This investigation was carried out to study the effect of a novel process of surface modification, surface nanostructuring by ultrasonic shot peening, on osteoblast proliferation and corrosion behavior of commercially pure titanium (c p-Ti) in simulated body fluid. A mechanically polished disc of c p-Ti was subjected to ultrasonic shot peening with stainless steel balls to create nanostructure at the surface. A nanostructure (<20 nm) with inhomogeneous distribution was revealed by atomic force and scanning electron microscopy. There was an increase of approximately 10% in cell proliferation, but there was drastic fall in corrosion resistance. Corrosion rate was increased by 327% in the shot peened condition. In order to examine the role of residual stresses associated with the shot peened surface on these aspects, a part of the shot peened specimen was annealed at 400°C for 1 hour. A marked influence of annealing treatment was observed on surface structure, cell proliferation, and corrosion resistance. Surface nanostructure was much more prominent, with increased number density and sharper grain boundaries; cell proliferation was enhanced to approximately 50% and corrosion rate was reduced by 86.2% and 41% as compared with that of the shot peened and the as received conditions, respectively. The highly significant improvement in cell proliferation, resulting from annealing of the shot peened specimen, was attributed to increased volume fraction of stabilized nanostructure, stress recovery, and crystallization of the oxide film. Increase in corrosion resistance from annealing of shot peened material was related to more effective passivation. Thus, the surface of c p-Ti, modified by this novel process, possessed a unique quality of enhancing cell proliferation as well as the corrosion resistance and could be highly effective in reducing treatment time of patients adopting dental and orthopedic implants of titanium and its alloys.
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http://dx.doi.org/10.1563/AAID-JOI-D-12-00006 | DOI Listing |
Ultrasonics
December 2024
Faculty of Information Technology, Beijing University of Technology, Beijing, China.
The thickness loss caused by corrosion is a vital factor that threatens the health of shell structures. It is significant to perform a non-destructive quantitative evaluation of corrosion-thinning defects in plate structures. Based on the laser ultrasonic guided wavefield scanning technology, this paper proposes an instantaneous wavenumber multi-shot fusion method, which improves the performance of the instantaneous wavenumber imaging method.
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December 2024
Department of Biomedical Engineering, Fudan University, Shanghai 200438, China; Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai, Shanghai 200032, China. Electronic address:
Micromachines (Basel)
October 2024
School of Microelectronics, Xidian University, Xi'an 710071, China.
Nanoscale
November 2024
School of Industrial Engineering, Purdue University, West Lafayette, IN 47906, USA.
Surface nanoengineering can significantly improve the mechanical properties and performance of metals, such as strength, hardness, fatigue, wear resistance, . In this work, we tailored the surface microstructure of GCr15 bearing steel within a thickness of approximately 800 μm using room temperature ultrasonic shot peening (USP) technology. Microstructure characterization studies reveal the formation of gradient nanosized spheroidal carbides and lath-shaped nano-martensite in the GCr15 bearing steel during the USP process.
View Article and Find Full Text PDFUltrasonics
January 2025
Schlegel-UW Research Institute for Aging, University of Waterloo, Waterloo, Canada. Electronic address:
Sparse matrix beamforming (SMB) is a computationally efficient reformulation of delay-and-sum (DAS) beamforming as a single sparse matrix multiplication. This reformulation can potentially dovetail with machine learning platforms like TensorFlow and PyTorch that already support sparse matrix operations. In this work, using SMB principles, we present the development of beamforming-integrated neural networks (BINNs) that can rationally infer ultrasound images directly from pre-beamforming channel-domain radiofrequency (RF) datasets.
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