Electrically focus-tuneable ultrathin lens for high-resolution square subpixels

Owing to the tremendous demands for high-resolution pixel-scale thin lenses in displays,we developed a graphenebased ultrathin square subpixel lens(USSL)capable of electrically tuneable focusing(ETF)with a performance competitive with that of a typical mechanical refractive lens.The fringe field due to a voltage bias in the graphene proves that our ETF-USSL can focus light onto a single point regardless of the wavelength of the visible light—by controlling the carriers at the Dirac point using radially patterned graphene layers,the focal length of the planar structure can be adjusted without changing the curvature or position of the lens.A high focusing efficiency of over 60% at a visible wavelength of 405 nm was achieved with a lens thickness of <13 nm,and a change of 19.42% in the focal length with a 9% increase in transmission was exhibited under a driving voltage.This design is first presented as an ETF-USSL that can be controlled in pixel units of flat panel displays for visible light.It can be easily applied as an add-on to high resolution,slim displays and provides a new direction for the application of multifunctional autostereoscopic displays.

Multiplexed DNA-functionalized graphene sensor with artificial intelligence-based discrimination performance for analyzing chemical vapor compositions

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(Al)-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.