Method for Obtaining Silver Nanoparticle Concentrations within a Porous Medium via Synchrotron X-ray Computed Microtomography
Bibliographic record
Abstract
Attempts at understanding nanoparticle fate and transport in the subsurface environment are currently hindered by an inability to quantify nanoparticle behavior at the pore scale (within and between pores) within realistic pore networks. This paper is the first to present a method for high resolution quantification of silver nanoparticle (nAg) concentrations within porous media under controlled experimental conditions. This method makes it possible to extract silver nanoparticle concentrations within individual pores in static and quasi-dynamic (i.e., transport) systems. Quantification is achieved by employing absorption-edge synchrotron X-ray computed microtomography (SXCMT) and an extension of the Beer-Lambert law. Three-dimensional maps of X-ray mass linear attenuation are converted to SXCMT-determined nAg concentration and are found to closely match the concentrations determined by ICP analysis. In addition, factors affecting the quality of the SXCMT-determined results are investigated: 1) The acquisition of an additional above-edge data set reduced the standard deviation of SXCMT-determined concentrations; 2) X-ray refraction at the grain/water interface artificially depresses the SXCMT-determined concentrations within 18.1 μm of a grain surface; 3) By treating the approximately 20 × 10(6) voxels within each data set statistically (i.e., averaging), a high level of confidence in the SXCMT-determined mean concentrations can be obtained. This novel method provides the means to examine a wide range of properties related to nanoparticle transport in controlled laboratory porous medium experiments.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".