Preparation of Carbon-Silicon Doping Composite Adsorbent Material for Removal of VOCs
Bibliographic record
Abstract
The adsorption-desorption combined process has been considered as a promising method for the industrial VOCs (volatile organic compounds) treatment. Herein, a carbon-silicon composite adsorbent material has been prepared for the removal of VOCs at lower potential flammable risk. The preparation involves two main steps: Extrusion forming and thermal treatment. The carboxymethyl cellulose and silicate were adopted as binder and fire retardant respectively. The molding and inflaming retarding mechanisms were proposed and discussed. Results show that the newly prepared doping combined material is micro-mesoporous with a specific surface area of 729 m2/g. The maximum adsorption capacity of carbon-silicon doping combined material to p-xylene is observed to be 292 mg/g. The adsorption is found to be favorable, which is well described by the Yoon-Nelson model and Freundlich isotherm. The combined material is also found to possess reversible adsorption to p-xylene; without sacrificing (<2%) too much adsorption capacity after five adsorption-desorption cycles. The composite materials have an increased ignition temperature of at least 40 °C compared with raw carbon material. These findings suggest that the obtained composite material possesses good adsorption capacity and flame-retardant properties.
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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 | 0.001 | 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".