70th Year Anniversary of Carbon Nanotube Discovery—Focus on Real World Solutions
Bibliographic record
Abstract
This Special Issue reprint highlights seventy years since the discovery of carbon nanotubes (CNTs) in 1952 by Russian scientists LV Radushkevich and VM Lukyanovich in what was then the Union of Soviet Socialist Republics or the USSR. It also reflects the popularization by the well-known Japanese scientist S Iijima, since 1991, of carbon nanostructures, including CNTs, towards nanotechnology as a forever impactful and inspiring field. For researchers, academics, and teachers of all levels, from novice to expert to guru, the articles contained within this Special Issue are themed aroundsustainability from nanotechnology, pertaining to diameter measurement, rotor system molecular simulation, ultra-high tensile strength, energy, filtration via membrane distillation, environmental remediation using adsorption, ionic liquids as curing agents, biosensors and bioinspired sensors, and electrical/mechanical properties of polymer nanocomposites. The CNT is a legacy 1D nanomaterial, only after which was 2D graphene able to emerge.This is a global and mixed-country authorship Special Issue, with this reprint containing articles from Canada, Saudi Arabia, China, Russia, Spain, India, Japan, the United Arab Emirates (UAE), the USA, Germany, Turkiye, and Malaysia. The Guest Editor is from Singapore and began publishing critical work on the CNT-polymer interface towards the mechanical properties of the nanocomposite up to 20 years ago.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.041 | 0.028 |
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".