Amine-functionalized lignin for CO2 capture. 1: Phenolation- enhanced amine grafting
Bibliographic record
Abstract
To address the growing need for the development of a sustainable platform for CO 2 capture, this study investigates the utilization of lignin as a biodegradable support for the fabrication of amine-containing CO 2 adsorbents. The synergetic effect of phenolation followed by the Mannich amination on lignin has been extended to CO 2 adsorption. Lignin was first subjected to phenolation (PL) to enhance its ability for attaching a greater number of amine-containing species. Extensive screening was conducted using five different polyamines to determine the optimum formulation for the Mannich process. The performance of diethylenetriamine-functionalized lignin (AL) and aminated PL (APL) was comprehensively evaluated under various operational conditions, including dry and humid CO 2 environments, different CO 2 concentrations, working temperatures, and cyclic CO 2 adsorption-desorption. In the presence of 15 % CO 2 /N 2 at 25 °C, APL was found to capture more CO 2 compared to AL under both dry and wet conditions, with a maximum CO 2 uptake of 1.45 mmol/g at 35 % relative humidity (RH), representing a 46 % increase compared to dry condition. • Biodegradable lignin was used as a sustainable support for amine-based CO 2 adsorbents. • Lignin was functionalized with five polyamines via Mannich reaction to optimize CO 2 capture performance. • Phenolation improved lignin's capacity to anchor more amine-containing species. • Amine functionalization after lignin phenolation showed superior CO 2 uptake. • Amine-functionalized phenolated lignin exhibited stable cyclic CO 2 adsorption.
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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 | 0.000 | 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 | 0.000 | 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 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".