Enhancing elemental visualization imaging accuracy on irregular material surfaces with an auto-focus laser-induced
Shangyong Zhao1, Jingyi Wu2, Zhangrong Li2
1Zhejiang A&F University, College of Optical, Mechanical and Electrical Engineering, Hangzhou, 311300, PR China; Tsinghua University, State Key Laboratory of Power System Operation and Control, Department of Electrical Engineering, Beijing, 100084, PR China.
Abstract:
Laser-induced breakdown spectroscopy (LIBS) elemental visualization imaging plays a valuable role in analyzing the distribution of elements on material surfaces, yet it continues to face significant challenges in accurately scanning irregular surfaces. Conventional static ns-LIBS systems suffer from notable signal attenuation and reduced analytical accuracy due to defocus effects. Existing hardware-based optimizations (such as precision displacement platforms) and numerical compensation algorithms each have limitations, the former is complex and inefficient, while the latter fails to fundamentally address the loss in signal-to-noise ratio. To tackle this issue, this study innovatively introduces dynamic focal adjustment technology into LIBS imaging and proposes an auto-focus LIBS (AF-LIBS) imaging method. By integrating a motor-driven focusing mirror module, the system tracks surface height variations in real time and dynamically adjusts the laser focal position, thereby ensuring plasma excitation stability at the physical root. Experimental results demonstrate that for complex surfaces with height variations of approximately 26 mm, the relative standard deviation (RSD) of characteristic spectral line intensities within regions of interest was significantly reduced to below 9.65% through a zoning strategy. Compared with traditional static ns-LIBS imaging, the proposed AF-LIBS method improves the stability of spectral signal intensity by a factor of 8.12. Thus, this work fundamentally addresses a core limitation in applying LIBS imaging to irregular surfaces-the defocus problem-effectively reducing signal uncertainty, delivering better analytical precision, and providing more reliable elemental distribution images. It greatly enhances the analytical reliability of LIBS in complex scenarios, extending its application beyond flat or simply shaped samples. This breakthrough opens up new possibilities in fields such as biomedical research, cultural heritage archaeology, geological sample analysis, online processing, and new energy materials, effectively promoting the transition of LIBS technology from laboratory settings to practical field-based in situ analytical applications.
