Realization of a universal quantum pressure standard
Bibliographic record
Abstract
Abstract We report the realization of the first cold-atom primary standard. This standard is based on a universal law governing quantum diffractive collisions between particles that allows an experimental determination of the velocity averaged total collision cross section, the only parameter required to quantify the pressure or flux of particles given a sensor particle collision rate measurement. Using an ensemble of 87Rb sensor atoms, we show that this new quantum pressure standard can be applied to gases of both atomic (He, Ar, and Xe) and molecular species ( N 2 , C O 2 , and H 2 ), surpassing the scope of existing orifice flow pressure standards. We verify the accuracy of this new standard using an ionization gauge (IG) calibrated for N2 by an orifice flow standard. The gauge calibration factors determined by the cold atom and orifice flow standards differ by less than 0.5% and, thus, agree within their uncertainties of 2% and 2.8% respectively. Using this standard, we evaluate the response of two different IGs to a variety of different gas species and report variations of up to 20% for their measured calibration factors. We also observe a non-linear response of the IG readings for CO2 gas. Finally, we demonstrate the use of a magneto-optical trap (MOT) as a transfer standard to extend the measurement range by a factor of 100 to include pressures up to P ~ 10−5 Pa.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".