Graphene has attracted large interest in photonic applications owing to its promising optical properties, especially its ability to absorb light over a broad wavelength range, which has lead to several studies on pure monolayer graphene-based photodetectors. However, the maximum responsivity of these photodetectors is below 10 mA W À 1 , which significantly limits their potential for applications. Here we report high photoresponsivity (with high photoconductive gain) of 8.61 A W À 1 in pure monolayer graphene photodetectors, about three orders of magnitude higher than those reported in the literature, by introducing electron trapping centres and by creating a bandgap in graphene through band structure engineering. In addition, broadband photoresponse with high photoresponsivity from the visible to the mid-infrared is experimentally demonstrated. To the best of our knowledge, this work demonstrates the broadest photoresponse with high photoresponsivity from pure monolayer graphene photodetectors, proving the potential of graphene as a promising material for efficient optoelectronic devices.

The pursuit of optoelectronic devices operating in the mid-infrared regime is driven by both fundamental interests and envisioned applications ranging from imaging, sensing to communications. Despite continued achievements in traditional semiconductors, notorious obstacles such as the complicated growth processes and cryogenic operation preclude the usage of infrared detectors. As an alternative path towards high-performance photodetectors, hybrid semiconductor/graphene structures have been intensively explored. However, the operation bandwidth of such photodetectors has been limited to visible and near-infrared regimes. Here we demonstrate a mid-infrared hybrid photodetector enabled by coupling graphene with a narrow bandgap semiconductor, Ti2O3 (Eg = 0.09 eV), which achieves a high responsivity of 300 A W−1 in a broadband wavelength range up to 10 µm. The obtained responsivity is about two orders of magnitude higher than that of the commercial mid-infrared photodetectors. Our work opens a route towards achieving high-performance optoelectronics operating in the mid-infrared regime.

Terahertz (THz) frequency technology has many potential applications in nondestructive imaging, spectroscopic sensing, and high-bit-rate free-space communications, with an optical modulator being a key component. However, it has proved challenging to achieve high-speed modulation with a high modulation depth across a broad bandwidth of THz frequencies. Here, we demonstrate that a monolithically integrated graphene modulator can efficiently modulate the light intensity of the THz radiation from a THz quantum cascade laser with a 100% modulation depth for certain region of the pumping current, as a result of the strongly enhanced interaction between the laser field and the graphene enabled by this integration scheme. Moreover, the small area of the resulting device in comparison to existing THz modulators enables a faster modulation speed, greater than 100 MHz, which can be further improved through optimized designs of the laser cavity and modulator architectures. Furthermore, as the graphene absorption spectrum is broadband in nature, our integration scheme can be readily scaled to other wavelength regions, such as the mid-infrared, and applied to a broad range of other optoelectronic devices.

Spatiotemporal instabilities are widespread phenomena resulting from complexity and nonlinearity. In broad-area edge-emitting semiconductor lasers, the nonlinear interactions of multiple spatial modes with the active medium can result in filamentation and spatiotemporal chaos. These instabilities degrade the laser performance and are extremely challenging to control. We demonstrate a powerful approach to suppress spatiotemporal instabilities using wave-chaotic or disordered cavities. The interference of many propagating waves with random phases in such cavities disrupts the formation of self-organized structures such as filaments, resulting in stable lasing dynamics. Our method provides a general and robust scheme to prevent the formation and growth of nonlinear instabilities for a large variety of high-power lasers.

Random lasers are a special class of laser in which light is confined through multiple scattering and interference process in a disordered medium, without a traditional optical cavity. They have been widely studied to investigate fundamental phenomena such as Anderson localization, and for applications such as speckle-free imaging, benefitting from multiple lasing modes. However, achieving controlled localized multi-mode random lasing at long wavelengths, such as in the terahertz (THz) frequency regime, remains a challenge.Here, we study devices consisting of randomly-distributed pillars fabricated from a quantum cascade gain medium, and show that such structures can achieve transversemagnetic polarized (TM) multi-mode random lasing, with strongly localized modes at THz frequencies. The weak short-range order induced by the pillar distribution is sufficient to ensure high quality-factor modes that have a large overlap with the active material.Furthermore, the emission spectrum can be easily tuned by tailoring the scatterer size and filling fraction. These "designer" random lasers, realized using standard photolithography 2 techniques, provide a promising platform for investigating disordered photonics with predesigned randomness in the THz frequency range, and may have potential applications such as speckle-free imaging.

The strong quantum confinement effect in lead selenide (PbSe) colloidal quantum dots (CQDs) allows to tune the bandgap of the material, covering a large spectral range from mid-to near infrared (NIR). Together with the advantages of low-cost solution processability, flexibility and easy scale-up production in comparison to conventional semiconductors especially in the mid-to near infrared range, PbSe CQDs have been a promising material for infrared optoelectronic applications. In this study, we synthesized monodisperse and high purity PbSe CQDs and then demonstrated the photodetectors working at different wavelengths up to 2.8 µm. Our high quality PbSe CQDs show clear multiple excitonic absorption peaks. PbSe CQD films of different thicknesses were deposited on interdigitated platinum electrodes by a simple drop casting technique to make the infrared photodetectors. At room temperature, the high performances of our PbSe CQD photodetectors were achieved with maximum responsivity, detectivity and external quantum efficiency of 0.96 A/W, 8.13 × 10 9 Jones and 78% at 5V bias. Furthermore, a series of infrared LEDs with a broad wavelength range from 1.5 μm to 3.4 μm was utilized to demonstrate the performance of our fabricated photodetectors with various PbSe CQD film thicknesses.

Machine olfaction is an intelligent system that combines a cross-sensitivity chemical sensor array and an effective pattern recognition algorithm for the detection, identification, or quantification of various odors. Data collected by the sensor array are the multivariate time series signals with a complex structure, and these signals become more difficult to analyze due to sensor drift. In this work, we focus on improving the classification performance under sensor drift by using the deep learning method, which is popular nowadays. Compared with other methods, our method can effectively tackle sensor drift by automatically extracting features, thus not only removing the complexity of designing the hand-made features but also making it pervasive for a variety of application in machine olfaction. Our experimental results show that the deep learning method can learn the features that are more robust to drift than the original input and achieves high classification accuracy. C 2015 Wiley Periodicals, Inc.

In this article, we introduce the concept of model confidence bounds (MCB) for variable selection in the context of nested models. Similarly to the endpoints in the familiar confidence interval for parameter estimation, the MCB identifies two nested models (upper and lower confidence bound models) containing the true model at a given level of confidence. Instead of trusting a single selected model obtained from a given model selection method, the MCB proposes a group of nested models as candidates and the MCB's width and composition enable the practitioner to assess the overall model selection uncertainty. A new graphical tool—the model uncertainty curve (MUC)—is introduced to visualize the variability of model selection and to compare different model selection procedures. The MCB methodology is implemented by a fast bootstrap algorithm that is shown to yield the correct asymptotic coverage under rather general conditions. Our Monte Carlo simulations and real data examples confirm the validity and illustrate the advantages of the proposed method.