Bibliographic record
Abstract
This study assesses the impact of clayey materials’ properties on biofilm formation within the context of point-of-use water treatment systems such as ceramic water filters (CWFs). CWFs were manufactures using clayey materials from different countries by mixing with sawdust and water, and then fired in a kiln. Due to the influence of clayey properties on the quality and duration of CWFs, this study focused to establish a standardization process for clayey selection criteria for ceramic filter factories around the world. To do this, well-established geosciences, environmental and geotechnical engineering methodologies were used. Physical characteristics of clayey materials can be determined through grain size analysis, and liquid limit and plastic limit tests. Mineralogical composition can be determined using X-ray diffraction (XRD) analysis. ICP-MS analysis identifies metals in sawdust fired ashes. Pseudomonas Fluorescens Migula was used as model organism to assess biofilm formation on both clayey materials and CWFs. Three clayey materials from Guatemala, Canada, and Guinea-Bissau were selected for this study. The Guatemalan clayey belonged to poorly-graded sand with silt contains four identifiable minerals: quartz, muscovite, montmorillonite and albite, and its CWF contains quartz, muscovite, and albite. The Canadian clayey was mainly made of quartz, muscovite, and kaolinite and defined as poorly-graded sand, however, its CWF contains quartz, muscovite and hematite. The clayey material from Guinea-Bissau contains quartz, kaolinite, dickite, and montmorillonite and belongs to poorly-graded sand, and its CWF was made of quartz and hematite, respectively. The average biofilm formation coverages for Guatemala, Canada, and Guinea-Bissau clayey materials were 20.02% ± 6.65%, 19.27% ± 4.59%, and 9.88% ± 5.01%, respectively, while average biofilm formation coverages for Guatemala, Canada, and Guinea-Bissau CWFs are 13.08% ± 4.12%, 10.39% ± 5.05%, and 8.50% ± 5.35%, respectively. 11 elements including Na, Mg, K, Cr, Mn, Fe, Co, Ni, Cu, Zn, and As were identified and quantified in sawdust ashes after firing process. High concentrations of Cr, Ni, Cu, Zn metals in general hinder biofilm formation, while Na, Mg, and Fe can accelerate biofilm formation, thus incorporation of ash can impact final CWF bulk geochemistry. Compared to previous studies, our study showed similar trends when P. fluorescens were used on diverse materials; biofilm formation on Canada clayey material containing kaolinite was higher than on Guinea-Bissau clayey material, which contained montmorillonite. Moreover, in Guatemala clayey, albite contained Na+, which can be exchanged with H+ in the culture medium to increase bacterial attachment on the positively charged
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".