Uncooled Broadband Photodetection via Light Trapping in Conformal PtTe2-Silicon Nanopillar Heterostructures.

Dirac semimetals have demonstrated significant attraction in the field of optoelectronics due to their unique bandgap structure and high carrier mobility. Combining them with classical semiconductor materials to form heterojunctions enables broadband optoelectronic conversion at room temperature. However, the low light absorption of layered Dirac semimetals substantially limits the device's responsivity in the infrared band. Herein, a three-dimensional (3D) heterostructure, composed of silicon nanopillars (SiNPs) and a conformal PtTe2 film, is proposed and demonstrated to enhance the photoresponsivity for uncooled broadband detection. The light trapping effect in the 3D heterostructure efficiently promotes the interaction between light and PtTe2, while also enhancing the light absorption efficiency of silicon, which enables the enhancement of the device responsivity across a broadband spectrum. Experimentally, the PtTe2-SiNPs heterojunction device demonstrates excellent photoelectric conversion behavior across the visible, near-infrared, and long-wave infrared (LWIR) bands, with its responsivity demonstrating an order-of-magnitude improvement compared to the counterparts with planar silicon heterojunctions. Under 11 µm laser irradiation, the noise equivalent power (NEP) can reach 1.76 nW·Hz-1/2 (@1 kHz). These findings offer a strategic approach to the design and fabrication of high-performance broadband photodetectors based on Dirac semimetals.

Noninvasive detection of mechanical prosthetic heart valve disorder.

Auscultation is a widely used efficient technique by cardiologists for detecting the heart conditions. Since the mechanical prosthetic heart valves are widely used today, it is important to develop a simple and efficient method to detect abnormal mechanical valves. In this paper, the mechanical prosthetic heart valve sounds are analyzed by using different power spectral density (PSD) estimation techniques. To improve the classification accuracy of heart sounds, we propose two different feature extraction schemes, i.e., a modified local discriminant bases (LDB) scheme and a Hilbert-Huang Transform (HHT)-based scheme. A database of 150 heart sounds is used in this study and an average classification accuracy of 97.3% is achieved for both the two feature extraction schemes, when a generic linear discriminant analysis (LDA) classifier is used in the classification stage.

[Spectral analysis and LDB based classification of heart sounds with mechanical prosthetic heart valves].

Auscultation, the act of listening for heart sounds to aid in the diagnosis of various heart diseases, is a widely used efficient technique by cardiologists. Since the mechanical prosthetic heart valves are widely used today, it is important to develop a simple and efficient method to detect abnormal mechanical valves. The study on five different mechanical valves showed that only the case of perivalvular leakage could be detected by spectral estimation. Though it is possible to classify different mechanical valves by using time-frequency components of the signal directly, the recognition rate is merely 84%. However, with the improved local discriminant bases (LDB) algorithm to extract features from heart sounds, the recognition rate is 97.3%. Experimental results demonstrated that the improved LDB algorithm could improve classification rate and reduce computational complexity in comparison with original LDB algorithm.