Metal-organic frameworks as hypergolic additives for hybrid rockets
Bibliographic record
Abstract
Hybrid rocket propulsion can contribute to reduce launch costs by simplifying engine design and operation. Hypergolic propellants, i.e. igniting spontaneously and immediately upon contact between fuel and oxidizer, further simplify system integration by removing the need for an ignition system. Such hybrid engines could also replace currently popular hypergolic propulsion approaches based on extremely toxic and carcinogenic hydrazines. Here we present the first demonstration for the use of hypergolic metal-organic frameworks (HMOFs) as additives to trigger hypergolic ignition in conventional paraffin-based hybrid engine fuels. HMOFS are a recently introduced class of stable and safe hypergolic materials, used here as a platform to bring readily tunable ignition and combustion properties to hydrocarbon fuels. We present an experimental investigation of the ignition delay (ID, the time from first contact with an oxidizer to ignition) of blends of HMOFs with paraffin, using White Fuming Nitric Acid (WFNA) as the oxidizer. The majority of measured IDs are under 10 ms, significantly below the upper limit of 50 ms required for functional hypergolic propellant, and within the ultrafast ignition range. A theoretical analysis of the performance of HMOFs-containing fuels in a hybrid launcher engine scenario also reveals the effect of the HMOF mass fraction on the specific impulse (Isp) and density impulse (ρIsp). The use of HMOFs to produce paraffin-based hypergolic fuels results in a slight decrease of the Isp and ρIsp compared to that of pure paraffin, similar to the effect observed with Ammonia Borane (AB), a popular hypergolic additive. HMOFs however have a much higher thermal stability, allowing for convenient mixing with hot liquid paraffin, making the manufacturing processes simpler and safer compared to other hypergolic additives such as AB.
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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".