Quantifying Liquid-Solid Mass Transfer in a Trickle Bed Using $${T}_{2}-{T}_{2}$$ Relaxation Exchange NMR
Bibliographic record
Abstract
Abstract Measurement of the liquid-solid mass transfer coefficient within a trickle bed (i.e. gas-liquid flow within a packed bed) of porous silica pellets is achieved through the use of $${T}_{2}-{T}_{2}$$ T 2 - T 2 relaxation exchange nuclear magnetic resonance (NMR). Compared to many conventional measurement techniques, the NMR method enables measurement of mass transport using pellets of real commercial interest. Mass transfer coefficients measured using the NMR technique over a range of liquid Reynolds number, 0.2 $$\le R{e}_{\mathrm{L}}\le $$ ≤ R e L ≤ 1.4, are compared to a number of literature correlations, with values measured using the NMR method falling within the range predicted by the correlations. The results demonstrate the importance of considering both the flow conditions and the type of pellets used to develop mass transport correlations in trickle beds. This novel NMR application may be utilized in the future to screen catalyst pellets in trickle beds for optimal mass transport properties.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".