A new electrolyte for molten carbonate decarbonization
Bibliographic record
Abstract
Abstract The molten Li 2 CO 3 transformation of CO 2 to oxygen and graphene nanocarbons (GNCs), such as carbon nanotubes, is a large scale process of CO 2 removal to mitigate climate change. Sustainability benefits include the stability and storage of the products, and the GNC product value is an incentive for carbon removal. However, high Li 2 CO 3 cost and its competitive use as the primary raw material for EV batteries are obstacles. Common alternative alkali or alkali earth carbonates are ineffective substitutes due to impure GNC products or high energy limitations. A new decarbonization chemistry utilizing a majority of SrCO 3 is investigated. SrCO 3 is much more abundant, and an order of magnitude less expensive, than Li 2 CO 3 . The equivalent affinities of SrCO 3 and Li 2 CO 3 for absorbing and releasing CO 2 are demonstrated to be comparable, and are unlike all the other alkali and alkali earth carbonates. The temperature domain in which the CO 2 transformation to GNCs can be effective is <800 °C. Although the solidus temperature of SrCO 3 is 1494 °C, it is remarkably soluble in Li 2 CO 3 at temperatures less than 800 °C, and the electrolysis energy is low. High purity CNTs are synthesized from CO 2 respectively in SrCO 3 based electrolytes containing 30% or less Li 2 CO 3 .
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".