Nanoenergetic Materials: From Materials to Applications
Bibliographic record
Abstract
Both nanoscience and nanotechnology have undoubtedly contributed significantly to the development of thermite-based nanoenergetic materials (NEMs) with tunable and tailorable combustion performance and their subsequent integration into devices. Specifically, this review article reflects the immense paybacks in designing and fabricating ordered/disordered assembly of energetic materials over multiple length scales (from nano- to milli-scales) in terms of realization of desired reaction rates and sensitivity. Besides presenting a critical review of present advancements made in the synthesis of NEMs, this article touches upon aspects related to various applications concomitantly. The article concludes with the author's summary of the insurmountable challenges and the road ahead toward the deployment of nanoenergetic materials in practical applications. The real challenge lies in the ability to preserve the self-assembly of fuel and oxidizer nanoparticles achieved at the nanoscale while synthesizing macroscale energetic formulations using advanced fabrication techniques both in bulk and thin film forms. Most importantly, these self-assembled NEMs have to exhibit excellent combustion performance at reduced sensitivity to external stimuli such as electrostatic discharge (ESD), friction and impact.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".