Ultra-small defect-engineered UiO-66 on cellulose nanocrystal template for advanced carbon dioxide capture membrane
Bibliographic record
Abstract
Global warming and associated climate change, primarily driven by greenhouse gas emissions, are no longer a forecast but are now undeniable realities. Although membrane technology presents a highly cost-effective approach for carbon dioxide (CO 2 ) capture, further research is required to overcome the inherent trade-off between selectivity and permeability to achieve enhanced performance. A novel defect-engineered ultrasmall cellulose nanocrystal (CNC)-templated UiO-66 MOF (CNC-UiO-66 hybrid) was synthesized to improve the performance of Pebax membranes. The elongated geometry of the CNC-UiO-66 hybrid creates extended facilitated transport channels for CO 2 , while the highly defective structure, induced by the presence of CNC during synthesis, enhances coordination interactions with both CO 2 and the polymer matrix. As a result, Pebax incorporated with CNC-UiO-66 demonstrated increased crystallinity and thermal stability. The incorporation of as little as 1 wt% of the CNC-UiO-66 hybrid into Pebax membranes achieved a remarkable CO 2 permeability of 1442 Barrer and a selectivity of 40, surpassing the Robeson upper bound (2008) for CO 2 /N 2 separation. Cost analysis suggested that this membrane could reduce carbon capture costs to 62 USD per tonne, 10 USD less than conventional membranes. These results highlight the potential of CNC-UiO-66 hybrid membranes for efficient and cost-effective CCUS applications, particularly in flue gas treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 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".