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.

Rational design of 3D dendritic TiO2 nanostructures with favorable architectures

Controlling the morphology and size of titanium dioxide (TiO2) nanostructures is crucial to obtain superior photocatalytic, photovoltaic, and electrochemical properties. However, the synthetic techniques for preparing such structures, especially those with complex configurations, still remain a challenge because of the rapid hydrolysis of Ti-containing polymer precursors in aqueous solution. Herein, we report a completely novel approach-three- dimensional (3D) TiO2 nanostructures with favorable dendritic architectures-through a simple hydrothermal synthesis. The size of the 3D TiO2 dendrites and the morphology of the constituent nano-units, in the form of nanorods, nanoribbons, and nanowires, are controlled by adjusting the precursor hydrolysis rate and the surfactant aggregation. These novel configurations of TiO2 nanostructures possess higher surface area and superior electrochemical properties compared to nanoparticles with smooth surfaces. Our findings provide an effective solution for the synthesis of complex TiO2 nano-architectures, which can pave the way to further improve the energy storage and energy conversion efficiency of TiO 2-based devices.

Some surface profiles of a strip after plane-strain indentation by rigid bodies with serrated surfaces

This paper is concerned with the surface profiles of a strip after rigid bodies with serrated (saw-teeth) surfaces indent the strip and are subsequently removed. Plane-strain conditions are assumed. This has application in roughness transfer of final metal forming process. The effects of the semi-angle of the teeth, the depth of indentation and the friction on the contact surface on the profile are considered.

Tracking people in 3D using position, size and shape

This paper presents a prototype tracking system for tracking people in enclosed indoor environments where there is a high rate of occlusions. The system uses a stereo camera for acquisition, and is capable of disambiguating occlusions using a combination of depth map analysis, a two step ellipse fitting people detection process, the use of motion models and Kalman filters and a novel fit metric, based on computationally simple object statistics. Testing shows that our fit metric outperforms commonly used position based metrics and histogram based metrics, resulting in more accurate tracking of people.

2D-3D Face Recognition Based on PCA and Feature Modelling

Hybrid face recognition, using image (2D) and structural (3D) information, has explored the fusion of Nearest Neighbour classifiers. This paper examines the effectiveness of feature modelling for each individual modality, 2D and 3D. Furthermore, it is demonstrated that the fusion of feature modelling techniques for the 2D and 3D modalities yields performance improvements over the individual classifiers. By fusing the feature modelling classifiers for each modality with equal weights the average Equal Error Rate improves from 12.60% for the 2D classifier and 12.10% for the 3D classifier to 7.38% for the Hybrid 2D+3D clasiffier.