11 research outputs found

    Extending the defect tolerance of halide perovskite nanocrystals to hot carrier cooling dynamics

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    Defect tolerance is a critical enabling factor for efficient lead-halide perovskite materials, but the current understanding is primarily on band-edge (cold) carriers, with significant debate over whether hot carriers can also exhibit defect tolerance. Here, this important gap in the field is addressed by investigating how intentionally-introduced traps affect hot carrier relaxation in CsPbX3 nanocrystals (X = Br, I, or mixture). Using femtosecond interband and intraband spectroscopy, along with energy-dependent photoluminescence measurements and kinetic modelling, it is found that hot carriers are not universally defect tolerant in CsPbX3, but are strongly correlated to the defect tolerance of cold carriers, requiring shallow traps to be present (as in CsPbI3). It is found that hot carriers are directly captured by traps, instead of going through an intermediate cold carrier, and deeper traps cause faster hot carrier cooling, reducing the effects of the hot phonon bottleneck and Auger reheating. This work provides important insights into how defects influence hot carriers, which will be important for designing materials for hot carrier solar cells, multiexciton generation, and optical gain media

    Fusion Domain-Adaptation CNN Driven by Images and Vibration Signals for Fault Diagnosis of Gearbox Cross-Working Conditions

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    The vibration signal of gearboxes contains abundant fault information, which can be used for condition monitoring. However, vibration signal is ineffective for some non-structural failures. In order to resolve this dilemma, infrared thermal images are introduced to combine with vibration signals via fusion domain-adaptation convolutional neural network (FDACNN), which can diagnose both structural and non-structural failures under various working conditions. First, the measured raw signals are converted into frequency and squared envelope spectrum to characterize the health states of the gearbox. Second, the sequences of the frequency and squared envelope spectrum are arranged into two-dimensional format, which are combined with infrared thermal images to form fusion data. Finally, the adversarial network is introduced to realize the state recognition of structural and non-structural faults in the unlabeled target domain. An experiment of gearbox test rigs was used for effectiveness validation by measuring both vibration and infrared thermal images. The results suggest that the proposed FDACNN method performs best in cross-domain fault diagnosis of gearboxes via multi-source heterogeneous data compared with the other four methods

    Partial Transfer Ensemble Learning Framework: A Method for Intelligent Diagnosis of Rotating Machinery Based on an Incomplete Source Domain

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    Most cross-domain intelligent diagnosis approaches presume that the health states in training datasets are consistent with those in testing. However, it is usually difficult and expensive to collect samples under all failure states during the training stage in actual engineering; this causes the training dataset to be incomplete. These existing methods may not be favorably implemented with an incomplete training dataset. To address this problem, a novel deep-learning-based model called partial transfer ensemble learning framework (PT-ELF) is proposed in this paper. The major procedures of this study consist of three steps. First, the missing health states in the training dataset are supplemented by another dataset. Second, since the training dataset is drawn from two different distributions, a partial transfer mechanism is explored to train a weak global classifier and two partial domain adaptation classifiers. Third, a particular ensemble strategy combines these classifiers with different classification ranges and capabilities to obtain the final diagnosis result. Two case studies are used to validate our method. Results indicate that our method can provide robust diagnosis results based on an incomplete source domain under variable working conditions

    Current Progress in Bioactive Ceramic Scaffolds for Bone Repair and Regeneration

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    Bioactive ceramics have received great attention in the past decades owing to their success in stimulating cell proliferation, differentiation and bone tissue regeneration. They can react and form chemical bonds with cells and tissues in human body. This paper provides a comprehensive review of the application of bioactive ceramics for bone repair and regeneration. The review systematically summarizes the types and characters of bioactive ceramics, the fabrication methods for nanostructure and hierarchically porous structure, typical toughness methods for ceramic scaffold and corresponding mechanisms such as fiber toughness, whisker toughness and particle toughness. Moreover, greater insights into the mechanisms of interaction between ceramics and cells are provided, as well as the development of ceramic-based composite materials. The development and challenges of bioactive ceramics are also discussed from the perspective of bone repair and regeneration
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