Synthesis and Characterization of Tertiary Nanothermite CNMs/Al/KClO<sub>4</sub> with Enhanced Combustion Characteristics
Bibliographic record
Abstract
Abstract Much attention has been directed toward the development of new energetic materials to achieve the increasingly demanding performance of high‐speed propulsion systems. Nanothermite is one of the main approaches for the development of new energetic materials by the close integration of oxidizer and metal fuel. This study is devoted to evaluating the impact of different carbon nanomaterials (graphene oxide, reduced graphene oxide, carbon nanotubes, and carbon nanofibers) on the thermal behavior of nanothermites based on potassium perchlorate and nano aluminium powder. Nanothermite compositions were prepared using a conventional sonication method. The morphology of nanothermites was characterized by a scanning electron microscope (SEM) coupled with energy dispersive spectroscopy (EDS), which confirmed that the nanoparticles are homogeneously dispersed without agglomeration. The structure of nanothermite was also characterized by Fourier Transform Infrared Spectroscopy (FTIR), X‐ray diffraction (XRD), and Raman spectroscopy. The impact of carbon nanomaterials on the combustion behavior of nanothermite was evaluated by thermal gravimetric analysis (TGA), differential scanning calorimetry (DSC), and bomb calorimetry. There was good agreement between results from DSC and bomb calorimetry. In general, the total heat released improved with the addition of carbon nanomaterials and particularly graphene oxide, which generated the highest increase in the heat of combustion. In addition, the maximum decomposition temperature shifted to a lower temperature, which indicates enhanced ignition characteristics. This is the first time reporting on the synthesis and characterization of tertiary nanothermites based on nano‐aluminum, potassium perchlorate, and carbon nanosize materials. It can be concluded that these novel nanothermite compositions exhibit dramatically improved properties as demonstrated by a 200 % increase in the heat of combustion with only a 5 % addition of graphene oxide. Moreover, the ignition temperature decreased from 545.1 °C to 508.7 °C enhancing the overall combustion characteristics.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".