Terahertz imaging with metamaterials for biological applications

Roh, Yeeun; Lee, Sang-Hun; Kwak, J. W.; Song, Hyun Seok; Shin, Sera; Kim, Yun Kyung; Wu, Jeong Weon; Ju, Byeong−Kwon; Kang, Boyoung; Seo, Minah

doi:10.1016/j.snb.2021.130993

Cited by 50 publications

(18 citation statements)

References 27 publications

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“…Although the operation in the THz gap region (1–10 THz) is limited by the strong material absorption, the employment of LiNbO 3 -based metasurfaces may ensure efficient generation and shaping of THz radiation in this region by optical rectification, thanks to the enhanced linear and nonlinear response provided by the phonon polaritons. THz sources in this frequency range can be strategical to investigate materials’ crystal structure and its dynamics and to perform vibrational spectroscopy of macromolecules. − We also expect a distinct impact of these technologies, given the promising applications of THz radiation in biology and medicine , and for the detection of explosives, weapons and drugs in security applications . In this framework, a seminal work recently demonstrated the capability of LiNbO 3 -based metasurfaces to efficiently generate broadband THz radiation around 1 THz .…”

Section: Linbo3 Metasurfaces For Nonlinear Quantum and Electro-opticsmentioning

confidence: 99%

“…THz sources in this frequency range can be strategical to investigate materials' crystal structure and its dynamics 144 and to perform vibrational spectroscopy of macromolecules. 144−146 We also expect a distinct impact of these technologies, given the promising applications of THz radiation in biology and medicine 147,148 and for the detection of explosives, weapons and drugs in security applications. 149 In this framework, a seminal work recently demonstrated the capability of LiNbO 3 -based metasurfaces to efficiently generate broadband THz radiation around 1 THz.…”

Section: Perspective Applications To Nonlinear Meta-opticsmentioning

confidence: 99%

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Lithium Niobate Meta-Optics

et al. 2022

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The rapid development of optical metasurfaces, 2D ensembles of engineered nanostructures, is presently underpinning a steady drive toward the miniaturization of many optical functionalities and devices. The list of material platforms for optical metasurfaces is rapidly expanding as, over the past few years, we have witnessed a surge in establishing meta-optical elements from high-index, highly transparent materials with strong nonlinear and electro-optic properties. In particular, crystalline lithium niobate (LiNbO 3 ), already a prime material for integrated photonics, has shown great promise for novel meta-optical components, thanks to its large electro-optical coefficient and secondorder nonlinear response and its broad transparency window ranging from the visible to the mid-infrared. Recent advances in nanofabrication technology have indeed marked a new milestone in the miniaturization of LiNbO 3 platforms, hence enabling the first demonstrations of LiNbO 3 -based metasurfaces. These seminal works set a steppingstone toward the realization of ultrathin monolithic nonlinear light sources, efficient quantum sources of correlated photon pairs, as well as electro-optical modulators. Here, we review these recent advances by providing a perspective on their potential applications and examining the possible setbacks and limitations of these emerging technologies.

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Section: Linbo3 Metasurfaces For Nonlinear Quantum and Electro-opticsmentioning

confidence: 99%

Section: Perspective Applications To Nonlinear Meta-opticsmentioning

confidence: 99%

Lithium Niobate Meta-Optics

et al. 2022

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“…Terahertz radiation positioned between the microwave and visible regions has scientifically and technologically strengthened diverse fields, including security, communications, medical, biomedical imaging, nanoscale microscopy, and photoacoustic imaging. , THz electromagnetic waves have been mainly applied for spectroscopy purposes at an early stage, with the advantage that they have a manifold of collective intermolecular and intramolecular modes in the broadband spectrum . Low photon energy in these regimes is also desirable for the nondestructive sensing of bio/chemical species. , In particular, the ultrabroadband (0.1–3.0 THz, 100 μm ∼ 3 mm, by solid-state devices, or 0.1–20 THz, 15 μm ∼ 3 mm, by THz air photonics) is beneficial for spectroscopy and detection, as it requires selectivity with which it can sufficiently distinguish the intrinsic absorption fingerprints of various molecules . Nevertheless, most research has been limited to solid-type samples because it is challenging to detect trace amounts of molecules due to the significant mismatch between the THz wavelength and the size of the molecules, resulting in a very low absorption cross-section.…”

Section: Recent Trends In Thz Sensing and Imaging Applicationsmentioning

confidence: 99%

“…8 Low photon energy in these regimes is also desirable for the nondestructive sensing of bio/ chemical species. 9,10 In particular, the ultrabroadband (0.1−3.0 THz, 100 μm ∼ 3 mm, by solid-state devices, or 0.1−20 THz, 15 μm ∼ 3 mm, by THz air photonics) is beneficial for spectroscopy and detection, as it requires selectivity with which it can sufficiently distinguish the intrinsic absorption fingerprints of various molecules. 11 Nevertheless, most research has been limited to solid-type samples because it is challenging to detect trace amounts of molecules due to the significant mismatch between the THz wavelength and the size of the molecules, resulting in a very low absorption cross-section.…”

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confidence: 99%

Frontiers in Terahertz Imaging Applications beyond Absorption Cross-Section and Diffraction Limits

Lee

Park

et al. 2022

ACS Photonics

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Label-free imaging technology is highly desirable for various bioengineering, medicine, and chemistry applications. Most existing optical imaging techniques require labeling of the biospecimens and have limitations that may affect the intrinsic properties of the target species. The electromagnetic waves in the terahertz (THz) spectral range can be an excellent alternative to visible light, which provides plentiful vibrational signatures of many molecules with very low photon energy (1 THz is equivalent to 4 meV), completely free from damage to the biomaterials. For this reason, THz waves possessing a broadband spectrum have emerged as a critical technology for fundamental research in bio/chemical detection and medical imaging, as well as in solid-state physics, chemistry, material science, and highly anticipated 6G next-generation telecommunications. The limited performance of THz waves as a sensing or imaging tool has been a considerable drawback resulting from low sensitivity or diffraction-limited low spatial resolution. Nevertheless, many successes in obtaining increased sensitivity with additional nanostructures and improving spatial resolution with geometric beam shaping opened up a way for highly efficient real-time THz imaging. From this Perspective, recent trends in innovative THz sensing and imaging research deserve an introduction regarding the level of reliability and sensitivity that can evolve into an actual medical device and other applications. It can also be expected to enable progress in analysis algorithms (compressive phase retrieval, reconstruction, or machine/deep learning), enabling better data sampling, denoising, deblurring, and efficient computing cost, thus finally providing a leap forward in the THz imaging area beyond the absorption cross-section and diffraction limits.

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“…A high-resolution terahertz(THz) image is conducive to image analysis in real-world applications, such as nondestructive detection [1][2][3], medical imaging [4][5][6][7][8], surveillance and security [9,10], food safety testing [11,12], amongst others. Nevertheless, spatial resolution of THz images is limited by the hardware of THz imaging system and the intrinsic long wavelength of THz wave.…”

Section: Introductionmentioning

confidence: 99%

A Low- and High-Resolution Terahertz Image Pair Construction Method With Gradient Fusion for Learning-Based Super-Resolution

et al. 2022

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Quality and feature quantity of low-and high-resolution image pairs for training are directly related to performance of learning-based super-resolution methods. In order to accurately increase learning features to the low-and high-resolution image pairs, we proposed a gradient degradation model of terahertz(THz) images to describe the process of a high-resolution THz image's gradient map is how degraded to corresponding low-resolution THz image's gradient map. And with the proposed model, we presented a low-and high-resolution THz image pair construction method with gradient fusion for learningbased super-resolution. As gradient maps are fused to training pairs, performance of learning-based superresolution methods for THz images could be improved. In addition, we applied the low-and high-resolution THz image pair construction method with gradient fusion to very deep super-resolution(VDSR) method and zero shot super-resolution(ZSSR) method, named VDSR method with gradient fusion and ZSSR method with gradient fusion, respectively. To evaluate the performance of these two improved methods, comparison experiments with passive millimeter-wave images and our THz lab data are presented. The experimental results show that the performance of the VDSR method with gradient fusion and the ZSSR method with gradient fusion have significant improvements in peak signal-to-noise ratio and structural similarity index measure than the VDSR method and the ZSSR method, respectively. It indicates that performance of learning-based super-resolution methods for THz images could be improved by applying the proposed low-and high-resolution THz image pair construction method with gradient fusion.

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Terahertz imaging with metamaterials for biological applications

Cited by 50 publications

References 27 publications

Lithium Niobate Meta-Optics

Lithium Niobate Meta-Optics

Frontiers in Terahertz Imaging Applications beyond Absorption Cross-Section and Diffraction Limits

A Low- and High-Resolution Terahertz Image Pair Construction Method With Gradient Fusion for Learning-Based Super-Resolution

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