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The past decade has witnessed a rapid growth of graphene plasmonics and their applications in different fields. Compared with conventional plasmonic materials, graphene enables highly confined plasmons with much longer lifetimes. Moreover, graphene plasmons work in an extended wavelength range, i.e., mid-infrared and terahertz regime, overlapping with the fingerprints of most organic and biomolecules, and have broadened their applications towards plasmonic biological and chemical sensors. In this review, we discuss intrinsic plasmonic properties of graphene and strategies both for tuning graphene plasmons as well as achieving higher performance by integrating graphene with plasmonic nanostructures. Next, we survey applications of graphene and graphene-hybrid materials in biosensors, chemical sensors, optical sensors, and sensors in other fields. Lastly, we conclude this review by providing a brief outlook and challenges of the field. Through this review, we aim to provide an overall picture of graphene plasmonic sensing and to suggest future trends of development of graphene plasmonics.

 

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 MoS₂and 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.

 

Atomically thin 2D materials exhibit strong intralayer covalent bonding and weak interlayer van der Waals interactions, offering unique high in-plane strength and out-of-plane flexibility. While atom-thick nature of 2D materials may cause uncontrolled intrinsic/extrinsic deformation in multiple length scales, it also provides new opportunities for exploring coupling between heterogeneous deformations and emerging functionalities in controllable and scalable ways for electronic, optical, and optoelectronic applications. In this review, we discuss (i) the mechanical characteristics of 2D materials, (ii) uncontrolled inherent deformation and extrinsic heterogeneity present in 2D materials, (iii) experimental strategies for controlled heterogeneous deformation of 2D materials, (iv) 3D structure-induced novel functionalities via crumple/wrinkle structure or kirigami structures, and (v) heterogeneous strain-induced emerging functionalities in exciton and phase engineering. Overall, heterogeneous deformation offers unique advantages for 2D materials research by enabling spatial tunability of 2D materials' interactions with photons, electrons, and molecules in a programmable and controlled manner.