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Record W2062424232 · doi:10.1088/0964-1726/18/9/095048

Simultaneous measurement of temperature and tensile loading using superstructure FBGs developed by laser direct writing of periodic on-fiber metallic films

2009· article· en· W2062424232 on OpenAlex
Hamidreza Alemohammad, Ehsan Toyserkani

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

VenueSmart Materials and Structures · 2009
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsUniversity of Waterloo
FundersOntario Ministry of Research and Innovation
KeywordsMaterials scienceFiber Bragg gratingSuperstructureComposite materialLaserUltimate tensile strengthFiberThermalOpticsOptoelectronicsStructural engineeringWavelength

Abstract

fetched live from OpenAlex

This paper addresses the development of superstructure fiber Bragg gratings (FBGs) by laser-assisted direct writing of on-fiber metallic films. A novel laser direct write method is characterized to fabricate periodic films of silver nanoparticles on the non-planar surface of as-fabricated FBGs. Silver films with a thickness of 9 µm are fabricated around a Bragg grating optical fiber. The performance of the superstructure FBG is studied by applying temperature and tensile stress on the fiber. An opto-mechanical model is also developed to predict the optical response of the synthesized superstructure FBG under thermal and structural loadings. The results show that the reflectivity of sidebands in the reflection spectrum can be tuned up to 20% and 37% under thermal and structural loadings, respectively. In addition, the developed superstructure FBG is used for simultaneous measurement of force and temperature to eliminate the inherent limitation of regular FBGs in multi-parameter sensing.

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 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 score0.006
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.012
GPT teacher head0.218
Teacher spread0.206 · 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