Normalization of underwater laser-induced breakdown spectroscopy using acoustic signals measured by a hydrophone

A novel system for precise identification and explainability analysis based on multimodal learning combining laser-induced breakdown spectroscopy and laser-induced plasma acoustic signals

This study presents an innovative approach to identify copper types using Laser-Induced Breakdown Spectroscopy (LIBS) in conjunction with Laser-Induced Plasma Acoustic (LIPA) signals. Traditionally overlooked, plasma acoustic signals can indeed provide valuable insights into plasma characteristics essential for copper identification. This study pioneers a cross-modal learning technique, integrating LIBS and LIPA signals, and employs a Support Vector Machine (SVM) for classification. To enhance feature extraction, Principal Component Analysis (PCA) reduces data dimensionality, while SHapley Additive exPlanations (SHAP) assess feature contributions, aiding feature selection. The combined model demonstrates high identification accuracy, and the interpretability analysis deepens our understanding of feature roles in copper detection. This framework not only boosts LIBS-based identification accuracy but also advances the theoretical foundation for multi-modal data fusion in material analysis.

Multimodal LIBS-FLIPA fusion with frame segmentation for robust plastic classification via advanced LIPA processing

The global increase in plastic waste, exceeding 400 million tons annually, underscores the urgent need for efficient plastic sorting and recycling. Laser-induced breakdown spectroscopy (LIBS) shows potential in this area, but its practical application is limited by challenges such as plasma fluctuations and low robustness. To address these limitations, we introduce laser-induced plasma acoustic (LIPA) signals and propose the frame-segmentation LIPA (FLIPA) algorithm to enhance LIBS analysis. This innovative algorithm reduces the number of variables in LIPA by 99% while optimizing computational efficiency and classification accuracy. Additionally, a multimodal fusion technique, LIBS-FLIPA, is developed to integrate LIBS and FLIPA at the feature level. The results indicate that LIBS-FLIPA significantly improves classification accuracy, robustness, and generalization, effectively mitigating overfitting risks. This study provides novel, to the best of our knowledge, solutions to challenges in LIBS analysis and proposes an innovative approach for robust plastic sorting, advancing the methodologies of LIBS research.

Data Fusion of Acoustic and Optical Emission from Laser-Induced Plasma for In Situ Measurement of Rare Earth Elements in Molten LiCl-KCl

Molten salts have a significant potential for use as next-generation nuclear reactor coolants and in pyroprocessing for the recycling of used nuclear fuel. However, the molten salt composition needs to be known at all times, and high temperatures and intense ionizing radiation pose challenges for the monitoring instrumentation. Although the technique of laser-induced breakdown spectroscopy (LIBS) has been studied for in situ measurements of molten salts, trials to improve its monitoring accuracy using chemometrics are lacking. In this study, a data fusion technique using the LIBS optical and laser-induced acoustic (LIA) signals was investigated to enhance the measurement accuracy for molten salt monitoring. Prediction models were constructed using the partial least-squares method, and the variable importance in projection scores was analyzed to evaluate the effect of incorporating the LIA signal into the analysis. This study investigates rare earth elements Eu, Er, and Pr found not only in nuclear but also in other settings such as laser and magnetic materials. The analysis of LIBS data without data fusion resulted in a root-mean-square error of prediction (RMSEP) of 0.0774-0.0913 wt %, whereas the prediction model using data fusion led to approximately 18-40% enhanced RMSEP (0.0461-0.0679 wt %). The results suggest that fusing the LIBS data with the simultaneously recorded LIA data can improve the monitoring accuracy of rare earth element composition in molten salts.

Acoustic characteristics of laser-induced plasmas from the forming dynamics perspective

The acoustic signal has demonstrated its capabilities in assisting laser-induced breakdown spectroscopy (LIBS) measurements. In this study, the acoustic characteristics of laser-induced plasmas (LIPs) under different levels of energy deposition were analyzed, and their correlation with LIP forming dynamics was investigated. In the deposited energy space, two zones in the acoustic pressure and duration were observed, featuring a clear transition point in 100 mJ. The analysis based on self-emission spectra and images suggested that this transition is a result of the change in plasma forming dynamics. Above 100mJ, the plasma temperature and electron density were saturated; thus, any further increase in deposited energy only contributes to the plasma size. In this regime, the acoustic wave from the significantly elongated plasma no longer satisfied the ideal spherical assumption. The observation was also strengthened by the analysis in the frequency domain. Moreover, the correlation between acoustic and radiation signals was also changed significantly with plasma forming dynamics. This study offers a systematic analysis of LIP acoustic signals on the deposited energy space. The potential of using acoustic measurement to interpret the plasma forming dynamics was demonstrated, which could be beneficial for the successful implementations of acoustic-aided LIBS.

Underwater determination of calcium and strontium ions in oilfield produced water by laser-induced breakdown spectroscopy (LIBS)

Laser-induced breakdown spectroscopy (LIBS) was applied to the determination of scaling ions in oilfield-produced water employing underwater measurements. Initially, the stability of plasma was verified using four different optical setups and expansion of the laser beam, and a combination of an achromatic lens with a meniscus lens were necessary to stabilize the plasma. Preliminary experiments demonstrated that only the determinations of Ca(II) and Sr(II) ions were feasible while the signal for the Mg(II) ion was absent and the sensitivity for Ba(II) was very low. The laser pulse repetition rate was evaluated and rates of 10 and 20 Hz provided a more stable breakdown in water compared to repetition rates of 2 to 7 Hz, besides imparting higher intense signals. The increase in salinity showed a small matrix effect, decreasing the sensitivities of the calibration curves by 8-13% when standard solutions with a salinity of 30‰ were used instead of water. Under optimized conditions with a laser pulse energy of 31 mJ, gate delay of 300 ns, gate width of 5.0 μs, repetition rate of 10 Hz, and accumulation of 500 laser shots, a linear range from 25 to 150 mg L-1 was obtained, with limits of detection of 0.58 and 0.85 mg L-1 for Ca(II) and Sr(II), respectively. The underwater determination of scaling ions in produced water by LIBS provided results that do not significantly differ from those obtained by inductively coupled plasma atomic emission spectroscopy (ICP OES) at a confidence level of 95%, with relative errors of up to 5.2%. These results demonstrate the potential of underwater LIBS measurements as an analytical tool for the determination of alkaline-earth metal ions in produced water, which can help the oil industry to overcome the problems related to scale formation.

ICESat-2 laser data denoising algorithm based on a back propagation neural network

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) photon data is the emerging satellite-based LiDAR data, widely used in surveying and mapping due to its small photometric spot and high density. Since ICESat-2 data collect weak signals, it is difficult to denoise in shallow sea island areas, and the quality of the denoising method will directly affect the precision of bathymetry. This paper proposes a back propagation (BP) neural network-based denoising algorithm for the data characteristics of shallow island reef areas. First, a horizontal elliptical search area is constructed for the photons in the dataset. Suitable feature values are selected in the search area to train the BP neural network. Finally, data with a geographic location far apart, including daily and nightly data, are selected respectively for experiments to test the generality of the network. By comparing the results with the confidence labels provided in the official documents of the ATL03 dataset, the DBSCAN algorithm, and the manual visual interpretation, it is proved that the denoising algorithm proposed in this paper has a better processing effect in shallow island areas.