degree of resolution is surpassing the level of human perceivability, a differentiated approach to further develop this technology is necessary. One such approach can be the application of an optically focusing device onto the display panels that controls the optical paths of individual pixels in order to facilitate holographic display. Nevertheless, conventional lenticular lenses [1] limit the design of the displays due to its high thickness, whereas conventional ultrathin diffractive lenses lack focusing controllability. Therefore, the realization of innovative technologies such as 3D display, [2] virtual reality (VR), [3] augmented reality (AR), [4] and so on requires both ultrathin and focus-tunable optical devices. Recently, microsized tunable lenses based on phase delays in anisotropic liquid crystals [5] are used to control focal lengths, and dielectric elastomer actuators (DAEs) [6] have demonstrated significant potential as electrically tunable flat lenses. Moreover, tunable metasurfaces [7] using phase-changing materials such as graphene or combinations of complexes like microelectromechanical systems (MEMS) [8] and DAEs have also demonstrated their potential. While recent studies on nanoscale diffractive lenses demonstrate their potential as possible candidates for thin-film display applications, their narrow focal ranges limit their application. Graphene, however, may realize focal controllability for its unique optoelectric property; due to its unique band structure among 2D materials, its carriers can be controlled by adjusting the Fermi level. Furthermore, due to the bandgap property of graphene, the intraband excitation of carriers is dominant over the interband excitation of carriers, which results in enhanced photonic transmission and reduced absorbance. Utilizing this property, graphene-based ultrathin focusing device is fabricated that alters its optical characteristics when direct-current voltage is applied producing vertical fringe-specific electric field. The proposed device demonstrates 8.6% change in focal length and 48.85% focusing efficiency at wavelength of 405 nm. Overall, this study on electrically tunable ultrathin microlens introduces potential for holographic displays and expands the research scope in future display technologies.

This study presents a new technology that can detect and discriminate individual chemical vapors to determine the chemical vapor composition of mixed chemical composition in situ based on a multiplexed DNA-functionalized graphene (MDFG) nanoelectrode without the need to condense the original vapor or target dilution. To the best of our knowledge, our artificial intelligence (AI)-operated arrayed electrodes were capable of identifying the compositions of mixed chemical gases with a mixed ratio in the early stage. This innovative technology comprised an optimized combination of nanodeposited arrayed electrodes and artificial intelligence techniques with advanced sensing capabilities that could operate within biological limits, resulting in the verification of mixed vapor chemical components. Highly selective sensors that are tolerant to high humidity levels provide a target for “breath chemovapor fingerprinting” for the early diagnosis of diseases. The feature selection analysis achieved recognition rates of 99% and above under low-humidity conditions and 98% and above under humid conditions for mixed chemical compositions. The 1D convolutional neural network analysis performed better, discriminating the compositional state of chemical vapor under low- and high-humidity conditions almost perfectly. This study provides a basis for the use of a multiplexed DNA-functionalized graphene gas sensor array and artificial intelligence-based discrimination of chemical vapor compositions in breath analysis applications.

This study presents a new technology that can detect and discriminate individual chemical vapors to determine the chemical vapor composition from mixed chemical composition in situ based on a multiplexed DNA-functionalized graphene (MDFG) nano-electrode without condensing the original vapor or target dilution. To the best of our knowledge, AI (Artificial Intelligence)-operated arrayed-electrodes identified the composition of mixed chemical gas with mixed ratio of it in early stage. This innovative technology comprises an optimized combination of nano-deposited arrayed electrodes and artificial intelligence techniques with advanced sensing capabilities that are comparable to the biological limit, resulting to verify mixed vapor chemical components. Highly selective sensors that are tolerant to high humidity levels provide a target for “breath chemo-vapor fingerprinting” for the early diagnosis of diseases. The feature selection analysis achieves recognition rates of 99% and above under low-humidity conditions and 98% and above in humid conditions for mixed chemical compositions. The 1D convolutional neural network analysis performed better with discriminates the compositional state of chemical vapor under low- and high-humidity conditions almost perfectly. This study provides a basis for the use of multiplexed DNA-functionalized graphene gas sensor array and artificial intelligence discrimination of chemical vapor compositions in breath analysis applications.