Sustainable solutions: the role of gel-based adsorbents in CO₂ capture
Bibliographic record
Abstract
This review examines the potential of novel gel-based adsorbents as effective solutions for capturing carbon dioxide (CO2) amid rising global emissions. Addressing the urgent need for sustainable and energy-efficient adsorption technologies, the article highlights the unique characteristics of various gel-based adsorbents, particularly those derived from renewable resources. These materials exhibit advantageous three-dimensional structures that enhance high-capacity and energy-efficient CO2 capture. Key features include a porous three-dimensional structure and tunable viscoelastic properties that contribute to significant adsorption capacity, selectivity, and optimised energy recovery. Additionally, the review analyzes the design and performance of the latest generations of sustainable gel adsorbents, often integrated with nanomaterials, ionic liquids, or biosurfactants, demonstrating the synergistic effects of these combinations on performance enhancement. Recent advances in carbon capture, utilisation, and storage (CCUS) technologies related to these adsorbents are examined. The economic and environmental benefits of hybrid systems were outlined, emphasising their inherent sustainability and essential role in the transition to a low-carbon economy. Finally, future research directions focused on optimising gel structures were proposed to enhance scalability and efficiency, a crucial step in facilitating sustainable development and strengthening climate resilience in the face of ongoing environmental challenges.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".