ReviewNanomaterials (Basel, Switzerland)2024
Machine Learning as a "Catalyst" for Advancements in Carbon Nanotube Research.
Review in Nanomaterials (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- 3D visualization of graphene and carbon nanotubes using Python: a study.Journal of molecular modeling · 2026Article
- CMOS-Compatible Fabrication Module for Sub-100 nm TiN and TaN Pillar Electrodes for Carbon Nanotube Test Structures.Nanomaterials (Basel, Switzerland) · 2026Article
- A Comprehensive Review on Filled Carbon Nanotubes: Synthesis, Properties and Applications.Chemical reviews · 2026Review
- Chemically-informed active learning enables data-efficient multi-objective optimization of self-healing polyurethanes.Chemical science · 2026Article
- Smart nanobiocatalysts for waste-to-biofuel conversion: integrating Nano-Bio interfaces and AI-driven design.Frontiers in bioengineering and biotechnology · 2026Review
- Advances in Carbon Nanotubes: Synthesis, Properties, and Cutting-Edge Applications.Nanomaterials (Basel, Switzerland) · 2025Article
- Advancing Carbon Nanotube Fibers: Addressing Challenges from Production to Application.ACS central science · 2025Review
- Convergence of Nanotechnology and Machine Learning: The State of the Art, Challenges, and Perspectives.International journal of molecular sciences · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
The synthesis, characterization, and application of carbon nanotubes (CNTs) have long posed significant challenges due to the inherent multiple complexity nature involved in their production, processing, and analysis. Recent advancements in machine learning (ML) have provided researchers with novel and powerful tools to address these challenges. This review explores the role of ML in the field of CNT research, focusing on how ML has enhanced CNT research by (1) revolutionizing CNT synthesis through the optimization of complex multivariable systems, enabling autonomous synthesis systems, and reducing reliance on conventional trial-and-error approaches; (2) improving the accuracy and efficiency of CNT characterizations; and (3) accelerating the development of CNT applications across several fields such as electronics, composites, and biomedical fields. This review concludes by offering perspectives on the future potential of integrating ML further into CNT research, highlighting its role in driving the field forward.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.