Measurement of Lateral Resolution in Confocal Raman Spectroscopy Using Indium Arsenide Nanowires
Bibliographic record
Abstract
ABSTRACT Lateral resolution is a key figure of merit for spectroscopy across all applications. Confocal Raman spectroscopy is able to provide chemical and structural information with submicrometre resolution, resulting in widespread use across multiple disciplines of science and technology. However, the lack of agreed‐upon measurement standards and appropriate reference samples has hindered uptake. Here, we report the development and demonstration of a reference sample based on indium arsenide (InAs) semiconducting nanowires for measuring the lateral resolution of confocal Raman spectroscopy with a pathway for traceability to the International System of Units (SI). An interlaboratory comparison involving 15 participants from 11 countries has been conducted to rigorously test and demonstrate the suitability of the sample and the method. The study identified required revisions to the measurement protocol to improve the consistency of data analysis and that the long‐term operational stability of the reference sample requires further improvement. Based on a revised data analysis protocol, the method delivered consistent results at the 95% confidence level for eight of the nine participants who returned full datasets. Outcomes from this study have contributed to the publication of a new international standard (ISO 23124:2024).
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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 | 0.002 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".