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Record W4415105030 · doi:10.1016/j.matlet.2025.139656

Morphed graphene as reinforcement for oil-well class G cement composites

2025· article· en· W4415105030 on OpenAlexaff
Luca Lavagna, Mattia Bartoli, Matteo Pavese, Maria Camila Belduque Correa, Alberto Tagliaferro, Francisco C. Robles Hernández

Bibliographic record

VenueMaterials Letters · 2025
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsUniversity of Ontario Institute of Technology
Fundersnot available
KeywordsGrapheneFlexural strengthComposite numberCementReinforcementPorosityDispersion (optics)Fracture (geology)Compressive strength

Abstract

fetched live from OpenAlex

Morphed graphene (MG) has only recently been put forward as the perfect reinforcement composite material for structure composites due to its unique mechanical properties. This article addresses the possibility of applying MG as a toughener phase in composites of cement for oil-well. MG was synthesized from petroleum coke through control milling and incorporated into cement with varying concentrations (0.1–1 %). The mechanical behavior of MG-reinforced cement demonstrated significant improvements, including enhanced fracture energy, flexural strength, and compression strength. Electron microscopy morphological analysis confirmed that MG effectively reduced porosity and improved particle cohesion. • Advanced morphed graphene–cement composite with enhanced performance. • Morphed graphene ensures better dispersion and bonding. • Boosted mechanical properties: strength and toughness.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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

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 head0.010
GPT teacher head0.239
Teacher spread0.230 · 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 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

Citations1
Published2025
Admission routes1
Has abstractyes

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