Nanofluids and Nanocomposite Membranes for Enhanced CO2 Capture: A Comprehensive Review
Bibliographic record
Abstract
Abstract The increasing concentration of greenhouse gasses in Earth's atmosphere is a critical concern, of which 75% of carbon dioxide (CO2) emissions are from the combustion of fossil fuels. This rapid increase in emissions led to irredeemable damages to ecosystems, such as climate change and acid rain. As a result, industries and academia have focused on developing innovative and cost-effective technologies for CO2 capture and storage (CCS). Physical/chemical absorption using amine and membrane-based technologies is generally used in CCS systems. However, the inherent technical and cost-effective limitations of these techniques directed their attention toward applying nanotechnologies for CCS systems. Here, the researchers have focused on infusing nanoparticles (NPs) into existing CCS technologies. The NPs could either be suspended in a base fluid to create nanofluids (NFs) or infused with membrane base materials to create nanocomposite membranes for enhanced carbon capture capabilities. This review paper investigates the manufacturing methods, characterization techniques, and various mechanisms to analyze the impact of nanoparticles-infused nanofluids and nanocomposite membranes for CO2 capture. Finally, the paper summarizes the factors associated with the two technologies and then outlines the drawbacks and benefits of incorporating NPs for CCS applications.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".