Feasibility of online optical diagnostics during gas-phase synthesis of few-layer graphene based on elastic light scattering measurements
Bibliographic record
Abstract
Gas-phase synthesis is a promising method for scalable production of high-quality free-standing few-layer graphene (FLG) particles. This study assesses the feasibility of elastic light scattering in characterizing particle morphology and distinguishing FLG from soot-like particles that may also be produced concurrently during gas-phase synthesis. FLG particle morphology is modeled based on tomographic electron microscopy images of FLG particles produced within a plasma reactor, whereas synthetic soot particles are generated via a cluster–cluster aggregation algorithm based on morphological parameters typical of flame soot. Light scattering properties of ensembles of synthetic FLG and soot particles are simulated via the discrete dipole approximation (DDA) and the multi-sphere T-matrix method, respectively. Angle-resolved scattering properties of ensembles of FLG and soot particles are analyzed to evaluate the feasibility of scattering-based diagnostics and identify potential measurement configurations for characterizing particle morphology. Overall, certain scattering properties, especially the depolarization ratio, are observed to be sensitive to the distinctive morphological aspects of FLG and soot, which highlights the promise of light scattering-based diagnostics for characterizing morphology during gas-phase synthesis of FLG. • Application-relevant functionality of few-layer graphene (FLG) depends on morphology. • Soot-like particles and crumpled FLG exhibit distinctive scattering behaviors. • Scattering can distinguish FLG from soot based on a single detection angle. • Depolarization ratio is potent in characterizing morphology of crumpled FLG.
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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 | 0.002 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".