Carbon Isotopic Measurements of Nanotubes to Differentiate Carbon Sources
Bibliographic record
Abstract
Abstract Stable carbon isotope (δ(13C)) analysis can provide information concerning the starting materials and the production process of a material. Carbon nanotubes (CNTs) are produced using a variety of starting materials, catalysts, and production methods. The use of δ(13C) as a tool to infer the nature of starting materials to gain insight into the mechanics of CNT growth was evaluated. The production process of NRC’s SWCNT-1 was traced via the δ(13C) measurement of the available starting materials, intermediate products, and the final product. As isotopic fractionation is likely negligible at high temperatures, the δ(13C) value of the starting materials was reflected in the δ(13C) value of the final CNT product. For commercially available CNTs, the estimated δ(13C) values of identified starting materials were related to the δ(13C) signatures of CNTs. Using this information and the δ(13C) values of CNTs, the nature of unknown carbon sources was inferred for some samples. The use of δ(13C) analysis may be used as a tracer to differentiate between those processes that use relatively 13C-depleted carbon source(s) such as carbon monoxide, methane, or natural gas, and those that do not.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".