Testing Silica-Encapsulated DNA Molecules with Iron Nanocore as a Groundwater Tracer in Fractured Silurian Dolostone Bedrock
Bibliographic record
Abstract
DNA-based tracers have recently been used as groundwater tracers, primarily in granular aquifer media. Given the possibility of simultaneously applying and distinguishing multiple tracers with distinct DNA labels, they offer unique opportunities for tracing distinct pathways between injection and arrival points. They are synthesised by adsorbing DNA molecules on a silica or magnetite nanocore and encapsulated with a silica layer to protect the molecule against extreme temperatures, pH, and microbial attack. These nanotracers can be exponentially amplified, pushing the detection sensitivity down to one molecule, thus helping to mitigate the narrower detection range associated with conventional solutes. However, when co-injected, both tracers are expected to follow the same preferential flow paths in a connected fracture network but with distinct travel times. While solute tracers are attenuated by diffusion into the matrix enhanced by sorption, the nanotracer mobility is dominated by advective transport enhanced by size exclusion. Considering the uncertainty in the nanotracer mobility, especially in bedrock aquifers, pairing these tracers provides complementary insight into the nature and variability of the fracture pathways and rates.In this study, we co-injected a novel DNA-based nanotracer with magnetite nanocore (acronym: SiDNAMag) and Uranine in a Silurian dolostone aquifer under controlled natural gradient flow conditions to characterise fracture connectivity, groundwater velocities and diffusion process influences. The experiment was conducted at a toluene-contaminated site in Guelph, Canada, where depth-discrete multilevel systems (MLSs) were installed for 3D monitoring, improving insights on spatial variability in tracer transport. The tracer solution was injected at 0.5 L/min over a 1.6 m vertical interval, isolated with straddle packers in an upgradient well 10.5 m from the modestly pumped (0.11 L/min) extraction well and monitored from 15 MLSs comprising 82 ports. Using temporal moment analysis, we compared the transport of SiDNAMag to Uranine and observed the preferential flow geometries through the fractured dolostone aquifer. SiDNAMag showed an earlier breakthrough (2.25 h) compared to Uranine (6.25 h) at the extraction well with higher average velocity. 2.5% of Uranine mass was recovered, while SiDNAMag recovery was unquantifiable due to intermittent detection in the extraction well. SiDNAMag was predominantly detected at depths below the injection interval compared to Uranine, suggesting an influence of density on particle mobility. Preferential pathways also exist in the zone above the injection interval, evidenced by early detection of Uranine in the shallow ports of MLSs between the injection and extraction wells.These findings enhance our understanding of fracture connectivity and the delineation of dominant flow pathways in the dolostone aquifer. They also provide insights into the variability of discrete fracture pathways within the 3D field domain, supporting the generation of fracture networks that accurately represent field conditions. Using HydroGeosphere, a discrete fracture matrix (DFM) numerical flow and transport model, these networks can be used to evaluate remediation strategies effectively. Although this study reveals SiDNAMag as a promising tool for groundwater tracing in fractured dolostone aquifers, a critical aspect of understanding its transport behaviour lies in examining the effects of groundwater chemistry and aquifer mineralogy on SiDNAMag.
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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) | 0.000 | 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".