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Propane dehydrogenation (PDH) is a crucial approach for propylene production. However, commonly used CrOx–based catalysts have issues including easy sintering at elevated reaction temperatures and relying on high acidity supports. In this work, we develop a strategy, to strongly anchor and isolate active sites against their commonly observed aggregation during reactions, by taking advantage of the net trap effect in chromate intercalated Zn–Cr layered hydroxides as precursors. Furthermore, the intercalated chromate overcomes the collapse of traditional layered hydroxides during their transformation to metal oxide, thus exposing more available active sites. A joint fine modulation including crystal structure, surface acidity, specific surface area, and active sites dispersion is performed on the final mixed metal oxides for propane dehydrogenation. As a result, Zn1Cr2–CrO4 2−–MMO delivers attractive propane conversion (~27%) and propylene selectivity (>90%) as compared to other non–noble–metal–based catalysts. [ABSTRACT FROM AUTHOR]

Introduction: Accurate classification of single-trial electroencephalogram (EEG) is crucial for EEG-based target image recognition in rapid serial visual presentation (RSVP) tasks. P300 is an important component of a single-trial EEG for RSVP tasks. However, single-trial EEG are usually characterized by low signal-to-noise ratio and limited sample sizes. Methods: Given these challenges, it is necessary to optimize existing convolutional neural networks (CNNs) to improve the performance of P300 classification. The proposed CNN model called PSAEEGNet, integrates standard convolutional layers, pyramid squeeze attention (PSA) modules, and deep convolutional layers. This approach arises the extraction of temporal and spatial features of the P300 to a finer granularity level. Results: Compared with several existing single-trial EEG classification methods for RSVP tasks, the proposed model shows significantly improved performance. The mean true positive rate for PSAEEGNet is 0.7949, and the mean area under the receiver operating characteristic curve (AUC) is 0.9341 (p < 0.05). Discussion: These results suggest that the proposed model e [ABSTRACT FROM AUTHOR]

Introduction: Acupoint localization is integral to Traditional Chinese Medicine (TCM) acupuncture diagnosis and treatment. Employing intelligent detection models for recognizing facial acupoints can substantially enhance localization accuracy. Methods: This study introduces an advancement in the YOLOv8-pose keypoint detection algorithm, tailored for facial acupoints, and named YOLOv8-ACU. This model enhances acupoint feature extraction by integrating ECA attention, replaces the original neckmodule with a lighter Slim-neckmodule, and improves the loss function for GIoU. Results: The YOLOv8-ACU model achieves impressive accuracy, with an mAP@0.5 of 97.5% and an mAP@0.5-0.95 of 76.9% on our self-constructed datasets. It also marks a reduction in model parameters by 0.44M, model size by 0.82 MB, and GFLOPs by 9.3%. Discussion: With its enhanced recognition accuracy and efficiency, along with good generalization ability, YOLOv8-ACU provides significant reference value for facial acupoint localization and detection. This is particularly beneficial for Chinese medicine practitioners engaged in facial acupoint research and intelligent detection. [ABSTRACT FROM AUTHOR]

We introduce a charge separation model in this work to explain the mechanism of enhanced photoconductivity of polycrystalline lead salt photoconductors. Our results show that this model could clarify the heuristic fabrication processes of such lead salt detectors that were not well understood and often considered mysterious for nearly a century. The improved lifetime and performance of the device, e.g., responsivity, are attributed to the spatial separation of holes and electrons, hence less possibility of carrier recombination. This model shows that in addition to crystal quality the size of crystallites, the depth of outer conversion layer, and doping concentration could all affect detector performance. The simulation results agree well with experimental results and thus offer a very useful tool for further improvement of lead salt detectors. The model was developed with lead salt family of photoconductors in mind, but may well be applicable to a wider class of semiconducting films. [ABSTRACT FROM AUTHOR]

The CaF2 nano-structures grown by thermal vapor deposition are presented. Significant responsivity improvement (>200%) of mid-infrared PbSe detectors incorporating a 200 nm nano-structured CaF2 coating was observed. The detector provides a detectivity of 4.2×1010 cm ⋅ Hz1/2/W at 3.8 lm, which outperforms all the reported un-cooled PbSe detectors. Structural investigations show that the coating is constructed by tapered-shape nanostructures, which creates a gradient refractive-index profile. Analogy to moth-eye antireflective mechanism, the gradient refractive-index nanostructures play the major roles for this antireflection effect. Some other possible mechanisms that help enhance the device performance are also discussed in the work. [ABSTRACT FROM AUTHOR]