Purification and sidewall functionalization of multiwalled carbon nanotubes and resulting bioactivity in two macrophage models
Bibliographic record
Abstract
This study examined the consequences of surface carboxylation of multiwalled carbon nanotubes (MWCNT) on bioactivity. Since commercial raw MWCNT contain impurities that may affect their bioactivity, HCl refluxing was exploited to purify raw "as-received" MWCNT by removing the amorphous carbon layer on the MWCNT surface and reducing the metal impurities (e.g. Ni). The removal of amorphous carbon layer was confirmed by Raman spectroscopy and thermogravimetric analysis. Furthermore, the HCl-purified MWCNT provided more available reaction sites, leading to enhanced sidewall functionalization. The sidewall of HCl-purified MWCNT was further functionalized with the -COOH moiety by HNO(3) oxidation. This process resulted in four distinct MWCNT: raw, purified, -COOH-terminated raw MWCNT, and -COOH-terminated purified MWCNT. Freshly isolated alveolar macrophages from C57Bl/6 mice were exposed to these nanomaterials to determine the effects of the surface chemistry on the bioactivity in terms of cell viability and inflammasome activation. Inflammasome activation was confirmed using inhibitors of cathepsin B and Caspase-1. Purification reduced the cell toxicity and inflammasome activation slightly compared to raw MWCNT. In contrast, functionalization of MWCNT with the -COOH group dramatically reduced the cytotoxicity and inflammasome activation. Similar results were seen using THP-1 cells supporting their potential use for high-throughput screening. This study demonstrated that the toxicity and bioactivity of MWCNT were diminished by removal of the Ni contamination and/or addition of -COOH groups to the sidewalls.
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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) | 0.001 | 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 | 0.001 | 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".