Few-layer MoS₂nanosheets are homogeneously incorporated in PMMA and show good nonlinear optical responses, suggesting a promising candidate for laser optical limiting materials.

We have demonstrated a high-average-power, high-repetition-rate optical terahertz (THz) source based on difference frequency generation (DFG) in the GaSe crystal by using a near-degenerate 2 μm intracavity KTP optical parametric oscillator as the pump source. The power of the 2 μm dual-wavelength laser was up to 12.33 W with continuous tuning ranges of 1988.0–2196.2 nm/2278.4–2065.6 nm for two waves. Different GaSe cystal lengths have been experimentally investigated for the DFG THz source in order to optimize the THz output power, which was in good agreement with the theoretical analysis. Based on an 8 mm long GaSe crystal, the THz wave was continuously tuned from 0.21 to 3 THz. The maximum THz average power of 1.66 μW was obtained at repetition rate of 10 kHz under 1.48 THz. The single pulse energy amounted to 166 pJ and the conversion efficiency from 2 μm laser to THz output was $1.68\times {10}^{-6}$. The signal-to-noise ratio of the detected THz voltage was 23 dB. The acceptance angle of DFG in the GaSe crystal was measured to be 0.16°.

The imaging diagnosis and prognostication of different degrees of traumatic brain injury (TBI) is very important for early care and clinical treatment. Especially, the exact recognition of mild TBI is the bottleneck for current label-free imaging technologies in neurosurgery. Here, we report an automatic evaluation method for TBI recognition with terahertz (THz) continuous-wave (CW) transmission imaging based on machine learning (ML). We propose a new feature extraction method for biological THz images combined with the transmittance distribution features in spatial domain and statistical distribution features in normalized gray histogram. Based on the extracted feature database, ML algorithms are performed for the classification of different degrees of TBI by feature selection and parameter optimization. The highest classification accuracy is up to 87.5%. The area under the curve (AUC) scores of the receiver operating characteristics (ROC) curve are all higher than 0.9, which shows this evaluation method has a good generalization ability. Furthermore, the excellent performance of the proposed system in the recognition of mild TBI is analyzed by different methodological parameters and diagnostic criteria. The system can be extensible to various diseases and will be a powerful tool in automatic biomedical diagnostics.

A humidity sensor based on Fabry-Perot interferometer (FPI) and intracavity sensing of a fiber ring laser is proposed and experimentally demonstrated. A compact humidity-sensitive fiber-optic FPI is developed and inserted in a fiber ring laser. Because the output power of the fiber laser is modulated by the reflection loss of the FPI at different ambient humidity, intracavity humidity sensing is induced. The experiment shows that the relative output power of the fiber laser has a good linear response to ambient humidity from 25 %RH to 95 %RH and the humidity sensitivity of 0.202 dB/%RH is obtained with a narrow 3-dB bandwidth and high signal-to-noise ratio (SNR). Furthermore, the excellent performance of the sensor for remote humidity detection is demonstrated. The sensor also shows a low temperature cross-sensitivity, fast time response, and good stability. The proposed sensor has a great potential in high-capacity sensor network and remote detections.

The early diagnosis of blast-induced traumatic brain injury (bTBI) is of great clinical significance for prognostication and treatment. Here, we report a new strategy for early bTBI diagnosis through serum and cerebrospinal fluid (CSF) based on terahertz time-domain spectroscopy (THz-TDS). The spectral differences of serum and CSF for different degrees of experimental bTBI in rats have been demonstrated in the early period. In addition, the THz spectra of total protein in the hypothalamus and hippocampus were investigated at different time points after blast exposure, which both showed clear differences with time increasing compared with that in the normal brain. This might help to explain the neurological symptoms caused by bTBI. Moreover, based on the THz absorption spectra of serum and CSF, the principal component analysis and machine learning algorithms were performed to automatically identify the degree of bTBI. The highest diagnostic accuracy was up to 95.5%. It is suggested that this method has potential as an alternative method for high-sensitive, rapid, label-free, economical and early diagnosis of bTBI.