Spectrochemical microanalysis of aluminum alloys by laser-induced breakdown spectroscopy: identification of precipitates
Bibliographic record
Abstract
Multielemental microanalysis of commercially available aluminum alloys has been performed in air by laser-induced breakdown spectroscopy (LIBS) by use of UV laser pulses with energies below 10 microJ. It is shown that the LIBS technique is capable of detecting the elemental composition of particles less than 10 microm in size, such as precipitates in an aluminum alloy matrix, by using single laser shots. Chemical mapping with a lateral resolution of approximately 10 microm of the distribution of precipitates in the surface plane of a sample was also carried out. Two main types of precipitate, namely, Mn-Fe-Cu (type I) and Mg-Cu (type II), were unambiguously distinguished in our LIBS experiments, in good agreement with x-ray microanalysis measurements. The relative standard deviations of emission of the main minor constituent elements (Cu, Mg, Mn) of the aluminum 2024 alloy range from 33% to 39% when laser shots on the precipitates are included in the analysis but decrease to a range from 5.3% to 7.4% when laser shots are taken only on the matrix material, excluding the precipitates.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".