Carbon nanofibers encapsulated by polyethylene glycol increase the mechanical properties and durability of OPC mortars
Bibliographic record
Abstract
Carbon nanofibers (CNF) are promising additives for the reinforcement of cementitious materials. However, due to their high specific surface and hydrophobic nature, CNF are difficult to disperse in ordinary Portland cement (OPC) mortars when using conventional plasticizers. In this work, we evaluate the production of mortars with CNF encapsulated by a thin layer of polyethylene glycol (PEG) covalently grafted at their surface (CNF@PEG). CNF@PEGs were chemically prepared using two methods with PEG of various molecular weights. In all cases, the dispersion of the CNF@PEG in water as well as in a simulated cement pore solution was improved. Remarkably, mortars fabricated with CNF@PEG4k (PEG of molecular weight 4000 g/mol) exhibited a 20 % increase in compressive strength, 10 % increase in flexural strength and 25 % increase of the Young's modulus measured after 7 days, in comparison to a reference mortar. Finally, electrochemical impedance spectroscopy (EIS) was used to assess the ionic resistivity of the mortar. Mortars containing CNF@PEG4K had an ionic resistivity 43 % higher than the reference sample at 28 days leading to better durability. These results demonstrate that encapsulation of CNF by PEG is a successful strategy to improve the mechanical performance and durability of CNF-containing OPC mortars. • Polyethylene glycol, PEG, is grafted on carbon nanofibers, CNF, to form core@shell CNF@PEG. • Unlike CNF, CNF@PEG are dispersible in cement pore solution without aggregate formation. • Mortars containing CNF@PEG exhibit higher compressive strength and flexural strength. • CNF@PEG has a higher electrical resistivity and lower chloride permeability with an expected durability improvement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".