Field-effect transistor (FET)-based biosensors allow label-free detection of biomolecules by measuring their intrinsic charges. The detection limit of these sensors is determined by the Debye screening of the charges from counter ions in solutions. Here, we use FETs with a deformed monolayer graphene channel for the detection of nucleic acids. These devices with even millimeter scale channels show an ultra-high sensitivity detection in buffer and human serum sample down to 600 zM and 20 aM, respectively, which are ∼18 and ∼600 nucleic acid molecules. Computational simulations reveal that the nanoscale deformations can form 'electrical hot spots' in the sensing channel which reduce the charge screening at the concave regions. Moreover, the deformed graphene could exhibit a band-gap, allowing an exponential change in the source-drain current from small numbers of charges. Collectively, these phenomena allow for ultrasensitive electronic biomolecular detection in millimeter scale structures.

Conventional imaging and recognition systems require an extensive amount of data storage, pre-processing, and chip-to-chip communications as well as aberration-proof light focusing with multiple lenses for recognizing an object from massive optical inputs. This is because separate chips (i.e., flat image sensor array, memory device, and CPU) in conjunction with complicated optics should capture, store, and process massive image information independently. In contrast, human vision employs a highly efficient imaging and recognition process. Here, inspired by the human visual recognition system, we present a novel imaging device for efficient image acquisition and data pre-processing by conferring the neuromorphic data processing function on a curved image sensor array. The curved neuromorphic image sensor array is based on a heterostructure of MoS2 and poly(1,3,5-trimethyl-1,3,5-trivinyl cyclotrisiloxane). The curved neuromorphic image sensor array features photon-triggered synaptic plasticity owing to its quasi-linear time-dependent photocurrent generation and prolonged photocurrent decay, originated from charge trapping in the MoS2-organic vertical stack. The curved neuromorphic image sensor array integrated with a plano-convex lens derives a pre-processed image from a set of noisy optical inputs without redundant data storage, processing, and communications as well as without complex optics. The proposed imaging device can substantially improve efficiency of the image acquisition and recognition process, a step forward to the next generation machine vision.

Hybrid structures of graphene and metal nanoparticles (NPs) have been actively investigated as higher quality surface enhanced Raman spectroscopy (SERS) substrates. Compared with SERS substrates, which only contain metal NPs, the additional graphene layer provides structural, chemical, and optical advantages. However, the two-dimensional (2D) nature of graphene limits the fabrication of the hybrid structure of graphene and NPs to 2D. Introducing three-dimensionality to the hybrid structure would allow higher detection sensitivity of target analytes by utilizing the three-dimensional (3D) focal volume. Here, we report a mechanical self-assembly strategy to enable a new class of 3D crumpled graphene-gold (Au) NPs hybrid nanoplasmonic structures for SERS applications. We achieve a 3D crumpled graphene-Au NPs hybrid structure by the delamination and buckling of graphene on a thermally activated, shrinking polymer substrate. We also show the precise control and optimization of the size and spacing of integrated Au NPs on crumpled graphene and demonstrate the optimized NPs' size and spacing for higher SERS enhancement. The 3D crumpled graphene-Au NPs exhibits at least 1 order of magnitude higher SERS detection sensitivity than that of conventional, flat graphene-Au NPs. The hybrid structure is further adapted to arbitrary curvilinear structures for advanced, in situ, nonconventional, nanoplasmonic sensing applications. We believe that our approach shows a promising material platform for universally adaptable SERS substrate with high sensitivity.

Graphene has been widely explored for flexible, high-performance photodetectors due to its exceptional mechanical strength, broadband absorption, and high carrier mobility. However, the low stretchability and limited photoabsorption of graphene have restricted its applications in flexible and highly sensitive photodetection systems. Various hybrid systems based on photonic or plasmonic nanostructures have been introduced to improve the limited photoresponsivity of graphene photodetectors. In most cases, the hybrid systems succeeded in the enhancement of photoresponsivity, but showed limited mechanical stretchability. Here, we demonstrate a stretchable photodetector based on a crumpled graphene-gold nanoparticle (AuNP) hybrid structure with ∼1200% enhanced photoresponsivity, compared to a conventional flat graphene-only photodetector, and exceptional mechanical stretchability up to a 200% tensile strain. We achieve plasmonically enhanced photoresponsivity by integrating AuNPs with graphene. By crumpling the hybrid structure, we realize mechanical stretchability and further enhancement of the optical absorption by densification. We also demonstrate that our highly stretchable photodetector with enhanced photoresponsivity can be integrated on a contact lens and a spring structure. We believe that our stretchable, high performance graphene photodetector can find broad applications for conformable and flexible optical sensors and dynamic mechanical strain sensors.

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