Density wave (DW) order is believed to be correlated with superconductivity in the recently discovered high-temperature superconductor La₃Ni₂O₇. However, experimental investigations of its evolution under high pressure are still lacking. Here, we explore the quasiparticle dynamics in bilayer nickelate La₃Ni₂O₇ single crystals using ultrafast optical pump-probe spectroscopy under high pressures up to 34.2 GPa. At ambient pressure, the temperature-dependent relaxation dynamics demonstrate a phonon bottleneck effect due to the opening of an energy gap around 151 K. The energy scale of the DW-like gap is determined to be 66 meV by the Rothwarf-Taylor model. Combined with recent experiential results, we propose that this DW-like transition at ambient pressure and low temperature is spin density wave (SDW). With increasing pressure, this SDW order is significantly suppressed up to 13.3 GPa before it completely disappears around 26 GPa. Remarkably, at pressures above 29.4 GPa, we observe the emergence of another DW-like order with a transition temperature of approximately 135 K, which is probably related to the predicted charge density wave (CDW) order. Our study provides the experimental evidences of the evolution of the DW-like gap under high pressure, offering critical insights into the correlation between DW order and superconductivity in La₃Ni₂O₇.

In absorption spectroscopy, analysis of multicomponent gas mixtures becomes challenging when absorption features overlap (blended spectra). Here we propose a gas sensor which can accurately identify the species and retrieve the concentrations of components in a gaseous mixture in a broad spectrum. The sensor integrates a mid-infrared dual-frequency comb laser source for spectrum acquisition and a deep learning algorithm for spectral analysis. The sensor was tested on gas phase mixtures of methane, acetone and water vapor. A prototype sensor was assessed in realistic scenarios in real time. We also systematically analyzed and presented explicit visualizations to explain the underlying working mechanism of the algorithms.

An efficient method for the detection of acetone, an important biomarker in exhaled breath, by utilizing wavelength modulation spectroscopy (WMS) with a mid-infrared distributed feedback interband cascade laser (DFB-ICL) is presented. The performance of the DFB-ICL laser is characterized, and its linewidth is measured to be $\mbox{(3}.\mbox{39} \pm 0.\mbox{15)}$$\text{(3}.\text{39}\pm 0.\text{15)}$ MHz by beating the output with an auxiliary mid-IR frequency comb over 50 ms. The measured asymmetric acetone absorption profile with the center wavelength around 3367 nm is fitted by the pseudo-Voigt function. The phase shift between the intensity and frequency modulations is determined and used for processing the results. The sensor is tested by performing measurements with calibration mixtures of acetone in nitrogen, and the detection limits of 0.58 ppm and 0.12 ppm were determined with 1 s and 60 s signal averaging times, respectively. Our work shows that wavelength modulation spectroscopy with a DFB-ICL laser complimented by a multipass cell for higher sensitivity can be efficiently used for acetone detection and is promising for sensitive breath analysis.

Coexistent optical parametric oscillation (OPO) and stimulated Raman scattering (SRS) are demonstrated in an X-cut KTiOAsO₄ (KTA) crystal. The 30-mm-long KTA crystal is placed within a diode-end-pumped acousto-optically (AO) Q-switched Nd:YAG laser cavity to construct an intracavity optical parametric oscillator. Coexistent Raman conversion of the fundamental wave is observed from the KTA crystal. With a diode power of 7.43 W and a pulse repetition rate (PRR) of 20 kHz, a signal (1535.0 nm) power of 0.92 W is obtained, corresponding to a diode-to-signal conversion efficiency of 12.4%. A first-Stokes (1091.4 nm) power of 0.17 W is obtained.

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.