Bibliographic record
Abstract
Abstract In 1976, a Canadian group described in detail for the first time a new ion beam analytical method based on the elastic recoil of target nuclei collided with high energy heavy incident ions. In this case 25–40‐MeV35Cl impinged on a multilayer C or Cu (backing)/LiF or LiOH/Cu (30–150 nm)/LiF or LiOH and H, Li, O and F recoiled atoms were detected. These exemplified the main characteristics of elastic recoil detection analysis (ERDA): its sensitivity to depth distribution and its ability to detect light elements in heavy substrates. In 1979, the use of megaelectronvolt energy4He beams permitted the use of ERDA to be extended to depth profiling of hydrogen isotopes in the near surface region of solids. ERDA has rapidly been revealed to be an excellent alternative to resonant nuclear reaction spectrometry for hydrogen determination in solids. Despite its less advantageous performance with respect to its lower depth resolution, lower analyzable depth, comparable sensitivity and more restricting irradiation and detection geometry, some ERDA features have made its development in ion beam analysis (IBA) laboratories worldwide easier; these are simultaneous access to1H and2H depth distributions, access to single‐ended Van de Graaff accelerators compared with tandem or cyclotrons, the ability to be combined with Rutherford backscattering spectrometry (RBS) and a less damaging effect on the investigated targets compared to those generated by heavy ions. The development of detection devices and the analytical capabilities offered by high‐energy heavy ion‐induced ERDA in material sciences for profiling light, medium and large mass number elements give this method a wide area in which to progress as illustrated by discussions at ion beam‐related technique conferences.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".