Natural iron-aluminosilicate as potential solid precursor for supplementary cementitious materials: A comparative study with other aluminosilicates
Bibliographic record
Abstract
The objective of this study was to investigate the impact of the geographic and climatic conditions on laterites properties and on geopolymerization based-laterite. Four different laterite deposits in the four geographical zones of Cameroon were studied. This included the center, north, south and west corners of Cameroon, having chemical composition of SiO 2 + Al 2 O 3 + Fe 2 O 3 = 88.94, 87.6, 89.13 and 78.97%, respectively. The center and south laterites from the black forest, with high pluviometry and relative humidity, show significant amounts of Fe 2 O 3 . While the west laterite from grass field - mountainous areas and the north-laterite from plain arid and semi-arid climate still show lower iron concentrations. The IR absorption bands of the different laterites appear between 1007 and 1047 cm −1 ; characteristic bands of aluminosilicate. The BET (Brunauer-Emmett-Teller) Specific surface area values are comprised in the range of [21.9, 24.1 m 2 /g] for non-calcined laterite and between [45.6 and 123.5 m 2 /g] for laterites calcined at 550 °C and 575 °C. The main particle size values are 5.71, 6.37, 7.43 and 8.45 μm for center-laterite, west-laterite, north laterite and south-laterite, respectively. Although, they differ in the degree of laterization, all the laterites present almost total conversion to geopolymers, due to the presence of amorphous kaolinite and reactive goethite. However, the iron content has significant impact on the globular microstructure. The particle size of laterites, their high values of BET surface area and their significant reactivity make them promising substitutes to metakaolin and other supplementary cementitious materials.
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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) | 0.001 | 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".