A novel methodology for assessing the environmental sustainability of ionic liquids used for CO<sub>2</sub> capture
Bibliographic record
Abstract
Ionic liquids (ILs) have been proposed as suitable sorbents for CO 2 capture because of their high CO 2 absorption capacity, thermal stability, negligible vapour pressure and physico-chemical tunability. However, the environmental implications of ILs are currently largely unknown because of a lack of data. The issue is further complicated by their complex chemical structures and numerous precursors for which environmental data are scarce or non-existent. In an attempt to address this issue, this paper presents a new methodology for estimating life cycle environmental impacts of novel ILs, with the aim of aiding synthesis and selection of more sustainable CO 2 sorbents. The methodology consists of four main steps: (1) selection of an appropriate IL and synthesis route; (2) construction of a life cycle tree; (3) life cycle assessment; and (4) recommendations for improvements. The application of the methodology is illustrated using trihexyltetradecylphosphonium 1,2,4-triazolide ([P 66614 ][124Triz]), a promising IL for CO 2 capture currently under development. Following the above steps, the paper demonstrates how the data obtained from laboratory synthesis of the IL can be scaled up to industrial production to estimate life cycle impacts and identify environmental hotspots. In this particular case, the main hotspots are the precursors used in the synthesis of the IL. Comparison of impacts with monoethanolamine (MEA), currently the most widely-used CO 2 sorbent, suggests that [P 66614 ][124Triz] has much higher impacts than MEA, including global warming potential. However, human toxicity potential is significantly higher for MEA. Therefore, the proposed methodology can be used to optimise the design of ILs and to guide selection of more sustainable CO 2 sorbents. Although the focus is on ILs, the methodology is generic and can be applied to other chemicals under development.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".