Carbon Nanotube Assembly and Integration for Applications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Carbon nanotubes (CNTs) have attracted significant interest due to their unique combination of properties including high mechanical strength, large aspect ratios, high surface area, distinct optical characteristics, high thermal and electrical conductivity, which make them suitable for a wide range of applications in areas from electronics (transistors, energy production and storage) to biotechnology (imaging, sensors, actuators and drug delivery) and other applications (displays, photonics, composites and multi-functional coatings/films). Controlled growth, assembly and integration of CNTs is essential for the practical realization of current and future nanotube applications. This review focuses on progress to date in the field of CNT assembly and integration for various applications. CNT synthesis based on arc-discharge, laser ablation and chemical vapor deposition (CVD) including details of tip-growth and base-growth models are first introduced. Advances in CNT structural control (chirality, diameter and junctions) using methods such as catalyst conditioning, cloning, seed-, and template-based growth are then explored in detail, followed by post-growth CNT purification techniques using selective surface chemistry, gel chromatography and density gradient centrifugation. Various assembly and integration techniques for multiple CNTs based on catalyst patterning, forest growth and composites are considered along with their alignment/placement onto different substrates using photolithography, transfer printing and different solution-based techniques such as inkjet printing, dielectrophoresis (DEP) and spin coating. Finally, some of the challenges in current and emerging applications of CNTs in fields such as energy storage, transistors, tissue engineering, drug delivery, electronic cryptographic keys and sensors are considered.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it