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Record W4372336988 · doi:10.1038/s41467-023-38354-3

Single-pulse terahertz spectroscopy monitoring sub-millisecond time dynamics at a rate of 50 kHz

2023· article· en· W4372336988 on OpenAlexafffund
Nicolas Couture, Wei Cui, Markus Lippl, Rachel Ostic, Défi Junior Jubgang Fandio, Eeswar Kumar Yalavarthi, Aswin Vishnuradhan, Angela Gamouras, Nicolas Y. Joly, Jean‐Michel Ménard

Bibliographic record

VenueNature Communications · 2023
Typearticle
Languageen
FieldEngineering
TopicTerahertz technology and applications
Canadian institutionsNational Research Council CanadaMax Planck - University of Ottawa Centre for Extreme and Quantum PhotonicsUniversity of Ottawa
FundersNational Research Council CanadaCanada Foundation for InnovationNatural Sciences and Engineering Research Council of CanadaGovernment of Canada
KeywordsMillisecondTerahertz radiationPulse (music)SpectroscopyMaterials scienceDynamics (music)Nuclear magnetic resonanceOptoelectronicsPhysicsOpticsDetectorAcoustics

Abstract

fetched live from OpenAlex

Slow motion movies allow us to see intricate details of the mechanical dynamics of complex phenomena. If the images in each frame are replaced by terahertz (THz) waves, such movies can monitor low-energy resonances and reveal fast structural or chemical transitions. Here, we combine THz spectroscopy as a non-invasive optical probe with a real-time monitoring technique to demonstrate the ability to resolve non-reproducible phenomena at 50k frames per second, extracting each of the generated THz waveforms every 20 μs. The concept, based on a photonic time-stretch technique to achieve unprecedented data acquisition speeds, is demonstrated by monitoring sub-millisecond dynamics of hot carriers injected in silicon by successive resonant pulses as a saturation density is established. Our experimental configuration will play a crucial role in revealing fast irreversible physical and chemical processes at THz frequencies with microsecond resolution to enable new applications in fundamental research as well as in industry.

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 score0.297
Threshold uncertainty score0.692

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.011
GPT teacher head0.255
Teacher spread0.244 · 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

Citations34
Published2023
Admission routes2
Has abstractyes

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