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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 OpenAlexafffund
Yuhui Liu, Qi Yue, Yangjian Cai, Xiaoyi Bao, Song Gao

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations10
Published2025
Admission routes2
Has abstractyes

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