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(a),(b) Examples of spinodal decomposition patterns resulting from spin-coating of C 5 thiol-passivated 2 nm diame-
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Au nanocrystals spin-coated onto silicon from toluene form cellular networks. A quantitative statistical crystallography analysis shows that intercellular correlations drive the networks far from statistical equilibrium. Spin-coating from hexane does not produce cellular structure, yet a strong correlation is retained in the positions of nanocrysta...
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... [due primarily to vapor pres- sure differences: P vp hexane 4P vp toluene at 300 K]. Thus, the probability of establishing a supercritical Marangoni number is much greater for hexane. Despite this, cellular networks do not form. Instead, for conden- sation from hexane structures ranging from isolated ''droplets'' to labyrinthine patterns [Figs. 4(a) and 4(b)] are observed. These patterns bear a striking qualitative similarity to those produced via spinodal phase separa- tion ...
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... correlated aggregates [18,19]. Thiele et al. [20] have used pair correlation func- tions to identify spatially correlated centers arising from spinodal dewetting. An alternative approach is to con- struct a Voronoi tessellation based on the mass centers of the aggregates and calculate its entropy. A Voronoi tessel- lation (not shown) for Fig. 4(a) yields S 1:39, again significantly lower than that expected for a Poisson dis- tribution of points [but remarkably similar to the value obtained for Fig. 1(c)]. Hence, it appears that, for con- densation from both hexane and toluene, nanocrystal overlayers comprise strongly spatially correlated aggre- gates. The presence of a ...
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... the nanoparticle arrangements we observe therefore most likely arise via spinodal decomposition, there is a key difference with the spinodal mechanism postulated by Ge and Brus [19]. This is illustrated in Fig. 4(c), an image taken under a toluene-nanocrystal solution where it is clear that nanocrystal islands have nucleated. Figure 4(c) (and work by Lin et al. [23]) illustrates that for Au nanocrystals both spinodal and conventional nucleation pathways may contribute in es- tablishing an overlayer morphology. Furthermore, Tanaka [18] has carried ...
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... is illustrated in Fig. 4(c), an image taken under a toluene-nanocrystal solution where it is clear that nanocrystal islands have nucleated. Figure 4(c) (and work by Lin et al. [23]) illustrates that for Au nanocrystals both spinodal and conventional nucleation pathways may contribute in es- tablishing an overlayer morphology. Furthermore, Tanaka [18] has carried out a detailed theoretical study of visco- elastic phase separation in binary systems. ...
Citations
... The Voronoi tessellation may be used to determine the statistical entropy of a cellular network; a quantity that defines the level of order or disorder within a network, and highlights any faults or deviations within the structure [103]. ...
Self-assembly of molecular building blocks to produce extended two-dimensional arrangements is a first step on the journey towards the design and construction of molecular systems with a predictable structure and functionality. By employing scanning probe techniques such as scanning tunneling microscopy (STM) and scanning tunneling spectroscopy (STS), an understanding of these self-assembled structures on the single-molecule level can be obtained. When combined with other techniques such as x-ray photoelectron spectrocopy (XPS) and x-ray standing wave analysis (NIXSW), the adsorption geometries and reaction pathways can be extracted to give a completed picture of each system. Within this thesis the self-assembly properties of porphyrin molecules are investigated using scanning probe and photoelectron techniques. Porphyrin molecules have been selected due to their ability to participate in chemical reactions and thier excellent thermal stability. The topography of tetraphenyl porphyrin (TPP) on Au(111) is studied, the local structure and ordering is determined using STM and the average surface arrangement is determined using LEED. The unit cell of the TPP is calculated as well as the commensurability of the lattice with the surface atoms. In addition to an as-deposited close-packed phase, two other visually different phases of TPP on Au(111) are presented and characterised. The arrangement and ordering of the TPP molecules in each phase is quantified using the Voronoi tessellation. The details of the electronic properties of TPP is investigated using STS and kelvin probe force microscopy (KPFM). Using these techniques three electronically distinct species are defined, TPP in the saddle conformation, TPP adsorbing atop a gold atom, and a metalated TPP molecule. Information on the chemical properties of these porphyrin phases is presented, achieved using XPS, by which an additional phase of TPP is observed and further insight into the chemical composition of each phase is achieved. From the XPS fitting a full structural characterisation of each phase is performed using NIXSW. From this the adsorption sites of each chemical species are defined and the heights above the surface extracted. In this thesis the experimental details for all three phases of TPP are presented. The use of a combination of methods allows for a complete picture of system to be built and for access to details that would be otherwise inaccessible. This particular combination of techniques, when used together, can give mechanistic details about the proceedings of chemicals reactions, which has previously been unattainable.
... Over time, this produces one of a number of distinct, spatially correlated equilibrium or nonequilibrium structures. These structures have previously been variously classified as 12,18 "labyrinthine"/"fingerlike" when the nanoparticle growth is worm-like/branching, "cellular" if the nanoparticles fully enclose pockets of substrate, or conversely "islands" when the nanoparticles cluster together into isolated areas. If the solvent has not yet fully evaporated, "holes"/ "pores" of substrate can form on a largely "liquid" surface. ...
Currently, researchers spend significant time manually searching through large volumes of data produced during scanning probe imaging to identify specific patterns and motifs formed via self-assembly and self-organisation. Here, we use a combination of Monte Carlo simulations, general statistics and machine learning to automatically distinguish several spatially-correlated patterns in a mixed, highly varied dataset of real AFM images of self-organised nanoparticles. We do this regardless of feature-scale and without the need for manually labelled training data. Provided that the structures of interest can be simulated, the strategy and protocols we describe can be easily adapted to other self-organised systems and datasets.
... self-assembly is a process wherein the interaction between nanoparticles results in final structure. As a result of evaporation induced dewetting, various structures such as isolated islands, labyrinthine structures, fractal structures, circular rings etc. have also been observed[100][101][102][103][104][105][106][107][108][109][110] . Thus, it becomes imperative to understand the differences in the self-assembly of dodecanethiol capped gold nanoparticles with and without addition of excess surfactant. ...
Monodisperse Gold Nanoparticles: Synthesis, Self-assembly and Fabrication of Floating Gate Memory Devices-Thesis 2013
... Many natural systems lead to tessellations of space into cellular structures with varying degree of order, as in soap froths, polycrystalline materials, biological tissues, fluid convection, galaxy clusters, or graphene-based networks [1][2][3][4]. In these patterns, sharp boundaries separate homogeneous regions with sizes and other statistics which can be characterized, including topological relations between domains, down to near 100 nm [5]. ...
... As seen in Fig. 2, Lewis' law fits accurately the data for 4 ≤ n ≤ 10. In other systems [5], systematic deviations for all n indicate that physical forces must be considered, besides topological constraints. In our case, the deviations for short and large n are statistical. ...
We report the experimental observation of a submicron cellular structure on the surface of silicon targets eroded by an ion plasma. Analysis by atomic force microscopy allows us to assess the time evolution and show that the system can be described quantitatively by the convective Cahn-Hilliard equation, found in the study of domain coarsening for a large class of driven systems. The space-filling trait of the ensuing pattern relates it to evolving foams. Through this connection, we are actually able to derive the coarsening law for the pattern wavelength from the nontrivial topological dynamics of the cellular structure. Thus, the study of the topological properties of patterns in nonvariational spatially extended systems emerges as complementary to morphological approaches to their challenging coarsening properties.
... Examples of passive set-ups include (i) the "meniscus technique" where a meniscus with a contact line is created in a geometric confinement, e.g., in a sphere-on-flat [41,45,47] or ring-on-flat [26,81] geometry, between two parallel plates [39], or in the wedge between two plates or crossed cylinders [63]; (ii) the deposition of a single drop onto a substrate where it evaporates freely [30,33,80]; and (iii) the deposition of flat films onto a substrate using spin-coating [82][83][84]. ...
When a simple or complex liquid recedes from a smooth solid substrate it often leaves a homogeneous or structured deposit behind. In the case of a receding non-volatile pure liquid the deposit might be a liquid film or an arrangement of droplets depending on the receding speed of the meniscus and the wetting properties of the system. For complex liquids with volatile components as, e.g., polymer solutions and particle or surfactant suspensions, the deposit might be a homogeneous or structured layer of solute - with structures ranging from line patterns that can be orthogonal or parallel to the receding contact line via hexagonal or square arrangements of drops to complicated hierarchical structures. We review a number of recent experiments and modelling approaches with a particular focus on mesoscopic hydrodynamic long-wave models. The conclusion highlights open question and speculates about future developments.
... Functional soft materials composed of many nanoparticles often appear in complex structure both as two-dimensional films [1, 2] and as three-dimensional super-crystal structures [3]–[5]. These structures emerge in various processes of self-assembly, most of which utilize liquid degrees of freedom and non-equilibrium methods [6, 7]. ...
... In comparison to classical conduction, single-electron conduction processes have numerous advantages, e.g., the absence of dissipation due to heating, and the non-linear current– voltage characteristics, which are important in nanoelectronics. Experimentally, a large non-linearity of the current–voltage curve was found for self-assembled nanoparticle films on substrates, which appears to be fairly well correlated with the film structure [1, 18, 19]. The random structure of nanoparticle films on substrates can be adequately modeled by planar graphs (nanonetworks) [18, 21]. ...
... Different assembly processes make use of the liquid phase, that provides the nanoparticle diffusion and affects the interaction. The assembly with fast evaporation of the liquid [1, 9] induces additional constraints to the particle motion and interaction. The interaction itself between pairs of the nanoparticles moving in a liquid is taking place at short distances and depends on the type of functionalization of their surfaces [24], as mentioned above. ...
Using numerical modeling we study the emergence of structure and structure-related non-linear conduction properties in self-assembled nanoparticle films. In particular, we show how different nanoparticle networks emerge within assembly processes with molecular bio-recognition binding. We then simulate the charge transport under voltage bias via single-electron tunnelings through the junctions between nanoparticles on such networks. We show how the regular nanoparticle array and topologically inhomogeneous nanonetworks affect the charge transport. We find long-range correlations in the time series of charge fluctuation at individual nanoparticles and of flow along the junctions within the network. These correlations explain the occurrence of a large non-linearity in the simulated and experimentally measured current–voltage characteristics and non-Gaussian fluctuations of the current at the electrode.
... However, experiments increasingly focus on complex situations like the evolution of multilayer films of partially miscible 21 or immiscible 4 liquids, complex fluids like polymer blends that might undergo dewetting or/and decomposition 21,22,23 or solutions of polymers, nanoparticles, colloids or polymer blends with interacting convective motion, phase separation, evaporation/condensation and evolving rheology. 24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39 Theoretical descriptions exist, however, only for a small part of the experimentally known complex scenario and phenomena involving free surface thin films. Recent advances include a fully nonlinear thin film description in long wave approximation for two layers of immiscible liquids under air 40,41,42,43,44 and between two plates, 45 the analysis of the dewetting behaviour on chemically or topographically heterogeneous substrates, 46,47,48,49 the study of the dynamics of depinning of a driven drop on a heterogeneous substrate, 50 the description of films with surface active nanoparticles, 51 the inclusion of evaporation/condensation in the thin film description. ...
A dynamical model is proposed to describe the coupled decomposition and profile evolution of a free surface film of a binary mixture. An example is a thin film of a polymer blend on a solid substrate undergoing simultaneous phase separation and dewetting. The model is based on model-H describing the coupled transport of the mass of one component (convective Cahn-Hilliard equation) and momentum (Navier-Stokes-Korteweg equations) supplemented by appropriate boundary conditions at the solid substrate and the free surface. General transport equations are derived using phenomenological non-equilibrium thermodynamics for a general non-isothermal setting taking into account Soret and Dufour effects and interfacial viscosity for the internal diffuse interface between the two components. Focusing on an isothermal setting the resulting model is compared to literature results and its base states corresponding to homogeneous or vertically stratified flat layers are analysed. Comment: Submitted to Physics of Fluids
... So, triangular and polyhedral nanoparticles were obtained in the presence of leaf extracts [9]. A large spectrum of new metal nanoparticles and of nanoparticles with special molecular shells was developed by using reaction mixtures containing thioles and surfactants [10] [11]. ...
Regular dendrit-like metal nanoparticles and core-shell nanoparticles were formed by the reduction of mixtures of tetrachloroaurate and silver nitrate solutions with ascorbic acid at room temperature in two- and three-step procedures. The formation of these particles was found in batch experiments as well as in micro flow-through processes using static micromixers. The characteristic diameters of 4-branched star particles were in the range between 60 and 100 nm. The typical particles consist of four metal cores which are embedded in a common shell. Additionally, particles with five and more metallic cores were formed, to some extent, and aggregates of the 4-branched particles also were formed. Larger aggregates and network-like structures of connected star particles were formed after sedimentation. The properties of the formed particles are dependent on the educt concentrations as well as on the order of mixing steps and on the time interval between them. Obviously, the relation of nucleation and particle growth in relation to the concentrations of metal ions determines the composition and the properties of formed nanoparticles. So, star-like particles are observed in case of nucleation of Au in absence of silver ions but with silver deposition after short nucleation time. Spherical core shell particles are formed in case of silver salt addition after complete reduction of tetrachloroaurate in flow-through experiments with sufficient residence time between both mixing steps. Polymer layers are always found in the form of a second outer shell even if the polymer solutions are added in an early stage of particle formation.
... Emergent structures of cellular networks are resembling of soap froths [7] or patterns of nano-particles self-assembled through nonlinear dynamic processes [8,9]. Typically, a pattern of cells appears when nano-particles are immersed in a liquid film, which is then allowed to evaporate until holes of different sizes open-up leaving particles in the walls between the holes [9,10]. ...
... Emergent structures of cellular networks are resembling of soap froths [7] or patterns of nano-particles self-assembled through nonlinear dynamic processes [8,9]. Typically, a pattern of cells appears when nano-particles are immersed in a liquid film, which is then allowed to evaporate until holes of different sizes open-up leaving particles in the walls between the holes [9,10]. Generally, the structure of the patterns effects the physical processes on them, such as current transport [11]. ...
... which is most often found in experiments [9]. Using the condition (3) the number of independent parameters in (4) is reduced ...
We present new algorithm for growth of non-clustered planar graphs by aggregation of cells with given distribution of size and constraint of connectivity k=3 per node. The emergent graph structures are controlled by two parameters--chemical potential of the cell aggregation and the width of the cell size distribution. We compute several statistical properties of these graphs--fractal dimension of the perimeter, distribution of shortest paths between pairs of nodes and topological betweenness of nodes and links. We show how these topological properties depend on the control parameters of the aggregation process and discuss their relevance for the conduction of current in self-assembled nanopatterns.
Although scanning probe microscopy (SPM) techniques have allowed researchers to interact with the nanoscale for decades now, little improvement has been made to the incredibly manual, time consuming process of setting up, running, and analysing the results of these experiments, often arising due to the constantly varying shape of the probe apex. Unlike traditional computing methods, machine learning methods (with neural networks in particular) are considerably more capable of automating subjective tasks such as these, and we are only just beginning to explore the potential applications of this technology in SPM. In this thesis we explore a number of areas where machine learning could potentially massively change the way we go about SPM experimentation. We begin by discussing the history, theory, and experimental concepts of scanning tunnelling microscopy (STM), atomic force microscopy (AFM), and normal-incidence-x-ray standing wave (NIXSW). We then explore the makeup of a neural network and demonstrate how they can be applied to a variety of use-cases in SPM, including classification and policy prediction. Moving to the experimental chapters, we first discuss how we can successfully distinguish between STM tip states of the H:Si(100), Au(111) and Cu(111) surfaces. We also show that by adapting this network to work in real time, we improve performance while requiring on the order of 100x less data. We next discuss our attempts to combine these networks with expert examples to intelligently maintain tip apex sharpness during experimentation, envisioning an end-to-end automatic experiment. Because one of the main difficulties in applying machine learning is the frequent need to manually label data, we then show how we can use Monte Carlo simulations of self-organised AFM nanostructures to automatically label training data for a network, and then combine it with classical statistics and preprocessing to find specific structures in a mixed, messy dataset of real, experimental AFM images. As part of this, we also build a network to denoise experimental images. Finally, we present NIXSW results from an investigation into the temperature dependence of H20@C60, discussing the potential to use unsupervised clustering techniques to distinguish between noisy human-indistinguishable spectra to overcome limitations in data collection.
