9 research outputs found
Preliminary studies of the impact of synthesis method on Reduced Graphene Oxide-Titanium Composite
No full textThere are two current major challenges aroused by the continued usage of fossil fuels as the energy source, which are the production of high levels of carbon dioxide (CO₂), resulting in global warming, and concerning the use of energy resources. There is a clear need to explore new prospects for CO₂ capture to prevent it from penetrating into the atmosphere. Carbon Capture and Conversion (CCC) method is one of the alternative solutions in carbon management. The synthesized reduced graphene oxide-Titanium (rGO-TiO₂) composites used in this preliminary study is the CCC material which will potentially capture the carbon dioxide (CO₂) and convert it into a hydrocarbon fuel such as methane. The aim of this preliminary study is to examine the impact of synthesis method and raw material to synthesize the rGO-TiO₂ composite. The photocatalytic activity was measured by using the Gas Chromatograph (GC) while the optical properties were measured by using Electrochemical Impedance Spectroscopy (EIS) and fluorescent spectrometer (PL). The EIS, PL and GC results confirms that the synthesize method and raw materials were affect the optical properties and the photocatalytic performance of the rGO-TiO₂. The rGO-TiO₂(H1) which was synthesized using the TBT powder via Hydrothermal method shows the best electrical properties and lowest recombination rate of the photogenerated electron-hole pairs compared to the other samples. The rGO-TiO₂(H1) also shows the highest photoreduction performance with 0.722 ųmol/gcat methane yield
Biofingerprint detection of corona virus using Raman spectroscopy: a novel approach
No full textAbstract Coronavirus disease-19 (COVID-19) is caused by SARS-CoV-2, a highly contagious respiratory virus that has caused a global pandemic. Despite the urgent need for effective diagnostic screening technologies, ideal methods for COVID-19 detection have not yet been developed. To address this issue, we developed a Raman spectroscopy technique for rapid and sensitive on-site detection of SARS-CoV-2, utilizing the unique spectral fingerprint of molecular vibrations. The proposed technique is non-invasive and label-free that enables the detection of molecular vibrations, providing a unique spectral fingerprint for different molecules. Raman spectra from 75 positive and 75 negative swab samples were analyzed, processed by smoothening and baseline correction of spectral data. The peaks in the processed data were detected and assigned based on literature peak, with peaks specific to positive samples used for detection with minimal false positives. These peaks were attributed to various molecules, including amino acids in proteins, glycoproteins, lipids, and protein structures. Our Raman spectroscopy technique provides a reliable and non-invasive approach for the detection of SARS-CoV-2, with potential to expand to other infectious agents. This method has significant implications for global health, aiding in effective control measures against COVID-19
Effect of monsoonal clustering for pm10 concentration Prediction in Keningau, Sabah using principal component analysis
No full textParticulate matter (PM) has caught scientific attention in scientific research due to its harmful effect on human health. While prediction is essential for future development in Keningau, temporal clustering in Keningau has yet to be studied. Thus, this research aims to determine whether monsoonal clustering is required for meteorological and pollutant concentration data collected in Keningau. Missing data is first imputed using Nearest Neighbour Method (NNM). Then, wind direction and wind speed are converted into northern (Wy) and eastern (Wx) component of wind speed. Data is then temporal clustered based on monsoonal season (NEM, IM4, SWM, IM10). Both clustered and unclustered data are analysed using principal component (PC) analysis (PCA). The findings revealed that humidity in PC1 with average EV (explained variation) of 93.92 ± 0.52 contribute the most variation of PM10, followed by Wx in PC2 with average EV of 3.51 ± 0.48. Regression analysis shows that humidity and PM10 are negatively moderate to strongly correlated except for IM4 (intermonsoon April), which may be due to dry climate during the season. As for Wx, it has weak correlation with PM10. This may be due to location of Keningau at western part of Crocker range. However, the spread of PM10 due to eastern wind causes weak to zero correlation. Due to consideration of dry climate as revealed by the findings from IM4 cluster, there is need for data collected by Keningau to be clustered by monsoon
Pattern Recognition for Human Diseases Classification in Spectral Analysis
No full textPattern recognition is a multidisciplinary area that received more scientific attraction during this period of rapid technological innovation. Today, many real issues and scenarios require pattern recognition to aid in the faster resolution of complicated problems, particularly those that cannot be solved using traditional human heuristics. One common problem in pattern recognition is dealing with multidimensional data, which is prominent in studies involving spectral data such as ultraviolet visible (UV/Vis), infrared (IR), and Raman spectroscopy data. UV/Vis, IR, and Raman spectroscopy are well-known spectroscopic methods that are used to determine the atomic or molecular structure of a sample in various fields. Typically, pattern recognition consists of two components: exploratory data analysis and classification method. Exploratory data analysis is an approach that involves detecting anomalies in data, extracting essential variables, and revealing the data’s underlying structure. On the other hand, classification methods are techniques or algorithms used to group samples into a predetermined category. This article discusses the fundamental assumptions, benefits, and limitations of some well-known pattern recognition algorithms including Principal Component Analysis (PCA), Kernel PCA, Successive Projection Algorithm (SPA), Genetic Algorithm (GA), Partial Least Square Regression (PLS-R), Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), Partial Least Square-Discriminant Analysis (PLS-DA) and Artificial Neural Network (ANN). The use of UV/Vis, IR, and Raman spectroscopy for disease classification is also highlighted. To conclude, many pattern recognition algorithms have the potential to overcome each of their distinct limits, and there is also the option of combining all of these algorithms to create an ensemble of methods
Pattern Recognition for Human Diseases Classification in Spectral Analysis
No full textPattern recognition is a multidisciplinary area that received more scientific attraction during this period of rapid technological innovation. Today, many real issues and scenarios require pattern recognition to aid in the faster resolution of complicated problems, particularly those that cannot be solved using traditional human heuristics. One common problem in pattern recognition is dealing with multidimensional data, which is prominent in studies involving spectral data such as ultraviolet-visible (UV/Vis), infrared (IR), and Raman spectroscopy data. UV/Vis, IR, and Raman spectroscopy are well-known spectroscopic methods that are used to determine the atomic or molecular structure of a sample in various fields. Typically, pattern recognition consists of two components: exploratory data analysis and classification method. Exploratory data analysis is an approach that involves detecting anomalies in data, extracting essential variables, and revealing the data’s underlying structure. On the other hand, classification methods are techniques or algorithms used to group samples into a predetermined category. This article discusses the fundamental assumptions, benefits, and limitations of some well-known pattern recognition algorithms including Principal Component Analysis (PCA), Kernel PCA, Successive Projection Algorithm (SPA), Genetic Algorithm (GA), Partial Least Square Regression (PLS-R), Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), Partial Least Square-Discriminant Analysis (PLS-DA) and Artificial Neural Network (ANN). The use of UV/Vis, IR, and Raman spectroscopy for disease classification is also highlighted. To conclude, many pattern recognition algorithms have the potential to overcome each of their distinct limits, and there is also the option of combining all of these algorithms to create an ensemble of methods
Composition Dependence Structural and Optical Properties of Silicon Germanium (SiχGe1−χ) Thin Films
No full textThis study investigates the structural and optical characteristics of Silicon Germanium (SiGe) thin films with varying compositions and annealing temperatures for potential use in electronic and optoelectronic devices. Si0.8Ge0.2 and Si0.9Ge0.1 films were deposited onto a high-temperature quartz substrate and annealed at 600 °C, 700 °C, and 800 °C before being evaluated using an X-Ray Diffractometer (XRD), Atomic Force Microscopy (AFM), and a UV-Vis Spectrometer for structural and optical properties. The results show that increasing the annealing temperature results in an increase in crystalline size for both compositions. The transmittance for Si0.8Ge0.2 decreases slightly with increasing temperature, while Si0.9Ge0.1 remains constant. The optical band gap for Si0.9Ge0.1 thin film is 5.43 eV at 800 °C, while Si0.8Ge0.2 thin film is 5.6 eV at the same annealing temperature. XRD data and surface analysis reveal significant differences between the band edges of SiGe nano-structure materials and bulk crystals. However, the possibility of a SiGe nano-crystal large band gap requires further investigation based on our study and related research works
Effect of Al₂O₃, RHF, and RHA on gamma shielding and mechanical properties of TeO₂-Based glass using Phy-X/PSD
No full textRadiation attenuation behaviour of TeO₂–ZnO–Bi₂O₃–Na₂O–Er₂O₃ with the addition of Al₂O₃ nanoparticles (NP), rice husk fibre (RHF), and rice husk ash (RHA) was investigated using Phy-X/PSD software. The mass attenuation coefficients of the chosen glasses were measured from 0.015 to 3 MeV. The results showed that increasing the RHF concentration of the glasses or decreasing the Al₂O₃ component of the glass system enhances the mass attenuation coefficient of the material. At low energy, there is a significant difference between the mass attenuation coefficients values of the samples with the lowest and highest Al₂O₃ content, where the difference is 0.05118 cm2/g at 0.015 MeV. TZNERHF had higher mass attenuation coefficients (0.05402 cm2/g), and TZNETiAl8 had the highest mass attenuation coefficients, with a value of 0.05357 cm2/g at 1.173 MeV. The results showed a decreasing trend in the half-value layer (HVL), tenth value layer (TVL), and mean free path (MFP) when the density increased from 5.12555 to 5.23882 g/cm3 and attenuation ability improved. Subsequently, the influence of Al₂O₃ on Zeff values became more considerable as Zeff values increased. The shielding qualities of their glass samples are also superior to commercial window glass and common shielding concrete. Thus, the glass samples can be used with excellent advantage as radiation shielding materials. The findings showed that adding RHF and lower Al₂O₃ enhanced the studied samples' radiation shielding properties. The results show that the TZNERHF glass sample has the highest mechanical properties. Thus, the TZNERHF sample is better than the other glass samples
Missing Value Imputation for PM10 Concentration in Sabah using Nearest Neighbour Method(NNM) and Expectation-Maximization (EM) Algorithm
No full textMissing data in large data analysis has affected further analysis conducted on dataset. To fill in missing data, Nearest Neighbour Method(NNM) and Expectation Maximization(EM) algorithm are the two most widely used methods. Thus, this research aims to compare both methods by imputing missing data of air quality in five monitoring stations (CA0030, CA0039, CA0042, CA0049, CA0050) in Sabah, Malaysia. PM10 (particulate matter with aerodynamic size below 10 microns) dataset in the range from 2003-2007(Part A) and 2008-2012 (Part B) are used in this research. To make performance evaluation possible, missing data is introduced in the datasets at 5 different levels(5%, 10%, 15%, 25% and 40%). The missing data is imputed by using both NNM and EM algorithm. The performance of both data imputation methods is evaluated using performance indicators(RMSE, MAE, IOA, COD) and regression analysis. Based on performance indicators and regression analysis, NNM performs better compared to EM in imputing data for stations CA0039, CA0042 and CA0049. This may be due to air quality data missing at random (MAR). However, this is not the case for CA0050 and part B of CA0030. This may be due to fluctuation that could not be detected by NNM. Accuracy evaluation using Mean Absolute Percentage Error (MAPE) shows that NNM is more accurate imputation method for most of the cases
Methods of optical spectroscopy in detection of virus in infected samples: A review
No full textDue to the recent COVID-19 pandemic that occurred worldwide since 2020, scientists and researchers have been studying methods to detect the presence of the virus causing COVID-19 disease, namely SARS-CoV-2. Optical spectroscopy is a method that employs the interaction of light in detecting virus on samples. It is a promising method that might help in detecting the presence of SARS-CoV-2 in samples. Four optical spectroscopy methods are discussed in this paper: ultraviolet (UV), infrared (IR), Raman spectroscopy and fluorescence spectroscopy. UV and IR spectroscopy differ in wavelength range (less than 400 nm for UV, more than 700 nm for IR). Raman spectroscopy involves shift in wavelength due to scattering of light. Fluorescence spectroscopy involves difference in wavelength between absorbed and emitted light due to vibrational relaxation. These four methods had been proven to differentiate healthy samples from virus-infected samples. UV spectroscopy is useful in determining presence of virus based on 260 nm/280 nm absorbance ratio. However, its usefulness is limited due to its destructive properties on virus at sufficiently high intensity. Meanwhile, IR spectroscopy has becoming popular in studies involving virus samples. Mid-infrared (MIR) spectroscopy is most commonly used among IR spectroscopy as it usually provides useful information directly from spectral data. Near infrared (NIR) spectroscopy is also used in studying virus samples, but additional methods such as principal component analysis (PCA) and partial least squares (PLS) are required to process raw spectral data and to identify molecules based on spectral peaks. On the other hand, Raman spectroscopy is useful because spectral data can be analyzed directly in identifying vibrational modes of specific molecules in virus samples. Fluorescence spectroscopy relies on interaction between viral particles and fluorescent tags for the detection of virus based on improvement or quenching of fluorescent signal. Due to non-invasive properties of virus samples, IR, Raman and fluorescence spectroscopy will be used more often in future studies involving virus detection in infected samples
