Development and implementation of promising technologies in JSC EDB Fakel
Bibliographic record
Abstract
The enterprise is mastering a number of technologies for obtaining functional materials for monopropellant thruster and Hall-effect thruster. The article describes advances in the development of a catalyst for the decomposition of hydrazine with high physicochemical characteristics. The activity of the catalyst assessed by chemisorption H2. The maximum values obtained in the range of 700–750 °C and amounted to more than 1000 µmol/g. The mechanical strength of the granules determined as a result of dynamic and static tests: 0.5 % and 16 MPa. The specific surface area according to the BET method was about 110 m2/g. As part of the work on a new low-temperature emissive material, samples of electrides mayenite obtained by solidphase synthesis. The results of laboratory tests indicate that the emission current has reached 1 mA, but at the moment long-term stable operation of the emitter has not been achieved. As part of the work on testing the application of a hafnium nitride barrier coating, prototypes of coatings on standard parts of the cathode assembly obtained and investigated. The coating is characterized by uniform thickness and color, and high adhesion to substrate. In continuation of the work, it is planned to conduct fire tests as part of the engine.
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 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".