ABSTRACT
Carbon-based electrodes are used in flow batteries to provide active centers for vanadium redox reactions. However, strong controversy exists about the exact origin of these centers. This study systematically explores the influence of structural and functional groups on the vanadium redox reactions at carbon surfaces. Pyridine, phenol and butyl containing groups are attached to carbon felt electrodes. To establish a unique comparison between the model and real-world behavior, both non-activated and commercially used thermally activated felts serve as a substrate. Results reveal enhanced half-cell performance in non-activated felt with introduced hydrophilic functionalities. However, this cannot be transferred to the thermally activated felt. Beyond a decrease in electrochemical activity, a reduced long-term stability can be observed. This work indicates that thermal treatment generates active sites that surpass the effect of functional groups and are even impeded by their introduction.
ABSTRACT
The degradation and aging of carbon felt electrodes is a main reason for the performance loss of Vanadium Redox Flow Batteries over extended operation time. In this study, the chemical mechanisms for carbon electrode degradation are investigated and distinct differences in the degradation mechanisms on positive and negative electrodes have been revealed. A combination of surface analysis techniques such as X-ray photoelectron spectroscopy (XPS), Raman spectroscopy, and Electrochemical Impedance Spectroscopy (EIS) was applied for this purpose. In addition to understanding the chemical and physical alterations of the aged electrodes, a thermal method for reactivating aged electrodes was developed. The reactivation process was successfully applied on artificially aged electrodes as well as on electrodes from a real-world industrial vanadium redox flow battery system. The aforementioned analysis methods provided insight and understanding into the chemical mechanisms of the reactivation procedure. By applying the reactivation method, the lifetime of vanadium redox flow batteries can be significantly extended.
ABSTRACT
Beta-lactamase (ß-lactamase) produced by different bacteria confers resistance against ß-lactam-containing drugs. The gene encoding ß-lactamase is plasmid-borne and can easily be transferred from one bacterium to another during conjugation. By such transformations, the recipient also acquires resistance against the drugs of the ß-lactam family. ß-Lactam antibiotics play a vital significance in clinical treatment of disastrous diseases like soft tissue infections, gonorrhoea, skin infections, urinary tract infections, and bronchitis. Herein, we report a prediction classifier named as ßLact-Pred for the identification of ß-lactamase proteins. The computational model uses the primary amino acid sequence structure as its input. Various metrics are derived from the primary structure to form a feature vector. Experimentally determined data of positive and negative beta-lactamases are collected and transformed into feature vectors. An operating algorithm based on the artificial neural network is used by integrating the position relative features and sequence statistical moments in PseAAC for training the neural networks. The results for the proposed computational model were validated by employing numerous types of approach, i.e., self-consistency testing, jackknife testing, cross-validation, and independent testing. The overall accuracy of the predictor for self-consistency, jackknife testing, cross-validation, and independent testing presents 99.76%, 96.07%, 94.20%, and 91.65%, respectively, for the proposed model. Stupendous experimental results demonstrated that the proposed predictor "ßLact-Pred" has surpassed results from the existing methods.
Subject(s)
Proteins , beta-Lactamases , Algorithms , Amino Acid Sequence , Neural Networks, Computer , beta-Lactamases/geneticsABSTRACT
Disorders of the heart and blood vessels are named cardiovascular disease. 'The heart's proper functionality is of an utmost necessity for the survival of life. The death rate due to heart disease, has been increased rapidly. Cardiovascular illness is believed the deadliest cause of death across the globe. From the facts and figures shared by the WHO (World Health Organization) 17.9 Million human lost their lives due to cardiovascular diseases. This research is carried out for the effective diagnosis of heart disease using the heart disease dataset available on the UCI Machine Repository. Heart disease diagnosis with an optimization algorithm can be fruitful in terms of higher accuracy and sensitivity. Finding an acceptable optimal solution among multiple solutions for a specific problem is known as optimization. Different machine learning algorithms have been applied as Support Machine Vector (SVM), K-Nearest Neighbor (KNN), Naïve Bayes (NB), Artificial Neural Network (ANN), Random Forest (RF), and Gradient Descent Optimization (GDO). Intelligent Cardiovascular Disease Prediction Empowered with Gradient Descent Optimization model produces the optimal results among under consideration classification algorithms. 98.54 % accuracy has been achieved by the GDO based model while performance evaluation it. 99.43% sensitivity (recall) and 97.76% precision have also been recorded. From the prediction results of the system, it's satisfactory to utilize it for cardiovascular disease diagnosis. The proposed system will be helpful for the analysis of cardiovascular disease.
