Thrust characteristics of nano-carbon/Al/oxygenated salt nanothermites for micro-energetic applications
Bibliographic record
Abstract
Combustion within small motors is key in the application-specific development of nanothermite-based micro-energetic systems. This study evaluates the performance of nanothermite mixtures in a converging-diverging nozzle and an open tube. Mixtures were prepared using nano-aluminum (n-Al), potassium perchlorate (KClO4), and different carbon nanomaterials (CNMs) including graphene-oxide (GO), reduced GO, carbon nanotubes (CNTs) and nanofibers (CNFs). The mixtures were packed at different densities and ignited by laser beam. Performance was measured using thrust measurement, high-speed imaging, and computational fluid dynamics modeling, respectively. Thrust, specific impulse (ISP), volumetric impulse (ISV), as well as normalized energy were found to increase notably with CNM content. Two distinctive reaction regimes (fast and slow) were observed in combustion of low and high packing densities (20% and 55%TMD), respectively. Total impulse (IFT) and ISP were maximized in the 5% GO/Al/KClO4 mixture, producing 7.95 mN·s and 135.20 s respectively at 20%TMD, an improvement of 57% compared to a GO-free sample (5.05 mN·s and 85.88 s). CFD analysis of the motors over predicts the thrust generated but trends in nozzle layout and packing density agree with those observed experimentally; peak force was maximized by reducing packing density and using an open tube. The numerical force profiles fit better for the nozzle cases than the open tube scenarios due to the rapid nature of combustion. This study reveals the potential of GO in improving oxygenated salt-based nanothermites, and further demonstrates their applicability for micro-propulsion and micro-energetic applications.
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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 | 0.000 | 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".