Effect of granular bentonite size and needle-punched fibers on virus transport through compacted and geosynthetic clay liners permeated with KCl
Bibliographic record
Abstract
The safe disposal of pathogenic waste from H1N1 outbreaks poses challenges for municipal solid waste (MSW) landfill operations. Needle-punched (NP) geosynthetic clay liners (GCLs) containing granular bentonite (GB) are widely used to contain various contaminants. However, GB particle size distribution, needle-punching reinforcement, and exposure to saline leachates can adversely influence hydraulic performance. Understanding the sorption, hydraulic, and diffusion characteristics of H1N1 under field conditions is essential. Current research on pathogenic waste behavior in MSW landfill environments, particularly the impact of GB size on osmotic efficiency in the presence of viral contamination, remains limited. This study employed H1N1 to evaluate the sorption capacity, hydraulic behavior, and diffusion parameters of GBs with varying grain sizes, alongside powdered bentonite (PB). Additionally, the study assessed the role of NP fibers in GCLs on H1N1 permeation under chemo-mechanical loading. Results show robust sorption across all GBs, with the finest GB and PB achieving optimal diffusion and retardation characteristics; in contrast, coarser GBs significantly increase fluid permeation rates. The GCL exhibited approximately one order of magnitude higher permeation under identical chemo-mechanical loading due to preferential flow through the NP fibre. These findings provide mechanistic insights into bentonite–virus interactions and serve as a preliminary basis for designing barrier systems to manage pandemic-related wastes safely.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".