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Record W3023743114 · doi:10.1088/2515-7647/ab8f89

Coulomb blocking of sequential tunnel ionization in complex systems

2020· article· en· W3023743114 on OpenAlexaff
G. Bart, I Schubert, A. M. Parks, P. B. Corkum, Thomas Brabec

Bibliographic record

VenueJournal of Physics Photonics · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLaser-Matter Interactions and Applications
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsIonizationTunnel ionizationQuantum tunnellingAtomic physicsElectronCoulombCoulomb blockadeCharge (physics)Block (permutation group theory)PhysicsBlocking (statistics)ChemistryIonCondensed matter physicsQuantum mechanicsVoltage

Abstract

fetched live from OpenAlex

Abstract When atoms and molecules are exposed to intense low frequency laser fields, the dominant response is sequential tunnel ionization of charge states with increasing ionization potential. Sequential ionization is assumed to proceed as separate one electron processes. The theoretical analysis developed here reveals that in complex systems sequential tunnel ionization can be inhibited by Coulomb blocking. When ionization potentials of subsequent charge states are close to each other, multiple tunneling events can occur during a half cycle and in close proximity, so that a tunneled electron can block the next tunneling electron. In sub-nm clusters driven by near infrared single-cycle pulses, Coulomb blocking reduces two-electron sequential tunneling by up to 2-3 orders of magnitude.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.041
GPT teacher head0.287
Teacher spread0.246 · 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 designSimulation or modeling
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

Citations2
Published2020
Admission routes1
Has abstractyes

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