Role and impact of surfactants in carbon nanotube dispersions and sorting
Bibliographic record
Abstract
Abstract Carbon nanotubes (CNTs) are proving to be versatile nanomaterials that exhibit superior and attractive electrical, optical, chemical, physical, and mechanical properties. Different kinds of CNTs exist, and their associated properties have been actively explored and widely exploited from fundamental studies to practical applications. Obtaining high‐quality CNTs in large volumes is desirable, especially for scalable electronic, photonic, chemical, and mechanical systems. At present, abundant but random CNTs are synthesized by various growth methods including arc discharge, chemical vapor deposition, and molecular beam epitaxy. An economical way to secure pristine CNTs is to disperse the raw soot of CNTs in solutions, from which purified CNTs are collected via sorting methods. Individual CNTs are generally hydrophobic, not readily soluble, requiring an agent, known as a surfactant to facilitate effective dispersions. Furthermore, the combination of surfactants, polymers, DNA, and other additives can enhance the purity of specific types of CNTs in confidence dispersions. With highly‐pure CNTs, designated functional devices are built to demonstrate improved performance. This review surveys and highlights the essential roles and significant impacts of surfactants in dispersing and sorting 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 | 0.001 | 0.001 |
| 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".