A Facile and Effective Method for Size Sorting of Large Flake Graphene Oxide
Bibliographic record
Abstract
The size of the building blocks fundamentally governs physical performances of the macro-scale graphene based structures since larger building blocks usually yields better mechanical and electrical properties.Density gradient centrifugation method has emerged as a versatile and scalable method for sorting colloidal 2D nanomaterials.This paper provides a facile and effective size sorting approach to graphene oxide (GO) flakes with large sizes up to 40 µm.The GO flakes were dispersed within distilled water.The centrifugation process parameters were calculated with respect to specific size ranges of GO flakes.Scanning electron microscopy utilized to prove the effectiveness of the separation process.Image processing analysis showed GO flakes with specific size ranges can be separate from aqueous suspension by controlling rotational speed and centrifugation time.The process was performed by using common benchtop centrifuges with low intensity centrifugal fields which requires low investment for a scalable process.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".