5 research outputs found
Synthesis of MgO nanoparticles via the sol-gel method for antibacterial applications, investigation of optical properties and comparison with commercial MgO
No full textMagnesium oxide nanoparticles (MgO-NPs) were synthesized using the solution-gelation (sol-gel) technique. For comparison, US Research Nanomaterials, Inc has supplied MgO with very high purity and used without further purification. Both nanoparticles were subjected to X-ray diffraction (XRD) pattern analysis for structural investigation. The XRD measurements showed an incredibly crystalline cubic structure. Morphological studies were carried out using scanning electron microscopy (SEM) measurements to examine the nanoparticles’ size and shape. SEM analysis of the synthesized sample’s morphology revealed a flake-like shape, while a spherical-like structure was observed in the case of the commercial MgO. Using X-ray peak broadening analysis, the Scherrer method was used to assess the crystallite size. A substantial correlation was found between the Scherrer formula and the average particle size of the MgO-NPs, which was determined through SEM analysis. The energy gap calculations were ascertained by plotting the photon energy utilizing the Tauc equation with the measurements of UV–visible absorption spectroscopy. Both nanoparticles were used against the bacterial activity of Streptococcus and Serratia bacteria. The results showed that synthesized nanoparticles exhibited greater effectiveness against bacterial activity than commercial ones
Synthesis of MgO Nanoparticles via the Sol–Gel Method: A DFT+U Analysis and Antibacterial Efficacy Comparison with Commercial Counterparts
No full textA sol–gel technique was utilized to synthesize magnesium oxide nanoparticles (MgO NPs) and compared to commercially available MgO NPs obtained from US Research Nanomaterials, Inc. X-ray diffraction (XRD) analysis revealed a highly crystalline cubic structure for both samples. Scanning electron microscopy (SEM) examinations showed that the synthesized MgO was nanoflakes (NFs), while the commercial MgO exhibited a spherical structure. The size of the crystallites was determined using the Scherrer method, showing a strong correlation with the SEM examination. In conjunction with the Tauc equation, UV–visible absorption spectroscopy was used to calculate the energy gap. The dielectric constants are determined for the entire spectrum and show very low values in the visible spectrum. Comparative antibacterial studies against Staphylococcus aureus (S. aureus) and Pseudomonas aeruginosa (P. aeruginosa) bacteria demonstrated that the synthesized NFs exhibited greater efficacy. Density functional theory (DFT) calculations using the Generalized Gradient Approximation with a Hubbard correction (GGA + U) revealed that the electronic characteristics of the MgO structure were primarily influenced by the oxygen 2p orbitals, with the band gap significantly impacted by the U parameter for oxygen. The determined band gaps for the synthesized and commercially available MgO nanostructure strongly agree with the experimental values. Density of states (DOS) analysis confirmed the difference in energy gap between the two nanostructures. The increased band gap of the synthesized NFs was associated with their reduced dimensions and unique structure, enhancing their antibacterial efficacy by facilitating superior attachment to bacterial cell surfaces
Classifying electrocardiograph waveforms using trained deep learning neural network based on wavelet representation
Get PDFDue to the rise in cardiac patients, an automated system that can identify different heart disorders has been created to lighten and distribute the duty of physicians. This research uses three different electrocardiograph (ECG) signals as indicators of a person's cardiac problems: Normal sinus rhythm (NSR), arrhythmia (ARR), and congestive heart failure (CHF). The continuous wavelet transform (CWT) provides the mechanism for classifying the 190 individual cases of ECG data into a 2-dimensional time-frequency representation. In this paper, the modified GoogLeNet is used for ECG data classification. Using a transfer learning approach and adjustments to parts of the output layers, ECG classification was conducted and the effectiveness of convolutional neural network (CNN) designs was tested. By comparing the results that the optimized neural network and GoogLeNet both had classification accuracy about of 80% and 100%, respectively. The GoogLeNet provide the best result in term of accuracy and training time
Electrocardiogram Waveforms Diagnosis based on Wavelet Representation and SqueezeNet Model
Full text linkAArrhythmia is an irregular in a person's beating heart that can happen occasionally. Heart rhythm problems can have disastrous results and seriously endanger health. Visually analyzing ECG data might be complex due to its large amount of information. Designing an automated method to assess the massive amount of ECG data is crucial. This research shows continuous wavelet transform (CWT) and deep learning strategies to automate detection and classification processes to examine three different ECG signals: congestive heart failure (CHF), normal sinus rhythm (NSR), and arrhythmia (ARR). CWT converts ECG signals into scalogram images for noise reduction and feature extraction. In deep learning, the modified SqueezeNet is employed to recognize the output of CWT, which is produced by the input of the ECG data. The proposed technique achieved 83.3%, 100%, and 94.7% accuracy in detecting CHF, NSR, and ARR. A comprehensive approach for classifying arrhythmias has been proposed, in which scalogram pictures of ECG waves are trained using the SqueezeNet model. The outcomes are superior to other current techniques and will significantly reduce wrong diagnose
Mapping geographical inequalities in access to drinking water and sanitation facilities in low-income and middle-income countries, 2000-17
Full text linkBACKGROUND: Universal access to safe drinking water and sanitation facilities is an essential human right, recognised in the Sustainable Development Goals as crucial for preventing disease and improving human wellbeing. Comprehensive, high-resolution estimates are important to inform progress towards achieving this goal. We aimed to produce high-resolution geospatial estimates of access to drinking water and sanitation facilities.METHODS: We used a Bayesian geostatistical model and data from 600 sources across more than 88 low-income and middle-income countries (LMICs) to estimate access to drinking water and sanitation facilities on continuous continent-wide surfaces from 2000 to 2017, and aggregated results to policy-relevant administrative units. We estimated mutually exclusive and collectively exhaustive subcategories of facilities for drinking water (piped water on or off premises, other improved facilities, unimproved, and surface water) and sanitation facilities (septic or sewer sanitation, other improved, unimproved, and open defecation) with use of ordinal regression. We also estimated the number of diarrhoeal deaths in children younger than 5 years attributed to unsafe facilities and estimated deaths that were averted by increased access to safe facilities in 2017, and analysed geographical inequality in access within LMICs.FINDINGS: Across LMICs, access to both piped water and improved water overall increased between 2000 and 2017, with progress varying spatially. For piped water, the safest water facility type, access increased from 40·0% (95% uncertainty interval [UI] 39·4-40·7) to 50·3% (50·0-50·5), but was lowest in sub-Saharan Africa, where access to piped water was mostly concentrated in urban centres. Access to both sewer or septic sanitation and improved sanitation overall also increased across all LMICs during the study period. For sewer or septic sanitation, access was 46·3% (95% UI 46·1-46·5) in 2017, compared with 28·7% (28·5-29·0) in 2000. Although some units improved access to the safest drinking water or sanitation facilities since 2000, a large absolute number of people continued to not have access in several units with high access to such facilities (>80%) in 2017. More than 253 000 people did not have access to sewer or septic sanitation facilities in the city of Harare, Zimbabwe, despite 88·6% (95% UI 87·2-89·7) access overall. Many units were able to transition from the least safe facilities in 2000 to safe facilities by 2017; for units in which populations primarily practised open defecation in 2000, 686 (95% UI 664-711) of the 1830 (1797-1863) units transitioned to the use of improved sanitation. Geographical disparities in access to improved water across units decreased in 76·1% (95% UI 71·6-80·7) of countries from 2000 to 2017, and in 53·9% (50·6-59·6) of countries for access to improved sanitation, but remained evident subnationally in most countries in 2017.INTERPRETATION: Our estimates, combined with geospatial trends in diarrhoeal burden, identify where efforts to increase access to safe drinking water and sanitation facilities are most needed. By highlighting areas with successful approaches or in need of targeted interventions, our estimates can enable precision public health to effectively progress towards universal access to safe water and sanitation.FUNDING: Bill & Melinda Gates Foundation
