Electrodeposition of lead dioxide on Fe electrode: application to the degradation of Indigo Carmine dye

Journal of Applied Electrochemistry - The modification of an iron electrode was carried out according to the following two steps. In a first step, a cathodic reduction is performed to form a film...     

Can lanthanum doping enhance catalytic performance of silver in direct propylene epoxidation over NaMoAg/SiO_2?

In the attempt to reduce surface free energy of silica to improve interaction of silica with silver, silica was doped by different amounts of low surface energy lanthanum, La_2O_3, through impregnation. The doped and undoped silica were used as supports for preparation of Na/Ag/Mo/La_2O_3-SiO_2 catalysts. Catalytic performances of the catalysts were evaluated in direct epoxidation of propylene(DPO) using molecular oxygen under atmospheric pressure and without adding hydrogen. Adding 5 wt%La to the Na/Ag/Mo/SiO_2 catalyst improves both the catalysts electivity in DPO and its stability over 20h of time-on-stream.The characterization results show that La_2O_3 species interact strongly with silver particles on the silica surface which result in significant improvement in the dispersion profile of silver and marked decrease in the size of silver nanoparticles(AgNPs). The estimated mean diameter of AgNPs is ca. 4.0 nm in Na/Ag/Mo/5 wt%La_2O_3-SiO_2, which is smaller than that(53.9 nm) found in Na/Ag/SiO_2. The presence of subnanometer AgNPs on Ag/La_2O_3-SiO_2 prior addition of MoO_3 and NaCl to the sample can enhance the mutual electronic synergism between Ag, MoO_3 and Na for selective production of propylene oxide.     

A hybrid knowledge and ensemble classification approach for prediction of venous thromboembolism

Clinical narratives such as progress summaries, lab reports, surgical reports, and other narrative texts contain key biomarkers about a patient's health. Evidence-based preventive medicine needs accurate semantic and sentiment analysis to extract and classify medical features as the input to appropriate machine learning classifiers. However, the traditional approach of using single classifiers is limited by the need for dimensionality reduction techniques, statistical feature correlation, a faster learning rate, and the lack of consideration of the semantic relations among features. Hence, extracting semantic and sentiment-based features from clinical text and combining multiple classifiers to create an ensemble intelligent system overcomes many limitations and provides a more robust prediction outcome. The selection of an appropriate approach and its interparameter dependency becomes key for the success of the ensemble method. This paper proposes a hybrid knowledge and ensemble learning framework for prediction of venous thromboembolism (VTE) diagnosis consisting of the following components: a VTE ontology, semantic extraction and sentiment assessment of risk factor framework, and an ensemble classifier. Therefore, a component-based analysis approach was adopted for evaluation using a data set of 250 clinical narratives where knowledge and ensemble achieved the following results with and without semantic extraction and sentiment assessment of risk factor, respectively: a precision of 81.8% and 62.9%, a recall of 81.8% and 57.6%, an F measure of 81.8% and 53.8%, and a receiving operating characteristic of 80.1% and 58.5% in identifying cases of VTE.     

Electrophoretic deposition of graphene oxide on carbon brush as bioanode for microbial fuel cell operated with real wastewater

Microbial fuel cell (MFC) is a promising technology for simultaneous wastewater treatment and energy harvesting. The properties of the anode material play a critical role in the performance of the MFC. In this study, graphene oxide was prepared by a modified hummer's method. A thin layer of graphene oxide was incorporated on the carbon brush using an electrophoretic technique. The deoxygenated graphene oxide formed on the surface of the carbon brush (RGO-CB) was investigated as a bio-anode in MFC operated with real wastewater. The performance of the MFC using the RGO-CB was compared with that using plain carbon brush anode (PCB). Results showed that electrophoretic deposition of graphene oxide on the surface of carbon brush significantly enhanced the performance of the MFC, where the power density increased more than 10 times (from 33 mWm^?2 to 381 mWm^?2). Although the COD removal was nearly similar for the two MFCs, i.e., with PCB and RGO-CB; the columbic efficiency significantly increased in the case of RGO-CB anode. The improved performance in the case of the modified electrode was related to the role of the graphene in improving the electron transfer from the microorganism to the anode surface, as confirmed from the electrochemical impedance spectroscopy measurements.     

Unconstrained Hand Dorsal Veins Image Database and Recognition System

Hand veins can be used effectively in biometric recognition since they are internal organs that, in contrast to fingerprints, are robust under external environment effects such as dirt and paper cuts. Moreover, they form a complex rich shape that is unique, even in identical twins, and allows a high degree of freedom. However, most currently employed hand-based biometric systems rely on hand-touch devices to capture images with the desired quality. Since the start of the COVID-19 pandemic, most hand-based biometric systems have become undesirable due to their possible impact on the spread of the pandemic. Consequently, new contactless hand-based biometric recognition systems and databases are desired to keep up with the rising hygiene awareness. One contribution of this research is the creation of a database for hand dorsal veins images obtained contact-free with a variation in capturing distance and rotation angle. This database consists of 1548 images collected from 86 participants whose ages ranged from 19 to 84 years. For the other research contribution, a novel geometrical feature extraction method has been developed based on the Curvelet Transform. This method is useful for extracting robust rotation invariance features from vein images. The database attributes and the veins recognition results are analyzed to demonstrate their efficacy.     

Identification of factors that accelerate hydrogen production by Clostridium butyricum RAK25832 using casamino acids as a nitrogen source

The ability of Clostridium butyricum RAK25832 to use casamino acids as a nitrogen source was investigated. Strain RAK25832 showed the capacity to utilize different types of carbon sources. With glucose as a carbon source (10 g/L), the preferred final concentration of casamino acids was 26.67 g/L, with a cumulative hydrogen production, production rate, and yield of 2505 mL H₂/L, 160 mL/h, and 1.81 mol H₂/mol glucose, respectively. Eighteen metal elements were screened to identify the most important metals for biohydrogen production, and four elements were optimized. The optimal medium composition was MgCl₂·6H₂O (0.1 g/L), K₂HPO₄·3H₂O (6.67 g/L), NaHCO₃ (2.6 g/L), and FeCl₂·4H₂O (0.002 g/L). Vitamin supplementation of the medium showed no significant effect on hydrogen production. Under the optimized conditions, cumulative hydrogen production reached 3074 mL H₂/L. This is the first study to demonstrate the use of casamino acids as a nitrogen source by C. butyricum.     

Learned Stochastic Mobility Prediction for Planning with Control Uncertainty on Unstructured Terrain

Motion planning for planetary rovers must consider control uncertainty in order to maintain the safety of the platform during navigation. Modeling such control uncertainty is difficult due to the complex interaction between the platform and its environment. In this paper, we propose a motion‐planning approach whereby the outcome of control actions is learned from experience and represented statistically using a Gaussian process regression model. This mobility prediction model is trained using sample executions of motion primitives on representative terrain, and it predicts the future outcome of control actions on similar terrain. Using Gaussian process regression allows us to exploit its inherent measure of prediction uncertainty in planning. We integrate mobility prediction into a Markov decision process framework and use dynamic programming to construct a control policy for navigation to a goal region in a terrain map built using an onboard depth sensor. We consider both rigid terrain, consisting of uneven ground, small rocks, and nontraversable rocks, and also deformable terrain. We introduce two methods for training the mobility prediction model from either proprioceptive or exteroceptive observations, and we report results from nearly 300 experimental trials using a planetary rover platform in a Mars‐analogue environment. Our results validate the approach and demonstrate the value of planning under uncertainty for safe and reliable navigation.     

Synthesis,Structure Characterization and Biological Activity of Co (II), Cu (II), and Zn (II) Complexes with (Z)‐3‐((3‐hydroxybenzylidene)amino)pyridin‐1‐ium 4‐(dodecan‐4‐yl)benzenesulfonate Surfactant

(Z)‐3‐((3‐hydroxybenzylidene)amino)pyridin‐1‐ium4‐dodecylbenzenesulfonate anionic surfactant and its cobalt, copper and zinc complexes were synthesized (I, I‐Co, I‐Cu and I‐Zn). The chemical structures of it were characterized by elemental analysis, FTIR, ¹H‐NMR, UV–Vis spectroscopy and atomic adsorption. The effects of the chemical structures of the synthesized anionic surfactant and the type of transition metals on the surface activity are presented in this paper. The thermodynamic parameters show that adsorption and micellization processes are spontaneous. The results of biological activity measurements showed that the synthesized compounds have a great efficiency against gram positive (Bacillus subtilis and Staphylococcus aureus), gram‐ negative bacteria (Pseudomonas aeruginosa and Escherichia coli) and fungi (Candida albicans and Aspergillus niger) as well as the sulfate reducing bacteria (Desulfomonas pigra). The complexation of the anionic surfactant with Co²⁺, Cu²⁺ and Zn²⁺ increase the antimicrobial activity values.     

Evaluation of Some Nonionic Surfactants Derived From Vanillin as Corrosion Inhibitors for Carbon Steel During Drilling Processes

Four eco‐friendly nonionic surfactants based on vanillin were investigated as corrosion inhibitors against carbon steel dissolution during the drilling process in the oil field. The corrosion inhibition efficiencies of the tested compounds were determined using weight loss, electrochemical polarization, and electrochemical impedance techniques. The data obtained show that the nonionic surfactants prevent the corrosion of drilling tools and their inhibition efficiency increased with an increase in their concentration. Tafel curves revealed that the surfactants under study act as mixed inhibitors. The adsorption of the inhibitors on carbon steel surface decreases the double‐layer capacitance. The inhibition efficiencies of the surfactants were influenced by their chemical structure and surface activity.