The BaaS liquid level measurement and control device of 5000m3 storage tank in oilfield based on visual sensing aims to solve the safety, environmental protection and digital problems of tank level monitoring in the field of petroleum and petrochemical energy. The device consists of three modules: level measurement, data analysis, and safety and environmental protection. Relying on intelligent visual perception and control, blockchain and other technologies, the remote digitization of the 5000m3 storage tank level signal in the oilfield is realized, and the blockchain + knowledge graph + privacy computing + Internet of Things creates a trusted data base to realize the real-time matching of product objects and ledgers.
Conventional research on surface-enhanced Raman scattering (SERS)-based pH sensors often depends on nanoparticle aggregation, whereas the variability in nanoparticle aggregation gives rise to poor repeatability in the SERS signal. Herein, we fabricated a gold nanorod array platform via an efficient evaporative self-assembly method. The platform exhibits great SERS sensitivity with an enhancement factor of 5.6 × 107 and maintains excellent recyclability and reproducibility with relative standard deviation (RSD) values of less than 8%. On the basis of the platform, we developed a highly sensitive bovine serum albumin (BSA)-coated 4-mercaptopyridine (4-MPy)-linked (BMP) SERS-based pH sensor to report pH ranging from pH 3.0 to pH 8.0. The intensity ratio variation of 1004 and 1096 cm–1 in 4-MPy showed excellent pH sensitivity, which decreased as the surrounding pH increased. Furthermore, this BMP SERS-based pH sensor was employed to measure the pH value in C57BL/6 mouse blood. We have demonstrated that the pH sensor has great advantages such as good stability, reliability, and accuracy, which could be extended for the design of point-of-care devices.
Abstract CXC chemokine receptor 4 (CXCR4) is recently regarded as a valuable biomarker for triple‐negative breast cancer (TNBC) metastasis but lacks available imaging reagents. Surface‐enhanced resonance Raman scattering (SERRS) with resonant dyes has emerged as a powerful tool for single‐cell imaging because of the electronically enhanced vibrational fingerprint signals. However, resonant Raman signals are often overwhelmed by accompanying fluorescence backgrounds. To address this, two black hole quenchers (BHQs) are designed as visible resonance Raman reporters with absolutely nonfluorescent readouts. Ultrafast spectroscopy elucidates that the nonfluorescent mechanism of the reporters originates from the ultrafast internal conversion at the subpicosecond scale that quenches the excited states of fluorescence. SERRS nanoprobes (NPs) decorated with such reporters exhibit strong Raman enhancement (5.82 × 10 6 ), the femtomolar‐level limit of detection as well as unrivaled photostability (τ s = 26516 s), outperforming that of crystal violet‐decorated counterparts. When conjugation of a CXCR4 antagonist, these fluorescence‐free SERRS NPs allow for photostable imaging of CXCR4 on TNBC cells at the single‐cell level, and for monitoring the expression variation during combined drug treatment. To the best of the available knowledge, this is the first example of absolutely nonfluorescent Raman reporters for single‐cell SERRS imaging.
Bearings are essential components of rotating machinery used in mechanical systems, and fault diagnosis of bearings is of great significance to the operation and maintenance of mechanical equipment. Deep learning is a popular method for bearing fault diagnosis, which can effectively extract the in-depth information of fault signals, thus achieving high fault diagnosis accuracy. However, due to the complex deep structure of deep learning, most deep learning methods require more time and resources for bearing fault diagnosis. This paper proposes a bearing fault diagnosis method combining feature engineering and fuzzy broad learning. First, time domain, frequency domain, and time-frequency domain features are extracted from the bearing signals. Then the stability and robustness indexes of these features are evaluated to complete the feature engineering. The features obtained by feature engineering are used as the input of the fault diagnosis model, and three sets of experimental data validate the model. The experimental results show that the proposed method can achieve the bearing fault diagnosis accuracy of 96.43% on the experimental bench data, 100% on the Case Western Reserve University dataset, and 100% on the centrifugal pump bearing fault dataset, with a time of approximately 0.28 s. The results show that this method has the advantages of accuracy, rapidity, and stability of bearing fault diagnosis.
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