Sustainable Carbon Nanomaterials and their Applications
Bibliographic record
Abstract
Sustainable Carbon Nanomaterials and Their Applications explores the role of carbon-based nanomaterials in promoting global sustainability. With climate change and environmental concerns rising, the book highlights the need for high-performance, eco-friendly materials. It showcases the unique properties of carbon nanomaterials and their potential across energy, environmental remediation, biomedicine, catalysis, and computational sciences. Featuring insights from leading researchers, the book presents an integrated view of sustainable carbon nanomaterials, from their responsible sourcing and green synthesis methods to advanced characterization techniques and real-world applications. This resource is essential for scientists, engineers, and students in nanotechnology, materials science, and environmental engineering, emphasizing that carbon nanomaterials are key to building a cleaner, safer, and more sustainable future. Key features • Takes a critical approach to the topic of carbon nanomaterials • Covers the sourcing of sustainable materials, their synthesis, characterization and classification • Discusses the latest applications spanning biology to sensing, catalysis to green energy, and environmental/analytical applications • Covers the identification of sustainable sources and how a sustainable source can be valorised to produce a nanomaterial • Explains how sustainability can translate to green research efforts (from source to product)
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.015 |
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".