Characterization of the remediation of chromium ion contamination with bentonite by terahertz time-domain spectroscopy

Effect of crystal-water on the optical and dielectric characteristics of calcium sulfate in the THz band

The effect of crystal-water contents on the optical properties and dielectric characteristics of calcium sulfate in the THz band is investigated. The complex dielectric constant and conductivity are analyzed using the Drude-Smith model. The refractive index and absorption coefficient are linearly increased with the content of crystal-water, and the corresponding linear fitting lines of R2 over 0.97 are obtained. The dielectric properties of calcium sulfate are significantly affected by the crystal-water content. These results indicate that a new method to quantitative measurement of the crystal-water content in hydrous minerals is provided.

Terahertz Time-Domain Spectroscopic Characteristics of Typical Metallic Minerals

Accurate identification and understanding of various metallic minerals are crucial for deciphering geological formations, structures, and ages. Giving their pivotal role as essential natural resources, a microscopic exploration of metallic minerals becomes imperative. Traditional analytical methods, while helpful, exhibit certain limitations. However, terahertz time-domain spectroscopy, distinguished by its high signal-to-noise ratio, expansive frequency band, and low incident wave energy, is a promising complement to conventional techniques in characterizing metallic minerals. This study employs terahertz time-domain spectroscopy to examine samples of Stibnite, Sphalerite, Galena, and Pyrite originating from diverse geological conditions. The vibrations of molecules within these metallic minerals induce discernible changes in the terahertz spectra. Our findings untiate the extensive potential of terahertz time-domain spectroscopy in the characterization of metallic minerals, affirming its considerable practical value in mineral resource exploration.

Identification of heavy metal pollutants in wheat by THz spectroscopy and deep support vector machine

This paper proposes to detect heavy metal pollutants in wheat using terahertz spectroscopy and deep support vector machine (DSVM). Five heavy metal pollutants, arsenic, lead, mercury, chromium, and cadmium, were considered for detection in wheat samples. THz spectral data were pre-processed by wavelet denoising. DSVM was introduced to further enhance the accuracy of the SVM classification model. According to the relationship between the accuracy and the training time with the number of hidden layers ranging from 1 to 4, the model performs the best when the hidden layer network has three layers. Besides, using the back-propagation algorithm to optimize the entire DSVM network. Compared with Deep neural network (DNN) and SVM models, the comprehensive evaluation index of the proposed model optimized by DSVM has the highest accuracy of 91.3 %. It realized the exploration enhanced the classification accuracy of the heavy metal pollutants in wheat.

Image restoration algorithm for terahertz FMCW radar imaging

The terahertz frequency modulation continuous-wave (THz FMCW) imaging technology has been widely used in non-destructive testing applications. However, THz FMCW real-aperture radar usually has a small depth of field and poor lateral resolution, thus restricting the high-precision imaging application. This paper proposes a 150-220 GHz FMCW Bessel beam imaging system, effectively doubling the depth of field and unifying the lateral resolution compared to the Gaussian beam quasi-optical system. Moreover, a THz image restoration algorithm based on local gradients and convolution kernel priors is proposed to eliminate further the convolution effect introduced by the Bessel beam, thereby enhancing the lateral resolution to 2 mm. It effectively improves the image under-restoration or over-restoration caused by the mismatch between the ideal and actual point spread function. The imaging results of the resolution test target and semiconductor device verify the advantages of the proposed system and algorithm.

Probing the Molecular Dynamics of Aqueous Binary Solutions with THz Time-Domain Ellipsometry

Using a customized time-domain ellipsometer operating in the THz range, the molecular dynamics of a liquid binary solution based on water and isopropyl alcohol (2-propanol) is investigated. The setup is capable of detecting small changes in the optical properties of the mixture within a single measurement. The complex dielectric response of samples with different concentrations is studied through the direct measurement of the ellipsometric parameters. The results are described using an effective Debye model, from which the relaxation parameters associated with different activation energies can be consistently extracted. Significant deviations between experimental data and the theoretical expectations at an intermediate volume percentage of 2-propanol in water are observed and interpreted as produced by competing effects: the creation/destruction of hydrogen bonding on the one hand, and the presence of cluster/aggregation between water and alcohol molecules on the other.

Chemometric Analysis of a Ternary Mixture of Caffeine, Quinic Acid, and Nicotinic Acid by Terahertz Spectroscopy

Caffeine, quinic acid, and nicotinic acid are among the significant chemical determinants of coffee quality. This study develops a chemometric model to quantify these compounds in ternary mixtures analyzed by terahertz time-domain spectroscopy (THz-TDS). A data set of 480 THz spectra was obtained from 80 samples. Combinations of data preprocessing methods, including normalization (Z-score, min-max scaling, Mie baseline removal) and dimensionality reduction (principal component analysis (PCA), factor analysis (FA), independent component analysis (ICA), locally linear embedding (LLE), non-negative matrix factorization (NMF), isomap), and prediction models (partial least-squares regression (PLSR), support vector regression (SVR), multilayer perceptron (MLP), convolutional neural network (CNN), gradient boosting) were analyzed for their prediction performance (totaling to 4,711,685 combinations). Results show that the highest quantification performance was achieved at a root-mean-square error of prediction (RMSEP) of 0.0254 (dimensionless mass ratio), using min-max scaling and factor analysis for data preprocessing and multilayer perceptron for prediction. Effects of preprocessing, comparison of prediction models, and linearity of data are discussed.