Probing oxidative aging in natural ester nanofluids using FTIR and frequency domain spectroscopy
Bibliographic record
Abstract
Natural ester insulating liquids are promising alternatives to mineral oils due to their biodegradability and lower environmental impact. Oxidation is less problematic in sealed systems; however, under conditions where oxygen exposure or elevated temperatures are present, the aging behavior of natural esters still warrants attention. In this study, oxidative aging in a canola-based natural ester and its nanoparticle-enhanced variants was monitored using Fourier Transform Infrared Spectroscopy (FTIR) and Frequency Domain Spectroscopy (FDS). Nanofluids were prepared with SiO 2 and TiO 2 nanoparticles of varying sizes (5–30 nm) and concentrations (0.05–0.25 wt%) using a two-step dispersion method. Oxidative aging was induced following the ASTM D2440 protocol. FTIR analysis revealed a superior molecular stability in oxidation-related functional groups in nanofluids containing TiO 2 , particularly at 5 nm, indicating improved resistance to chemical degradation. Complementary FDS measurements showed lower conductivity and dissipation factors in aged TiO 2 -based nanofluids, confirming enhanced dielectric stability at the molecular level. These spectroscopic diagnostics demonstrate the effectiveness of TiO 2 nanoparticles in mitigating oxidation pathways and preserving dielectric integrity under thermal-oxidative stress. The results highlight the value of FTIR and FDS as non-destructive monitoring tools and contribute to the design of more durable, high-performance natural ester insulating fluids for advanced power systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".