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Record W4324132462 · doi:10.1021/acs.analchem.2c03575

International Interlaboratory Comparison of Thermogravimetric Analysis of Graphene-Related Two-Dimensional Materials

2023· article· en· W4324132462 on OpenAlexaff
Pei Lay Yap, Farzaneh Farivar, Åsa Jämting, Victoria A. Coleman, S J P Gnaniah, Elisabeth Mansfield, Pu Cheng, S. M. Landi, Marcus Vinícius David, Emmanuel Flahaut, Mohammed Aizane, Michael Barnes, M. Gallerneault, M. Dominique Locatelli, Sébastien Jacquinot, Carlton Gray Slough, Jörg Menzel, Stefan Schmölzer, Lingling Ren, Andrew J. Pollard, Dušan Lošić

Bibliographic record

VenueAnalytical Chemistry · 2023
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsNational Research Council Canada
FundersAustralian Research CouncilHorizon 2020 Framework ProgrammeEuropean CommissionMinistério da Ciência, Tecnologia e InovaçãoConselho Nacional de Desenvolvimento Científico e TecnológicoGraphene Flagship
KeywordsThermogravimetric analysisGrapheneChemistryOxideThermal stabilityMoistureNanotechnologyProcess engineeringMaterials scienceOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

Research on graphene-related two-dimensional (2D) materials (GR2Ms) in recent years is strongly moving from academia to industrial sectors with many new developed products and devices on the market. Characterization and quality control of the GR2Ms and their properties are critical for growing industrial translation, which requires the development of appropriate and reliable analytical methods. These challenges are recognized by International Organization for Standardization (ISO 229) and International Electrotechnical Commission (IEC 113) committees to facilitate the development of these methods and standards which are currently in progress. Toward these efforts, the aim of this study was to perform an international interlaboratory comparison (ILC), conducted under Versailles Project on Advanced Materials and Standards (VAMAS) Technical Working Area (TWA) 41 “Graphene and Related 2D Materials” to evaluate the performance (reproducibility and confidence) of the thermogravimetric analysis (TGA) method as a potential new method for chemical characterization of GR2Ms. Three different types of representative and industrially manufactured GR2Ms samples, namely, pristine few-layer graphene (FLG), graphene oxide (GO), and reduced graphene oxide (rGO), were used and supplied to ILC participants to complete the study. The TGA method performance was evaluated by a series of measurements of selected parameters of the chemical and physical properties of these GR2Ms including the number of mass loss steps, thermal stability, temperature of maximum mass change rate ( T p ) for each decomposition step, and the mass contents (%) of moisture, oxygen groups, carbon, and impurities (organic and non-combustible residue). TGA measurements determining these parameters were performed using the provided optimized TGA protocol on the same GR2Ms by 12 participants across academia, industry stakeholders, and national metrology institutes. This paper presents these results with corresponding statistical analysis showing low standard deviation and statistical conformity across all participants that confirm that the TGA method can be satisfactorily used for characterization of these parameters and the chemical characterization and quality control of GR2Ms. The common measurement uncertainty for each parameter, key contribution factors were identified with explanations and recommendations for their elimination and improvements toward their implementation for the development of the ISO/IEC standard for chemical characterization of GR2Ms.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.021
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.109

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.019
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0040.003
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0020.003
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.024
GPT teacher head0.348
Teacher spread0.324 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations19
Published2023
Admission routes1
Has abstractyes

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