Controlled Transition Metal Nucleated Growth of Carbon Nanotubes by Molten Electrolysis of CO2
Bibliographic record
Abstract
The electrolysis of CO2 in molten carbonate has been introduced as an alternative mechanism to synthesize carbon nanomaterials inexpensively at high yield. Until recently, CO2 was thought to be unreactive, making its removal a challenge. CO2 is the main cause of anthropogenic global warming and its utilization and transformation into a stable, valuable material provides an incentivized pathway to mitigate climate change. This study focuses on controlled electrochemical conditions in molten lithium carbonate to split CO2 absorbed from the atmosphere into carbon nanotubes (CNTs), and into various macroscopic assemblies of CNTs, which may be useful for nano-filtration. Different CNT morphologies were prepared electrochemically by variation of the anode and cathode composition and architecture, variation of the electrolyte composition pre-electrolysis processing, and variation of the current application and current density. Individual CNT morphologies’ structures and the CNT molten carbonate growth mechanisms are explored using SEM (scanning electron microscopy), TEM (transmission electron micrsocopy), HAADF (high angle annular dark field), EDX (energy dispersive xray), X-ray diffraction), and Raman methods. The principle commercial technology for CNT production had been chemical vapor deposition, which is an order of magnitude more expensive, generally requires metallo-organics, rather than CO2 as reactants, and can be highly energy and CO2 emission intensive (carries a high carbon positive, rather than negative, footprint).
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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".