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High-repetition rate intense single-cycle terahertz source for ion field emission applications

2025· article· W7124151690 on OpenAlexaff
L. Guiramand, Alexandre Fahy, Matteo De Tullio, F. Exertier, Saïd Idlahcen, Jonathan Houard, G. Costa, Thibault Godin, X. Ropagnol, F. Blanchard, Angela Vella, Ammar Hideur

Bibliographic record

Venuenot available
Typearticle
Language
FieldEngineering
TopicAdvanced Materials Characterization Techniques
Canadian institutionsÉcole de Technologie Supérieure
FundersEuropean Commission
KeywordsTerahertz radiationOptical rectificationLithium niobateYtterbiumIonRectificationLaserCrystal (programming language)

Abstract

fetched live from OpenAlex

We report on the first demonstration of terahertz-assisted ions field-emission at high repetition rate. This has been enabled by the development of an intense single-cycle terahertz (THz) source. This highly-stable source is based on optical rectification in a lithium niobate crystal driven by an industrial-grade ytterbium laser. Using a discrete tilted pulse front pumping configuration, this efficient THz source has been successfully used to drive THz-assisted atom probe tomography (APT) analysis of metals. This work paves the way for a broader exploitation of THz-assisted APT analysis of complex samples at the atomic-scale.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.914
Threshold uncertainty score1.000

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 head0.007
GPT teacher head0.240
Teacher spread0.233 · 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.

Study designBench or experimental
Domainnot available
GenreMethods

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

Citations0
Published2025
Admission routes1
Has abstractyes

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