Polymer-Filled Composite Porous Catalytic Particles for Hydrodynamic Studies in Trickle-Bed Reactors
Bibliographic record
Abstract
A simple and scaleable method is proposed for suppressing internal porosity in various porous particles that are widely applied in packed-bed catalytic systems. The method is based on a complete filling of the pores by a cross-linked organic polymer. The particles were first loaded with the volume of organic monomer and cross-linker needed to achieve complete blockage of the porosity, and subsequently, in situ radical polymerization was performed with heating under reduced atmosphere. The method is shown to be adaptable for particles of various shapes, sizes, and porous structures. Selective and complete pore filling has been confirmed by nitrogen physisorption measurements, thermogravimetric analysis, and scanning electron microscopy. The different analyses confirm uniform pore blocking. The difference in contact angle before and after polymer impregnation was found to be negligible. The system is especially suitable for packed-bed hydrodynamic studies to disentangle extragranular hydrodynamic effects (such as axial dispersion) from intraparticle mass transfer effects, which are key to scaling down/up trickle-bed reactors. The liquid holdup and Péclet number for a trickle-bed reactor packed with polymer-impregnated spherical particles and glass beads, respectively, were compared for the compatibility of the method. For given superficial liquid and gas velocities corresponding to the pulsing flow regime, the external liquid holdup and Péclet number were found to be correspondingly close for both particles.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".