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Record W2592018446 · doi:10.1177/0003702817693233

Broadband Mid-Infrared Stand-Off Reflection–Absorption Spectroscopy Using a Pulsed External Cavity Quantum Cascade Laser

2017· article· en· W2592018446 on OpenAlexafffund
Xunchen Liu, Inseok Chae, N. Miriyala, Dongkyu Lee, Thomas Thundat, Seonghwan Kim

Bibliographic record

VenueApplied Spectroscopy · 2017
Typearticle
Languageen
FieldChemistry
TopicSpectroscopy and Laser Applications
Canadian institutionsUniversity of CalgaryUniversity of Alberta
FundersNational Natural Science Foundation of ChinaCanada Research ChairsNational Science Foundation
KeywordsQuantum cascade laserMaterials scienceLaser linewidthLaserOpticsSpectroscopyAbsorption spectroscopyInfrared spectroscopyInfraredOptoelectronicsAnalytical Chemistry (journal)ChemistryPhysics

Abstract

fetched live from OpenAlex

Broadband mid-infrared molecular spectroscopy is essential for detection and identification of many chemicals and materials. In this report, we present stand-off mid-infrared spectra of 1,3,5-trinitro-1,3,5-triazine or cyclotrimethylene trinitramine (RDX) residues on a stainless-steel surface measured by a broadband external cavity quantum cascade laser (QCL) system. The pulsed QCL is continuously scanned over 800 cm −1 in the molecular fingerprint region and the amplitude of the reflection signal is measured by either a boxcar-averager-based scheme or a lock-in-amplifier-based scheme with 1 MHz and 100 kHz quartz crystal oscillators. The main background noise is due to the laser source instability and is around 0.1% of normalized intensity. The direct absorption spectra have linewidth resolution around 0.1 cm −1 and peak height sensitivity around 10 −2 due to baseline interference fringes. Stand-off detection of 5–50 µg/cm 2 of RDX trace adsorbed on a stainless steel surface at the distance of 5 m is presented.

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), Science and technology studies, Insufficient payload (model declined to judge)
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 score0.081
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.027
GPT teacher head0.318
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.

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

Citations7
Published2017
Admission routes2
Has abstractyes

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