Search

We present a facile strategy to achieve color-tunability room-temperature phosphorescence (RTP) nanoprobes by doping mineral acids (i.e., boric acid and phosphoric acid) in an organic silicon scaffold through a cross-linking process. Such RTP nanoprobes exhibit inherent tunable phosphorescence (from 420–650 nm) with long lifetime (emission lasting for ∼5–15 s, RTP lifetime: ∼0.53–2.11 s) and high quantum yields (∼13.1–43.0%). Therefore, the as-prepared nanoprobes enable multiple imaging in live cells with a high signal-to-background ratio value of ∼52. [ABSTRACT FROM AUTHOR]

We present a facile strategy to achieve purely organic multi-colour room-temperature phosphorescence (RTP) films by doping typical fused-ring compounds into a poly(vinyl alcohol) matrix. Such RTP films demonstrate inherent RTP emission ranging from green to red with a long lifetime and high quantum yield (QY) (lifetime: ∼0.56 ms, QY: ∼35.4%). We further exploit such high-performance RTP films for dynamic information encryption. [ABSTRACT FROM AUTHOR]

Bearing failures often result from compound faults, where the characteristics of these compound faults span across multiple domains. To tackle the challenge of extracting features from compound faults, this paper proposes a novel fault detection method based on the Legendre multiwavelet transform (LMWT) combined with envelope spectrum analysis. Additionally, to address the issue of identifying suitable wavelet decomposition coefficients, this paper introduces the concept of relative energy ratio. This ratio assists in identifying the most sensitive wavelet coefficients associated with fault frequency bands. To assess the performance of the proposed method, the results obtained from the LMWT method are compared with those derived from the empirical wavelet transform (EWT) method using different datasets. Experimental findings demonstrate that the proposed method exhibits more effective frequency spectrum segmentation and superior detection performance across various experimental conditions. [ABSTRACT FROM AUTHOR]

Efficiently diagnosing bearing faults is of paramount importance to enhance safety and reduce maintenance costs for rotating machinery. This paper introduces a novel bearing fault diagnosis method (LW-BPNN), which combines the rich properties of Legendre multiwavelet bases with the robust learning capabilities of a BP neural network (BPNN). The proposed method not only addresses the limitations of traditional deep networks, which rely on manual feature extraction and expert experience but also eliminates the complexity associated with designing and training deep network architectures. To be specific, only two statistical parameters, root mean square (RMS) and standard deviation (SD), are calculated on different Legendre multiwavelet decomposition levels to thoroughly represent more salient and comprehensive fault characteristics by using several scale and wavelet bases with various regularities. Then, the mapping relation between the extracted features and the health conditions of the bearing is automatically learned by the simpler BPNN classifier rather than the complex deep network structure. Finally, a few experiments on a popular bearing dataset are implemented to verify the effectiveness and robustness of the presented method. The experimental findings illustrate that the proposed method exhibits a high degree of precision in diagnosing various fault patterns. It outperforms other methods in terms of diagnostic accuracy, making it a viable and promising solution for real-world industrial applications in the field of rotating machinery. [ABSTRACT FROM AUTHOR]