Parametric amplification in single-walled carbon nanotube nanoelectromechanical resonators

The low quality factor (Q) of Single-walled carbon nanotube (SWNT) resonators has limited their sensitivity in sensing application. To this end, we employ the technique of parametric amplification by modulating the spring constant of SWNT resonators at twice the resonant frequency, and achieve 10 times Q enhancement. The highest Q obtained at room temperature is around ~700, which is 3-4 times better than previous Q record reported for doubly-clamped SWNT resonators. Furthermore, efficient parametric amplification is found to only occur in the catenary vibration regime. Our results open up the possibility to employ light-weight and high-Q carbon nanotube resonators in single molecule and atomic mass sensing.Comment: 14 pages, 3 figure

Ultrafast photocurrent measurement of the escape time of electrons and holes from carbon nanotube PN junction photodiodes

Ultrafast photocurrent measurements are performed on individual carbon nanotube PN junction photodiodes. The photocurrent response to sub-picosecond pulses separated by a variable time delay {\Delta}t shows strong photocurrent suppression when two pulses overlap ({\Delta}t = 0). The picosecond-scale decay time of photocurrent suppression scales inversely with the applied bias VSD, and is twice as long for photon energy above the second subband E22 as compared to lower energy. The observed photocurrent behavior is well described by an escape time model that accounts for carrier effective mass.Comment: 8 pages Main text, 4 Figure

Ultrafast photocurrent measurement of the escape time of electrons and holes from carbon nanotube PN junction photodiodes

Ultrafast photocurrent measurements are performed on individual carbon nanotube PN junction photodiodes. The photocurrent response to sub-picosecond pulses separated by a variable time delay {\Delta}t shows strong photocurrent suppression when two pulses overlap ({\Delta}t = 0). The picosecond-scale decay time of photocurrent suppression scales inversely with the applied bias VSD, and is twice as long for photon energy above the second subband E22 as compared to lower energy. The observed photocurrent behavior is well described by an escape time model that accounts for carrier effective mass.Comment: 8 pages Main text, 4 Figure

Carb­oxy­methyl ursolate monohydrate

In the title compound, C28H50O5·H2O, all of the six-membered rings of the penta­cyclic triterpene skeleton adopt chair conformations. In the crystal, mol­ecules are linked by O—H⋯O and C—H⋯O hydrogen bonds

Reconfigurable Intelligent Surface Based Orbital Angular Momentum: Architecture, Opportunities, and Challenges

Orbital angular momentum (OAM) has gained a lot of attention due to its potential in enhancing the spectral efficiency for wireless communications. Using different OAM modes, multiple independent data streams are simultaneously transmitted by using spatial distribution of helical phase, which enables OAM as a new form of multiple access technique for wireless communications. Controlling the phases of incoming electromagnetic waves, the reconfigurable intelligent surface (RIS) is suitable for implementing OAM. In this article, an RIS-based OAM framework is introduced. The basic concepts and features of RIS and OAM are presented. Then classifications and comparisons of different RIS-based OAM schemes are summarized. Simulation results verify that RIS-based OAM transmission can achieve nearly 100 percent higher spectral efficiency of wireless communication systems compared to the conventional RIS scheme

Use of graphene as protection film in biological environments

Corrosion of metal in biomedical devices could cause serious health problems to patients. Currently ceramics coating materials used in metal implants can reduce corrosion to some extent with limitations. Here we proposed graphene as a biocompatible protective film for metal potentially for biomedical application. We confirmed graphene effectively inhibits Cu surface from corrosion in different biological aqueous environments. Results from cell viability tests suggested that graphene greatly eliminates the toxicity of Cu by inhibiting corrosion and reducing the concentration of Cu(2+) ions produced. We demonstrated that additional thiol derivatives assembled on graphene coated Cu surface can prominently enhance durability of sole graphene protection limited by the defects in graphene film. We also demonstrated that graphene coating reduced the immune response to metal in a clinical setting for the first time through the lymphocyte transformation test. Finally, an animal experiment showed the effective protection of graphene to Cu under in vivo condition. Our results open up the potential for using graphene coating to protect metal surface in biomedical application

Over-the-Air Split Machine Learning in Wireless MIMO Networks

In split machine learning (ML), different partitions of a neural network (NN) are executed by different computing nodes, requiring a large amount of communication cost. To ease communication burden, over-the-air computation (OAC) can efficiently implement all or part of the computation at the same time of communication. Based on the proposed system, the system implementation over wireless network is introduced and we provide the problem formulation. In particular, we show that the inter-layer connection in a NN of any size can be mathematically decomposed into a set of linear precoding and combining transformations over MIMO channels. Therefore, the precoding matrix at the transmitter and the combining matrix at the receiver of each MIMO link, as well as the channel matrix itself, can jointly serve as a fully connected layer of the NN. The generalization of the proposed scheme to the conventional NNs is also introduced. Finally, we extend the proposed scheme to the widely used convolutional neural networks and demonstrate its effectiveness under both the static and quasi-static memory channel conditions with comprehensive simulations. In such a split ML system, the precoding and combining matrices are regarded as trainable parameters, while MIMO channel matrix is regarded as unknown (implicit) parameters.Comment: 15 pages, 13 figures, journal pape

Bayesian Learning for Double-RIS Aided ISAC Systems with Superimposed Pilots and Data

Reconfigurable intelligent surface (RIS) has great potential to improve the performance of integrated sensing and communication (ISAC) systems, especially in scenarios where line-of-sight paths between the base station and users are blocked. However, the spectral efficiency (SE) of RIS-aided ISAC uplink transmissions may be drastically reduced by the heavy burden of pilot overhead for realizing sensing capabilities. In this paper, we tackle this bottleneck by proposing a superimposed symbol scheme, which superimposes sensing pilots onto data symbols over the same time-frequency resources. Specifically, we develop a structure-aware sparse Bayesian learning framework, where decoded data symbols serve as side information to enhance sensing performance and increase SE. To meet the low-latency requirements of emerging ISAC applications, we further propose a low-complexity simultaneous communication and localization algorithm for multiple users. This algorithm employs the unitary approximate message passing in the Bayesian learning framework for initial angle estimate, followed by iterative refinements through reduced-dimension matrix calculations. Moreover, the sparse code multiple access technology is incorporated into this iterative framework for accurate data detection which also facilitates localization. Numerical results show that the proposed superimposed symbol-based scheme empowered by the developed algorithm can achieve centimeter-level localization while attaining up to $96\%$ of the SE of conventional communications without sensing capabilities. Moreover, compared to other typical ISAC schemes, the proposed superimposed symbol scheme can provide an effective throughput improvement over $133\%$