Bibliographic record
Abstract
Engineering of defects located in-grain or at grain boundary is central to the development of functional materials and nanomaterials. While there is a recent surge of interest in the formation, migration, and annihilation of defects during ion and plasma irradiation of bulk (3D) materials, the detailed behavior in low-dimensional materials remains most unexplored and especially difficult to assess experimentally. A new hyperspectral Raman imaging scheme providing high selectivity and diffraction-limited spatial resolution is here adapted to examine plasma-induced damage in a polycrystalline graphene film grown by chemical vapor deposition on copper substrates and then transferred on silicon substrates. For experiments realized in nominally pure argon plasmas at low pressure, spatially resolved Raman conducted before and after each plasma treatment shows that the defect generation in graphene films exposed to very low-energy (11 eV) ion bombardment follows a 0D defect curve, while the domain boundaries tend to develop as 1D defects. Surprisingly and contrary to common expectations of plasma-surface interactions, damage generation at grain boundaries is slower than within the grains. Inspired by recent modeling studies, this behavior can be ascribed to a lattice reconstruction mechanism occurring preferentially at domain boundaries and induced by preferential atom migration and adatom-vacancy recombination. Further studies were realized to compare the impact of different plasma environments promoting either positive argon ions, metastable argon species, or VUV-photons on the damage formation dynamics. While most of the defect formation is due to knock-on collisions by 11-eV argon ions, the combination with VUV-photon or metastable atom irradiation is found to have a very different impact. In the former, the photons are mainly thought to clean the films from PMMA residues due to graphene transfer from copper to silicon substrates. On the other hand, in conditions with both ion and metastable atom irradiation, the surface de-excitation of the latter seem to greatly enhance the self-healing of the grain boundaries due to an increase of the local energy deposition. Finally, these experiments were used as building blocks to examine the formation of chemically doped graphene film in such plasmas using argon mixed with either traces of N- or B-bearing gases. While preferential n-type doping was observed in graphene domains in nitrogen-containing plasmas, preferential p-type doping was observed at grain boundaries in boron-containing plasmas.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".