Activated carbon impregnation with ag and cu composed nanoparticles for <i>escherichia coli</i> contaminated water treatment
Bibliographic record
Abstract
This study aimed to produce modified granular activated carbon (GAC) by low concentrations of silver (Ag) and/or copper (Cu) nanoparticles with antibacterial capacity for application in water purification. Modified porous materials were produced from the vacuum impregnation method, at the concentrations (mass of metal/mass of activated carbon) of Ag 8 x 10−4 g/g (Ag 0.08 %), Cu 1 x 10−2 g/g (Cu 1 %), and Ag 8 x 10−4 g/g + Cu 1 x 10−2 g/g (Ag 0.08 % Cu 1 %). The reduction of the metal salts in NPs of Ag and Cu in their metallic forms or oxides was carried out by the thermal decomposition method. The characterization of the produced porous material was performed by x‐ray diffraction, programmed temperature reduction, scanning electron microscopy, x‐ray dispersive energy spectroscopy, electron transmission microscopy, and specific surface area measurements: Brunauer, Emmet, and Teller; micropore area (t method); pore size distribution (DA method); and volume and diameter of micropores (HK method) and mesopores (BJH method). In the structure of the material produced, Ag and Cu metal compounds and AgO and CuO oxides were identified, with average crystallite sizes of < 60 nm. The efficiency of the inactivation of Escherichia coli was more significant in the GAC modified with the combination of NPs (GAC/NP‐AgCu) (6.4 log 10 units), evidencing the synergistic effect of the metals when compared to GAC/NP‐Ag (0.56 log10 units) and GAC/NP‐Cu (1.31 log10 units) modified porous material. Thus, these antibacterial materials can be used for application in water purification, improving the bacteriological quality of water intended for human consumption, noting that the low concentration of metals used can provide exceptional process efficiency.
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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".