Nonlinear Optical Saturable Absorption Properties of 2D VP Nanosheets and Application as SA in a Passively Q-Switched Nd:YVO4 Laser.

Two-dimensional (2D) violet phosphorus (VP) plays a significant role in the applications of photonic and optoelectronic devices due to its unique optical and electrical properties. The ultrafast carrier dynamics and nonlinear optical absorption properties were systematically investigated here. The intra- and inter-band ultrafast relaxation times of 2D VP nanosheets were measured to be ~6.83 ps and ~62.91 ps using the pump-probe method with a probe laser operating at 1.03 µm. The nonlinear absorption coefficient ßeff, the saturation intensity Is, the modulation depth ΔR, and the nonsaturable loss were determined to be -2.18 × 104 cm/MW, 329 kW/cm2, 6.3%, and 9.8%, respectively, by using the Z-scan and I-scan methods, indicating the tremendous saturable absorption property of 2D VP nanosheets. Furthermore, the passively Q-switched Nd:YVO4 laser was realized with the 2D VP nanosheet-based SA, in which the average output power of 700 mW and the pulse duration of 478 ns were obtained. These results effectively reveal the nonlinear optical absorption characteristics of VP nanosheets, demonstrating their outstanding light-manipulating capabilities and providing a basis for the applications of ultrafast optical devices. Our results verify the excellent saturable absorption properties of 2D VP, paving the way for its applications in pulsed laser generation.

Dual-Comb Gas Sensor Integrated with a Neural Network-Based Spectral Decoupling Algorithm of Overlapped Spectra for Gas Mixture Sensing.

Cross-interference among absorptions severely affects the ability to achieve accurate gas concentration retrieval through gas molecular specificity. In this study, a novel dual gas sensor was proposed to separate methane and water absorbance from the blended spectra of their mixture in the mid-infrared (MIR) band by employing a neural network algorithm. To address the scarcity of experimental data, the neural network was trained over a simulated data set constructed with the same distribution as the experimental ones. The system takes advantages of the broadband spectra to provide high-quality comb data and allows the neural network to establish an accurate spectral decoupling function. In addition, a feature absorption peak screening mechanism was proposed to achieve more accurate concentration retrieval, which avoids the prediction error introduced by interrogating the only peak of the separated spectra. The promising results of the systematic evaluation have demonstrated the feasibility of our methods in practical detections.

Adaptively Optimized Gas Analysis Model with Deep Learning for Near-Infrared Methane Sensors.

Noise significantly limits the accuracy and stability of retrieving gas concentration with the traditional direct absorption spectroscopy (DAS). Here, we developed an adaptively optimized gas analysis model (AOGAM) composed of a neural sequence filter (NSF) and a neural concentration retriever (NCR) based on deep learning algorithms for extraction of methane absorption information from the noisy transmission spectra and obtaining the corresponding concentrations from the denoised spectra. The model was trained on two data sets, including a computationally generated one and the experimental one. We have applied this model for retrieving methane concentration from its transmission spectra in the near-infrared (NIR) region. The NSF was implemented through an encoder-decoder structure enhanced by the attention mechanism, improving robustness under noisy conditions. Further, the NCR was employed based on a combination of a principal component analysis (PCA) layer, which focuses the algorithm on the most significant spectral components, and a fully connected layer for solving the nonlinear inversion problem of the determination of methane concentration from the denoised spectra without manual computation. Evaluation results show that the proposed NSF outperforms widely used digital filters as well as the state-of-the-art filtering algorithms, improving the signal-to-noise ratio by 7.3 dB, and the concentrations determined with the NCR are more accurate than those determined with the traditional DAS method. With the AOGAM enhancement, the optimized methane sensor features precision and stability in real-time measurements and achieves the minimum detectable column density of 1.40 ppm·m (1σ). The promising results of the present study demonstrate that the combination of deep learning and absorption spectroscopy provides a more effective, accurate, and stable solution for a gas monitoring system.

Probing methane in air with a midinfrared frequency comb source.

We employed a midinfrared frequency comb source for methane detection in ambient air. The transmitted spectra over a bandwidth of about 500 nm were recorded with an optical spectrum analyzer under various experimental conditions of different path lengths. The normalized absorption spectra were compared and fitted with simulations, yielding quantitative values of concentrations of methane and water vapor in the ambient air. The 3σ detection limit was ∼6.6×10-7 cm-1 in ambient air for a broad spectral range, achieved with a path length of ∼590 m. This approach provides a broad spectral range, a large dynamic range, high sensitivity, and accurate calibration. The performed analysis of the residuals shows that an excellent agreement between the measured and calculated spectral profiles was obtained.

Single-frequency CaWO4 Raman amplifier at 1178 nm.

Single-frequency crystalline Raman amplifications at 1178 nm were demonstrated. The seeding laser was generated from a single-frequency continuous wave fiber Raman amplifier. Three stages of Raman amplifications from CaWO4 single crystals were realized with a pulsed 1064 nm Nd:YAG laser as the pumping source. The final output pulse energy at 1178 nm was 26.7 mJ, and the pulse width was 2.9 ns, corresponding to a peak power of 5.2 MW. The overall Raman amplification ratio was up to 4.6×10(6). The linewidth was less than 500 MHz.

Four-wavelength laser based on intracavity BaWO4 Raman conversions of a dual-wavelength Q-switched Nd:YLF laser.

By using diode-end-pumped acousto-optically Q-switched intracavity Raman laser configurations, we demonstrate a four-wavelength laser emitting at 1047.0, 1053.0, 1159.4 and 1166.8 nm. Two Nd:YLiF4 crystals are employed to generate 1047.0-nm and 1053.0-nm laser radiations. These two lasers are then frequency converted by a BaWO4 Raman crystal to generate 1159.4-nm and 1166.8-nm first-Stokes waves. With pulse synchronization realized, we obtain the maximum output powers of 427, 418, 423 and 332 mW for 1047.0-nm, 1053.0-nm, 1159.4-nm and 1166.8-nm lasers, respectively. The total optical-to-optical conversion efficiency is 15.1%.

Iodine-filter-based high spectral resolution lidar for atmospheric temperature measurements.

This paper presents a method for measuring atmosphere temperature profile using a single iodine filter as frequency discriminator. This high spectral resolution lidar (HSRL) is a system reconfigured with the transmitter of a mobile Doppler wind lidar and with a receiving subsystem redesigned to pass the backscattering optical signal through the iodine cell twice to filter out the aerosol scattering signal and to allow analysis of the molecular scattering spectrum, thus measuring temperatures. We report what are believed to be the first results of vertical temperature profiling from the ground to 16 km altitude by this lidar system (power-aperture product=0.35 Wm(2)). Concurrent observations of an L band radiosonde were carried out on June 14 and August 3, 2008, in good agreement with HSRL temperature profiles.