Analysis of the Hg-Na arc-W cathode interactions at increasingly high Na contents
Bibliographic record
Abstract
Summary form only given, as follows. The effect of increasingly high sodium contents on the attachment conditions of a DC mercury-sodium (Hg-Na) arc discharge on a tungsten (W) cathode is studied. A physical model based on the collisionless assumption is used for the description of the cathode sheath. The solution of this model; i.e. the plasma heat flux versus the cathode surface temperature, is coupled to a thermal model for the cathode bulk for a self-consistent solution of the problem under diffuse attachment conditions. Domains of existence for self-sustaining operation of the system in the electron temperature at the sheath edge - cathode surface temperatures space (T/sub e/-T/sub s/ space) are obtained versus the Na content in the arc discharge and reveal the higher cathode surface temperature requirements as the Na content increases. For a typical set of HPS lamp operating conditions (p=1 atm, p/sub Hg/=0.9P, p/sub Na/=0.1P, I=3.2 A), the model predicts melting of the pure W cathode tip. The substantial increase of the operating tip temperature at increasingly high Na content is attributed to the increasingly high density of Na/sup +/ ions, itself associated with the much lower ionization potential of Na with respect to Hg, which must be sustained by the electron emission processes. Lowering the W cathode tip work function brings the operating temperatures to lower values in agreement with well-known experimental facts.
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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) | 0.002 | 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".