Highly sensitive and reproducible CNTs@Ag modified Flower-Like silver nanoparticles for SERS situ detection of transformer Oil-dissolved furfural

Wan, Fu; Lei, Yu; Wang, Changding; He, Honglin; Lei, Jia; Wang, Tingting; Chen, Weigen

doi:10.1016/j.saa.2022.121067

Cited by 9 publications

(5 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…27 The industry requirement for furfural content in insulating oil varies in different application scenarios: when determining whether the insulation system is severely aged, the threshold is 4 mg/L. 26 This is supported by Domun's research and summary, which found that the furfural content in the oil of many operational transformers is typically around 2 mg/L. 28 For monitoring the abnormal aging, the most stringent requirement is 0.1 mg/L.…”

Section: ■ Results and Discussionmentioning

confidence: 99%

“…As a comparison, Somekawa et al used laser Raman spectroscopy to detect dissolved furfural in insulating oil without using any enhancement methods, and achieved an LOD of 14.4 ppm, corresponding to 11.5 mg/L . Wan et al utilized surface-enhanced Raman spectroscopy to detect dissolved furfural in insulating oil and achieved an LOD of 2.24 mg/L . Wang et al used extraction and electrochemical methods to obtain an LOD of 0.03 μg/g for furfural in oil, corresponding to 0.024 mg/L …”

Section: Resultsmentioning

confidence: 99%

“…25 Wan et al utilized surface-enhanced Raman spectroscopy to detect dissolved furfural in insulating oil and achieved an LOD of 2.24 mg/L. 26 Wang et al used extraction and electrochemical methods to obtain an LOD of 0.03 μg/g for furfural in oil, corresponding to 0.024 mg/L. 27 The industry requirement for furfural content in insulating oil varies in different application scenarios: when determining whether the insulation system is severely aged, the threshold is 4 mg/L.…”

Section: ■ Results and Discussionmentioning

confidence: 99%

See 2 more Smart Citations

Centrifugal Extraction-Assisted Fiber-Enhanced Raman Spectroscopy for Online Detection of Trace Furfural in Oil Power Equipment

Song,

Chen,

Wang

et al. 2023

Anal. Chem.

Self Cite

View full text Add to dashboard Cite

Oil-paper insulated equipment is integral in power conversion and supports low-loss electricity transport. As a characteristic byproduct of the oil-paper insulation system, the realization of efficient detection of furfural in oil is crucial to the safe operation of the power grid. We proposed a novel approach using dual-enhanced Raman spectroscopy for sensing trace liquid components. This method employs a centrifugal extractor to separate and enrich the targeted components, achieving selective enhancement. The optimal phase ratio was determined to be 30:1. A liquid-core fiber was used to optimize the laser transmission efficiency and Raman signal collection efficiency, resulting in a nonselective signal enhancement of 44.86. It also investigated the impact of intermolecular interactions on the shift of Raman spectra, identifying the reasons for the differences in Raman signals between pure furfural, furfural in oil, and furfural in water. A batch of samples with furfural dissolved in insulation oil was measured using this system and achieved a limit of detection of 0.091 mg/L. The stability of the dual-enhanced Raman platform was experimentally verified with a spectral intensity fluctuation of 0.68%. This method is fast, stable, adaptable, and suitable for the detection of a wide range of liquid ingredients.

show abstract

Section: ■ Results and Discussionmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: ■ Results and Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Centrifugal Extraction-Assisted Fiber-Enhanced Raman Spectroscopy for Online Detection of Trace Furfural in Oil Power Equipment

Song,

Chen,

Wang

et al. 2023

Anal. Chem.

Self Cite

View full text Add to dashboard Cite

show abstract

“…The univariate linear regression model (ULR) is a well-known method for quantifying aging characteristics in transformer oil [ 54 , 55 , 56 ]. This method often analyzes only one characteristic peak, ignoring the rich information contained in multiple characteristic peaks or even the full spectrum, but also the intensity of one characteristic peak used for modeling is unstable, which leads to inaccurate predictions, thus limiting its application in acetone quantification.…”

Section: Introductionmentioning

confidence: 99%

Quantitative Analysis of Acetone in Transformer Oil Based on ZnO NPs@Ag NWs SERS Substrates Combined with a Stoichiometric Model

Zhang

Lei

Song

et al. 2022

IJMS

Self Cite

View full text Add to dashboard Cite

Acetone is an essential indicator for determining the aging of transformer insulation. Rapid, sensitive, and accurate quantification of acetone in transformer oil is highly significant in assessing the aging of oil-paper insulation systems. In this study, silver nanowires modified with small zinc oxide nanoparticles (ZnO NPs@Ag NWs) were excellent surface-enhanced Raman scattering (SERS) substrates and efficiently and sensitively detected acetone in transformer oil. Stoichiometric models such as multiple linear regression (MLR) models and partial least square regressions (PLS) were investigated to quantify acetone in transformer oil and compared with commonly used univariate linear regressions (ULR). PLS combined with a preprocessing algorithm provided the best prediction model, with a correlation coefficient of 0.998251 for the calibration set, 0.997678 for the predictive set, a root mean square error in the calibration set (RMSECV = 0.12596 mg/g), and a prediction set (RMSEP = 0.11408 mg/g). For an acetone solution of 0.003 mg/g, the mean absolute percentage error (MAPE) was the lowest among the three quantitative models. For a concentration of 7.29 mg/g, the MAPE was 1.60%. This method achieved limits of quantification and detections of 0.003 mg/g and 1 μg/g, respectively. In general, these results suggested that ZnO NPs@Ag NWs as SERS substrates coupled with PLS simply and accurately quantified trace acetone concentrations in transformer oil.

show abstract

“…Wan et al achieved a high sensitivity in situ detection of furfural (2.25 mg/L) by the modification of the silver nanoparticles on the silver-coated carbon nanotubes. It is well known that the long-term realtime monitoring of furfural in oil is very important during the operation of the transformer oil, which require the SERS has long-term stability when using SERS, but the long-term stability of the SERS substrate during the transformer operation has not been investigated at present [16]. The effect of a temperature-dependent chemisorption on the intensity of the surface enhanced Raman spectra was studied by Chandra Nath Roy et al in 2016 [17].…”

mentioning

confidence: 99%

Functionalised B‐Cu@F‐Ag@Au trimetallic nanomaterials with long term stability for rapid and highly sensitive in situ SERS detection of furfural in transformer oil

et al. 2022

Self Cite

View full text Add to dashboard Cite

The surface-enhanced Raman scattering (SERS) substrates enable a highly sensitive detection of furfural in the transformer oil. However, detection substrates with long-term stability are still extremely challenging. In this work, we anchored the thiol-containing coupling agents 2, 5-dimercapto-1, 3, 4-thiadiazole (DMTD) and 1, 4-benzenedithiol (BDT) on the surface of bubble copper (B-Cu) and flower-like silver nanoparticles (F-Ag), respectively. The three-dimensional SERS detection substrates with long-term stability by using a combination of chemical reduction and self-assembly methods were constructed. The substrate has a minimum detection limit of 10 −9 M for rhodamine B in oil with an enhancement factor of up to 2.23 � 10 7 . Importantly, the three-crystal B-Cu@F-Ag 1 @Au 5 substrate was used for the detection of furfural in the transformer oil with a detection limit of 2 mg/L and a relative standard deviation value of 2.46%. After 60 days of a simulated operation, the detection signal of furfural in the transformer oil samples at 75°C and still reached the initial value of 77.53%, indicating that the substrate has a good long-term stability. This triple frame structured SERS detection platform shows great potential in tracking furfural in the aging transformer oil mixing systems. | INTRODUCTIONPower transformers are the core equipment in the power transmission systems. During the actual operation of a transformer, its oil-paper insulation system is subjected to electrical, thermal and mechanical stress, and it produces aging characteristics [1][2][3][4]. As a typical aging characteristic, furfural can be used to accurately evaluate the aging degree of the transformer insulation system [5,6]. Chromatographic based spectrophotometry and high-performance liquid chromatography are routine detection methods for furfural in the transformer oil. These methods are expensive, and they are easily affected by the surrounding environmental factors and cannot complete the detection of a complex oil system [7,8]. To achieve a nondestructive and in situ detection of furfural dissolved in oil, Toshihiro et al. applied laser Raman spectroscopy to detect the standard sample of furfural in the transformer oil for the first time, and the minimum detection limit could reach only 104 mg/L [9], which cannot meet the furfural detection accuracy standard (4 mg/L) of the transformer oil after severe aging.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

show abstract

Highly sensitive and reproducible CNTs@Ag modified Flower-Like silver nanoparticles for SERS situ detection of transformer Oil-dissolved furfural

Cited by 9 publications

References 36 publications

Centrifugal Extraction-Assisted Fiber-Enhanced Raman Spectroscopy for Online Detection of Trace Furfural in Oil Power Equipment

Centrifugal Extraction-Assisted Fiber-Enhanced Raman Spectroscopy for Online Detection of Trace Furfural in Oil Power Equipment

Quantitative Analysis of Acetone in Transformer Oil Based on ZnO NPs@Ag NWs SERS Substrates Combined with a Stoichiometric Model

Functionalised B‐Cu@F‐Ag@Au trimetallic nanomaterials with long term stability for rapid and highly sensitive in situ SERS detection of furfural in transformer oil

Contact Info

Product

Resources

About