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Record W4411852180 · doi:10.17163/ings.n34.2025.03

Characterization of graphene oxide synthesized through a modified Hummers method

2025· article· en· W4411852180 on OpenAlexaff
Wilson Navas-Pinto, Duncan Cree, Lee D. Wilson, Germán Barrionuevo, Xavier Sánchez-Sánchez, Héctor Calvopiña

Bibliographic record

VenueIngenius · 2025
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsGrapheneCharacterization (materials science)OxideMaterials scienceGraphene oxide paperNanotechnologyChemical engineeringEngineeringMetallurgy

Abstract

fetched live from OpenAlex

Graphene oxide (GO) has garnered significant interest due to its exceptional and tunable properties, which make it a promising candidate for a wide range of engineering applications, including composite material fabrication and water treatment. In this study, GO was synthesized from graphite flakes using a modified Hummers method involving a reduced amount of sulfuric acid. The resulting material was characterized using scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), and X-ray photoelectron spectroscopy (XPS). These techniques enabled a clear differentiation between the morphology of the synthesized GO and that of the original graphite. The GO exhibited a substantially altered structure, with increased thickness likely due to the incorporation of oxygen-containing functional groups on its basal plane. FTIR analysis confirmed the presence of characteristic functional groups such as hydroxyl, carbonyl, and carboxyl. XPS analysis revealed that the elemental composition of the synthesized GO consisted of approximately 69.7% carbon and 29.9% oxygen, with a trace amount of sulfur attributed to the reagents used in the synthesis. The observed changes in morphology and composition suggest the successful synthesis of GO with potential for functionalization and application in diverse engineering contexts.

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)0.000
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 score0.151
Threshold uncertainty score0.329

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.028
GPT teacher head0.332
Teacher spread0.304 · 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

Citations4
Published2025
Admission routes1
Has abstractyes

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