Chemical warfare agents (CWAs) are potential threats to civil society and defence personnel. In recent years, many efforts has been deployed to develop a scalable, rapid and accurate detection system to identify trace amount of CWAs. Here we report a graphene-based field-effect transistor (GFET) sensor able to detect 800 ppb of dimethyl methyl phosphonate (DMMP), a simulant of the nerve agent sarin. We observe enhanced sensitivity when the GFET sensor is exposed to few mWs of UV light. Back gate measurements performed before and during exposures to the analyte allow us to investigate the sensing mechanism while monitoring the induced changes in carrier concentration and mobility in graphene.

Development of graphene-based field-effect transistor (GFET) sensors for a simulant of chemical warfare agents

Liquid-phase exfoliation (LPE) is a low-cost and scalable technique for producing a wide range of van der Waals nanomaterials that can be incorporated into existing laboratory sample and industrial material production. Liquid-phase exfoliated nanomaterials have the potential to produce devices quickly and at low-cost, with colloidal dispersions easily adaptable to existing production methods. Other methods of nanomaterial production, such as chemical vapour deposition (CVD) and mechanical exfoliation can be costly, time-consuming and require complicated equipment to produce comparatively small area devices. Such methods are excellent for laboratory-scale samples to examine physical properties and produce proof of concept devices. However, LPE can and will bridge the gap towards real-world applications that require faster, easier and more cost-effective production methods. In this work, we investigate the saturable absorption and Kerr nonlinearity of graphene fabricated by LPE and CVD. 

Thin films of LPE graphene were produced on BK7 glass substrates, and compared to CVD graphene transferred onto an identical substrate. Through careful consideration of the concentration of graphene dispersion and deposition methods, very thin films of graphene can be prepared. Atomic force microscopy (AFM) measurements showed effective bi-layer graphene thickness for the LPE samples. Z-scan measurements performed with 180 fs pulses at a wavelength of 1030 nm reveal that both LPE and CVD graphene display strong saturable absorption characteristics, with a nonlinear absorption coefficient (*β*) approaching -104 cm/GW and a Kerr nonlinearity (*n2*) of -1 cm2/GW. Such strong saturable absorption is ideal for devices such as mode-lockers for ultrafast pulsed lasers. The magnitude of the nonlinear absorption coefficient of the LPE graphene increases with pulse duration, *τ*, up to 105 cm/GW at around *τ* = 10 ps. These results pave the way for the use of LPE graphene in nonlinear optical applications such as frequency generation and mode locking.

Nonlinear optical properties of graphene: liquid-phase exfoliation vs chemical vapour deposition

In one-class novelty detection, a model learns solely on the in-class data to single out out-class instances. Autoencoder (AE) variants aim to compactly model the in-class data to reconstruct it exclusively, thus differentiating the in-class from out-class by the reconstruction error. However, compact modeling in an improper way might collapse the latent representations of the in-class data and thus their reconstruction, which would lead to performance deterioration. Moreover, to properly measure the reconstruction error of high-dimensional data, a metric is required that captures high-level semantics of the data. To this end, we propose Discriminative Compact AE (DCAE) that learns both compact and collapse-free latent representations of the in-class data, thereby reconstructing them both finely and exclusively. In DCAE, (a) we force a compact latent space to bijectively represent the in-class data by reconstructing them through internal discriminative layers of generative adversarial nets. (b) Based on the deep encoder's vulnerability to open set risk, out-class instances are encoded into the same compact latent space and reconstructed poorly without sacrificing the quality of in-class data reconstruction. (c) In inference, the reconstruction error is measured by a novel metric that computes the dissimilarity between a query and its reconstruction based on the class semantics captured by the internal discriminator. Extensive experiments on public image datasets validate the effectiveness of our proposed model on both novelty and adversarial example detection, delivering state-of-the-art performance.

Discriminative Multi-level Reconstruction under Compact Latent Space for One-Class Novelty Detection

M3-8 Applied Physics (DAPI) / Physique appliquée (DPAI)

technical paper

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OS T1.2 Machine Learning 1

ICPR 2020 is the premier international conference on Pattern Recognition, gathering leading researchers from around the world to present their latest achievements in Pattern Analysis and Recognition, applied Artificial Intelligence and Machine Learning. The topic of the year is " Putting Artificial intelligence to work on Patterns". 
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Jaewoo Park

3

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Development of graphene-based field-effect transistor (GFET) sensors for a simulant of chemical warfare agents

Nonlinear optical properties of graphene: liquid-phase exfoliation vs chemical vapour deposition

Discriminative Multi-level Reconstruction under Compact Latent Space for One-Class Novelty Detection

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