Thermal Properties of MWCNT-rGO-MgO-Incorporated Alkali-Activated Engineered Composites
Bibliographic record
Abstract
This study evaluates the influence of multiwall carbon nanotubes (MWCNTs), reduced graphene oxide (rGO), and magnesium oxide (MgO) on the thermal conductivity of alkali-activated engineered composites (AAECs). Thirty-two ambient-cured AAECs consisting of two types of powdered-form reagents/activators (type 1—calcium hydroxide: sodium meta silicate = 1:2.5; type 2—calcium hydroxide: sodium sulfate 2.5:1), two dosages of MgO (0 and 0.5%) of MgO, three percentages (0, 0.3%, and 0.6%) of MWCNTs/rGO, and binary (45% ground granulated blast furnace slag ‘GGBFS’ and 55% Class C fly ash ‘FA-C’) and ternary combinations (40% GGBFS, 25% FA-C and 35% class F fly ash ‘FA-F’) of industrial-waste-based source materials, silica sand, and polyvinyl alcohol (PVA) fiber were developed using the ‘one-part dry mix’ technique. Problems associated with the dispersion and agglomeration of nanomaterials during production were avoided through the use of defined ultra-sonication with a high-shear mixing protocol. The impact of the combination of source materials, activators, and MgO/MWCNT/rGO dosages and their combinations on the thermal properties of AAECs is evaluated and discussed based on temperature–time history and thermal conductivity/diffusivity properties along with micro-structural characteristics. It was found that the change in temperature of the AAECs decreased during testing with the addition of MWCNTs/rGO/MgO. The thermal conductivity and diffusivity of AAECs increased with the increase in MWCNT/rGO/MgO contents due to the formation of additional crystalline reaction products, improved matrix connectivity, and high conductivity of nanomaterials. MWCNT AAECs showed the highest thermal conductivity of 0.91–1.26 W/mK with 49% enhancement compared to control AAECs followed by rGO AAECs. The study confirmed the viability of producing MgO/MWCNT/rGO-incorporated AAECs with enhanced thermal properties.
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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 | 0.000 | 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 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".