Computer-aided design of graphene and 2D materials synthesis via magnetic inductive heating of 11 transition metals
Bibliographic record
Abstract
Abstract We performed numerical simulations to determine the effect of the most influential operating parameters on the performance of a radio frequency (RF) induction-heating system in which RF magnetic fields inductively heat metal foils to grow graphene. The thermal efficiency of the system depends on the geometry as well as on the materials’ electrical conductivity and skin depth. The process is simulated under specific graphene and two-dimensional (2D) materials growth conditions using finite elements software in order to predict the transient temperature and magnetic field distribution during standard graphene and 2D materials growth conditions. The proposed model considers different coil Helmholtz-like geometries and 11 metal foils, including Ag, Au, Cu, Ni, Co, Pd, Pt, Rh, Ir, Mo, and W. In each case, an optimal window of process variables ensuring a temperature range of 1035 °C–1084 °C or 700 °C–750 °C suitable for graphene and MoS 2 growth, respectively, was found. Temperature gradients calculated from the simulated profiles between the edge and the center of the substrate showed a thermal uniformity of less than ∼2% for coinage metals like Au, Ag, and Cu and up to 7% for Pd. Model validation was performed for graphene growth on copper. Due to its limited heat conductivity, good heating uniformity was obtained. As a consequence, full coverage of monolayer graphene on copper with few defects and a grain domain size of ∼2 µ m was obtained. The substrate temperature reached ∼1035 °C from ambient after only ∼90 s, in excellent agreement with model predictions. This allows for improved process efficiency in terms of fast, localized, homogeneous, and precise heating with energy saving. Due to these advantages, inductive heating has great potential for large-scale and rapid manufacturing of graphene and 2D materials.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".