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Record W4414971344 · doi:10.1002/jrs.70054

Measurement of Lateral Resolution in Confocal Raman Spectroscopy Using Indium Arsenide Nanowires

2025· article· en· W4414971344 on OpenAlexaff
Sebastian Wood, Keith R. Paton, Ruth Rawcliffe, Tomas Peach, Tehseen Adel, Alessio Sacco, Hugo Kerdoncuff, Matthieu Paillet, A. Zahab, Paul Finnie, Li‐Lin Tay, Jeongyong Kim, HyukSang Kwon, Nobuyasu Itoh, Shinsuke Kashiwagi, Andrea Mario Rossi, Yaxuan Yao, Lingling Ren, Elif Özçeri, Gemma Rius, David Steinmetz, Andrea Richter, Thomas Dieing, A. Tempez, O. Lancry, Marc Chaigneau, Angela R. Hight Walker, Erlon H. Martins Ferreira, Maxim Shkunov, Fernando A. Castro

Bibliographic record

VenueJournal of Raman Spectroscopy · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSpectroscopy Techniques in Biomedical and Chemical Research
Canadian institutionsNational Research Council Canada
FundersNational Key Research and Development Program of ChinaNational Institute of Standards and Technology
KeywordsRaman spectroscopyNanowireSample (material)SpectroscopyConfocalResolution (logic)Gallium arsenideTraceabilityCharacterization (materials science)

Abstract

fetched live from OpenAlex

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).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.853

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.335
Teacher spread0.319 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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