An Experimental Investigation of Environmentally Friendly Concrete Reinforced With Graphene Nanoplatelets
Bibliographic record
Abstract
Carbon Dioxide (𝐶𝑂 2 ) is the principal greenhouse gas that contributes to increasing the greenhouse effect, which has a significant adverse impact on the environment.There are large quantities of carbon dioxide emissions associated with cement production.Production of cement is estimated to be responsible for approximately 8% of global greenhouse gas emissions.Besides its environmental impact, concrete also exhibits low tensile and ductility, which can result in cracks.In recent years, carbon nanomaterials, such as Carbon Nanotubes (CNT), Carbon Nanofibers (CNFs), Graphene Nanoplatelets (GNPs), Graphene Oxide (GO), and Reduced Graphene Oxide (rGO), have gained increasing attention from the building industry and scientific community due to their exceptional mechanical and physical properties.In fact, the use of carbon nanomaterials is considered an environmentally friendly method of enhancing the mechanical properties of concrete.The purpose of this study is to investigate the effects of GNPs on the mechanical properties of concrete using the wet dispersion method.In addition to using ultrasonic treatment to prepare GNP dispersions, a superplasticizer is also used to facilitate the dispersion of GNPs in water.Concrete specimens were prepared with 0.25 wt% GNPs.Compressive, flexural and tensile strength of concrete at age of 7 days were assessed.As a result of adding GNPs, strength of concrete specimens was enhanced.Furthermore, when GNPs were incorporated into concrete specimens, compressive, flexural and tensile strength was increased by 19, 8.7 and 9.1 % respectively.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".