Adsorption Mechanisms and AI‐Driven Discovery of Biomass‐Based CO <sub>2</sub> Sorbents
Bibliographic record
Abstract
Abstract The pressing need to reduce carbon dioxide emissions has driven recent advances in carbon capture technologies. Among these, adsorption has emerged as one of the most efficient and promising methods for CO 2 sequestration. This review provides a comprehensive analysis of recent progress in biomass‐derived activated carbon (AC) as a sustainable solution for carbon capture. It explores the influence of various biomass precursors, their composition, and the effects of chemical and thermal treatments on the textural properties and CO 2 adsorption capacity of AC. The role of functional groups and pore structures in enhancing adsorption performance, particularly under humid conditions, is also examined. Additionally, the integration of artificial intelligence (AI)‐driven technologies in process modeling and the discovery of optimized bio‐based AC materials is highlighted. Classic adsorption kinetic models are reviewed to provide deeper insights into CO 2 adsorption mechanisms and the efficiency of bio‐based AC. The discussion underscores the necessity of continued research to enhance the properties, scalability, and cost‐effectiveness of bio‐based AC while leveraging AI‐driven innovations to advance carbon capture and storage (CCS) solutions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".