Simultaneous on-chip sensing of multiple greenhouse gases in a complex gas environment is highly desirable in industry, agriculture, and meteorology, but remains challenging due to their ultralow concentrations and mutual interference. Porous microstructure and extremely high surface areas in metal-organic frameworks (MOFs) provide both excellent adsorption selectivity and high gases affinity for multigas sensing. Herein, it is described that integrating MOFs into a multiresonant surface-enhanced infrared absorption (SEIRA) platform can overcome the shortcomings of poor selectivity in multigas sensing and enable simultaneous on-chip sensing of greenhouse gases with ultralow concentrations. The strategy leverages the near-field intensity enhancement (over 1500-fold) of multiresonant SEIRA technique and the outstanding gas selectivity and affinity of MOFs. It is experimentally demonstrated that the MOF-SEIRA platform achieves simultaneous on-chip sensing of CO 2 and CH 4 with fast response time (<60 s), high accuracy (CO 2 : 1.1%, CH 4 : 0.4%), small footprint (100 × 100 µm 2), and excellent linearity in wide concentration range (0-2.5 × 10 4 ppm). Additionally, the excellent scalability to detect more gases is explored. This work opens up exciting possibilities for the implementation of all-in-one, real-time, and on-chip multigas detection as well as provides a valuable toolkit for greenhouse gas sensing applications.

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The growth of the chemical industry has brought tremendous challenges to chemical sensing technology. Chemical sensors based on metamaterials have great potential because of their label-free and non-destructive characteristics. However, metamaterials applied in chemical sensing have mainly been investigated from the measurement of sample concentration or the determination of the dielectric properties at a fixed frequency. Here we present a metamaterial integrated microfluidic (MIM) sensor for the multi-band sensing for dielectric property of chemicals, which is promising for the identification of chemicals. The MIM sensor mainly consists of multiple pair of high sensitive symmetrical double split-ring resonators (DSRRs) and meandering microfluidic channels with a capacity of only 4 μL. A dielectric model has been innovatively established and experimentally verified to accurately estimate the complex permittivity and thus realize the multi-band sensing of dielectric property of chemicals. With the increase in the number of resonators in the sensor, a dielectric spectrum like curve could be obtained for more detailed dielectric information. This work delivers a miniaturized, reusable, label-free and non-destructive metamaterial-microfluidic solution and paves a way of the multi-band sensing for dielectric property of chemicals.

We investigate the challenging problem of enabling multicast video service in emerging cognitive radio (CR) networks. We propose a cross-layer optimization approach to multicast video in CR networks. Specifically, we model CR video multicast as an optimization problem, while considering important design factors including scalable video coding, video rate control, spectrum sensing, dynamic spectrum access, modulation, scheduling, retransmission, and primary user protection. The objective is to optimize the overall received video quality as well as achieving proportional fairness among multicast users, while keeping the interference to primary users below a prescribed threshold. Although the problem can be solved using advanced optimization techniques, we propose a sequential fixing algorithm and a greedy algorithm with low complexity and proven optimality gap. Our simulations using MPEG-4 fine grained scalability (FGS) demonstrate the efficacy and superior performance of the proposed approach as compared with an alternative equal allocation scheme.

In this paper, we consider femtocell CR networks, where femto base stations (FBS) are deployed to greatly improve network coverage and capacity. We investigate the problem of generic data multicast in femtocell networks. We reformulate the resulting MINLP problem into a simpler form, and derive upper and lower performance bounds. Then we consider three typical connection scenarios in the femtocell network, and develop optimal and near-optimal algorithms for the three scenarios. Second, we tackle the problem of streaming scalable videos in femtocell CR networks. A framework is developed to captures the key design issues and trade-offs with a stochastic programming problem formulation. In the case of a single FBS, we develop an optimum-achieving distributed algorithm, which is shown also optimal for the case of multiple non-interfering FBS's. In the case of interfering FBS's, we develop a greedy algorithm that can compute near-opitmal solutions, and prove a closed-form lower bound on its performance.