Comprehensive analysis of water vapor sorption kinetics and mechanisms using biosorbent pellets from canola meal and oat hull
Bibliographic record
Abstract
Agricultural residues, specifically canola meal (CM) and oat hull (OH), have been innovatively utilized to develop biosorbents for sorbing water vapor from the air. These biomaterials are comprised of hydrophilic functional groups that effectively perform air dehydration. Air, being non-polar, was used as a model gas in this study to simulate gas dehydration. In the current research, these materials were formed into pellets to produce high-quality biosorbents with controlled size, shape, and enhanced moisture uptake capacity. CM (309.48 mg/g) and OH (233.07 mg/g) pellets had higher or comparable water sorption capacities than commercialized adsorbents used for drying gases. The mixed-order kinetic model described the sorption process well and identified both mass transfer and sorption on active site steps (R2 > 0.991 and χ2 < 16.0). Regarding the OH pellet, sorption on active sites was the predominant kinetic mechanism at the beginning, followed by intraparticle diffusion until equilibrium. However, sorption on CM pellets was delayed at 4.2 min at the initial stage, owing to the external mass transfer resistance; then, intraparticle diffusion controlled the process until equilibrium. Adding sodium lignosulfonate (LSNa) lowered the initial sorption rate but enhanced the water uptake and strength of pellets. The addition of LSNa resulted in a 25% and 14% increase in the water uptake capacity of oat hull and canola meal pellets, respectively; conversely, it also caused an increase in the delay time for sorption on CM pellets at the beginning of the process, extending it to 9.50 min.
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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".