Lehigh University

About: Lehigh University is a education organization based out in Bethlehem, Pennsylvania, United States. It is known for research contribution in the topics: Catalysis & Fracture mechanics. The organization has 12684 authors who have published 26550 publications receiving 770061 citations.

Role of acid-base interfacial bonding in adhesion

Abstract: The strength of macroscopic adhesive bonds of polymers is known to be directly proportional to the microscopic exothermic interfacial energy changes of bond formation, as measured by Dupre's 'work of adhesion'. Since the work of adhesion can be very appreciably increased by interfacial acid-base bonding with concomitant increases in adhesive bond strength, it is important to understand the acid-base character of polymers and of the surface sites of substrates or of the reinforcing fillers of polymer composites. The best known acid-base bonds are the hydrogen bonds; these are typical of acid-base bonds, with interaction energies dependent on the acidity of the hydrogen donor and on the basicity of the hydrogen acceptor. The strengths of the acidic or basic sites of polymers and of inorganic substrates can be easily determined by spectroscopic or calorimetric methods, and from this information one can start to predict the strengths of adhesive bonds. An important application of the new knowledge of interfac...

Facile removal of stabilizer-ligands from supported gold nanoparticles

Abstract: Metal nanoparticles that comprise a few hundred to several thousand atoms have many applications in areas such as photonics, sensing, medicine and catalysis. Colloidal methods have proven particularly suitable for producing small nanoparticles with controlled morphologies and excellent catalytic properties. Ligands are necessary to stabilize nanoparticles during synthesis, but once the particles have been deposited on a substrate the presence of the ligands is detrimental for catalytic activity. Previous methods for ligand removal have typically involved thermal and oxidative treatments, which can affect the size or morphology of the particles, in turn altering their catalytic activity. Here, we report a procedure to effectively remove the ligands without affecting particle morphology, which enhances the surface exposure of the nanoparticles and their catalytic activity over a range of reactions. This may lead to developments of nanoparticles prepared by colloidal methods for applications in fields such as environmental protection and energy production.

New search spaces for sequencing problems with application to job shop scheduling

Abstract: In this paper search heuristics are developed for generic sequencing problems with emphasis on job shop scheduling. The proposed methods integrate problem specific heuristics common to Operations Research and local search approaches from Artificial Intelligence in order to obtain desirable properties from both. The applicability of local search to a wide range of problems, and the incorporation of problem-specific information are both properties of the proposed algorithms. Two methods are proposed, both of which are based on novel definitions of solution spaces and of neighborhoods in these spaces. Applications of the proposed methodology are developed for job shop scheduling problems, and can be easily applied with any scheduling objective. To demonstrate effectiveness, the method is tested on the job shop scheduling problem with the minimum makespan objective. Encouraging results are obtained.

MIMO radar waveform design based on mutual information and minimum mean-square error estimation

Abstract: This paper addresses the problem of radar waveform design for target identification and classification. Both the ordinary radar with a single transmitter and receiver and the recently proposed multiple-input multiple-output (MIMO) radar are considered. A random target impulse response is used to model the scattering characteristics of the extended (nonpoint) target, and two radar waveform design problems with constraints on waveform power have been investigated. The first one is to design waveforms that maximize the conditional mutual information (MI) between the random target impulse response and the reflected waveforms given the knowledge of transmitted waveforms. The second one is to find transmitted waveforms that minimize the mean-square error (MSE) in estimating the target impulse response. Our analysis indicates that under the same total power constraint, these two criteria lead to the same solution for a matrix which specifies the essential part of the optimum waveform design. The solution employs water-filling to allocate the limited power appropriately. We also present an asymptotic formulation which requires less knowledge of the statistical model of the target