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Record W1966550930 · doi:10.1117/12.839519

Open path spectroscopy of methane using a battery operated vertical cavity surface-emitting laser system

2009· article· en· W1966550930 on OpenAlexaff
Matthew Dzikowski, Aleksandr Klyashitsky, Wolfgang Jäeger, J. Tulip

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2009
Typearticle
Languageen
FieldChemistry
TopicSpectroscopy and Laser Applications
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsVertical-cavity surface-emitting laserOptoelectronicsMaterials scienceSpectroscopyLaserTunable diode laser absorption spectroscopyTunable laserDiodeSemiconductor laser theoryOpticsWavelengthPhysics

Abstract

fetched live from OpenAlex

Tunable diode laser spectroscopy (TDLS) is a well-established method for trace gas detection. TDLS systems usually employ edge-emitting diodes with a distributed feedback configuration. Recently long wavelength vertical cavity surface emitting lasers (VCSEL) have emerged as an alternative source for spectroscopic applications. The relatively low cost, low power requirements and large tuning range of VCSELs make them particularly attractive for portable gas detection systems. In this paper we describe a battery-operated VCSEL spectroscopy system operating near 1650 nm for methane detection. Wavelength modulation spectroscopy (WMS) is commonly used in TDLS systems to improve sensitivity. WMS in these systems is usually implemented with a hardware based lock-in amplifier. We report on the construction of a new system with software WMS and compare its operation with a conventional system. The VCSEL TDLS system is used to probe the 2v3 band of methane over an open path. The relative contributions of optical and electrical noise to the system signal to noise ratio and minimum gas detection level is presented. Finally, challenges and future design considerations in VCSEL spectroscopy are discussed.

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 categoriesMeta-epidemiology (narrow)
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 score0.118
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0020.000
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 head0.263
Teacher spread0.248 · 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

Citations3
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicSpectroscopy and Laser ApplicationsFrench-language works237,207