Microstructural Thermal Zones in Reaction of Nanoenergetics
Bibliographic record
Abstract
Despite the potential of nanoenergetics as promising energy sources with high energy densities and fast energy release, our limited ability to predict combustion speeds restricts the utilization of nanoenergetics. Here, we provide a comprehensive analysis of thermal microstructures subject to heterogeneous reactions and propose a new scaling for combustion wave speeds. To control reaction heterogeneity, two different particle interfacial morphologies of physically mixed and core-shell Al/CuO nanoparticles were synthesized. The combustion dynamics and temperature fields were obtained at micrometer-scale resolutions using high-speed and infrared cameras. Experiments showed that the core-shell Al/CuO exhibiting less reaction heterogeneity had a faster wave speed than the physically mixed counterpart, although the measured chemical reaction rates were lower. By employing the thermal structure analysis, we found that the shortened preheat zone and the lengthened reaction zone, attributed to the lower reaction onset temperature and reaction rate, led to the increased wave speed despite the lower reaction rate in the core-shell Al/CuO. From these analyses, we developed a new scaling that describes the combustion wave speeds in nanoenergetics based on intrinsic properties and thermal structures for both morphologies.
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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 | 0.001 |
| 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".