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Precision Measurement of the First Ionization Potential of Nobelium

2018· article· en· W2810121142 on OpenAlexaff
Premaditya Chhetri, D. Ackermann, H. Backe, M. Block, B. Cheal, C. Droese, Ch. E. Düllmann, J. Even, R. Ferrer, F. Giacoppo, Stefan Götz, F. P. Heßberger, M. Huyse, O. Kaleja, J. Khuyagbaatar, P. Kunz, M. Laatiaoui, F. Lautenschläger, W. Lauth, N. Lecesne, L. Lens, E. Minaya Ramirez, A. Mistry, S. Raeder, P. Van Duppen, Thomas Walther, A. Yakushev, Z. Zhang

Bibliographic record

VenuePhysical Review Letters · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAtomic and Molecular Physics
Canadian institutionsTRIUMF
FundersScience and Technology Facilities CouncilHorizon 2020 Framework ProgrammeHelmholtz-GemeinschaftEuropean Commission
KeywordsIonizationAtomic physicsPhysicsIonQuantum mechanics

Abstract

fetched live from OpenAlex

One of the most important atomic properties governing an element's chemical behavior is the energy required to remove its least-bound electron, referred to as the first ionization potential. For the heaviest elements, this fundamental quantity is strongly influenced by relativistic effects which lead to unique chemical properties. Laser spectroscopy on an atom-at-a-time scale was developed and applied to probe the optical spectrum of neutral nobelium near the ionization threshold. The first ionization potential of nobelium is determined here with a very high precision from the convergence of measured Rydberg series to be 6.626 21±0.000 05 eV. This work provides a stringent benchmark for state-of-the-art many-body atomic modeling that considers relativistic and quantum electrodynamic effects and paves the way for high-precision measurements of atomic properties of elements only available from heavy-ion accelerator facilities.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.011
GPT teacher head0.249
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations67
Published2018
Admission routes1
Has abstractyes

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