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Record W4409018381 · doi:10.1016/j.pacs.2025.100715

Recent advances in optical fiber-based gas sensors utilizing light-induced acoustic/elastic techniques

2025· article· en· W4409018381 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenuePhotoacoustics · 2025
Typearticle
Languageen
FieldChemistry
TopicSpectroscopy and Laser Applications
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaTaishan Scholar Project of Shandong ProvinceTaishan Scholar Foundation of Shandong ProvinceNatural Science Foundation of Shandong ProvinceNational Natural Science Foundation of ChinaCanada Research ChairsCRC Health GroupScience and Technology Commission of Shanghai MunicipalityAlexander von Humboldt-Stiftung
KeywordsOptical fiberMaterials scienceFiber optic sensorOptoelectronicsOpticsAcoustic sensorFiberAcousticsComposite materialPhysics

Abstract

fetched live from OpenAlex

Gas sensing detects gas properties, such as physical, molecular, optical, thermodynamic, and dynamic properties. Light-induced acoustic techniques include monitoring the optical and physical properties of the gas. Fiber-based gas sensing is important because it offers several unique advantages compared to traditional gas sensing technologies, such as high sensitivity and accuracy, a compact and lightweight design, remote sensing capabilities, multiplexing, and distributed sensing. We review the recent developments in optical fiber-based gas sensors utilizing light-induced acoustic/elastic techniques based on photoacoustic spectroscopy, Brillouin scattering, and light-induced thermoelastic spectroscopy (LITES).

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.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.467
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
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.295
Teacher spread0.282 · 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