Preparation and Characterization of Aqueous Nanothermite Inks for Direct Deposition on SCB Initiators
Bibliographic record
Abstract
Abstract Nanothermites are a promising replacement energetic for many devices but their use has been limited by high sensitivity during processing, hazardous processing solvents, and time consuming deposition. Incorporating processing and deposition into a single step, especially if no organic solvents were used, could allow nanothermites to be applied safely in a wider range of applications. This work reports on the performance and characterization of direct‐deposited water processed nanothermite inks on semiconductor bridge (SCB) initiators. Specifically, it investigates the replacement of nanothermites processed by resonant mixing (Resodyn LabRAM) in the solvent N,N‐dimethylformamide (DMF) with nanothermites processed in water. Processing safety and mixture performance were then characterized. It was found that water processed nanothermites were stable for up to 480 min in a water bath at 50 °C only if both metal and metal oxide particles were coated with palmitic acid. In addition, water processed nanothermites were found to have better mixing intimacy, which resulted in better performance than nanothermite processed in DMF. Direct deposition of water processed nanothermites also mitigates electrostatic discharge (ESD) sensitivity, while the material remains wetted, improving processing safety dramatically. For the system investigated, it was found that processing at a solids loading of 30 vol.% resulted in a high density, high performance ink that was deposited directly onto the SCBs. This resulted in a 25 % reduction in the all fire threshold over traditional energetics. This mixing approach uses an environmentally friendly mixing medium, can result in a higher density final material, and allows safe one‐step mixing and deposition.
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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".