Operational Constraints of Detecting SARS-CoV-2 on Passive Samplers using Electronegative Filters: A Kinetic and Equilibrium Analysis
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide In developing an effective monitoring program for the wastewater surveillance of SARS-CoV-2 ribonucleic acid (RNA), the importance of sampling methodology is paramount. Passive sampling has been shown to be an effective tool to detect SARS-CoV-2 RNA in wastewater. However, the adsorption characteristics of SARS-CoV-2 RNA on passive sampling material are not well-understood, which further obscures the relationship between wastewater surveillance and community infection. In this work, adsorption kinetics and equilibrium characteristics were evaluated using batch-adsorption experiments for heat-inactivated SARS-CoV-2 (HI-SCV-2) adsorption to electronegative filters. Equilibrium isotherms were assessed or a range of total suspended solids (TSS) concentrations (118, 265, and 497 mg L –1 ) in wastewater, and a modeled q max of 7 × 10 3 GU cm –2 was found. Surrogate adsorption kinetics followed a pseudo-first-order model in wastewater with maximum concentrations achieved within 24 h. In both field and isotherm experiments, equilibrium behavior and viral recovery were found to be associated with wastewater and eluate TSS. On the basis of the results of this study, we recommend a standard deployment duration of 24–48 h and the inclusion of eluate TSS measurement to assess the likelihood of solids inhibition during analysis.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".