Surface modification of nitrogen-doped carbon nanotubes by ozone via atomic layer deposition
Bibliographic record
Abstract
The use of ozone as an oxidizing agent for atomic layer deposition (ALD) processes is rapidly growing due to its strong oxidizing capabilities. However, the effect of ozone on nanostructured substrates such as nitrogen-doped multiwalled carbon nanotubes (NCNTs) and pristine multiwalled carbon nanotubes (PCNTs) are not very well understood and may provide an avenue toward functionalizing the carbon nanotube surface prior to deposition. The effects of ALD ozone treatment on NCNTs and PCNTs using 10 wt. % ozone at temperatures of 150, 250, and 300 °C are studied. The effect of ozone pulse time and ALD cycle number on NCNTs and PCNTs was also investigated. Morphological changes to the substrate were observed by scanning electron microscopy and high resolution transmission electron microscopy. Brunauer-Emmett-Teller measurements were also conducted to determine surface area, pore size, and pore size distribution following ozone treatment. The graphitic nature of both NCNTs and PCNTs was determined using Raman analysis while x-ray photoelectron spectroscopy (XPS) was employed to probe the chemical nature of NCNTs. It was found that O3 attack occurs preferentially to the outermost geometric surface of NCNTs. Our research also revealed that the deleterious effects of ozone are found only on NCNTs while little or no damage occurs on PCNTs. Furthermore, XPS analysis indicated that ALD ozone treatment on NCNTs, at elevated temperatures, results in loss of nitrogen content. Our studies demonstrate that ALD ozone treatment is an effective avenue toward creating low nitrogen content, defect rich substrates for use in electrochemical applications and ALD of various metal/metal oxides.
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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) | 0.000 | 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".