Physico-Antibacterial Feature and SEM Morphology of Bio-Hydraulic Lime Mortars Incorporating Nano-Graphene Oxide and Binary Combination of Nano-Graphene Oxide with Nano Silver, Fly Ash, Zinc, and Titanium Powders
Bibliographic record
Abstract
The study evaluated the impact of graphene powders used as additives in the recipe of the experimental lime mortar to a mixture ratio of 1:2.5 of NHL3.5 hydraulic lime:fine sand. The content of binder, aggregate and water was kept constant, varying only the amount and the type of the added additives in relation to the amount of natural hydraulic lime NHL3.5. The following five types of experimental mortars were prepared as follows: reference mortar (without additive); mortars containing 1 wt.% GO and 5 wt.% GO powder; mortar with the following GO powders mixture: GO powder functionalized with silver nanoparticles and with fly ash (GO-Ag + GO-fly ash); mortar with the following GO powders mixture: GO with zinc oxide and with titanium oxide (GO-ZnO + GO-TiO2). The influence of the GO-based additive addition on the porosity, surface microstructure, and water sorption coefficient of the mortar samples was evaluated. The antibacterial effect of the mortar samples against three bacterial strains was also investigated. The best results were obtained for the experimental mortar containing GO-ZnO -TiO2, which showed improved experimental properties that potentially allow its use for the rehabilitation of heritage buildings.
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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 | 0.001 | 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".