Graphene oxide-Au nano particle coated quartz crystal microbalance biosensor for the real time analysis of carcinoembryonic antigen

A label-free quartz crystal microbalance (QCM) biosensor was developed for the selective and real-time estimation of carcinoembryonic antigen (CEA) through the present study. Graphene oxide-Au nanoparticles (GO-AuNPs) was in situ synthesised on the surface of the QCM electrode and the antibody of CEA (monoclonal anti-CEA from mouse) was covalently immobilized on this layer as the bioreceptor for CEA. Mercaptoacetic acid–EDC–NHS reaction mechanism was used for anti-CEA immobilization. The effect of oxygen plasma treatment of the QCM electrode surface before bioreceptor preparation on the performance of the biosensor was tested and was found promising. CEA solutions with various concentrations were analysed using the bioreceptors to estimate the sensitivity and detection limit of the biosensor. The biosensors selectively recognized and captured CEA biomolecules with a detection limit of 0.06 and 0.09 ng mL−1 of CEA for oxygen plasma-treated (E2) and untreated (E1) bioreceptors, respectively. The sensitivity was estimated at 102 and 79 Hz, respectively, for E2 and E1. Clinical serum samples were analysed and the results were found in good agreement with the ELISA analysis. Long term stability was also found to be excellent. Langmuir adsorption isotherm was also conducted using the experimental results

The TSA Degradation Process Within Cement-Based Materials in the Electrical Field Environment

The thaumasite form of sulfate attack (TSA) of cement-based materials is a complex degradation process. The degradation process needs abundant water, carbonate, sulfate and silicate. However, there is no detailed study on the TSA degradation process. X-Ray Diffraction (XRD) and chemical analyses were used to study the TSA degradation process based on the present studies. The results indicated that the degradation process of the electrical field was similar to the full immersion, with the main difference being the degradation rate. The electrical field can obviously accelerate the TSA degradation process. The degradation had progressed to the core of the sample, and the amount of pulp was found at 120 days. The degree of degradation of the MgSO4 full immersion affected only the surface of the sample at 360 days. The degree of degradation of the MgSO4 full immersion was the lowest at 360 days. Meanwhile, combining the XRD, chemical analysis data, the degradation rule was summarized as there being a fixed range to generate the pulp (the representative thaumasite form of sulfate attack), and the pH and the Ca/S need to meet the following conditions: 10.6

Training High Quality Spam-detection Models Using Weak Labels

To be effective in detecting spam in online content sharing networks, it is necessary that techniques used to detect spam have good precision, high recall, and the ability to adapt to new types of spam. A bottleneck in developing such machine learning techniques is the lack of availability of high quality labeled training data. Human labeling to obtain high quality labeled data is expensive and not scalable. Current approaches such as unsupervised learning or semi-supervised learning can only produce low quality labels. Generally, the present disclosure is directed to a weak supervision approach to train a machine learning model to detect spam content items. Weak labels are generated for content items in training data using various techniques such as rules that encode domain knowledge and/or anomaly detection techniques such as unsupervised machine learning/ clustering or semi-supervised machine learning. The accuracy of the various techniques is estimated based on observed agreements/ disagreements in the weak labels. The weak labels are combined into a single value (e.g., per content item) that is used as a probabilistic training label to train a machine learning model using supervised learning that is noise aware. In the training, a penalty is applied for deviation from the probabilistic label such that the penalty is higher for a label associated with a higher confidence and lower for a label associated with a lower confidence. The model thus trained can be used to detect spam content