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Record W4409215368 · doi:10.1063/5.0255950

Enhanced terahertz radiation from nanorod array targets irradiated by ultraintense laser pulses

2025· article· en· W4409215368 on OpenAlexaff
Hao Liu, J. Ruan, Zhangsen Chen, Huihui Song, Dan Wang, Shangqing Li, S. Mondal, X. Zhang, Shuhui Sun, Guoqian Liao, T. Ozaki, Yutong Li

Bibliographic record

VenuePhysics of Plasmas · 2025
Typearticle
Languageen
FieldEngineering
TopicTerahertz technology and applications
Canadian institutionsInstitut National de la Recherche Scientifique
FundersNational Natural Science Foundation of China
KeywordsPhysicsLaserTerahertz radiationRadiationPlasmaIrradiationNanorodOpticsOptoelectronicsNanotechnologyNuclear physics

Abstract

fetched live from OpenAlex

Ultraintense laser interactions with a metal foil offer an emerging approach toward the generation of intense terahertz (THz) radiation, and how to improve the THz generation efficiency remains an open question. Here, we report the enhanced generation of THz radiation from ultraintense laser-irradiated nanostructured targets where metallic nanorod arrays are fabricated on the front surface of foil targets. The influences of nanorod lengths on the THz radiation emitted from the foil rear surface are investigated experimentally. Compared to the case of flat foil targets, a maximum enhancement in the THz pulse energy by a factor of 2.3 is observed by varying the nanorod length, and the THz peak emission direction moves toward the target surface with longer nanorods. Measurements of escaping fast electrons imply that the boosted THz yield is attributed to the enhanced laser absorption, and thus, the substantial increase in the fast-electron number. Particle-in-cell simulations reproduce well the experimental results.

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 score0.062
Threshold uncertainty score0.625

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 head0.004
GPT teacher head0.199
Teacher spread0.195 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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