LOW-POWER LASER IGNITION OF ALUMINUM/METAL OXIDE NANOTHERMITES
Bibliographic record
Abstract
In this study, three different types of nanothermites, i.e., Al/CuO, MoO3, and Al/Bi2O3, were produced using the widely spread wet (i.e., isopropanol based) method. In addition to the above method, the three nanothermites were also produced using a Resodyn LabRAM mixer. A paraffin-coated spherical Al nanopowder (100 nm) was used as the fuel source, while the oxidizers were nanometric powders of CuO (40 nm), MoO3 (100 nm), and Bi2O3 (200 nm). The effect of nanothermite composition on the sensitivity for the tests of electrostatic discharge (ESD), impact, and friction was investigated. Scanning electron microscopy (SEM) was used to analyze the morphology and homogeneity of the nanothermites. Next, the nanothermites were thermally analyzed in terms of energy release, ignition temperature, and flame temperature using a thermogravimetric analysis differential and scanning calorimetry (TGA/DSC) technique. A low-power diode laser was used to evaluate the ability of different laser wavelengths (661, 532, and 445 nm) to produce the ignition energy needed for a specific thermite reaction. Low ignition delays (less than 15 ms) were obtained at approximately 300 mW laser power output for both Al/MoO3 and Al/Bi2O3 thermites. Finally, a forward-looking infrared camera was used to estimate the ignition and burning temperatures of the Al/MoO3 nanothermite.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".