Laser-induced breakdown spectroscopy (LIBS) as an emerging tool: figures, facts and future
Bibliographic record
Abstract
Laser-Induced Breakdown Spectroscopy (LIBS) is a method of optical emission spectroscopy that uses laser-generated plasma as the source of vaporization, atomization and excitation. A bibliographic study around the LIBS literature shows clearly the number of application areas related to LIBS and laser based techniques is still growing. There is no doubt that LIBS has become a fascinating technology with great promise. The benefits include no sample preparation, no consumables, every sample, real-time analysis, standoff measurements, and more. LIBS provides ppm sensitivity for elemental analysis and even offers molecular characterization based on database libraries and chemometrics. In the last decade, there has been a significant technological developments in the components (lasers, spectrometers, detectors) used in LIBS instruments as well as emerging needs to perform real time measurements under conditions to which conventional techniques cannot be applied. This opens the door for many applications and possibilities of developing field-deployable instruments. Recently, fiber lasers have become one of the hottest topics in photonics. In this presentation, we will report their use for LIBS analysis and will discuss their advantages and inconvenient. Also, we will give an overview about LIBS applications for real time analysis, we will discuss the LIBS instrumentation in terms of robustness, analytical performance and portability in comparison to conventional techniques. In addition, we will present some approaches to improve the LIBS sensitivity developed in our laboratory and elsewhere, such as laser-induced fluorescence coupled to LIBS, resonance enhanced LIBS, resonant ablation, etc.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".