Review on Advancements in Carbon Nanotubes: Synthesis, Purification, and Multifaceted Applications
Bibliographic record
Abstract
Since their discovery over two decades ago, carbon nanotubes (CNTs) have captivated researchers due to their exceptional electrical, optical, mechanical, and thermal properties, making them versatile candidates for various advanced applications. CNTs have transformed numerous scientific domains, including nanotechnology, electronics, materials science, and biomedical engineering. Their applications range from nanoelectronics, robust nanocomposites, and energy storage devices to innovative materials, sensors, conducting polymers, field emission sources, and Li-ion batteries. Furthermore, CNTs have found critical roles in biosensing, water purification, bone scaffolding, and targeted gene and drug delivery. The chemical reactivity and functional versatility of CNTs are profoundly influenced by their structural and physicochemical properties, such as surface area, surface charge, size distribution, surface chemistry, and purity. This review comprehensively explores the current state of CNT research, focusing on widely used synthesis, purification, and characterization techniques alongside emerging applications. By highlighting recent advancements and addressing unresolved challenges, it aims to present a novel perspective on the transformative potential of CNTs, fostering innovation across diverse scientific and technological fields.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".