Self-assembly of indole-2-carboxylic acid at graphite and gold surfaces

Model systems are critical to our understanding of self-assembly processes. As such, we have studied the surface self-assembly of a small and simple molecule, indole-2-carboxylic acid (I2CA). We combine density functional theory gas-phase (DFT) calculations with scanning tunneling microscopy to reveal details of I2CA assembly in two different solvents at the solution/solid interface, and on Au(111) in ultrahigh vacuum (UHV). In UHV and at the trichlorobenzene/highly oriented pyrolytic graphite (HOPG) interface, I2CA forms epitaxial lamellar structures based on cyclic OH⋯O carboxylic dimers. The structure formed at the heptanoic acid/HOPG interface is different and can be interpreted in a model where heptanoic acid molecules co-adsorb on the substrate with the I2CA, forming a bicomponent commensurate unit cell. DFT calculations of dimer energetics elucidate the basic building blocks of these structures, whereas calculations of periodic two-dimensional assemblies reveal the epitaxial effects introduced by the different substrates.

Supramolecular assembly of heterocirculenes in 2D and 3D

The results of a high-resolution ambient STM study of ‘sulflower’ (octathio[8]circulene) and ‘selenosulflower’ (sym-tetraselena-tetrathio[8]circulene) molecules, immobilized in a hydrogen-bonded matrix of trimesic acid (TMA) at the solid–liquid interface, are compared with the STM and X-ray structure of separate host and guest 2D and 3D crystals, respectively.

Halogen bonds in 2D supramolecular self-assembly of organic semiconductors

Weak interactions between bromine, sulphur, and hydrogen are shown to stabilize 2D supramolecular monolayers at the liquid–solid interface. Three different thiophene-based semiconducting organic molecules assemble into close-packed ultrathin ordered layers. A combination of scanning tunneling microscopy (STM) and density functional theory (DFT) elucidates the interactions within the monolayer. Electrostatic interactions are identified as the driving force for intermolecular Br⋯Br and Br⋯H bonding. We find that the S⋯S interactions of the 2D supramolecular layers correlate with the hole mobilities of thin film transistors of the same materials.

Two-dimensional self-assembly of a symmetry-reduced tricarboxylic acid

Investigations of the self-assembly of simple molecules at the solution/solid interface can provide useful insight into the general principles governing supramolecular chemistry in two dimensions. Here, we report on the assembly of 3,4′,5-biphenyl tricarboxylic acid (H3BHTC), a small hydrogen bonding unit related to the much-studied 1,3,5-benzenetricarboxylic acid (trimesic acid, TMA), which we investigate using scanning tunneling microscopy (STM) and density functional theory (DFT) calculations. STM images show that H3BHTC assembles by itself into an offset zigzag chain structure that maximizes the surface molecular density in favor of maximizing the number density of strong cyclic hydrogen bonds between the carboxylic groups. The offset geometry creates “sticky” pores that promote solvent coadsorption. Adding coronene to the molecular solution produces a transformation to a high-symmetry host–guest lattice stabilized by a dimeric/trimeric hydrogen bonding motif similar to the TMA flower structure. Finally, we show that the H3BHTC lattice firmly immobilizes the guest coronene molecules, allowing for high-resolution imaging of the coronene structure.

Self-assembly of a halogenated molecule on oxide-passivated Cu(110)

The supramolecular self-assembly of brominated molecules was investigated and compared on Cu(110) and Cu(110)[BOND]O(2×1) surfaces under ultrahigh vacuum. By using scanning tunnelling microscopy, we show that brominated molecules form a disordered structure on Cu(110), whereas a well-ordered supramolecular network is observed on the Cu(110)[BOND]O(2×1) surface. The different adsorption behaviors of these two surfaces are described in terms of weakened molecule–substrate interactions on Cu(110)[BOND]O(2×1) as opposed to bare Cu(110). The effect of oxygen-passivation is to suppress debromination and it can be a convenient approach for investigating other self-assembly processes on copper-based substrates.

Molecular self-assembly on graphene

The formation of ordered arrays of molecules via self-assembly is a rapid, scalable route towards the realization of nanoscale architectures with tailored properties. In recent years, graphene has emerged as an appealing substrate for molecular self-assembly in two dimensions. Here, the first five years of progress in supramolecular organization on graphene are reviewed. The self-assembly process can vary depending on the type of graphene employed: epitaxial graphene, grown in situ on a metal surface, and non-epitaxial graphene, transferred onto an arbitrary substrate, can have different effects on the final structure. On epitaxial graphene, the process is sensitive to the interaction between the graphene and the substrate on which it is grown. In the case of graphene that strongly interacts with its substrate, such as graphene/Ru(0001), the inhomogeneous adsorption landscape of the graphene moiré superlattice provides a unique opportunity for guiding molecular organization, since molecules experience spatially constrained diffusion and adsorption. On weaker-interacting epitaxial graphene films, and on non-epitaxial graphene transferred onto a host substrate, self-assembly leads to films similar to those obtained on graphite surfaces. The efficacy of a graphene layer for facilitating planar adsorption of aromatic molecules has been repeatedly demonstrated, indicating that it can be used to direct molecular adsorption, and therefore carrier transport, in a certain orientation, and suggesting that the use of transferred graphene may allow for predictible molecular self-assembly on a wide range of surfaces.

Substrate, molecular structure, and solvent effects in 2D self-assembly via hydrogen and halogen bonding

Recently, halogen···halogen interactions have been demonstrated to stabilize two-dimensional supramolecular assemblies at the liquid–solid interface. Here we study the effect of changing the halogen, and report on the 2D supramolecular structures obtained by the adsorption of 2,4,6-tris(4-bromophenyl)-1,3,5-triazine (TBPT) and 2,4,6-tris(4-iodophenyl)-1,3,5-triazine (TIPT) on both highly oriented pyrolytic graphite and the (111) facet of a gold single crystal. These molecular systems were investigated by combining room-temperature scanning tunneling microscopy in ambient conditions with density functional theory, and are compared to results reported in the literature for the similar molecules 1,3,5-tri(4-bromophenyl)benzene (TBPB) and 1,3,5-tri(4-iodophenyl)benzene (TIPB). We find that the substrate exerts a much stronger effect than the nature of the halogen atoms in the molecular building blocks. Our results indicate that the triazine core, which renders TBPT and TIPT stiff and planar, leads to stronger adsorption energies and hence structures that are different from those found for TBPB and TIPB. On the reconstructed Au(111) surface we find that the TBPT network is sensitive to the fcc- and hcp-stacked regions, indicating a significant substrate effect. This makes TBPT the first molecule reported to form a continuous monolayer at room temperature in which molecular packing is altered on the differently reconstructed regions of the Au(111) surface. Solvent-dependent polymorphs with solvent coadsorption were observed for TBPT on HOPG. This is the first example of a multicomponent self-assembled molecular networks involving the rare cyclic, hydrogen-bonded hexamer of carboxylic groups, R66(24) synthon.

Comparison of decision tree, support vector machines, and Bayesian network approaches for classification of falls in Parkinson’s disease

Being able to accurately predict the risk of falling is crucial in patients with Parkinson’s dis- ease (PD). This is due to the unfavorable effect of falls, which can lower the quality of life as well as directly impact on survival. Three methods considered for predicting falls are decision trees (DT), Bayesian networks (BN), and support vector machines (SVM). Data on a 1-year prospective study conducted at IHBI, Australia, for 51 people with PD are used. Data processing are conducted using rpart and e1071 packages in R for DT and SVM, con- secutively; and Bayes Server 5.5 for the BN. The results show that BN and SVM produce consistently higher accuracy over the 12 months evaluation time points (average sensitivity and specificity > 92%) than DT (average sensitivity 88%, average specificity 72%). DT is prone to imbalanced data so needs to adjust for the misclassification cost. However, DT provides a straightforward, interpretable result and thus is appealing for helping to identify important items related to falls and to generate fallers’ profiles.

Knowledge discovery from tree databases using balanced optimal search

This research is a step forward in discovering knowledge from databases of complex structure like tree or graph. Several data mining algorithms are developed based on a novel representation called Balanced Optimal Search for extracting implicit, unknown and potentially useful information like patterns, similarities and various relationships from tree data, which are also proved to be advantageous in analysing big data. This thesis focuses on analysing unordered tree data, which is robust to data inconsistency, irregularity and swift information changes, hence, in the era of big data it becomes a popular and widely used data model.

A transcriptomic analysis of the kidney tissue of Tra catfish (pangasianodon hypophthalmus) reared in saline condition: De novo assembly, annotation, SNP discovery

Pangasianodon hypophthalmus is a commercially important freshwater fish used in inland aquaculture in the Mekong Delta, Vietnam. The current study using Ion Torrent technology generated EST resources from the kidney for Tra catfish reared at a salinity level of 9 ppt. We obtained 2,623,929 reads after trimming and processing with an average length of 104 bp. De novo assemblies were generated using CLC Genomic Workbench, Trinity and Velvet/Oases with the best overall contig performance resulting from the CLC assembly. De novo assembly using CLC yielded 29,940 contigs, and allowing identification of 5,710 putative genes when comppared with NCBI non-redundant database. A large number of single nucleotide polymorphisms (SNPs) were also detected. The sequence collection generated in our study represents the most comprehensive transcriptomic resource for P. hypophthalmus available to date.

Activity recognition using binary tree SVM

This paper presents an effective classification method based on Support Vector Machines (SVM) in the context of activity recognition. Local features that capture both spatial and temporal information in activity videos have made significant progress recently. Efficient and effective features, feature representation and classification plays a crucial role in activity recognition. For classification, SVMs are popularly used because of their simplicity and efficiency; however the common multi-class SVM approaches applied suffer from limitations including having easily confused classes and been computationally inefficient. We propose using a binary tree SVM to address the shortcomings of multi-class SVMs in activity recognition. We proposed constructing a binary tree using Gaussian Mixture Models (GMM), where activities are repeatedly allocated to subnodes until every new created node contains only one activity. Then, for each internal node a separate SVM is learned to classify activities, which significantly reduces the training time and increases the speed of testing compared to popular the `one-against-the-rest' multi-class SVM classifier. Experiments carried out on the challenging and complex Hollywood dataset demonstrates comparable performance over the baseline bag-of-features method.

Unravelling the self-assembly of hydrogen bonded NDI semiconductors in 2D and 3D

Supramolecular ordering of organic semiconductors is the key factor defining their electrical characteristics. Yet, it is extremely difficult to control, particularly at the interface with metal and dielectric surfaces in semiconducting devices. We have explored the growth of n-type semiconducting films based on hydrogen-bonded monoalkylnaphthalenediimide (NDI-R) from solution and through vapor deposition on both conductive and insulating surfaces. We combined scanning tunneling and atomic force microscopies with X-ray diffraction analysis to characterize, at the submolecular level, the evolution of the NDI-R molecular packing in going from monolayers to thin films. On a conducting (graphite) surface, the first monolayer of NDI-R molecules adsorbs in a flat-lying (face-on) geometry, whereas in subsequent layers the molecules pack edge-on in islands (Stranski–Krastanov-like growth). On SiO2, the NDI-R molecules form into islands comprising edge-on packed molecules (Volmer–Weber mode). Under all the explored conditions, self-complementary H bonding of the imide groups dictates the molecular assembly. The measured electron mobility of the resulting films is similar to that of dialkylated NDI molecules without H bonding. The work emphasizes the importance of H bonding interactions for controlling the ordering of organic semiconductors, and demonstrates a connection between on-surface self-assembly and the structural parameters of thin films used in electronic devices.

Two-step self-assembly of hierarchically-ordered nanostructures

Due to their unique size- and shape-dependent physical and chemical properties, highly hierarchically-ordered nanostructures have attracted great attention with a view to application in emerging technologies, such as novel energy generation, harvesting, and storage devices. The question of how to get controllable ensembles of nanostructures, however, still remains a challenge. This concept paper first summarizes and clarifies the concept of the two-step self-assembly approach for the synthesis of hierarchically-ordered nanostructures with complex morphology. Based on the preparation processes, two-step self-assembly can be classified into two typical types, namely, two-step self-assembly with two discontinuous processes and two-step self-assembly completed in one-pot solutions with two continuous processes. Compared to the conventional one-step self-assembly, the two-step self-assembly approach allows the combination of multiple synthetic techniques and the realization of complex nanostructures with hierarchically-ordered multiscale structures. Moreover, this approach also allows the self-assembly of heterostructures or hybrid nanomaterials in a cost-effective way. It is expected that widespread application of two-step self-assembly will give us a new way to fabricate multifunctional nanostructures with deliberately designed architectures. The concept of two-step self-assembly can also be extended to syntheses including more than two chemical/physical reaction steps (multiple-step self-assembly).

Generalized self-assembly of scalable two-dimensional transition metal oxide nanosheets

Two-dimensional (2D) transition metal oxide systems present exotic electronic properties and high specific surface areas, and also demonstrate promising applications ranging from electronics to energy storage. Yet, in contrast to other types of nanostructures, the question as to whether we could assemble 2D nanomaterials with an atomic thickness from molecules in a general way, which may give them some interesting properties such as those of graphene, still remains unresolved. Herein, we report a generalized and fundamental approach to molecular self-assembly synthesis of ultrathin 2D nanosheets of transition metal oxides by rationally employing lamellar reverse micelles. It is worth emphasizing that the synthesized crystallized ultrathin transition metal oxide nanosheets possess confined thickness, high specific surface area and chemically reactive facets, so that they could have promising applications in nanostructured electronics, photonics, sensors, and energy conversion and storage devices.