Effect of THz spectra of L-Arginine molecules by the combination of water molecules

Mechanistic Study of L-Rhamnose Monohydrate Dehydration Using Terahertz Spectroscopy and Density Functional Theory

L-rhamnose has recently gained attention for its potential to enhance vaccine antigenicity. To optimize its use as a vaccine adjuvant, it is important to understand the dehydration behavior of L-rhamnose monohydrate, which plays a critical role in modifying its physicochemical properties. This study investigated the spectroscopic characteristics of L-rhamnose and its monohydrate using terahertz time-domain spectroscopy (THz-TDS), Raman spectroscopy, and powder X-ray diffraction (PXRD). The results indicate that THz-TDS can more effectively distinguish the spectral features of these two compounds and can be used to reflect the structural changes in L-rhamnose monohydrate before and after dehydration. THz spectral data show that dehydration of L-rhamnose occurs at 100 °C, and continuous heating at 100 °C can complete the dehydration process within 6 min. Density functional theory (DFT) calculations revealed that water molecule vibrations significantly affect the THz absorption peaks. These findings indicate that removing water during dehydration causes substantial changes in molecular structure and dynamics. Overall, this study highlights the value of combining THz-TDS with DFT calculations to investigate the structures of carbohydrates and their hydrates, providing an accurate method for understanding the dehydration process and molecular interactions in hydrated systems. This approach holds significant importance for the development of effective vaccine adjuvants.

An intelligent sensing platform for detecting and identifying biochemical substances based on terahertz spectra

This paper presents the development of an intelligent sensing platform dedicated to accurately identifying terahertz (THz) spectra obtained from various biochemical substances. The platform currently has two distinct identification modes, which focus on identifying five amino acids, namely phenylalanine, methionine, lysine, leucine, and threonine, and five carbohydrates, namely aspartame, fructose, glucose, lactose monohydrate, and sucrose based on their THz spectra. The first mode, called One-dimensional THz Spectrum Identification (OTSI), combines THz time-domain spectroscopy (THz-TDS) with the proposed mini convolutional neural network (MCNN) model. THz-TDS detects biochemical substances, while the MCNN model identifies the THz spectra. The MCNN model has a simple structure and only needs to deal with the THz absorption coefficients of biochemical substances, which are less computationally intensive and easily converged. The model can achieve 99.07 % accuracy in identifying one-dimensional THz spectra of the ten biochemical substances. The second mode, THz Spectrum Image-based Identification (TSII), applies the YOLO-v5 target detection model to THz spectral image recognition. The YOLO-v5 model uses THz absorption peaks as identification features and can identify biochemical substances based on only one or several THz absorption peaks. The overall identifying accuracy of the YOLO-v5 model for ten biochemical substances is 96.20 %. We also compared the MCNN and YOLO-v5 models with other deep learning and machine learning models, which demonstrate that they have better performance. This feature broadens the platform's utility in biomolecular analysis and paves the way for further research and development in detecting and analyzing diverse biological compounds.

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.

Sensitive and Specific Detection of Carcinoembryonic Antigens Using Toroidal Metamaterial Biosensors Integrated with Functionalized Gold Nanoparticles

Carcinoembryonic antigen (CEA) is a biomarker that is highly expressed in cancer patients. Label-free, highly sensitive, and specific detection of CEA biomarkers can therefore greatly aid in the early detection and screening of cancer. This study presents a toroidal metamaterial biosensor integrated with functionalized gold nanoparticles (AuNPs) that demonstrated highly sensitive and specific detection of CEA using terahertz (THz) time-domain spectroscopy. In the biosensor, a closed-loop magnetic field formed an electrical confinement, resulting in a high sensitivity (287.8 GHz/RIU) and an ultrahigh quality factor (15.04). In addition, the integrated AuNPs with high refractive indices significantly enhanced the sensing performance of the biosensor. To explore the quantitative and qualitative detection of CEA, CEA biomarkers with various concentrations and four types of proteins were measured by the designed biosensor, achieving a limit of detection of 0.17 ng and high specificity. Even more significant, the proposed AuNP-integrated THz toroidal metamaterial biosensor demonstrates exceptional potential for use in technologies for cancer diagnosis and monitoring.

Quantitative detection of THz-ATR spectra of aqueous samples under strong-field terahertz wave

Owing to the characteristics of THz wave, the terahertz time-domain spectral (THz-TDS) system has potentials in the field of biological macromolecule detection. However, water with strong absorption effect on THz wave exists in most biological detection, so the research focus in this field is to study aqueous samples. In view of these, THz spectroscopy system has research value for qualitative and quantitative detection of α-lactose and its water-containing samples. This research used a THz-TDS system with LiNbO₃ crystal to generate strong THz wave that was used to test 0.29 mmol α-lactose samples with water content of 15 μL-930 μL by using attenuating total reflection (ATR) prism. The absorption peak at 0.53 THz is detected, and with the increase of water content, the curve of absorption spectrum is observed to move up on the whole. This research has a guiding role for the test and improvement of water content limit in this field.

Experimental Detection and Simulation of Terahertz Spectra of Aqueous L-Arginine

Terahertz (THz) wave is a good candidate for biological sample detection, because vibration and rotation energy levels of biomolecule are in THz band. However, the strong absorption of THz wave by water in biological samples hinders its development. In this paper, a method for direct detection of THz absorption spectra of L-arginine suspension was proposed by using a strong field THz radiation source combined with a polyethylene cell with micrometer thickness in a THz time-domain spectroscopy system. And the THz absorption spectrum of L-arginine solution was simulated by the density functional theory and the simulation result is in good agreement with the experimental results. Finally, the types of chemical bond interaction that cause the absorption peak are identified based on the experimental and simulation results. This work paves a way to investigate the THz absorption spectra and intramolecular interactions of aqueous biological samples.