Effect of annealing atmosphere on characteristics of kaolin-doped zinc oxide disks

This study elucidates the effect of the annealing environment (oxygen and nitrogen) on the properties of kaolin-doped ZnO disks. The findings showed that, in comparison to the sample annealed in O2 ambient, the sample annealed in N2 has a greater average grain size. The tensile stress, lattice constants, and crystallite size of the (002) plane all indicate that annealing under N2 enhanced the crystal quality of the ZnO disk. The bandgap energy shifted from 3.09 to 2.96 eV because of the doping and annealing processes. By annealing the kaolin-doped ZnO disk under N2 atmosphere, the intrinsic defects were almost eliminated. The nonlinear coefficient of the disks varied from 3.6 to 2.0. It was proven in this study that kaolin can be used as an effective dopant/additive for ZnO varistors

Spectral response to early detection of stressed oil palm seedlings using near-infrared reflectance spectra at region 900-1000 nm

A method was developed based on spectral analysis and classification models for early detection of water stress level in the leaves of oil palm seedlings. The healthy (well-watered: D0) and water-stressed (subjected to water stress for five days: D1-D5) leaves of oil palm seedlings were investigated to identify and classify the stress levels. The stress levels were grouped as light, moderate, and severe. The region 900–1000 nm was selected because it is highly correlated with water content, particularly in terms of first and second derivatives. The measured reflectance spectra at 900–1000 nm were pre-processed using smoothing, standard normal variate (SNV), and first and second Savitzky-Golay (SG) derivatives. Principal component analysis (PCA) was performed on several transformed datasets to reduce the reflectance spectral dimension and derive the principal components (PCs). Support vector machine (SVM) and linear discriminant analysis (LDA) classification models were employed to the scores of PCs to achieve six classification levels of water stress. Classification accuracy was assessed using the overall accuracy and confusion matrix of testing datasets. The SVM and PCA-LDA classification models predicted the water stress levels with high average overall classification accuracy of 92 % and 94 % using the smoothed + SNV + first derivative and smoothed + SNV spectral dataset, respectively. The findings confirmed the potential of 900–1000 nm region to distinguish the different levels of water stress in oil palm seedlings

Near-Infrared Spectroscopy as a Potential COVID-19 Early Detection Method: A Review and Future Perspective

The coronavirus disease 2019 (COVID-19) pandemic is a worldwide health anxiety. The rapid dispersion of the infection globally results in unparalleled economic, social, and health impacts. The pathogen that causes COVID-19 is known as a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). A fast and low-cost diagnosis method for COVID-19 disease can play an important role in controlling its proliferation. Near-infrared spectroscopy (NIRS) is a quick, non-destructive, non-invasive, and inexpensive technique for profiling the chemical and physical structures of a wide range of samples. Furthermore, the NIRS has the advantage of incorporating the internet of things (IoT) application for the effective control and treatment of the disease. In recent years, a significant advancement in instrumentation and spectral analysis methods has resulted in a remarkable impact on the NIRS applications, especially in the medical discipline. To date, NIRS has been applied as a technique for detecting various viruses including zika (ZIKV), chikungunya (CHIKV), influenza, hepatitis C, dengue (DENV), and human immunodeficiency (HIV). This review aims to outline some historical and contemporary applications of NIRS in virology and its merit as a novel diagnostic technique for SARS-CoV-2