CO<sub>2</sub> Utilization by Electrolytic Splitting to Carbon Nanotubes in Non‐Lithiated, Cost‐Effective, Molten Carbonate Electrolytes
Bibliographic record
Abstract
Abstract Carbon nanotubes (CNTs), have extraordinarily high tensile strength, electrical and thermal conductivity, and electrical storage capabilities. To date, CNTs have had limited use due to their high synthesis cost. The synthesis costs decrease when CNTs are prepared by transition metal nucleated molten electrolytic CO 2 splitting, rather than by conventional chemical vapor deposition or arc deposition techniques. In addition to cost, a second advantage of CNT electrosynthesis is that the process consumes the greenhouse gas CO 2 in its transformation to CNTs (and oxygen), providing a path to climate mitigation. However, electrolytic high yield, CNT syntheses has only been demonstrated with expensive lithiated electrolytes, such as pure or mixed Li 2 CO 3 . This study demonstrates, after several hundred failures, that carbon nanomaterials, can be synthesized from CO 2 in non‐lithiated electrolytes. In the new sodium barium carbonate electrolytes, lower mobility due to the absence of lithium ions is overcome by: i) low current density; or ii) the introduction of oxides, such as BaO, to induce graphene walls to facilitate passage of larger cations during the process of growing CNTs; and/or iii) addition of specific transition metal nucleation agents, such as Fe 2 O 3 . The electrolysis uses inexpensive anodes and cathodes to form carbon nanomaterials, including straight and coiled CNTs.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".