Nanothermites: Developments and Future Perspectives
Bibliographic record
Abstract
Ternary nanothermites are considered promising energetic materials and a key option to meet the increasing demand in the field of energetic and propulsion systems. The first part of this chapter outlines the advantages of using nanothermites over microthermites. Traditional composites of nanothermites based on n-Al and metallic oxides are described. Furthermore, efforts to improve the combustion characteristics of basic nanothermites mixtures are summarized. In the second part of the chapter, we introduce the benefits of using oxygenated salts as alternative oxidizers to metallic ones in enhancing the energetic properties of nanothermites. The impact of different carbon nanomaterials (graphene oxide, reduced graphene oxide, carbon nanotubes, and carbon nanofibers) on the combustion behavior of ternary nanothermites is discussed. Another focus is on the proper use of ternary nanothermites in micro-energetic devices. For this purpose, tuning the proportion of the oxidizer and fuel in ternary nanothermites is an important issue. In addition, the combustion propagation process, pressure, and thrust generating characteristics of ternary nanothermite mixtures in small tubes are examined.
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 | 0.001 | 0.000 |
| 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 | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".