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Bibliographic record
Abstract
This study was initiated for the purpose of eliminating long-life waste (LLFP) in nuclear reactors using fusion neutrons. Theoretical simulation studies have revealed that if nuclear fusion neutrons are irradiated to LLFP continuously for 10 years with the rating of 1019 cm-3 s-1, detoxification will proceed by transmutation. There is also a nuclear fusion reactor as a neutron source, but it is too large. The concept of In-flight Muon Catalysis Fusion has been proposed. Atom replaced by orbital electrons with negative muon has an atomic radius of 1/200, so the fusion cross section reaches 2000 barn at the d-t target. This reaction cross section is several hundred times larger than magnetic nuclear fusion using bare ions. Moreover, the kinetic energy of nuclei necessary for fusion is reduced from 100 keV to 1ï½2 keV. In order to utilize this muon fusion reaction, a mixture gas of deuterium and tritium at about 30 atm is targeted and irradiated with 10 keV muon particles. At the energy of α particles generated by this fusion (3.5 MeV), ions of 1ï½2 keV are generated in the target, and this ion and muon atom causes the next nuclear fusion reaction in a chain and steady state. This ion collides with the gas molecules in the target and disappears with a lifetime of about nanosecond, but the next ion is generated by new α particles generated in the next fusion reaction. The target is a high density neutral gas and does not need to be supported by a magnetic field. Since the target gas is overheated by the energy generated in the nuclear fusion reaction, the gas must be replaced at high speed. In the supersonic Laval nozzle, a high density Mach shock wave of 30 atm serves a nuclear fusion regime to which muons are injected. This supersonic nozzle replaces the gas combustion of the scramjet engine with nuclear fusion and has the potential to realize nuclear fusion within the range of existing technology.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.006 | 0.001 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.477 | 0.120 |
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; both teacher heads agree on what is shown here.
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".