Competitive Adsorption Behaviour of Binary Mixtures on Titanium Dioxide
Bibliographic record
Abstract
Exploration of the adsorption mechanism for mixtures at photocatalyst surfaces is a prerequisite for a full understanding of photocatalytic oxidation (PCO) technology for treatment of gaseous contaminants in indoor air applications. However, there has been very little work on the competitive adsorption of photocatalysts. In this article, an experimental and analytical study on the co‐adsorption of nine binary mixtures on a commercial PCO filter was investigated using a bench‐scale single‐pass continuous flow system. Adsorption tests were performed with a concentration of 500 ppb for the selected mixtures at various molar ratios. The experiments were performed at 40 % RH, 21 °C, and a gas flow rate of 10 L/min. Quantitative methods were developed to describe inhibitory and facilitatory effects on the adsorption of one component by the other. It was found that for the non‐polar mixture of p‐xylene and toluene, the polar mixture of MEK and acetone, and the polar/non‐polar mixture of MEK and p‐xylene, adsorption selectivity varied from 0.83–1.81, 1.80–1.21, and 2.60–1.78, respectively, when mixing ratio of each mixture changed from 1:2.33 to 2.33:1. In addition, a time‐dependent co‐adsorption model was developed and validated with the experimental results. It was concluded that competitive adsorption performance is dependent upon the composition of the gas mixture, the natures of the adsorbate and substrate, and the initial molar ratio of VOCs.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".