Antioxidative degradation mechanism of <scp>2‐mercaptobenzimidazole</scp> modified <scp>TEPA‐MCM</scp> ‐41 adsorbents for carbon capture from flue gas
Bibliographic record
Abstract
Abstract Solid amine adsorbents can efficiently adsorb CO 2 , but a significant problem is that amine groups are oxidized. In this research, tetraethylenepentamine‐impregnated MCM‐41 adsorbents (TEPA‐MCM‐41) were functionally modified with sulphur‐containing antioxidant 2‐mercaptobenzimidazole (described as antioxidant MB) and tns‐(2.4‐di‐tert‐butyl)‐phosphite (defined as antioxidant 168), respectively. The antioxidative degradation mechanism of 8% MB–50% TEPA‐MCM‐41 was analyzed by in situ diffuse reflectance infrared Fourier transform (in situ DRIFT) spectrum and high‐performance liquid chromatography/mass spectrometry (HPLC/mass). The CO 2 adsorption capacity of 50% TEPA‐MCM‐41 was 4.30 mmol/g under 15% CO 2 /85% N 2 , but decreased to 1.38 mmol/g after oxidation at 100°C for 42 h under 95% N 2 /5% O 2 certain condition. The CO 2 capacity of 8% MB–50% TEPA‐MCM‐41 reduced from 3.90 to 2.86 mmol/g. After 30 adsorption cycles under 5% O 2 /15% CO 2 /80% N 2 , the capacity of 8% MB–50% TEPA‐MCM‐41 also only decreased by 16.8%, while 50% TEPA‐MCM‐41 decreased by 63.2%. The reason for the excellent antioxidant stability of 8% MB–50% TEPA‐MCM‐41 is that MB scavenged free radicals from amine oxidation and decomposed the hydroperoxides produced by free radical reactions. The hydroperoxides were decomposed into alcohols (non‐radical products), which were eventually oxidized to sulphonic compounds. The MB modification inhibited the oxidative degradation of solid amine adsorbents guided for the production of antioxidant‐efficient adsorbents.
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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) | 0.001 | 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".