Nuclear Magnetic Resonance Investigation of Surface Relaxivity Modification by Paramagnetic Nanoparticles
Bibliographic record
Abstract
Abstract Nuclear magnetic resonance (NMR) measurements are routinely used to characterize pore size distributions in fluid saturated porous media. The principle of the NMR measurement is that the result is linked with pore size by a parameter named surface relaxivity. However, in natural porous media, surface relaxivity is not constant or well-known due to heterogeneous distributions of impurities on pore surfaces. To control pore surface relaxivity, we injected paramagnetic zirconia nanoparticles into silica porous media: glass bead packs and sandstone core samples. Adsorption of the nanoparticles onto the pore surfaces altered their surface relaxivity due to differences in relaxivity between the silica surfaces and the nanoparticles. NMR measurements of porous media saturated with zirconia nanoparticle dispersions and deionized (DI) water were compared to calculate amount of adsorbed zirconia nanoparticles and quantify the alteration of pore surface relaxivity. Our results indicate that adsorption of nanoparticles onto pore surfaces leaves fewer nanoparticles in dispersion within the pore space and alters surface relaxation on pore wall with attached nanoparticles. The overall relaxation rate of the porous medium is thus affected by adsorption, which changes the surface relaxation rate and the relaxation rate of the fluid within the pore space. Electrostatic interactions drive nanoparticle adsorption onto pore walls. When silica porous media, which have negative surface charge, are saturated with positively charged nanoparticles, the nanoparticles adsorb onto the pore surface. When the porous media are saturated with negatively charged nanoparticles, no adsorption occurs. Our work highlights the importance of surface chemistry and adsorption on nanoparticle behavior in porous media and suggests that fundamental NMR behavior of media may be controlled with targeted adsorption of suitable nanoparticles.
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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) | 0.001 | 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".