Self assembly of plasmonic core-satellite nano-assemblies mediated by hyperbranched polymer linkers

The morphology of plasmonic nano-assemblies has a direct influence on optical properties, such as localised surface plasmon resonance (LSPR) and surface enhanced Raman scattering (SERS) intensity. Assemblies with core-satellite morphologies are of particular interest, because this morphology has a high density of hot-spots, while constraining the overall size. Herein, a simple method is reported for the self-assembly of gold NPs nano-assemblies with a core-satellite morphology, which was mediated by hyperbranched polymer (HBP) linkers. The HBP linkers have repeat units that do not interact strongly with gold NPs, but have multiple end-groups that specifically interact with the gold NPs and act as anchoring points resulting in nano-assemblies with a large (~48 nm) core surrounded by smaller (~15 nm) satellites. It was possible to control the number of satellites in an assembly which allowed optical parameters such as SPR maxima and the SERS intensity to be tuned. These results were found to be consistent with finite-difference time domain (FDTD) simulations. Furthermore, the multiplexing of the nano-assemblies with a series of Raman tag molecules was demonstrated, without an observable signal arising from the HBP linker after tagging. Such plasmonic nano-assemblies could potentially serve as efficient SERS based diagnostics or biomedical imaging agents in nanomedicine.

Tunnelling current recognition through core-satellite gold nanoparticles for ultrasensitive detection of copper ions

We report a new method for ultrasensitive detection of Cu(2+), which is based on changes in the tunnelling recognition current across self-assembled core-satellite gold nanoparticles (GNPs) networks functionalised with amino acids (l-cysteine). The addition of copper ions induces the formation of GNP/l-cysteine/Cu(2+)/l-cysteine/GNP molecular junctions and generates a significant decrease in the resistance through the networks. The networks are ultrasensitive to over ten orders range of copper ion concentrations.

Self-assembled core-satellite gold nanoparticle networks for ultrasensitive detection of chiral molecules by recognition tunneling current

Chirality sensing is a very challenging task. Here, we report a method for ultrasensitive detection of chiral molecule l/d-carnitine based on changes in the recognition tunneling current across self-assembled core-satellite gold nanoparticle (GNP) networks. The recognition tunneling technique has been demonstrated to work at the single molecule level where the binding between the reader molecules and the analytes in a nanojunction. This process was observed to generate a unique and sensitive change in tunneling current, which can be used to identify the analytes of interest. The molecular recognition mechanism between amino acid l-cysteine and l/d-carnitine has been studied with the aid of SERS. The different binding strength between homo- or heterochiral pairs can be effectively probed by the copper ion replacement fracture. The device resistance was measured before and after the sequential exposures to l/d-carnitine and copper ions. The normalized resistance change was found to be extremely sensitive to the chirality of carnitine molecule. The results suggested that a GNP networks device optimized for recognition tunneling was successfully built and that such a device can be used for ultrasensitive detection of chiral molecules.

Hybrid polymer/metal nanoparticles with surface enhanced raman tracking for potential use in nanomedicine

This project was a preliminary step towards the development of novel methods for early stage cancer diagnosis and treatment. Diagnostic imaging agents with high Raman signal enhancement were developed based on tailored assemblies of gold nanoparticles, which demonstrated potential for non-invasive detection from deep under the skin surface. Specifically designed polymers were employed to assemble gold nanoparticles into controlled morphologies including dimers, nanochains, nanoplates, globular and core-satellite nanostructures. Our findings suggest that the Raman enhancement is strongly dependent on assembly morphology and can be tuned to adapt to the requirements of the diagnostic agent.

A biomimetic sensor for the detection of lead in water.

The monitoring of lead (II) ions (Pb(2+)) in water is essential for both human health and the environment. Herein, a simple yet innovative biosensor for Pb(2+) detection is presented. The sensor is developed by the self-assembly of gold nanoparticles (GNPs) core-satellite structure using naturally occurring tripeptide glutathione (GSH) as linker. The addition of Pb(2+) caused a red-to-blue color change and the localized surface plasmon resonance (LSPR) band was shifted to ca. 650nm. The limit of detection (LOD) is found to be 47.6nM (9.9ppb) by UV-vis spectroscopy with high selectivity against other heavy metals. This method offers a new strategy for heavy metal detection using functionalized GNPs.

Plasmonic nanorods and nanoparticle-assemblies

Die vorliegenden Dissertation beschäftigt sich mit plasmonischen Nanopartikeln, deren Wechselwirkung mit Licht in einer Plasmonenschwingung resultiert. Suspensionen dieser Partikel zeigen kräftige Farben, da sich die Resonanzfrequenz der Plasmonenschwingung im sichtbaren Bereich des elektromagnetischen Spektrum befindet. Durch die Veränderung interner (Material, Größe, Form) oder externer Parameter (Brechungsindex der Umgebung, Abstand zu anderen plasmonischen Partikeln) lässt sich die Farbe der Partikel verändern, eine Verschiebung der Resonanzfrequenz kann beobachtet werden. Ihre Sensitivität gegenüber äußeren Bedingungen ist der Grund, weshalb plasmonische Nanopartikel als Sensoren eingesetzt werden können. Wichtig ist hierbei nicht nur, dass die Partikel eine hohe Sensitivität zeigen, sondern auch die Möglichkeit, reproduzierbar Partikel zu synthetisieren, die experimentellen Anforderungen entsprechen. In der vorliegenden Arbeit wird das Wachstum von reinen Gold- und mit Silber beschichteten Goldnanostäbchen untersucht. Des Weiteren werden plasmonische Nanopartikel als Orientierungs-, Brechungsindex- und Abstandssensoren verwendet. Die Synthese von Goldnanostäbchen erfolgt auf nasschemischen Weg, ihr anisotropes Wachstum aus isotropen Keimen wird durch zahlreiche Faktoren beeinflusst. In diesem Zusammenhang wurde ein Wachstumsmodell entwickelt, das neben dem Vorhandensein eines Stabilisators auch die Rolle von Bromid- und Silberionen herausstellt, die durch selektive Adsorption das Wachstum bestimmter Kristallflächen inhibieren. Zudem konnte gezeigt werden, dass die Potentialdifferenz zwischen Reduktions- und Oxidationsmittel klein sein muss, um ein langsames selektives Wachstum zu gewährleisten. rnDurch das Aufwachsen einer dünnen Silberschicht auf Goldnanostäbchen verbessert sich deren Qualität im Bezug auf die heterogene Linienbreite. Der “Plasmonic Focusing Effect”, die Änderung der Steigung des linearen Zusammenhangs von Plasmonenresonanz und Aspektverhältnis, konnte theoretisch berechnet und experimentell verifiziert werden. Durch die Aufnahme zeitaufgelöster Spektren und die Untersuchung des Verlaufs der Reaktion wurden sowohl Reaktionsordnung, als auch Aktivierungsenergie ermittelt. Das so gefundene kinetische Model erlaubt zudem die Vorhersage des Reaktionsprodukts zu verschiedenen Zeiten. rnEinzelne Goldnanostäbchen wurden in einer Gelmatrix bei verschiedenen Temperaturen untersucht, die Aufnahme der zeitlichen Variation der polarisationsabhängigen Streuintensität konnte genutzt werden, um den Kollaps des Gels zu charakterisieren. Neben der Verwendung einzelner plasmonischer Nanopartikel wurden auch Dimere, bestehend aus zwei Goldnanokugeln, untersucht. Nach der Kalibrierung der Resonanzfrequenz gegenüber des Abstandes der beiden Partikel durch externe Methoden (Lichtstreuung, Cryo- Elektronenmikroskopie) wurde der so gefundene exponentielle Zusammenhang verwendet, um sowohl den Brechungsindex der Umgebung, als auch den Abstand der beiden Goldnanokugeln zu bestimmen. Des Weiteren wurden Goldnanopartikeldimere benutzt, um ein als Linker verwendetes thermoresponsives Elastin-Polymer bei verschiedenen Temperaturen zu charakterisieren. Neben Aggregaten aus zwei Goldnanokugeln wurden auch so genannte “core-satellite” Strukturen synthetisiert, die um einen großen Goldnanopartikelkern viele kleine Goldnanopartikel tragen. Diese Partikel haben eine theoretisch vorhergesagte höhere Sensitivität gegenüber Brechungsindexänderungen, was in ersten Experimenten gezeigt werden konnte.

Satellite tracking large numbers of individuals to infer population level dispersal and core areas for the protection of an endangered species

Aim: Tracking the dispersal patterns and habitat use of migratory species is necessary to delineate optimal areas for protection, with large sample sizes being more representative of the population. Here, we examine the dispersal patterns of a key Mediterranean loggerhead turtle (Caretta caretta) breeding population to identify priority foraging sites for protection. Location: Zakynthos Island, Greece and the wider Mediterranean. Method: We examined the dispersal patterns and foraging sites of 75 adult loggerheads (n = 38 males and 37 females) tracked from the breeding area of Zakynthos Island (Greece) from 2004 to 2011. We then combined our data with published sea turtle literature to identify key foraging sites for protection. Results: While both males and females exhibited similar dispersal patterns, about 25% males remained < 100 km of Zakynthos, whereas all females (except one) migrated > 200 km. Integration of our data with the wider literature isolated 10 core sites in proximity to existing protected areas, which could potentially protect 64% of the Zakynthos population, while five sites support individuals from at least 10 other loggerhead breeding populations. Main conclusions: Due to the widespread availability of neritic foraging grounds across the Mediterranean, sea turtles from Zakynthos exhibit disparate dispersal patterns. However, protecting only a few objectively defined important sites can encompass a large proportion of the foraging areas used and hence have considerable conservation benefit.

Fast searching algorithm for candidate satellite-node set in NLMG

The Node-based Local Mesh Generation (NLMG) algorithm, which is free of mesh inconsistency, is one of core algorithms in the Node-based Local Finite Element Method (NLFEM) to achieve the seamless link between mesh generation and stiffness matrix calculation, and the seamless link helps to improve the parallel efficiency of FEM. Furthermore, the key to ensure the efficiency and reliability of NLMG is to determine the candidate satellite-node set of a central node quickly and accurately. This paper develops a Fast Local Search Method based on Uniform Bucket (FLSMUB) and a Fast Local Search Method based on Multilayer Bucket (FLSMMB), and applies them successfully to the decisive problems, i.e. presenting the candidate satellite-node set of any central node in NLMG algorithm. Using FLSMUB or FLSMMB, the NLMG algorithm becomes a practical tool to reduce the parallel computation cost of FEM. Parallel numerical experiments validate that either FLSMUB or FLSMMB is fast, reliable and efficient for their suitable problems and that they are especially effective for computing the large-scale parallel problems.

Remote sensing and core data needed to support planning and policy decision making

A variety of sustainable development research efforts and related activities are attempting to reconcile the issues of conserving our natural resources without limiting economic motivation while also improving our social equity and quality of life. Land use/land cover change, occurring on a global scale, is an aggregate of local land use decisions and profoundly impacts our environment. It is therefore the local decision making process that should be the eventual target of many of the ongoing data collection and research efforts which strive toward supporting a sustainable future. Satellite imagery data is a primary source of data upon which to build a core data set for use by researchers in analyzing this global change. A process is necessary to link global change research, utilizing satellite imagery, to the local land use decision making process. One example of this is the NASA-sponsored Regional Data Center (RDC) prototype. The RDC approach is an attempt to integrate science and technology at the community level. The anticipated result of this complex interaction between research and the decision making communities will be realized in the form of long-term benefits to the public.

Crop Classification using Biologically-inspired Techniques with High Resolution Satellite Image

Remote sensing provides a lucid and effective means for crop coverage identification. Crop coverage identification is a very important technique, as it provides vital information on the type and extent of crop cultivated in a particular area. This information has immense potential in the planning for further cultivation activities and for optimal usage of the available fertile land. As the frontiers of space technology advance, the knowledge derived from the satellite data has also grown in sophistication. Further, image classification forms the core of the solution to the crop coverage identification problem. No single classifier can prove to satisfactorily classify all the basic crop cover mapping problems of a cultivated region. We present in this paper the experimental results of multiple classification techniques for the problem of crop cover mapping of a cultivated region. A detailed comparison of the algorithms inspired by social behaviour of insects and conventional statistical method for crop classification is presented in this paper. These include the Maximum Likelihood Classifier (MLC), Particle Swarm Optimisation (PSO) and Ant Colony Optimisation (ACO) techniques. The high resolution satellite image has been used for the experiments.

Urban satellite image classification using biologically inspired techniques

This paper focuses on optimisation algorithms inspired by swarm intelligence for satellite image classification from high resolution satellite multi- spectral images. Amongst the multiple benefits and uses of remote sensing, one of the most important has been its use in solving the problem of land cover mapping. As the frontiers of space technology advance, the knowledge derived from the satellite data has also grown in sophistication. Image classification forms the core of the solution to the land cover mapping problem. No single classifier can prove to satisfactorily classify all the basic land cover classes of an urban region. In both supervised and unsupervised classification methods, the evolutionary algorithms are not exploited to their full potential. This work tackles the land map covering by Ant Colony Optimisation (ACO) and Particle Swarm Optimisation (PSO) which are arguably the most popular algorithms in this category. We present the results of classification techniques using swarm intelligence for the problem of land cover mapping for an urban region. The high resolution Quick-bird data has been used for the experiments.

Cu 2p core-level photoemission spectrum of Sr2CuO3

We measure the Cu 2p X-ray photoemission spectrum (XPS) of Sr2CuO3 and analyze it by means of exact diagonalization calculations for (CunO3n)(4n-) clusters. In Sr2CuO3, the intensity ratio of the 3d(y) satellite to the 3d(10)(L) under bar main line is 0.35-0.4, which is evidently smaller than that in the other high-T-c related cuprates. We ascribe it as the smaller charge-transfer energy between the Cu 3d and O 2p. The origin of the broad main-line of Sr2CuO3 is also discussed.

Electronic structure of early 3d-transition-metal oxides by analysis of the 2p core-level photoemission spectra

The electronic structures of a wide range of early transition-metal (TM) compounds, including Ti and V oxides with metal valences ranging from 2+ to 5+ and formal d-electron numbers ranging from 0 to 2, have been investigated by a configuration-interaction cluster model analysis of the core-level metal 2p x-ray photoemission spectra (XPS). Inelastic energy-loss backgrounds calculated from experimentally measured electron-energy-loss spectra (EELS) were subtracted from the XPS spectra to remove extrinsic loss features. Parameter values deduced for the charge-transfer energy Delta and the d-d Coulomb repulsion energy U are shown to continue the systematic trends established previously for the late TM compounds, giving support to a charge-transfer mechanism for the satellite structures. The early TM compounds are characterized by a large metal d-ligand p hybridization energy, resulting in strong covalency in these compounds. Values for Delta and U suggest that many early TM compounds should be reclassified as intermediate between the charge-transfer regime and the Mott-Hubbard regime.

Multi-spectral satellite image classification using Glowworm Swarm Optimization

This paper investigates a new Glowworm Swarm Optimization (GSO) clustering algorithm for hierarchical splitting and merging of automatic multi-spectral satellite image classification (land cover mapping problem). Amongst the multiple benefits and uses of remote sensing, one of the most important has been its use in solving the problem of land cover mapping. Image classification forms the core of the solution to the land cover mapping problem. No single classifier can prove to classify all the basic land cover classes of an urban region in a satisfactory manner. In unsupervised classification methods, the automatic generation of clusters to classify a huge database is not exploited to their full potential. The proposed methodology searches for the best possible number of clusters and its center using Glowworm Swarm Optimization (GSO). Using these clusters, we classify by merging based on parametric method (k-means technique). The performance of the proposed unsupervised classification technique is evaluated for Landsat 7 thematic mapper image. Results are evaluated in terms of the classification efficiency - individual, average and overall.