Advancements in Carbon Capture, Utilization, and Storage (CCUS): A Comprehensive Review of Technologies and Prospects
Bibliographic record
Abstract
Carbon dioxide (CO2) is the most significant anthropogenic greenhouse gas (GHG), accounting for approximately 81% of total emissions, with methane (CH4), nitrous oxide (N2O), and fluorinated gases contributing the remainder. Rising atmospheric CO2 concentrations, driven primarily by fossil fuel combustion, industrial processes, and transportation, have surpassed the Earth’s natural sequestration capacity, intensifying climate change impacts. Carbon Capture, Utilization, and Storage (CCUS) offers a portfolio of solutions to mitigate these emissions, encompassing pre-combustion, post-combustion, oxy-fuel combustion, and direct air capture (DAC) technologies. This review synthesizes advancements in CO2 capture materials including liquid absorbents (amines, amino acids, ionic liquids, hydroxides/carbonates), solid adsorbents (metal–organic frameworks, zeolites, carbon-based materials, metal oxides), hybrid sorbents, and emerging hydrogel-based systems and their integration with utilization and storage routes. Special emphasis is given to CO2 mineralization using mine tailings, steel slag, fly ash, and bauxite residue, as well as biological mineralization employing carbonic anhydrase (CA) immobilized in hydrogels. The techno-economic performance of these pathways is compared, highlighting that while high-capacity sorbents offer scalability, hydrogels and biomineralization excel in low-temperature regeneration and integration with waste valorization. Challenges remain in cost reduction, material stability under industrial flue gas conditions, and integration with renewable energy systems. The review concludes that hybrid, cross-technology CCUS configurations combining complementary capture, utilization, and storage strategies will be essential to meeting 2030 and 2050 climate targets.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".