Nuclear magnetic resonance relaxation and diffusion properties of confined fluids in organic nanopores: A molecular dynamics study
Bibliographic record
Abstract
A series of molecular dynamics (MD) simulations was conducted to investigate the nuclear magnetic resonance (NMR) relaxation properties of confined fluids in kerogen nanopores. We examined how the longitudinal (T1) and transverse (T2) relaxation times vary as a function of pore size, pore shape, the presence of paramagnetic impurities, and Larmor frequency (ω). Given the challenges of nanoscale experiments, this approach offers an in-depth analysis of how these petrophysical factors influence the measurements of T1 and T2 relaxation times. Water and oil were included in the simulations to assess how fluid type affects the results. The findings show that the presence of kerogen significantly impacts the diffusion of water and oil in organic nanopores compared to bulk. To quantify the influence of pore structure, we systematically analyzed the diffusion and NMR properties of fluids under nanoconfinement in rectangular nanopores of varying sizes. We observed that smaller pore sizes lead to a reduction in the diffusion coefficients. When considering a more complex pore network (kerogen matrix), the values of T1 and T2 relaxation times decreased by three and five orders of magnitude, respectively, compared to the values obtained for rectangular pores. At a Larmor frequency of 400 MHz, both n-pentane and water in kerogen exhibited longer relaxation times than at lower frequencies. The presence of paramagnetic impurities in the system allowed us to obtain relaxation times in the order of magnitude of the experimental data reported by other authors for hydrocarbons confined in kerogen matrices. This study provides a detailed guide to using MD simulations to investigate the behavior of nanoconfined fluids in kerogen.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".