Volatile Organic Compound (VOC) Removal via Photocatalytic Oxidation Using TiO2 Coated Nanofilms
Bibliographic record
Abstract
In this paper, toluene removal via photocatalytic oxidation using TiO2 dip coated nanofilms is presented. Nanofilms were synthesized from bacterial cellulose using the electrospinning technique. The physical properties of the nanofilms were analyzed by scanning electron microscopy (SEM). The ratio of bacterial cellulose/nylon used in the spinning process was 0.165:1. The results from SEM showed that the structure of the TiO2 composite nanofilms was rutile crystalline with an average particle size of 20 nm, and synthesized nanofilms had an average size of 20 - 30 nm. The band gap energies of TiO2-dip coated nanofilms ranged from 3.18 - 3.21 eV. SEM results of TiO2 coated nanofilms suggested that the TiO2 was rather uniformly distributed onto the surface of the nanofilms. The actual amount of TiO2 coated on the nanofilms was estimated using thermogravimetric analysis (TGA) for 1x1 cm2 surface area. It was found that 0.1852, 0.2897 and 0.7275 mg of TiO2 were coated on the surface of the nanofilms for 1, 2.5 and 5 % (weight) TiO2 dosage, respectively. The photocatalytic activity of the nanofilms was tested for the removal of gaseous toluene in a photocatalytic reactor. Experimental conditions were set as follows: UV light intensity of approximately 2.7 mW.cm-2, flow rate of 0.2 L.min-1, and an initial toluene concentration of about 200±20 ppm, and a retention time at 200 min. The degradation rate of toluene increased with increasing dosage of TiO2 from 1, 2.5 and 5 %. The nanofilms at a 5 % dosage yielded the highest removal efficiency of 92.71 %, followed by the 2.5 and 1 % dosage, respectively.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".