CO <sub>2</sub> adsorption on pristine Cu-laden biomass-derived hydrochar
Bibliographic record
Abstract
Abstract The circular reuse of biowaste materials offers a promising pathway for creating environmentally sustainable CO 2 adsorbents. Pristine hydrochars derived from waste biomass provide a sustainable and affordable solution for CO 2 capture, eliminating the need for energy-demanding activation or chemical functionalization. In this study, we investigate the CO 2 adsorption performance of hydrochars synthesized via hydrothermal carbonization of copper-contaminated switchgrass. Unlike numerous biomass-based adsorbents previously reported, the hydrochars examined here are utilized without post-synthesis activation or chemical treatment, offering a low-energy, simplified approach to CO 2 capture. Using thermogravimetric analysis (TGA), the hydrochar produced at 220 °C for 6 h exhibited the highest CO 2 uptake of 0.38 ± 0.02 mmol g −1 (16.52 ± 0.78 mg g −1 ) at 30 °C, a remarkable value considering the material’s low specific surface area of 21.8 m 2 g −1 . To further investigate adsorption kinetics, pseudo-first-order, pseudo-second-order, and Avrami kinetic models were applied. The Avrami model provided the best fit, assuming different adsorption mechanisms co-exist. Furthermore, cyclic adsorption–desorption tests on HC-2h at 50 °C with 100%vol of CO 2 demonstrated a gradual decrease in capacity over three cycles (from 10.43 ± 0.70 to 7.67 ± 0.65 mg g −1 ), revealing partial capacity for regeneration and moderate stability. The research outcomes establish an initial stage for the ongoing development of hydrochars derived from contaminated biomass waste as a potential CO 2 adsorbent and open new avenues for integrating such materials into circular carbon management strategies where captured CO 2 may be valorized through utilization pathways.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".