2018
DOI: 10.1038/s41598-018-26140-x
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Noninvasive low-cycle fatigue characterization at high depth with photoacoustic eigen-spectrum analysis

Abstract: In this work, photoacoustic eigen-spectrum analysis was proposed for noninvasively characterizing the mechanical properties of materials. We theoretically predicted the relationship between the photoacoustic eigen-spectra of cylindrical optical absorbers and their mechanical properties. Experimental measurements of eigen-spectra extracted from photoacoustic coda waves agreed well with the theoretical predictions. We then applied the photoacoustic eigen-spectrum analysis for contactless monitoring of low-cycle … Show more

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Cited by 4 publications
(3 citation statements)
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References 49 publications
(63 reference statements)
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“…The frequency responses of the microparticles were also numerically modeled in COMSOL, and they are in good agreement with the experimental results. Xiaoxiang Gao et al has shown eigenvalue results for polystyrene microparticles having same elastic parameters (Young’s modulus and Poisson’s ratio) is in close proximity to the predicted eigenvalues for our PS20, thus confirming the validity of our results [ 20 ]. The eigenfrequencies depend inversely on the diameter of the microparticles.…”
Section: Resultssupporting
confidence: 89%
See 1 more Smart Citation
“…The frequency responses of the microparticles were also numerically modeled in COMSOL, and they are in good agreement with the experimental results. Xiaoxiang Gao et al has shown eigenvalue results for polystyrene microparticles having same elastic parameters (Young’s modulus and Poisson’s ratio) is in close proximity to the predicted eigenvalues for our PS20, thus confirming the validity of our results [ 20 ]. The eigenfrequencies depend inversely on the diameter of the microparticles.…”
Section: Resultssupporting
confidence: 89%
“…Xiaoxiang Gao et al has demonstrated that eigen vibration information of light-absorbing microstructures can be obtained from photoacoustic amplitudes enabling noncontact evaluation of elastic properties [ 19 ]. Noncontact and non-invasive low-cycle fatigue characterization of homogenous, isotropic, elastic cylinders via photoacoustic eigen-spectrum analysis have been shown [ 20 ]. A.B.…”
Section: Introductionmentioning
confidence: 99%
“…PA imaging has demonstrated great potential in biomedical applications, such as tumor detection, 11) flow measurement, 12) accurate intraoperative margin assessment, 13) etc. [14][15][16][17] The imaging quality of AR-PAM highly depends on the characteristics of the acoustical focus, including focal size and shape. The length and width of the focal region determine the depth of field and the lateral resolution of AR-PAM 18) respectively.…”
mentioning
confidence: 99%