Raman lidar for remote sensing of oil in water

Study on precise identification of remote bacterial species using multi-temporal LIBS optimized by plasma electron temperature coefficient of variation

BACKGROUND

Laser-Induced Breakdown Spectroscopy (LIBS) has demonstrated significant potential in microbial detection due to its rapid, non-contact, and multi-element analytical capabilities. However, remote detection is hindered by challenges such as signal attenuation and the high similarity of spectral features, which reduce classification accuracy. To address these issues, this study proposes a multi-temporal LIBS remote identification method optimized based on the plasma electron temperature coefficient of variation (CVT). By analyzing CVT values across different delay ranges, optimal time delays were selected and combined to amplify spectral differentiation, thereby improving classification performance.

RESULTS

A coaxial-design LIBS telemetry system with adjustable focus was constructed, and multi-substrate telemetry testing was conducted on 10 common pathogenic bacteria at distances of 5 m and 10 m. By optimizing multiple temporal delays within the 100-1000 ns range, the classification performance of single-temporal, dual-temporal, and multi-temporal spectral combinations was evaluated. The results showed that the multi-temporal approach improved the classification performance across all substrates. At a distance of 5 m, a 100 % identification rate was achieved for all substrates, with Precision, Recall, and F1-score all reaching 1.0. At 10 m, the identification rate for the aluminum substrate increased from 76 % to 93 %. In addition, the contribution of the four major elements, Ca, Na, C, and K, was found to account for up to 60 % of the classification results.

SIGNIFICANCE AND NOVELTY

It is demonstrated that the CVT-optimized multi-temporal LIBS technology effectively overcomes the signal attenuation bottleneck at long distances, significantly enhancing the robustness and analytical capability of remote microbial identification. This approach provides a novel method for remote detection in areas such as public safety, medical diagnostics, and military defense.

Remote detection and identification of plastics with hyperspectral Raman imaging lidar

We report a hyperspectral Raman imaging lidar system that can remotely detect and identify typical plastic species. The system is based on a frequency-doubled, Q-switched Nd:YAG laser operating at 532 nm and an imaging spectrograph equipped with a gated intensified CCD spectrometer. Stand-off detection of plastics is achieved at 6 m away with a relatively wide field of view of 1 × 150 mm2, thus providing the groundwork for better solutions in monitoring marine plastic pollution.

Remote visualization of underwater oil using a flash Raman lidar system

We propose and experimentally demonstrate a new, to the best of our knowledge, underwater monitoring system that incorporates Raman spectroscopy based on a flash lidar. We have visualized underwater oil at a 5 m distance by illuminating the area of around 15 cm diameter with an expanding laser beam at 532 nm and detecting the oil and water Raman images. By calibrating the oil Raman image with the water Raman image, the detection limit of liquid oil thickness has been estimated to be about 0.27 mm. Thus, the proposed technique provides the capability of effectively detecting oil leaks in underwater sea areas.

Laser waterless cleaning of residual organic solvents on the surface of polyurethane coatings

Residual organic solvents have a great impact on the physical and mental health of equipment operators in industry and agriculture. Laser waterless cleaning technology of residual organic solvents on the surface of polyurethane coatings has great application prospects and is a good way to tackle the pollution problem. In this paper, the evolutionary behavior of a laser waterless cleaning mechanism and substrate surface state is analyzed. The influence law of laser energy density and scanning speed on the residual solvent cleaning effect was investigated. The optimal laser cleaning parameters were obtained by comprehensive evaluation of the substrate surface cleaning effect and microscopic morphology. The peak of solvent characteristics before and after laser cleaning was detected by Raman spectroscopy. The results demonstrated that the laser cleaning effect was better with the increase of energy density or the decrease of scanning speed in the substrate damage range, and the best laser cleaning parameters were laser energy density of 0.24J/c m 2 and scanning speed of 500 mm/s. A significant reduction of the peak of Raman spectroscopy was found, reflecting the excellent effect of laser waterless cleaning of residual organic solvents.

Remote sensing oil in water with an all-fiber underwater single-photon Raman lidar

The detection of oil in water is of great importance for maintaining subsurface infrastructures such as oil pipelines. As a potential technology for oceanic application, an oceanic lidar has proved its advantages for remote sensing of optical properties and subsea materials. However, current oceanic lidar systems are highly power-consuming and bulky, making them difficult to deploy underwater to monitor oil in water. To address this issue, we have developed a compact single-photon Raman lidar by using a single-photon detector with high quantum efficiency and low dark noise. Due to the single-photon sensitivity, the detection of the relatively weak Raman backscattered signal from underwater oil was realized with a laser with a pulse energy of 1 µJ and a telescope with a diameter of 22.4 mm. An experimental demonstration was conducted to obtain the distance-resolved Raman backscatter of underwater oil of different thicknesses up to a distance of 12 m. The results indicate the single-photon Raman lidar's potential for inspecting underwater oil pipelines.