ABSTRACT
The phenomenon of phase change transition has been a fascinating research subject over decades due to a possibility of dynamically controlled materials properties, allowing the creation of optical devices with unique features. The present paper unravels the optical characteristics and terahertz (THz) dielectric permittivity of a novel phase change material (PCM), GeTe2, prepared by pulsed laser deposition (PLD) and their remarkable contrast in crystalline and amorphous states, in particular, a difference of 7 orders of magnitude in conductivity. The THz spectra were analyzed using the harmonic oscillator and Drude term. Using GeTe2 PLD films, we designed and prepared a THz metasurface in the form of periodic structure and revealed a possibility of tuning the THz resonance either by a thermal control or light-induced crystallization response, thus achieving the dynamic and tunable functionality of the metastructure. We propose controlling the state of metasurface by observing the intensity characteristics of the Raman peak of 155 cm-1. Density functional theory (DFT) modeling demonstrates that in the process of crystallization the mode intensity of 155 cm-1 assigned to Te-Te stretching in amorphous chain fragments decreases and disappears at full crystallization.
ABSTRACT
The liquid and lyophilized blood plasma of patients with benign or malignant thyroid nodules and healthy individuals were studied by terahertz (THz) time-domain spectroscopy and machine learning. The blood plasma samples from malignant nodule patients were shown to have higher absorption. The glucose concentration and miRNA-146b level were correlated with the sample's absorption at 1 THz. A two-stage ensemble algorithm was proposed for the THz spectra analysis. The first stage was based on the Support Vector Machine with a linear kernel to separate healthy and thyroid nodule participants. The second stage included additional data preprocessing by Ornstein-Uhlenbeck kernel Principal Component Analysis to separate benign and malignant thyroid nodule participants. Thus, the distinction of malignant and benign thyroid nodule patients through their lyophilized blood plasma analysis by terahertz time-domain spectroscopy and machine learning was demonstrated.
ABSTRACT
SIGNIFICANCE: The creation of fundamentally new approaches to storing various biomaterial and estimation parameters, without irreversible loss of any biomaterial, is a pressing challenge in clinical practice. We present a technology for studying samples of diabetic and non-diabetic human blood plasma in the terahertz (THz) frequency range. AIM: The main idea of our study is to propose a method for diagnosis and storing the samples of diabetic and non-diabetic human blood plasma and to study these samples in the THz frequency range. APPROACH: Venous blood from patients with type 2 diabetes mellitus and conditionally healthy participants was collected. To limit the impact of water in the THz spectra, lyophilization of liquid samples and their pressing into a pellet were performed. These pellets were analyzed using THz time-domain spectroscopy. The differentiation between the THz spectral data was conducted using multivariate statistics to classify non-diabetic and diabetic groups' spectra. RESULTS: We present the density-normalized absorption and refractive index for diabetic and non-diabetic pellets in the range 0.2 to 1.4 THz. Over the entire THz frequency range, the normalized index of refraction of diabetes pellets exceeds this indicator of non-diabetic pellet on average by 9% to 12%. The non-diabetic and diabetic groups of the THz spectra are spatially separated in the principal component space. CONCLUSION: We illustrate the potential ability in clinical medicine to construct a predictive rule by supervised learning algorithms after collecting enough experimental data.
