A novel scheme for ultrashort terahertz pulse generation over a gapless wide spectral range: Raman-resonance-enhanced four-wave mixing

High-sensitivity methane detection based on QEPAS and H-QEPAS technologies combined with a self-designed 8.7 kHz quartz tuning fork

Methane (CH4) is a greenhouse gas as well as being flammable and explosive. In this manuscript, quartz-enhanced photoacoustic spectroscopy (QEPAS) and heterodyne QEPAS (H-QEPAS) exploring a self-designed quartz tuning fork (QTF) with resonance frequency (f0) of ∼8.7 kHz was utilized to achieve sensitive CH4 detection. Compared with the standard commercial 32.768 kHz QTF, this self-designed QTF with a low f0 and large prong gap has the merits of long energy accumulation time and low optical noise. The strongest line located at 6057.08 cm-1 in the 2v3 overtone band of CH4 was chosen as the target absorption line. A diode laser with a high output power of > 30 mW was utilized as the excitation source. Acoustic micro-resonators (AmRs) were added to the sensor architecture to amplify the intensity of acoustic waves. Compared to the bare QTF, after the addition of AmRs, a signal enhancement of 149-fold and 165-fold were obtained for QEPAS and H-QEPAS systems, respectively. The corresponding minimum detection limits (MDLs) were 711 ppb and 1.06 ppm for QEPAS and H-QEPAS sensors. Furthermore, based on Allan variance analysis the MDLs can be improved to 19 ppb and 27 ppb correspondingly. Compared to the QEPAS sensor, the H-QEPAS sensor shows significantly shorter measurement timeframes, allowing for measuring the gas concentration quickly while simultaneously obtaining f0 of QTF.

High-precision prediction of blood glucose concentration utilizing Fourier transform Raman spectroscopy and an ensemble machine learning algorithm

Raman spectroscopy has gained popularity in analyzing blood glucose levels due to its non-invasive identification and minimal interference from water. However, the challenge lies in how to accurately predict blood glucose concentrations in human blood using Raman spectroscopy. This paper researches a novel integrated machine learning algorithm called Bagging-ABC-ELM. The optimal input weights and biases of extreme learning machine (ELM) model are obtained by artificial bee colony (ABC) algorithm. The bagging algorithm is used to obtain a better the stability of the model and higher performance than ELM algorithm. The results show that the mean value of coefficient of determination is 0.9928, and root mean square error is 0.1928. Compared to other regression models, the Bagging-ABC-ELM model exhibited superior prediction accuracy, robustness, and generalization capability. The Bagging-ABC-ELM model presents a promising alternative for analyzing blood glucose levels in clinical and research settings.

Local measurement of terahertz field-induced second harmonic generation in plasma filaments

The concept of Terahertz Field-Induced Second Harmonic (TFISH) Generation is revisited to introduce a single-shot detection scheme based on third order nonlinearities. Focused specifically on the further development of THz plasma-based sources, we begin our research by reimagining the TFISH system to serve as a direct plasma diagnostic. In this work, an optical probe beam is used to mix directly with the strong ponderomotive current associated with laser-induced ionization. A four-wave mixing (FWM) process then generates a strong second-harmonic optical wave because of the mixing of the probe beam with the nonlinear current components oscillating at THz frequencies. The observed conversion efficiency is high enough that for the first time, the TFISH signal appears visible to the human eye. We perform spectral, spatial, and temporal analysis on the detected second-harmonic frequency and show its direct relationship to the nonlinear current. Further, a method to detect incoherent and coherent THz inside plasma filaments is devised using spatio-temporal couplings. The single-shot detection configurations are theoretically described using a combination of expanded FWM models with Kostenbauder and Gaussian Q-matrices. We show that the retrieved temporal traces for THz radiation from single- and two-color laser-induced air-plasma sources match theoretical descriptions very well. High temporal resolution is shown with a detection bandwidth limited only by the spatial extent of the probe laser beam. Large detection bandwidth and temporal characterization is shown for THz radiation confined to under-dense plasma filaments induced by < 100 fs lasers below the relativistic intensity limit.

New temperature measurement method based on light-induced thermoelastic spectroscopy

A new temperature measurement method based on light-induced thermoelastic spectroscopy (LITES) was demonstrated for the first time, to the best of our knowledge, in this manuscript. According to the thermoelastic effect of quartz tuning fork (QTF), this technique retrieves the temperature on the basis of the resonance signal of QTF. Wavelength modulation spectroscopy (WMS) combined with the dual-line method was used to achieve temperature measurement. A QTF with high-frequency selectivity and high-quality factor (Δf0 = 2.5 Hz, Q-factor = 13104.9) was used as the detection element to suppress noise and improve the signal level. Two absorption lines of water vapor (H2O) located at 7153.749 cm-1 and 7154.354 cm-1 were selected as the target line. A single distributed feedback (DFB) diode laser was used to cover the two selected absorption lines simultaneously to reduce the complexity of the sensor system. A tube furnace capable of covering a temperature range from 400°C to 1000°C was adopted to verify the performance of this method. The relative error of the measured temperature was less than 5%, which indicated that the LITES temperature sensor has excellent detection accuracy. Compared to the widely used TDLAS temperature measuring method, this LITES-based technique has the merits of low cost, has no wavelength limitation, and is expected to be applied on more occasions.