Medical ultrasound transducers require matching layers to couple energy from the piezoelectric ceramic into the tissue. Composites of type 0-3 are often used to obtain the desired acoustic impedances, but they introduce challenges at high frequencies, i.e. non-uniformity, attenuation, and dispersion. This paper presents novel acoustic matching layers made as silicon-polymer 1-3 composites, fabricated by deep reactive ion etch (DRIE). This fabrication method is well-established for high-volume production in the microtechnology industry. First estimates for the acoustic properties were found from the iso-strain theory, while the Finite Element Method (FEM) was employed for more accurate modeling. The composites were used as single matching layers in 15 MHz ultrasound transducers. Acoustic properties of the composite were estimated by fitting the electrical impedance measurements to the Mason model. Five composites were fabricated. All had period 16 μm, while the silicon width was varied to cover silicon volume fractions between 0.17 and 0.28. Silicon-on-Insulator (SOI) wafers were used to get a controlled etch stop against the buried oxide layer at a defined depth, resulting in composites with thickness 83 μm. A slight tapering of the silicon side walls was observed; their widths were 0.9 μm smaller at the bottom than at the top, corresponding to a tapering angle of 0.3°. Acoustic parameters estimated from electrical impedance measurements were lower than predicted from the iso-strain model, but fitted within 5% to FEM simulations. The deviation was explained by dispersion caused by the finite dimensions of the composite and by the tapered walls. Pulse-echo measurements on a transducer with silicon volume fraction 0.17 showed a two-way -6 dB relative bandwidth of 50%. The pulse-echo measurements agreed with predictions from the Mason model when using material parameter values estimated from electrical impedance measurements. The results show the feasibility of the fabrication method and the theoretical description. A next step would be to include these composites as one of several layers in an acoustic matching layer stack.
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http://dx.doi.org/10.1016/j.ultras.2013.02.010 | DOI Listing |
Phys Chem Chem Phys
January 2025
Shenyang National Laboratory for Materials Science, Institute of Metal Research, Chinese Academy of Sciences, Shenyang, 110016, P. R. China.
It is a major challenge to obtain broadband microwave absorption (MA) properties using low dielectric or magnetic nanoparticle-decorated carbon composites due to the limited single conductive loss or polarization loss of the carbon materials used as substrates. Novel pure cellulose-derived graphite carbon (CGC) materials can be used as an exceptional substrate option due to their special defective graphitic carbon structure, which provides both conduction and polarization loss. Herein, CGC@ZnO composites were first synthesized by atomic layer deposition (ALD) for use as microwave absorbents.
View Article and Find Full Text PDFHeliyon
January 2025
Department of Electrical Engineering, Feng Chia University, Taichung, 407802, Taiwan.
This study presents an innovative glucose detection platform, featuring a highly sensitive, non-enzymatic glucose sensor. The sensor integrates nickel nanowires and a graphene thin film deposited on the gate region of an extended-gate electric double-layer field-effect transistor (EGEDL-FET). This unique combination of materials and device structure enables superior glucose sensing performance.
View Article and Find Full Text PDFJ Comput Assist Tomogr
January 2025
Department of Radiology, College of Medicine, University of Florida, Gainesville, FL.
Purpose: This study evaluated beam quality and radiation dosimetry of a CT scanner equipped with a novel detector and filtration technology called PureVision Optics (PVO). PVO features miniaturized electronics, a detector cut with microblade technology, and increased filtration in order to increase x-ray detection and reduce image noise.
Methods: We assessed the performance of two similar 320-detector CT scanners: one equipped with PVO and one without.
We propose an alternative data-free deep learning method using a physics-informed neural network (PINN) to enable more efficient computation of light diffraction from 3D optical metasurfaces, modeling of corresponding polarization effects, and wavefront manipulation. Our model learns only from the governing physics represented by vector Maxwell's equations, Floquet-Bloch boundary conditions, and perfectly matched layers (PML). PINN accurately simulates near-field and far-field responses, and the impact of polarization, meta-atom geometry, and illumination settings on the transmitted light.
View Article and Find Full Text PDFNeural Netw
January 2025
Department of Mathematics, Southern Methodist University, Dallas, 75275, TX, USA. Electronic address:
In this paper, we derive diffusion equation models in the spectral domain to study the evolution of the training error of two-layer multiscale deep neural networks (MscaleDNN) (Cai and Xu, 2019; Liu et al., 2020), which is designed to reduce the spectral bias of fully connected deep neural networks in approximating oscillatory functions. The diffusion models are obtained from the spectral form of the error equation of the MscaleDNN, derived with a neural tangent kernel approach and gradient descent training and a sine activation function, assuming a vanishing learning rate and infinite network width and domain size.
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