The modification of industrial grade metatitanic and application in SCR DeNO<sub><i>X</i></sub> catalyst preparation
Bibliographic record
Abstract
In this paper, industrial grade metatitanic (H2TiO3) from sulphate process was used as the raw material to prepare TiO2 through a series of modification. Granular and honeycomb SCR DeNOX catalysts were made by granulation and molding processes respectively with those TiO2 as the carrier material. The characterisation, mechanical strength and denitration performance of the carrier/catalyst samples were tested and compared with those of commercial anatase nano‐TiO2 products. The results showed that the TiO2 sample after the modified operation was remarkably improved in particle diameter, dispersion characteristic (average particle size from 15.79 to 1.746 µm) and the degree of crystallinity (from 93.7% to 98.1%), also the impurity content of K, Na, Ca, Mg, Al, S, etc. was significantly reduced; however, the grain size was increased (from 7.0 to 9.9 nm), and the specific surface area was decreased slightly (from 148.1 to 112.6 m2/g). As a whole, the characterisation was extremely close to commercial anatase nano‐TiO2. Catalyst samples prepared with TiO2 after the modified operation had an upgrade on catalytic activity (the maximum denitration rate of granular catalyst from 95.0% to 97.1%; honeycomb catalyst from 92.2% to 93.3%), and the mechanical properties of honeycomb catalysts have been enhanced significantly (compressive strength of axial direction changed from 0.1063 to 0.5965 Mpa). The work indicated that TiO2 prepared with the industrial grade metatitanic from sulphate process was suitable as a carrier material for SCR DeNOX catalyst production. Also the modification was necessary.
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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) | 0.000 | 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".