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Monitoring Oxidation in Natural Ester Insulating Liquids Through FTIR Spectroscopy Analysis

2024· article· en· W4412354331 on OpenAlex
Yazid Hadjadj, Refat Atef Ghunem, Alhaytham Y. Alqudsi

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.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicLubricants and Their Additives
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsFourier transform infrared spectroscopySpectroscopyMaterials scienceInfrared spectroscopyChemical engineeringChemistryOrganic chemistryEngineeringPhysics

Abstract

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This study investigates the thermal aging behavior of MIDEL eN1204, a natural ester-based insulating oil derived from rapeseed, using Fourier Transform Infrared (FTIR) spectroscopy as the primary analytical method. The oil samples were subjected to an intensive thermal aging process at 115°C for 1000 hours to replicate the long-term aging process that occurs in transformers. FTIR spectroscopy was employed to monitor shifts in key absorbance bands indicative of oxidative and hydrolytic degradation. The 1711 cm<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−1</sup> band, associated with carbonyl compounds, showed a significant increase in intensity, marking the oil's degradation. A differential normalization approach was introduced to enhance the detection of these aging effects. The results demonstrate the potential of FTIR spectroscopy to improve diagnostic processes of natural ester insulating liquids for maintaining transformer insulation systems.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.393
Threshold uncertainty score0.345

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 integrity0.0000.000
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 head0.009
GPT teacher head0.258
Teacher spread0.249 · 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

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Citations2
Published2024
Admission routes1
Has abstractyes

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