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Crystal growth and spectroscopic investigations of Dy:YAlO3 and Dy,Tm:YAlO3 crystals for a??3a?ˉ??m laser application
摘要： Dy3+:YAP and Dy3+,Tm3+:YAP single crystals have been successfully synthesized by Czochralski (CZ) method aiming for ~3 μm emission. The optical absorption and emission features of both crystals were studied in detail. A narrow and weak absorption band around 800 nm as well as an upconversion emission under the excitation of 802 nm in Dy3+:YAP crystal along with a weak ~3 μm emission were observed, indicating that Dy3+:YAP crystal is not suitable for direct pumping under 800 nm excitation. Tm3+ ion was introduced as a sensitizer for enhancing ~3 μm emission and found that co-doping with Tm3+ greatly improved the absorption capacity around 800 nm. An enhanced ~3 μm emission was observed in Dy3+,Tm3+:YAP crystal under 795 nm excitation. The energy transfer mechanisms between Dy3+ and Tm3+ ions and the energy transfer micro-parameters were discussed. Higher values of microscopic energy transfer constants between Dy3+ and Tm3+ ions were obtained, proving Tm3+ ion act as an effective sensitizer for Dy3+ ion in YAP crystal. Following the obtained optical results, Dy3+,Tm3+:YAP crystal could be a promising medium for 3 μm laser operation under 795 nm laser diode for practical applications.
关键词： optical absorption,energy transfer micro-parameters,3 μm emission,MIR laser,Dy3+,Tm3+:YAP crystal
更新于2025-09-23 15:21:01
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Artificial Intelligence Assisted Mid-Infrared Laser Spectroscopy In Situ Detection of Petroleum in Soils
摘要： A simple, remote-sensed method of detection of traces of petroleum in soil combining artificial intelligence (AI) with mid-infrared (MIR) laser spectroscopy is presented. A portable MIR quantum cascade laser (QCL) was used as an excitation source, making the technique amenable to field applications. The MIR spectral region is more informative and useful than the near IR region for the detection of pollutants in soil. Remote sensing, coupled with a support vector machine (SVM) algorithm, was used to accurately identify the presence/absence of traces of petroleum in soil mixtures. Chemometrics tools such as principal component analysis (PCA), partial least square-discriminant analysis (PLS-DA), and SVM demonstrated the effectiveness of rapidly differentiating between different soil types and detecting the presence of petroleum traces in different soil matrices such as sea sand, red soil, and brown soil. Comparisons between results of PLS-DA and SVM were based on sensitivity, selectivity, and areas under receiver-operator curves (ROC). An innovative statistical analysis method of calculating limits of detection (LOD) and limits of decision (LD) from fits of the probability of detection was developed. Results for QCL/PLS-DA models achieved LOD and LD of 0.2% and 0.01% for petroleum/soil, respectively. The superior performance of QCL/SVM models improved these values to 0.04% and 0.003%, respectively, providing better identification probability of soils contaminated with petroleum.
关键词： chemometrics,soil,artificial intelligence (AI),multivariate analysis,mid-infrared (MIR) laser spectroscopy,petroleum,quantum cascade lasers (QCLs)
更新于2025-09-23 15:19:57