR). For the analyzed cases, the obtained results show that the correlation coefficient is around 0.99, the energy respecting to the pure EMG signal is 98–104%, the SNR is between 16.64 and 20.40 dB and the mean absolute error (MAE) is in the range of −69.02 and −65.31 dB. It has been also applied on 18 real EMG signals, evaluating the percentage of energy respecting to the noisy signals. The proposed method adjusts the reduction level to the amplitude of each harmonic present in the analyzed noisy signals (synthetic and real), reducing the harmonics with no alteration of the desired signal.

Functionalization of carbon nanotubes using aminobenzene acids and electrochemical methods. Electroactivity for the oxygen reduction reaction

Functionalized carbon nanotubes (CNTs) using three aminobenzene acids with different functional groups (carboxylic, sulphonic, phosphonic) in para position have been synthesized through potentiodynamic treatment in acid media under oxidative conditions. A noticeable increase in the capacitance for the functionalized carbon nanotubes mainly due to redox processes points out the formation of an electroactive polymer thin film on the CNTs surface along with covalently bonded functionalities. The CNTs functionalized using aminobenzoic acid rendered the highest capacitance values and surface nitrogen content, while the presence of sulfur and/or phosphorus groups in the aminobenzene structure yielded a lower functionalization degree. The oxygen reduction reaction (ORR) activity of the functionalized samples was similar to that of the parent CNTs, independently of the functional group present in the aminobenzene acid. Interestingly, a heat treatment in N2 atmosphere with a very low O2 concentration (3125 ppm) at 800 °C of the CNTs functionalized with aminobenzoic acid produced a material with high amounts of surface oxygen and nitrogen groups (12 and 4% at., respectively), that seem to modulate the electron-donor properties of the resulting material. The onset potential and limiting current for ORR was enhanced for this material. These are promising results that validates the use of electrochemistry for the synthesis of novel N-doped electrocatalysts for ORR in combination with adequate heat treatments.

Successful functionalization of superporous zeolite templated carbon using aminobenzene acids and electrochemical methods

A novel and selective electrochemical functionalization of a highly reactive superporous zeolite templated carbon (ZTC) with two different aminobenzene acids (2-aminobenzoic and 4-aminobenzoic acid) was achieved. The functionalization was done through potentiodynamic treatment in acid media under oxidative conditions, which were optimized to preserve the unique ZTC structure. Interestingly, it was possible to avoid the electrochemical oxidation of the highly reactive ZTC structure by controlling the potential limit of the potentiodynamic experiment in presence of aminobenzene acids. The electrochemical characterization demonstrated the formation of polymer chains along with covalently bonded functionalities to the ZTC surface. The functionalized ZTCs showed several redox processes, producing a capacitance increase in both basic and acid media. The rate performance showed that the capacitance increase is retained at scan rates as high as 100 mV s−1, indicating that there is a fast charge transfer between the polymer chains formed inside the ZTC porosity or the new surface functionalities and the ZTC itself. The success of the proposed approach was also confirmed by using other characterization techniques, which confirmed the presence of different nitrogen groups in the ZTC surface. This promising method could be used to achieve highly selective functionalization of highly porous carbon materials.

Fluid-Fluid Reactions: Study of the Surface Renewal Theory in Chemical Engineering Courses

The Surface Renewal Theory (SRT) is one of the most unfamiliar models in order to characterize fluid-fluid and fluid-fluid-solid reactions, which are of considerable industrial and academicals importance. In the present work, an approach to the resolution of the SRT model by numerical methods is presented, enabling the visualization of the influence of different variables which control the heterogeneous overall process. Its use in a classroom allowed the students to reach a great understanding of the process.

Improving drug discovery using hybrid softcomputing methods

Virtual screening (VS) methods can considerably aid clinical research, predicting how ligands interact with drug targets. Most VS methods suppose a unique binding site for the target, but it has been demonstrated that diverse ligands interact with unrelated parts of the target and many VS methods do not take into account this relevant fact. This problem is circumvented by a novel VS methodology named BINDSURF that scans the whole protein surface in order to find new hotspots, where ligands might potentially interact with, and which is implemented in last generation massively parallel GPU hardware, allowing fast processing of large ligand databases. BINDSURF can thus be used in drug discovery, drug design, drug repurposing and therefore helps considerably in clinical research. However, the accuracy of most VS methods and concretely BINDSURF is constrained by limitations in the scoring function that describes biomolecular interactions, and even nowadays these uncertainties are not completely understood. In order to improve accuracy of the scoring functions used in BINDSURF we propose a hybrid novel approach where neural networks (NNET) and support vector machines (SVM) methods are trained with databases of known active (drugs) and inactive compounds, being this information exploited afterwards to improve BINDSURF VS predictions.