Simulation and Experimental Studies and Applications of Carbon Nanotubes and Graphenes in Engineering and Medicine
Bibliographic record
Abstract
Since their discoveries in 1990’s of last century and the beginning of this century, respectively, carbon nanotubes (CNTs) and graphenes have been extensively investigated. Investigations of the properties of CNTs and graphenes, either as single nanostructures or as components in nanoscale devices, such as molecular transporter or nanocomposites, have become one of the most active research directions in materials physics and chemistry and nanotechnology. The investigations include a wide range of studies on their electronic conductance properties, field emission properties, fracture and buckling properties, and thermal conductivity properties. The studies have exhibited extremely high strength and exceptional electronic and thermal properties of the nanostructures. The superior mechanical properties have generated a great motivation for mechanical engineers and scholars to explore potential applications of individual CNTs and graphenes, and nanoscale systems made from them. The efforts by the engineers and scholars have revealed wide potential applications of CNTs and graphenes in nanodevices in biological, medical, energy storage, sensor, and other engineering and medicine applications. The nanostructures have been found to hold substantial promises as nanosensors, oscillators, transistors, solar cells, molecular transporters, ultracapacitors, microbial detection, and diagnosis devices, etc. Extensive and comprehensive simulation and experimental studies on carbon nanotubes and graphenes will open a door for a wide range of their applications in engineering and medicine.This special issue is dedicated to the publication of recent developments in simulations and experiments of the two materials for their applications in engineering and medicine applications. A wide range of fundamentally theoretical, computational, experimental topics on modeling, and applications of the two materials are covered in the special issue. It is with great pleasure that we present this special issue that covers a very wide and varied range of subject areas in original research reports addressing nanoscale phenomena and their applications and reviews of emerging nanotechnology topics and research needs. The research papers in the special issue will be published in Vol. 3 Nos. 1 and 2 of the journal due to different submission and processing stages.We would like to extend our sincere thanks to the authors for their contributions, especially their precious time and efforts invested in the special issue.
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".