Circuit Topologies and Techniques for High Voltage Plasma Ignition
Bibliographic record
Abstract
In a pulsed plasma (discharge) application requiring sub-microsecond pulses (such as gas lasers), there are major advantages to ignite or breakdown the discharge at higher voltages (higher tens of kVs). This is especially true if the pulse is only a few tens of nanoseconds in duration. The advantage is, primarily, higher peak power through the gap discharge. Higher peak powers with a shorter pulse width in most cases favor increased efficiencies in the kinetic processes in the plasma, which, for instance, in gas lasers leads to high laser energy outputs for a given energy input. However, the challenge is to achieve shorter times to charge the peaking capacitor. Shorter charging times can be achieved using pulse compression stage(s) but are limited by the stray loop inductance. Achieving a higher voltage ignition level thus remains a major challenge in the pulse power industry. This paper proposes a new concept: the use of a magnetic switch to be inserted between the peaking capacitor and the gap to block a part of the peaking capacitor's rising voltage. This shortens the time during which an applied voltage is seen by the gap thus enabling it to be ignited at high voltages.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".