Formulation of Time Dependent Bloch NMR Equations for Computational Analyses of Nano Particles in Porous Media
Bibliographic record
Abstract
Nuclear Magnetic Resonance (NMR) has been very useful in the study of pore size distribution of porous materials and in molecular recognition.Important properties of the porous media have been shown to be very much dependent on the T 1 and T 2 relaxation times.The NMR transverse magnetization carries information on the pores' properties.This has been demonstrated by many experiments on porous media but analytical expressions showing the direct relationships between the pore features and the NMR parameters have been quite scarce in literature.In this study, formulation of time dependent Bloch NMR equation for computational analyses of nano particles in porous media has been presented.Since the nano particle is expected to be imaged in a nano-porous medium, we apply the transformation that makes the NMR transverse magnetization expressible in term of with porosity .Two new parameters which validate the transformation are properly defined in terms of the porosity, T 1 and T 2 relaxation parameters.The results obtained in this study can have applications in functional magnetic resonance imaging (fMRI), Petroleum exploration and well design, geological engineering and could be a frontier towards a very robust way of describing porousity and permeability in systems transporting particles of specific shape and form.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".