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 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".