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Record W4320205626 · doi:10.1002/lpor.202200446

Tunable Mid‐Infrared Detail‐Enhanced Imaging With Micron‐Level Spatial Resolution and Photon‐Number Resolving Sensitivity

2023· article· en· W4320205626 on OpenAlexafffund
Xuanke Zeng, Congying Wang, Hongyu Wang, Qinggang Lin, Zhenkuan Chen, Xiaowei Lu, Maijie Zheng, Jinyang Liang, Yi Cai, Shixiang Xu, Jingzhen Li

Bibliographic record

VenueLaser & Photonics Review · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSpectroscopy Techniques in Biomedical and Chemical Research
Canadian institutionsInstitut National de la Recherche Scientifique
FundersShenzhen Fundamental Research ProgramNatural Sciences and Engineering Research Council of CanadaNatural Science Foundation of Guangdong ProvinceNational Natural Science Foundation of China
KeywordsOpticsImage resolutionFemtosecondBandwidth (computing)Sensitivity (control systems)OptoelectronicsMaterials sciencePhotonDetectorMillimeterPhysicsLaserComputer scienceElectronic engineeringTelecommunications

Abstract

fetched live from OpenAlex

Abstract The underdevelopment of mid‐infrared (MIR) components and detectors greatly limits the spatial resolution and sensitivity of MIR imaging. To overcome these limitations, MIR detail‐enhanced imaging is enhanced via non‐degenerate optical parametric amplification (OPA) pumped by a femtosecond vortex pulse. This design renders MIR illumination into a visible image by nonlinear wavelength‐conversion, together with a high OPA gain, large spatial bandwidth, and remarkable sensitivity. These experiments show that the design can realize MIR imaging with a spatial resolution of up to 114 line pairs per millimeter and a 2D spatial bandwidth product of up to 62 900, over a spectral region tunable from 2.0 to 3.0 µm. Equally important, this setup simultaneously achieves excellent imaging sensitivity of 25 photons at room temperature. It is thought that this work provides a powerful way to realize effective real‐time MIR imaging with an excellent spatial resolution even in very weak illumination environments, which can benefit many applications from semiconductor material characterization and biomedical imaging to security.

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)0.001
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 score0.222
Threshold uncertainty score0.801

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.015
GPT teacher head0.305
Teacher spread0.291 · 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

Citations12
Published2023
Admission routes2
Has abstractyes

Explore more

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