Laser-induced breakdown plasma-based sensors

被引:0
作者
Griffin, Steven T. [1 ]
机构
[1] Univ Memphis, Ctr Appl Sensors, Memphis, TN 38152 USA
来源
CHEMICAL, BIOLOGICAL, RADIOLOGICAL, NUCLEAR, AND EXPLOSIVES (CBRNE) SENSING XI | 2010年 / 7665卷
关键词
Laser Induced Breakdown Spectroscopy; modeling; plasma; compressive sensing; signal processing; classification; PULSE LASER; ABLATION PLASMAS; DUST PARTICLES; SPECTROSCOPY; SAMPLES; SINGLE; NANOSECOND; ALUMINUM; SILICON; GRAINS;
D O I
10.1117/12.850095
中图分类号
TP7 [遥感技术];
学科分类号
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要
Laser Induced Breakdown Spectroscopy (LIBS) is dependent on the interaction between the initiating Laser sequence, the sampled material and the intermediate plasma states. Pulse shaping and timing have been empirically demonstrated to have significant impact on the signal available for active/passive detection and identification. The transient nature of empirical LIBS work makes data collection for optimization an expensive process. Guidance from effective computer simulation represents an alternative. This computational method for CBRNE sensing applications models the Laser, material and plasma interaction for the purpose of performance prediction and enhancement. This paper emphasizes the aspects of light, plasma, and material interaction relevant to portable sensor development for LIBS. The modeling structure emphasizes energy balances and empirical fit descriptions with limited detailed-balance and finite element approaches where required. Dusty plasma from partially decomposed material sample interaction with pulse dynamics is considered. This heuristic is used to reduce run times and computer loads. Computer simulations and some data for validation are presented. A new University of Memphis HPC/super-computer (similar to 15 TFLOPS) is used to enhance simulation. Results coordinated with related effort at Arkansas State University. Implications for ongoing empirical work are presented with special attention paid to the application of compressive sensing for signal processing, feature extraction, and classification.
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页数:8
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