MétaCan
Menu
Back to cohort
Record W4291551977 · doi:10.1364/ol.468358

Magnetometry based on the effect of laser-induced plasmas in a sodium-containing environment

2022· article· en· W4291551977 on OpenAlexaff
Guoguo Kang, Xiaoxu Zhang, Yixin Guo, Zong-Yu Guo, Luo Zhao, Weiqi Jin, Huiqi Zheng, Qiongying Ren, Hua Zhao

Bibliographic record

VenueOptics Letters · 2022
Typearticle
Languageen
FieldEngineering
TopicLaser-induced spectroscopy and plasma
Canadian institutionsOptech (Canada)
FundersNational Key Research and Development Program of China
KeywordsLaserOpticsMagnetometerMaterials scienceResonance (particle physics)SpectroscopyMagnetic fieldAtomic physicsPlasmaLarmor precessionResonance fluorescenceLaser-induced breakdown spectroscopyNuclear magnetic resonancePhysicsFluorescence

Abstract

fetched live from OpenAlex

The magneto-optical resonance response of sodium atoms generated by a high-energy solid-state pulse Nd:YAG laser is studied in different external magnetic fields. We investigate the resonance fluorescence signal of sodium atoms in a simulated sea fog environment based on the laser-induced plasma (LIP) effect. By ionizing an NaCl solution spray to generate sodium atoms in an atmospheric environment, we build a Bell–Bloom magneto-optical resonance system under laboratory conditions. With the help of laser-induced breakdown spectroscopy (LIBS) and extinction spectrum, we obtain sodium atoms with a lifetime of 250 µs. A narrowband tunable continuous wave (CW) 589-nm laser tuned at the D 2 line with a modulation frequency around the Larmor frequency is used as the pump beam to polarize sodium atoms in the test magnetic field. We find that the magneto-optical resonance signals vary with different external magnetic fields and the positions of the resonance signal are consistent with the theoretical values. An intrinsic magnetometric sensitivity of 620.4 pT in a 1-Hz bandwidth is achieved.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.341
Threshold uncertainty score0.649

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 integrity0.0000.001
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 head0.007
GPT teacher head0.187
Teacher spread0.180 · 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 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

Citations1
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueOptics LettersSame topicLaser-induced spectroscopy and plasmaFrench-language works237,207