Amine-Functionalized Lignin for CO <sub>2</sub> Capture─Part 2: A Double Amine Grafting Strategy
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Global warming has been exacerbated by the escalating emissions of greenhouse gases from fossil fuel power plants and other industrial and transportation sources. Postcombustion CO 2 capture by amine-containing materials has evolved into a burgeoning industry for mitigating point-source emissions. Nevertheless, the deployment of sustainable and biodegradable solid-supported amine adsorbents remains underexplored despite their tunable surface chemistry that can be leveraged for CO 2 adsorption. Therefore, the purpose of this study is to valorize lignin for CO 2 capture using a double amine functionalization method. Lignin was subjected to two separate grafting procedures: one with diethylenetriamine (DETA) to produce DETA-aminated lignin (DAL) and another procedure involving 3-aminopropyltrimethoxysilane (APTMS) and triaminosilane (TRI) to make APTMS- and TRI-grafted lignin. Subsequently, DAL was further grafted by TRI (TRI/DAL) to increase amine content and improve CO 2 capture. TRI/DAL was found to achieve a considerably higher CO 2 uptake than that of individually modified materials. In the presence of 15% dry CO 2 /N 2 at 25 °C, CO 2 uptake reached 1.31 mmol/g, representing a 72% increase compared to DAL (0.76 mmol/g) and more than 250% of TRI/Lignin-1 (0.37 mmol/g). Humidity further promoted CO 2 capture, with a CO 2 uptake of 1.84 mmol/g and an amine efficiency of 0.43 mol CO 2 /mol N at 55% relative humidity (RH). In addition to enhancing CO 2 uptake, the presence of moisture improved the cyclic stability of the material, with TRI/DAL retaining 97.5 and 95% of its initial capacity after ten cycles at 25 and 50 °C in the presence of 15% CO 2 /N 2 with 55% RH.
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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".