Graphene-based sensors for detection of heavy metals in water: a review

Graphene (G) is attracting significant attention because of its unique physical and electronic properties. The production of graphene through the reduction of graphene oxide (GO) is a low-cost method. The reduction of GO can further lead to electrically conductive reduced GO. These graphene-based nanomaterials are attractive for high-performance water sensors due to their unique properties, such as high specific surface areas, high electron mobilities, and exceptionally low electronic noise. Because of potential risks to the environment and human health arising from heavy-metal pollution in water, G-/GO-based water sensors are being developed for rapid and sensitive detection of heavy-metal ions. In this review, a general introduction to graphene and GO properties, as well as their syntheses, is provided. Recent advances in optical, electrochemical, and electrical detection of heavy-metal ions using graphene or GO are then highlighted. Finally, challenges facing G/GO-based water sensor development and outlook for future research are discussed.     

Possible excitation of a fast sodium beam by laser irradiation

We present results of model calculations concerning the fraction of excited atoms obtained by laser irradiation of a fast sodium atom beam (100–400 eV kinetic energy). We calculated relative absorption probabilities as a function of the intersection angle between the two beams, the atom-beam energy, the atom-beam energy spread and the atom-beam divergence. The fraction of excited atoms that can be obtained at 300 eV is at least a factor of 5 smaller than in the case of a thermal beam, due to the divergence and energy spread of the fast atom beam giving rise to a large Doppler broadening.     

Integrated microwave photonics

Microwave photonics (MWP) is an emerging field in which radio frequency (RF) signals are generated, distributed, processed and analyzed using the strength of photonic techniques. It is a technology that enables various functionalities which are not feasible to achieve only in the microwave domain. A particular aspect that recently gains significant interests is the use of photonic integrated circuit (PIC) technology in the MWP field for enhanced functionalities and robustness as well as the reduction of size, weight, cost and power consumption. This article reviews the recent advances in this emerging field which is dubbed as integrated microwave photonics. Key integrated MWP technologies are reviewed and the prospective of the field is discussed.     

Stepwise Adsorption of Alkoxy-Pyrene Derivatives onto a Lamellar,Non-Porous Naphthalenediimide-Template on HOPG

The development of new strategies for the preparation of multicomponent supramolecular assemblies is a major challenge on the road to complex functional molecular systems. Here we present the use of a non-porous self-assembled monolayer from uC₃₃-NDI-uC₃₃ , a naphthalenediimide symmetrically functionalized with unsaturated 33 carbon-atom-chains, to prepare bicomponent supramolecular surface systems with a series of alkoxy-pyrene ( PyrOR ) derivatives at the liquid/HOPG interface. While previous attempts at directly depositing many of these PyrOR units at the liquid/HOPG interface failed, the multicomponent approach through the uC₃₃-NDI-uC₃₃ template enabled control over molecular interactions and facilitated adsorption. The PyrOR deposition restructured the initial uC₃₃-NDI-uC₃₃ monolayer, causing an expansion in two dimensions to accommodate the guests. As far as we know, this represents the first example of a non-porous or non-metal complex-bearing monolayer that allows the stepwise formation of multicomponent supramolecular architectures on surfaces.     

Sequential gradient-restoration algorithm for the minimization of constrained functions—Ordinary and conjugate gradient versions

The problem of minimizing a functionf(x) subject to the constraint (x)=0 is considered. Here,f is a scalar,x ann-vector, and aq-vector. Asequential algorithm is presented, composed of the alternate succession of gradient phases and restoration phases.In thegradient phase, a nominal pointx satisfying the constraint is assumed; a displacement x leading from pointx to a varied pointy is determined such that the value of the function is reduced. The determination of the displacement x incorporates information at only pointx for theordinary gradient version of the method (Part 1) and information at both pointsx and for theconjugate gradient version of the method (Part 2).In therestoration phase, a nominal pointy not satisfying the constraint is assumed; a displacement y leading from pointy to a varied point is determined such that the constraint is restored to a prescribed degree of accuracy. The restoration is done by requiring the least-square change of the coordinates.If the stepsize of the gradient phase is ofO(), then x=O() and y=O(²). For sufficiently small, the restoration phase preserves the descent property of the gradient phase: the functionf decreases between any two successive restoration phases.This research, supported by the NASA Manned Spacecraft Center, Grant No. NGR-44-006-089, and by the Office of Scientific Research, Office of Aerospace Research, United States Air Force, Grant No. AF-AFOSR-828-67, is a condensation of the investigations reported in Refs. 1 and 2.     

The restoration of constraints in holonomic and nonholonomic problems

The first part of this paper considers a system described byp algebraic or transcendental equations involvingn variables, withn>p. A nominal state, not satisfying all the equations, is given. An iterative procedure is developed leading to a varied state satisfying all the equations. The procedure involves quasilinearization with an added optimality condition, namely, the requirement of least-square change of the coordinates. Two examples illustrating the rapid convergence of the algorithm are supplied.The second part considers a system described byn first-order differential equations involvingn state variables andm control variables. A nominal state and a nominal control, consistent with the boundary conditions, but not satisfying the equations, are given. An iterative procedure is developed leading to a varied state and a varied control consistent with the boundary conditions and the equations. The procedure involves quasilinearization with an added optimality condition, namely, the requirement of least-square change of the control and the state. Two examples illustrating the rapid convergence of the algorithm are supplied.The above procedures can be included in some of the iterative algorithms for minimizing functions or functionals involving variables subject to constraints, namely, gradient methods, whether ordinary, accelerated, or conjugate. Each gradient phase is alternated with a buffer phase, the restoration phase described here.This research, supported by the Office of Scientific Research, Office of Aerospace Research, United States Air Force, Grant No. AF-AFOSR-828-67, and by the NASA-Manned Spacecraft Center, Grant No. NGR-44-006-089, is a condensation of the investigations described in Refs. 1 and 2. Portions of this paper were presented by the senior author at the Second Hawaii International Conference on System Sciences, Honolulu, Hawaii, January 22–24, 1969. The authors are indebted to Professor H. Y. Huang and Mr. R. R. Iyer for helpful discussions.     

Sequential conjugate-gradient-restoration algorithm for optimal control problems. Part 1. Theory

This paper considers the problem of minimizing a functionalI which depends on the statex(t), the controlu(t), and the parameter π. Here,I is a scalar,x ann-vector,u anm-vector, and π ap-vector. At the initial point, the state is prescribed. At the final point, the state and the parameter are required to satisfyq scalar relations. Along the interval of integration, the state, the control, and the parameter are required to satisfyn scalar differential equations. First, the case of a quadratic functional subject to linear constraints is considered, and a conjugate-gradient algorithm is derived. Nominal functionsx(t),u(t), π satisfying all the differential equations and boundary conditions are assumed. Variations Δx(t), δu(t), Δπ are determined so that the value of the functional is decreased. These variations are obtained by minimizing the first-order change of the functional subject to the differential equations, the boundary conditions, and a quadratic constraint on the variations of the control and the parameter. Next, the more general case of a nonquadratic functional subject to nonlinear constraints is considered. The algorithm derived for the linear-quadratic case is employed with one modification: a restoration phase is inserted between any two successive conjugate-gradient phases. In the restoration phase, variations Δx(t), Δu(t), Δπ are determined by requiring the least-square change of the control and the parameter subject to the linearized differential equations and the linearized boundary conditions. Thus, a sequential conjugate-gradient-restoration algorithm is constructed in such a way that the differential equations and the boundary conditions are satisfied at the end of each complete conjugate-gradient-restoration cycle. Several numerical examples illustrating the theory of this paper are given in Part 2 (see Ref. 1). These examples demonstrate the feasibility as well as the rapidity of convergence of the technique developed in this paper. This research was supported by the Office of Scientific Research, Office of Aerospace Research, United States Air Force, Grant No. AF-AFOSR-72-2185. The authors are indebted to Professor A. Miele for stimulating discussions. Formerly, Graduate Studient in Aero-Astronautics, Department of Mechanical and Aerospace Engineering and Materials Science, Rice University, Houston, Texas.