International interlaboratory comparison of Raman spectroscopic analysis of CVD-grown graphene
Bibliographic record
Abstract
Abstract There is a pressing need for reliable, reproducible and accurate measurements of graphene’s properties, through international standards, to facilitate industrial growth. However, trustworthy and verified standards require rigorous metrological studies, determining, quantifying and reducing the sources of measurement uncertainty. Towards this effort, we report the procedure and the results of an international interlaboratory comparison (ILC) study, conducted under Versailles Project on Advanced Materials and Standards. This ILC focusses on the comparability of Raman spectroscopy measurements of chemical vapour deposition (CVD) grown graphene using the same measurement protocol across different institutes and laboratories. With data gathered from 17 participants across academia, industry (including instrument manufacturers) and national metrology institutes, this study investigates the measurement uncertainty contributions from both Raman spectroscopy measurements and data analysis procedures, as well as provides solutions for improved accuracy and precision. While many of the reported Raman metrics were relatively consistent, significant and meaningful outliers occurred due to differences in the instruments and data analysis. These variations resulted in inconsistent reports of peak intensity ratios, peak widths and the coverage of graphene. Due to a lack of relative intensity calibration, the relative difference reported in the 2D- and G peak intensity ratios ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:msub> <mml:mi>I</mml:mi> <mml:mrow> <mml:mn>2</mml:mn> <mml:mrow> <mml:mtext>D</mml:mtext> </mml:mrow> </mml:mrow> </mml:msub> </mml:mrow> <mml:mrow> <mml:mo>/</mml:mo> </mml:mrow> <mml:mrow> <mml:msub> <mml:mi>I</mml:mi> <mml:mrow> <mml:mtext>G</mml:mtext> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> ) was up to 200%. It was also shown that the standard deviation for <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:msub> <mml:mi mathvariant="normal">Γ</mml:mi> <mml:mrow> <mml:mn>2</mml:mn> <mml:mrow> <mml:mtext>D</mml:mtext> </mml:mrow> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> values reported by different software packages, was 15× larger for Lorentzian fit functions than for pseudo-Voigt functions. This study has shown that by adopting a relative intensity calibration and consistent peak fitting and data analysis methodologies, these large, and previously unquantified, variations can be significantly reduced, allowing more reproducible and comparable measurements for the graphene community, supporting fundamental research through to the growing graphene industry worldwide. This project and its findings directly underpin the development of the ISO/IEC standard ‘ DTS 21356-2—Nanotechnologies—Structural Characterisation of CVD-grown Graphene ’.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.017 | 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".