Nano Energy
·
2024
·
IF 16.8
He Liu,Deliang Li,Haoxiang Chu,Yong Ding,Zhiwei Fu,Xinan Yao,Jiayi Zhu,Jiaqi Yang,Ruonan Liu,Tao Xu,Simian Fu,Yiying Liu,Yixuan Han,Yanpeng Wang,Yue Zhao,Xiaoyu Cui,Ye Tian
Touch pad based on triboelectric nanogenerator are attracting attention in the field of wearable electronics and human-machine interaction. However, it is still a challenge to realize touch pads combining large-area metamorphic stretchability, high power triboelectric sensing, intelligent free-sliding recognition and availability in extreme environments. Here, we fabricate an ultra-stretchable triboelectric touch pad by integrating liquid metal and high performance hydrogel-based triboelectric sensor arrays, which achieves Transformer-assisted gesture recognition for drone flight direction control. The internal hydrogel electrode has great stretchability, electrical conductivity and anti-freeze properties. Encapsulated in Ecoflex with sandpaper micro-surfaces, it endows high triboelectric power density and pressure sensitivity. With screen-printed liquid metal, the integrated touch pad is fully ultra-stretchable and large-area strain stability. Combining Internet of Things, we build a Transformer-assisted human machine interface. Notably, slide gesture signal peak deviation is reduced by integral processing. The random free-sliding gesture signals are parsed by the Transformer algorithm with 96.83 % accuracy. Multi-threaded signal processing improves the response speed and throughput of the system. The accurate real-time gesture recognition for drone flight direction control in extreme environment is realized. This human machine interface can facilitate development of next generation intelligent remote interactive platforms.
研究领域:
可穿戴电子设备
·
人机交互
·
传感器技术
✓ 已验证
产品关联
DOI
高影响因子
Communications Chemistry
·
2024
·Vol. 7
·期 1
·1-11
·
IF 5.9
Yang Yiming,Sun Shuang,Yang Shuyuan,Yang Qin,Lu Xinqiong,Wang Xiaohao,Yu Quan,Huo Xinming,Qian Xiang
Structural annotation of small molecules in tandem mass spectrometry has always been a central challenge in mass spectrometry analysis, especially using a miniaturized mass spectrometer for on-site testing. Here, we propose the Transformer enabled Fragment Tree (TeFT) method, which combines various types of fragmentation tree models and a deep learning Transformer module. It is aimed to generate the specific structure of molecules de novo solely from mass spectrometry spectra. The evaluation results on different open-source databases indicated that the proposed model achieved remarkable results in that the majority of molecular structures of compounds in the test can be successfully recognized. Also, the TeFT has been validated on a miniaturized mass spectrometer with low-resolution spectra for 16 flavonoid alcohols, achieving complete structure prediction for 8 substances. Finally, TeFT confirmed the structure of the compound contained in a Chinese medicine substance called the Anweiyang capsule. These results indicate that the TeFT method is suitable for annotating fragmentation peaks with clear fragmentation rules, particularly when applied to on-site mass spectrometry with lower mass resolution.
研究领域:
分析化学
·
人工智能
·
生物信息学
✓ 已验证
DOI
PubMed
高影响因子
MICROCHEMICAL JOURNAL
·
2025
·
IF 5.1
Ying Yang,Liang Qi,Guanhua Xu,Zheng Li,Yi Tao
Ethanol precipitation is a pivotal manufacturing step in the production of Guanxinning tablets (GXN). However, current quality control methods for this process remain inadequate. To address this gap, we propose a novel analytical framework that combines Raman spectroscopy with state-of-the-art deep learning architectures, specifically integrating a Mamba State Space model with a Transformer model, to enable non-destructive and real-time monitoring of seven critical quality attributes (CQAs) throughout the ethanol precipitation process of GXN. A total of 160 ethanol precipitation samples were collected, and their Raman spectra were acquired using a 785 nm laser (35 mW) across a wavenumber range of 200–3200 cm −1 , enabling rapid spectral acquisition within 1 s per sample. The content of five bioactive compounds (danshensu, ligustrazine, ferulic acid, rosmarinic acid, and salvianolic acid B) was quantified via HPLC, while solid content and alcohol content were determined using standard methods. Spectral outliers were removed using PCA-Mahalanobis distance analysis, and the Kennard-Stone algorithm was employed to partition datasets into training and test sets (4:1 ratio) to ensure model generalizability. Comparative model evaluation revealed that Mamba State Space model outperformed Transformer model and linear models, achieving prediction coefficients ( R p 2 ) of 0.9419–0.9978 and root mean square error of prediction (RMSEP) values of 0.0129–0.0501, with exceptional accuracy for solid content ( R p 2 > 0.99). This approach reduces reliance on time-consuming HPLC analytical method, offering a 99.9 % reduction in analysis time while maintaining predictive accuracy (RMSEP ≤0.0501) and robustness ( R p 2 > 0.94). By embedding the Mamba State Space model into Raman spectroscopy-based ethanol precipitation process monitoring, this study establishes a robust quality by artificial intelligence framework for real-time quality control in traditional Chinese medicine (TCM) manufacturing, paving the way for smart process analytical technology (PAT) applications.
研究领域:
分析化学
·
制药工程
·
中药学
·
人工智能
✓ 已验证
产品关联
DOI
高影响因子
Analysis & Sensing
·
2024
·
IF 3.4
Yifeng Xu,Haixia Mei,Yu Bing,Fuyun Zhang,Ning Sui,Tingting Zhou,Xiaopeng Fan,Lijie Wang,Tong Zhang
Graphical In this article, a Sm−Pd bimetal doped SnO 2 -based MEMS C 2 H 2 sensor was prepared for detecting dissolved acetylene gas in transformer oil. It has the unique advantages of high sensitivity, high selectivity and low power consumption. Acetylene (C 2 H 2 ), as an important characteristic gas in transformer fault diagnosis, should be accurately detected and effectively distinguished from other dissolved gases (H 2 , CH 4 , C 2 H 6 , C 2 H 4 , CO, CO 2 ), which is crucial to determine whether the fault occurs and the fault type, but also faces challenges now. The rational design and employment of rare earth and noble metals are expected to address this issue. In this work, SnO 2 -3 at% Sm 2 O 3 -1 at% PdO based MEMS gas sensor was prepared to achieve high performance detection of C 2 H 2 which has a response value of 56 to 50 ppm C 2 H 2 , response/recovery time of 2 s/136 s, lower detection limit of 1 ppm, power consumption of 15.5 mW, and weak cross sensitivity to other transformer fault characteristic gases. Lewis acids and bases theory was used to explain the reason why rare earth Sm is a benefit element to improve selectivity to C 2 H 2 . The formation of oxygen vacancies and hetero junctions was used to explain the increased sensitivity of the material. This study proved the feasibility of rare earth and noble metals as potential additives to enable advanced gas-sensitive materials for highly selective transformer fault characteristic gas C 2 H 2 detection.
研究领域:
材料科学
·
传感器技术
·
电力设备监测
✓ 已验证
产品关联
DOI
高影响因子
Journal of Nanomaterials
·
2013
·
IF 1.547
M. Dong,L. P. Shen,H. Wang,H. B. Wang,J. Miao
Aluminum-nitride-(AlN)-transformer oil-based nanofluid was prepared by dispersing AlN nanoparticles in transformer oil. The composition-dependent electrical conductivity of AlN-transformer oil nanofluid was investigated at different ambient temperatures. The results indicate the nonlinear dependences of the electrical conductivity on volumetric fraction and temperature. In comparison to the pure transformer oil, the electrical conductivity of nanofluid containing 0.5% AlN nanoparticles has increased by 1057 times at 60°C. By considering the electrophoresis of the AlN nanoparticles, a straightforward electrical conductivity model is established to modulate and understand the experiment results.
研究领域:
材料科学
·
电气工程
✓ 已验证
产品关联
DOI
高影响因子
MOLECULES
·
2023
·Vol. 28
·期 6
·2444
·
IF 4.6
Yan Wang,Peng Dou,Xiaofeng You,Qing Liu,Zhaoyang Fei,Xian Chen,Zhuxiu Zhang,Jihai Tang,Mifen Cui,Xu Qiao
Resins have enormous potential in the removal of naphthenic acids (NAs) from transformer oil due to their rich porosity and high mechanical and diversified functionality, whereas their poor adsorption capacity limits application. In this work, the polystyrene–diethylamine resin (PS−DEA−x) was prepared by grafting diethylamine (DEA) onto chloromethylated polystyrene (PS−Cl) resin to efficiently adsorb cyclopentane carboxylic acid from transformer oil for the first time. The characterization analysis results indicated that amine contents were significantly enhanced with the increase in DEA. Particularly, resin with a molar ratio of 1:5 depending on chloromethyl to DEA (PS−DEA−5) exhibited the highest amine contents and efficient adsorption of cyclopentane carboxylic acid (static adsorption capacity up to 110.0 mg/g), which was about 5 times higher than that of the pristine PS−Cl. The thermodynamic and kinetic studies showed that the adsorption behaviors could be well fitted to the Langmuir isotherm equation and pseudo−second−order rate equation. Moreover, it was found that 1 g of the PS−DEA−5 can decontaminate about 760 mL transformer oil to meet reuse standards by a continuous stream, indicating its potential application in industry.Keywords:resin;diethylamine;chemical modification;cyclopentane carboxylic acid;transformer oil purification
研究领域:
化学工程
·
材料科学
✓ 已验证
产品关联
DOI
PubMed
高影响因子
Asia-Pacific Journal of Chemical Engineering
·
2023
·
IF 1.8
Kai Fei,Zhuo Yi Dong,Li Shi,Xuan Meng,Naiwang Liu
Transformer oil plays a critical role in ensuring the insulation and normal operation of power transformers. However, during operation, the transformer oil would undergo oxidation and degradation, leading to the formation of acids and polar compounds that increase its acidity and degrade its performance. Hence, the recycling and regeneration of aged transformer oil is an essential practice in transformer maintenance, contributing significantly to the extension of a transformer's operational lifespan and the reduction of potential risks. Adsorption separation is a preferred method for regeneration, offering simplicity and high efficiency. This study focuses on impregnated γ-Al 2 O 3 as an adsorbent for acidity removal in waste transformer oil. We conducted static adsorption experiments to investigate the optimal loading amount of the active component and the best calcination temperature for acid removal. XRD technique is employed to monitor the phase changes of the carrier during the calcination process. BET analysis is used to observe variations in the specific surface area and pore volume of the adsorbent during the modification process. DTG-TGA is utilized to assess the weight loss during the calcination process. FTIR is employed to observe changes in functional groups during modification and adsorption processes. Along with these investigations, a study is also carried out to assess the dynamic acid removal performance of the acid-removing adsorbent loaded into a fixed bed. The findings contribute to the development of sustainable methods for regenerating transformer oil, ensuring efficient and environmentally friendly operations.
研究领域:
化学工程
·
环境科学
✓ 已验证
产品关联
DOI
高影响因子
Agronomy-Basel
·
2025
·Vol. 15
·期 8
·1779
·
IF 3.4
Meichen Liu,Shengwei Zhang,Jing Gao,Bo Wang,Kedi Fang,Lu Liu,Shengwei Lv,Qian Zhang
The salinization of agricultural soils is a serious threat to farming and ecological balance in arid and semi-arid regions. Accurate estimation of soil water-soluble ions (calcium, carbonate, magnesium, and sulfate) is necessary for correct monitoring of soil salinization and sustainable land management. Hyperspectral ground-based data are valuable in soil salinization monitoring, but the acquisition cost is high, and the coverage is small. Therefore, this study proposes a two-stage deep learning framework with multispectral remote-sensing images. First, the wavelet transform is used to enhance the Transformer and extract fine-grained spectral features to reconstruct the ground-based hyperspectral data. A comparison of ground-based hyperspectral data shows that the reconstructed spectra match the measured data in the 450–998 nm range, with R2up to 0.98 and MSE = 0.31. This high similarity compensates for the low spectral resolution and weak feature expression of multispectral remote-sensing data. Subsequently, this enhanced spectral information was integrated and fed into a novel multiscale self-attentive Transformer model (MSATransformer) to invert four water-soluble ions. Compared with BPANN, MLP, and the standard Transformer model, our model remains robust across different spectra, achieving an R2of up to 0.95 and reducing the average relative error by more than 30%. Among them, for the strongly responsive ions magnesium and sulfate, R2reaches 0.92 and 0.95 (with RMSE of 0.13 and 0.29 g/kg, respectively). For the weakly responsive ions calcium and carbonate, R2stays above 0.80 (RMSE is below 0.40 g/kg). The MSATransformer framework provides a low-cost and high-accuracy solution to monitor soil salinization at large scales and supports precision farmland management.
研究领域:
遥感
·
土壤科学
·
农业监测
·
深度学习
·
环境监测
✓ 已验证
产品关联
DOI
高影响因子
IEEE Access
·
2025
·
IF 3.6
Jiandong Yao,Bo Zhou,Chuan Cai,Jun Gao,Peng Cheng,Kaimeng Shi,Yiheng Wei
The main insulation for high-voltage outlet devices in transformers consists of a complex arrangement of alternating multilayer oil channels and insulating pressboard, with the insulation margin approaching the design limit. As transformer voltage levels increase, high-voltage outlets are prone to sudden, unexplained discharge failures. Investigation of the causes of insulation margin reduction and the mechanisms behind these sudden failures is urgently needed to ensure the safe operation of transformers. In this study, a simulation model of a transformer outlet device was developed to analyze electric field distribution characteristics under operational conditions. The analysis revealed that the first oil gap in the stepped region of the oil–paper insulation is the weakest link in the insulation system. On the basis of the principle of equivalent electric field strength, a test oil cup was designed to explore the impact of cellulose particles on the electrical performance of insulating oil. The results indicate that under prolonged exposure to operational electric field strengths, cellulose particles aggregate, leading to a significant deterioration in the electrical performance of insulating oil. This study provides valuable insights into the correlation between particle aggregation and the reduction in safety margins of high-voltage outlet devices. These findings offer new theoretical support for tracing the causes of discharge failures in transformers.
研究领域:
电力系统绝缘技术
·
高电压工程
·
电气设备可靠性
·
绝缘材料老化
·
变压器运维
✓ 已验证
DOI
高影响因子
Journal of Cleaner Production
·
2024
·
IF 9.7
Yihua Qian,Haoyuan Luo,Yaohong Zhao,Yiyan Liu,Jiajin Huang,Liang Yu,Qibin Xia
Capturing dibutyl phthalate (DBP) impurities from transformer oils and purifying them is crucial for maintaining long-term sustainable transformer operation. Furthermore, this process significantly reduces the generation of waste from aged transformer oils, thereby contributing to environmental protection and sustainable development. In this study, various metal ion-modified wood activated carbons (ACs) were prepared using the wet impregnation method. The adsorption isotherms and adsorption kinetics studies reveal that Fe/AC exhibits a significant enhancement of more than 40% in DBP adsorption capacity compared to the original AC. Consequently, Fe/AC is the material with the highest adsorption capacity for DBP in transformer oils up to now. Furthermore, the desorption activation energy of DBP on Fe/AC was 46.623 kJ/mol, 1.73 times higher than original AC. Ultimately, the adsorption mechanism was explained using the Hard-Soft-Acid-Base (HSAB) principle, which demonstrated a stronger interaction between hard base DBP and Fe/AC due to the hard acidic nature of Fe 3+ and therefore a higher capture capacity of DBP by Fe/AC. The simplified preparation method and enhanced adsorption capacity of the modified Fe/AC make it an efficient and cost-effective approach to ensure reliable and sustained transformer operation, while also minimizing the generation of waste transformer oils.
研究领域:
环境科学
·
材料科学
✓ 已验证
DOI
高影响因子
SENSORS
·
2026
·Vol. 26
·期 16
·5291
·
IF 4
Xiaoqin Zhang,Hongbin Zhu,Hao Liu,Jin Cao,Han Shi,Shanyuan Niu
Methanol is a critical molecular marker for the early aging of oil-paper insulation, and its rapid detection is highly valuable for insulation condition assessment and the fault warning of power transformers. Widely used chromatographic methods require sophisticated pretreatment workflow and are not suitable for in situ monitoring. Non-destructive spectroscopic methods remain challenging due to the intrinsically small cross section of trace molecules in complex liquid environments. The rapid, direct detection of trace methanol in an oil mixture has yet to be demonstrated. In this study, a high-performance Raman-enhancing substrate was developed through hierarchical microstructure regulation, combining microscale light-trapping structures and nanoscale field-confinement sites to sense the weak Raman response of methanol in transformer oil. Direct detection of ppm-level methanol in the oil matrix was achieved, without additional adsorption enrichment or other complicated pretreatment procedures. The characteristic Raman band of methanol in transformer oil was identified, and a quantitative sensing method was established. Furthermore, the intrinsic temperature-dependent Raman response of methanol was investigated to evaluate the stability of its characteristic fingerprint bands over a broad temperature range. This work demonstrates a rapid, sensitive, and pretreatment-free spectroscopic strategy for trace methanol detection in complex oil matrices, and also sheds light on the high-sensitivity detection of small molecular markers in complex liquid environments.
✓ 已验证
产品关联
DOI
PubMed
高影响因子
SPECTROCHIMICA ACTA PART B-ATOMIC SPECTROSCOPY
·
2020
·
IF 3.086
Zhe Ye,Zhenghao Zhu,Ning Ma,Xiaohua Wang,Dingxin Liu,Aijun Yang,Huan Yuan,Mingzhe Rong
The deterioration of transformer oil caused by particle contamination, especially copper particle, seriously affects the safe operation of oil-immersed transformers. Therefore, it is very important for the detection of metal particles in transformer oil. A new method based on laser-induced breakdown spectroscopy (LIBS) was first applied in the transformer oil diagnosis field. By screening out effective spots, the correlation between Cu particle concentration and LIBS signal was obtained after testing a set of oil samples with concentration range from 0.54 to 12.4 μg/g; the calibration curves of Cu I 324.75 nm /CN 358.6 nm and Cu I 327.39 nm /CN 358.6 nm were therefore established. The results shown high linearity, and the determination coefficient R 2 of the two calibration curves in range from 0.54 to 3.1 μg/g were superior to 0.99. The limit of detection (LOD) inferred from Cu I 324.75 nm /CN 358.6 nm fitting curve was 0.77 μg/g and met the critical value specified by industry standards. Due to its flexibility, it can be used as a new-type of particle concentration detection method for transformer oil.
研究领域:
电气工程
·
分析化学
✓ 已验证
产品关联
DOI
高影响因子
危险品化学品经营许可证（带存储）