Laser‐induced breakdown spectroscopy/laser ablation coupled to inductively coupled plasma mass spectrometry (<scp>LIBS</scp>/<scp>LA</scp>‐<scp>ICPMS</scp>) for the forensic screening and discrimination of lead‐free solders
Bibliographic record
Abstract
The tandem LIBS/LA-ICPMS technique is a desirable tool for the multi-elemental determination, characterization, and classification of alloys as forensic evidence. In this study, LIBS/LA-ICPMS is validated for the forensic evaluation of lead-free solder alloys, which form valuable evidence from post-blast crime scenes involving homemade and improvised explosive devices. LIBS/LA-ICPMS is competitive with other spectroscopic-based forensic techniques as it is in situ, analyzes samples directly, and requires minimal destruction of the exhibit. Following a one-standard calibration technique, nine major (alloying metals) and trace elements (impurities or additives) are quantified in lead-free solders. Optimizing laser parameters and using Pb as a naturally occurring internal standard are shown to compensate for mass-dependent drift and matrix effects. The quantitative results of Pb-free certified reference materials align with certificate values and with results from two techniques in a cross-validation comparison, including electrothermal vaporization-inductively coupled plasma optical emission spectrometry and neutron activation analysis. Utilizing peak ratios in a model of principal component analysis is presented to identify key compositional differences among solders and provide a visual model for solder discrimination. Outcomes of this approach demonstrate the potential for associating or discriminating lead-free solders, including different solders from the same manufacturer. Together, this technique can establish chemical concordance among known and questioned materials and offers a utilitarian approach for the forensic assessment of trace evidence.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".