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Record W3200605378 · doi:10.1364/oe.438264

Ultrafast attosecond-magnetic-field generation of the charge migration process based on HeH<sup>2+</sup> and H<sub>2</sub> <sup>+</sup> electronically excited by circularly polarized laser pulses

2021· article· en· W3200605378 on OpenAlexaff
Qing-Yun Xu, Zhijie Yang, Yong-Lin He, Fang-Yan Gao, Huizhong Lu, Jing Guo

Bibliographic record

VenueOptics Express · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLaser-Matter Interactions and Applications
Canadian institutionsUniversité de Sherbrooke
FundersNational Natural Science Foundation of China
KeywordsAtomic physicsUltrashort pulseAttosecondExcited stateIonizationIonPhysicsExcitationElectronMagnetic fieldLaserOptics

Abstract

fetched live from OpenAlex

The ultrafast process by the electron in molecular ions from one site or region to another that has come to be known as charge migration (CM), which is of fundamental importance to photon induced chemical or physical reactions. In this work, we study the electron current and ultrafast magnetic-field generation based on CM process of oriented asymmetric (HeH 2+ ) and symmetric (H 2 + ) molecular ions. Calculated results show that they are ascribed to quantum interference of electronic states for these molecular ions under intense circularly polarized (CP) laser pulses. The two scenarios of (i) resonance excitation and (ii) direct ionization are considered through appropriately utilizing designed laser pulses. By comparison, the magnetic field induced by the scenario (i) is stronger than that of scenario (ii) for molecular ions. However, the scheme (ii) is very sensitive to the helicity of CP field, which is opposite to the scenario (i). Moreover, the magnetic field generated by H 2 + is stronger than that by HeH 2+ through scenario (i). Our findings provide a guiding principle for producing ultrafast magnetic fields in molecular systems for future research in ultrafast magneto-optics.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.175
Threshold uncertainty score0.935

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.008
GPT teacher head0.224
Teacher spread0.217 · 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 teacher head, 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

Citations6
Published2021
Admission routes1
Has abstractyes

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