Laser-Based Characterization and Classification of Functional Alloy Materials (AlCuPbSiSnZn) Using Calibration-Free...

Area of Science:

• Materials Science
• Analytical Chemistry
• Spectroscopy

Background:

• Functional alloys containing aluminum, copper, lead, silicon, tin, and zinc are crucial for electrotechnical and thermal applications.
• Accurate and rapid elemental analysis is essential for quality control and material development.

Purpose of the Study:

• To analyze functional alloy samples using laser-induced breakdown spectroscopy (LIBS).
• To quantitatively determine elemental concentrations and classify alloy samples.
• To evaluate the effectiveness of calibration-free LIBS (CF-LIBS) and machine learning techniques.

Main Methods:

• Utilized a Q-switched Nd laser (532 nm, 5 ns pulses) to ablate nine alloy samples.
• Employed compact Avantes spectrometers with CCD arrays to capture emission spectra.
• Performed quantitative analysis using calibration-free laser-induced breakdown spectroscopy (CF-LIBS) under local thermodynamic equilibrium (LTE) assumptions.
• Applied a random forest technique (RFT) for sample classification based on LIBS spectral data.

Main Results:

• CF-LIBS analysis showed good agreement with laser ablation time-of-flight mass spectrometry (LA-TOF-MS).
• The random forest technique achieved high classification accuracy, with out-of-bag estimation at ~98.89% and 10-fold cross-validation at ~99.12%.
• Demonstrated the capability to analyze elemental composition and classify functional metallic materials rapidly and without extensive sample preparation.

Conclusions:

• The integrated approach of LIBS, LA-TOF-MS, and machine learning (RFT) provides a powerful tool for analyzing functional metallic materials.
• This synergistic combination enables fast, preparation-free elemental analysis and classification.
• Highlights the potential of data-driven methods in conjunction with spectroscopic techniques for advanced material characterization.