Thermal Properties of MWCNT-rGO-MgO-Incorporated Alkali-Activated Engineered Composites
Why this work is in the frame
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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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it