Kinetics of low‐temperature catalytic combustion of ethylene at wet conditions for postharvest storage applications
Bibliographic record
Abstract
Abstract The study addresses the reduction of ethylene levels in postharvest storage applications using a selected Pd‐Zn‐Sn/TiO2 catalyst (US Patent 11065579) capable of reacting trace concentrations of ethylene at temperatures as low as 278 K and relative humidity as high as 90%. The rate law is derived from data collected using a constant volume batch reactor, and a model for a storage room with an associated isothermal packed bed reactor is developed. The amount of catalyst required to maintain an ethylene concentration of 0.1 ppmv in a room containing 20 tons of fruit having an ethylene metabolism of 0.1 μl/kg h was calculated as a function of air temperature and water content. While the catalyst can continuously remove ethylene from saturated, refrigerated air, the amount of catalyst required can be reduced significantly by incorporating conventional air conditioning solutions upstream of the catalyst bed. Such combined systems and their functions are discussed.
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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) | 0.002 | 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".