Mid-infrared multispectral confocal microscope using off-axis parabolic mirrors to study epiretinal membranes

Surface plasmons-phonons for mid-infrared hyperspectral imaging

Surface plasmons have proven their ability to boost the sensitivity of mid-infrared hyperspectral imaging by enhancing light-matter interactions. Surface phonons, a counterpart technology to plasmons, present unclear contributions to hyperspectral imaging. Here, we investigate this by developing a plasmon-phonon hyperspectral imaging system that uses asymmetric cross-shaped nanoantennas composed of stacked plasmon-phonon materials. The phonon modes within this system, controlled by light polarization, capture molecular refractive index intensity and lineshape features, distinct from those observed with plasmons, enabling more precise and sensitive molecule identification. In a deep learning-assisted imaging demonstration of severe acute respiratory syndrome coronavirus (SARS-CoV), phonons exhibit enhanced identification capabilities (230,400 spectra/s), facilitating the de-overlapping and observation of the spatial distribution of two mixed SARS-CoV spike proteins. In addition, the plasmon-phonon system demonstrates increased identification accuracy (93%), heightened sensitivity, and enhanced detection limits (down to molecule monolayers). These findings extend phonon polaritonics to hyperspectral imaging, promising applications in imaging-guided molecule screening and pharmaceutical analysis.

Long-infrared dual-wavelength linear-polarization-multiplexed confocal metalens based on an all-silicon dielectric

The metalens has vast applications in biomedicine and industrial manufacturing due to their ultrathin structure and vital ability to manipulate the properties of light waves for long-infrared systems. However, it is difficult for metalens to achieve the confocal function with high focusing efficiency, wide wavelength bandwidth, and low structural complexity. Here, we propose and experimentally demonstrate an all-silicon dielectric metalens composed of arrays of minimalist meta-atoms with a single rectangular nanopillar arranged on a periodic square lattice substrate, which realizes the confocal function of the orthogonal-linear-polarized light with wavelengths of 10.6 µm and 9.3 µm, with focusing efficiencies of 64.94% and 60.03%, respectively. Also, it reveals nearly the diffraction-limited focusing performance. In addition, the metalens can realize precise long-infrared thermal imaging. Moreover, the proposed metalens is compatible with the standard complementary metal oxide semiconductor processes, which can effectively reduce the manufacturing cost and provide a feasible solution for developing planar integrated multifunctional micro-nanophotonic devices in the long-infrared field.

Dielectric dispersion characteristics of the phospholipid bilayer with subnanometer resolution from terahertz to mid-infrared

There is growing interest in whether the myelinated nerve fiber acts as a dielectric waveguide to propagate terahertz to mid-infrared electromagnetic waves, which are presumed stable signal carrier for neurotransmission. The myelin sheath is formed as a multilamellar biomembrane structure, hence insights into the dielectric properties of the phospholipid bilayer is essential for a complete understanding of the myelinated fiber functioning. In this work, by means of atomistic molecular dynamics simulations of the dimyristoylphosphatidylcholine (DMPC) bilayer in water and numerical calculations of carefully layered molecules along with calibration of optical dielectric constants, we for the first time demonstrate the spatially resolved (in sub-nm) dielectric spectrum of the phospholipid bilayer in a remarkably wide range from terahertz to mid-infrared. More specifically, the membrane head regions exhibit both larger real and imaginary permittivities than that of the tail counterparts in the majority of the 1–100 THz band. In addition, the spatial variation of dielectric properties suggests advantageous propagation characteristics of the phospholipid bilayer in a relatively wide band of 55–85 THz, where the electromagnetic waves are well confined within the head regions.

Grade classification of human glioma using a convolutional neural network based on mid-infrared spectroscopy mapping

This study proposes a convolutional neural network (CNN)-based computer-aided diagnosis (CAD) system for the grade classification of human glioma by using mid-infrared (MIR) spectroscopic mappings. Through data augmentation of pixels recombination, the mappings in the training set increased almost 161 times relative to the original mappings. The pixels of the recombined mappings in the training set came from all of the one-dimensional (1D) vibrational spectroscopy of 62 (almost 80% of all 77 patients) patients at specific bands. Compared with the performance of the CNN-CAD system based on the 1D vibrational spectroscopy, we found that the mean diagnostic accuracy of the recombined MIR spectroscopic mappings at peaks of 2917 cm^-1 , 1539 cm^-1 and 1234 cm^-1 on the test set performed higher and the model also had more stable patterns. This research demonstrates that two-dimensional MIR mapping at a single frequency can be used by the CNN-CAD system for diagnosis and the research also gives a prompt that the mapping collection process can be replaced by a single-frequency IR imaging system, which is cheaper and more portable than a Fourier transform infrared microscopy and thus may be widely utilized in hospitals to provide meaningful assistance for pathologists in clinics.