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 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".