Controlled-bandgap silicon nitride nanomaterials : deterministic nitrogenation in high-density plasmas

To overcome major problems associated with insufficient incorporation of nitrogen in hydrogenated amorphous silicon nitride (a-SiNx:H) nanomaterials, which in turn impedes the development of controlled-bandgap nanodevices, here we demonstrate the possibility to achieve effective bandgap control in a broad range by using high-density inductively coupled plasmas. This achievement is related to the outstanding dissociation ability of such plasmas. It is shown that the compositional, structural, optical, and morphological properties of the synthesized a-SiNx:H nanomaterials can be effectively tailored through the manipulation of the flow rate ratio of the silane to nitrogen gases X. In particular, a wide bandgap of 5.21 eV can be uniquely achieved at a low flow rate ratio of the nitrogen to silane gas of 1.0, whereas typically used values often exceed 20.0. These results are highly-relevant to the development of the next-generation nanodevices that rely on the effective control of the functional nano-layer bandgap energies.

Plasma-enabled growth of ultralong straight, helical, and branched silica photonic nanowires

This article reports on the lowerature inductively coupled plasma-enabled synthesis of ultralong (up to several millimeters in length) SiO2 nanowires, which were otherwise impossible to synthesize without the presence of a plasma. Depending on the process conditions, the nanowires feature straight, helical, or branched morphologies. The nanowires are amorphous, with a near-stoichiometric elemental composition ([O] / [Si] =2.09) and are very uniform throughout their length. The role of the ionized gas environment is discussed and the growth mechanism is proposed. These nanowires are particularly promising for nanophotonic applications where long-distance and channelled light transmission and polarization control are required.

Biaxial fiber Bragg grating accelerometer using axial and transverse forces

We demonstrate the first biaxial fiber Bragg grating (FBG) accelerometer using axial and transverse forces. An inertial object is fixed at the middle of two FBGs inscribed in one fiber. The difference between the resonant wavelengths of the two FBGs can distinguish the acceleration in the axial direction, while being insensitive in the transverse direction. The average of the resonant wavelengths of the two FBGs can distinguish the acceleration in the transverse direction, while being insensitive in the axial direction. In the experiments, when the transverse direction was vertical, the crest-to-trough sensitivity at 5 Hz and resonant frequency of the average were 0.545 nm/g and 34.42 Hz, respectively. When the axial direction was vertical, those of the difference were 0.0454 nm/g and 900 Hz, respectively. For each FBG, the crest-to-trough sensitivity at 5 Hz and resonant frequency in the transverse/vertical direction were 24 and 1/26 times those in the axial/vertical direction, respectively.

Improvement of the mode II interface fracture toughness of glass fiber reinforced plastics/aluminum laminates through vapor grown carbon fiber interleaves

The effects of acid treatment, vapor grown carbon fiber (VGCF) interlayer and the angle, i.e., 0° and 90°, between the rolling stripes of an aluminum (Al) plate and the fiber direction of glass fiber reinforced plastics (GFRP) on the mode II interlaminar mechanical properties of GFRP/Al laminates were investigated. The experimental results of an end notched flexure test demonstrate that the acid treatment and the proper addition of VGCF can effectively improve the critical load and mode II fracture toughness of GFRP/Al laminates. The specimens with acid treatment and 10 g m−2 VGCF addition possess the highest mode II fracture toughness, i.e., 269% and 385% increases in the 0° and 90° specimens, respectively compared to those corresponding pristine ones. Due to the induced anisotropy by the rolling stripes on the aluminum plate, the 90° specimens possess 15.3%–73.6% higher mode II fracture toughness compared to the 0° specimens. The improvement mechanisms were explored by the observation of crack propagation path and fracture surface with optical, laser scanning and scanning electron microscopies. Moreover, finite element analyses were carried out based on the cohesive zone model to verify the experimental fracture toughness and to predict the interface shear strength between the aluminum plates and GFRP laminates.

Solution processable low bandgap diketopyrrolopyrrole (DPP) based derivatives : novel acceptors for organic solar cells

Novel low bandgap solution processable diketopyrrolopyrrole (DPP) based derivatives functionalized with electron withdrawing end capping groups (trifluoromethylphenyl and trifluorophenyl) were synthesized, and their photophysical, electrochemical and photovoltaic properties were investigated. These compounds showed optical bandgaps ranging from 1.81 to 1.94 eV and intense absorption bands that cover a wide range from 300 to 700 nm, attributed to charge transfer transition between electron rich phenylene-thienylene moieties and the electron withdrawing diketopyrrolopyrrole core. All of the compounds were found to be fluorescent in solution with an emission wavelength ranging from 600 to 800 nm. Cyclic voltammetry indicated reversible oxidation and reduction processes with tuning of HOMO-LUMO energy levels. Bulk heterojunction (BHJ) solar cells using poly(3-hexylthiophene) (P3HT) as the electron donor with these new acceptors were used for fabrication. The best power conversion efficiencies (PCE) using 1:2 donor-acceptor by weight mixture were 1% under simulated AM 1.5 solar irradiation of 100 mW cm-2. These findings suggested that a DPP core functionalized with electron accepting end-capping groups were a promising new class of solution processable low bandgap n-type organic semiconductors for organic solar cell applications.

A low-bandgap diketopyrrolopyrrole-benzothiadiazole-based copolymer for high-mobility ambipolar organic thin-film transistors

A new, solution-processable, low-bandgap, diketopyrrolopyrrole- benzothiadiazole-based, donor-acceptor polymer semiconductor (PDPP-TBT) is reported. This polymer exhibits ambipolar charge transport when used as a single component active semiconductor in OTFTs with balanced hole and electron mobilities of 0.35 cm2 V-1s-1 and 0.40 cm 2 V-1s-1, respectively. This polymer has the potential for ambipolar transistor-based complementary circuits in printed electronics.

A novel method of Fiber Bragg Grating (FBG) strain modulation and its application in FBG accelerometers

This thesis has systemically investigated the possibility of improving one type of optical fiber sensors by using a novel mechanism. Many parameters of the sensor have been improved, and one outcome of this innovation is that civil structures, such as bridges and high-rise buildings, may be operated more safely and used longer.

Interlaminar mechanical properties of carbon fiber reinforced plastic laminates modified with graphene oxide interleaf

By taking the advantage of the excellent mechanical properties and high specific surface area of graphene oxide (GO) sheets, we develop a simple and effective strategy to improve the interlaminar mechanical properties of carbon fiber reinforced plastic (CFRP) laminates. With the incorporation of graphene oxide reinforced epoxy interleaf into the interface of CFRP laminates, the Mode-I fracture toughness and resistance were greatly increased. The experimental results of double cantilever beam (DCB) tests demonstrated that, with 2 g/m2 addition of GO, the Mode-I fracture toughness and resistance of the specimen increase by 170.8% and 108.0%, respectively, compared to those of the plain specimen. The improvement mechanisms were investigated by the observation of fracture surface with scanning electron microscopies. Moreover, finite element analyses were performed based on the cohesive zone model to verify the experimental fracture toughness and to predict the interfacial tensile strength of CFRP laminates.

Extending genetic linkage analysis to diffusion tensor images to map single gene effects on brain fiber architecture

We extended genetic linkage analysis - an analysis widely used in quantitative genetics - to 3D images to analyze single gene effects on brain fiber architecture. We collected 4 Tesla diffusion tensor images (DTI) and genotype data from 258 healthy adult twins and their non-twin siblings. After high-dimensional fluid registration, at each voxel we estimated the genetic linkage between the single nucleotide polymorphism (SNP), Val66Met (dbSNP number rs6265), of the BDNF gene (brain-derived neurotrophic factor) with fractional anisotropy (FA) derived from each subject's DTI scan, by fitting structural equation models (SEM) from quantitative genetics. We also examined how image filtering affects the effect sizes for genetic linkage by examining how the overall significance of voxelwise effects varied with respect to full width at half maximum (FWHM) of the Gaussian smoothing applied to the FA images. Raw FA maps with no smoothing yielded the greatest sensitivity to detect gene effects, when corrected for multiple comparisons using the false discovery rate (FDR) procedure. The BDNF polymorphism significantly contributed to the variation in FA in the posterior cingulate gyrus, where it accounted for around 90-95% of the total variance in FA. Our study generated the first maps to visualize the effect of the BDNF gene on brain fiber integrity, suggesting that common genetic variants may strongly determine white matter integrity.

Brain fiber architecture, genetics, and intelligence: a high angular resolution diffusion imaging (HARDI) study

We developed an analysis pipeline enabling population studies of HARDI data, and applied it to map genetic influences on fiber architecture in 90 twin subjects. We applied tensor-driven 3D fluid registration to HARDI, resampling the spherical fiber orientation distribution functions (ODFs) in appropriate Riemannian manifolds, after ODF regularization and sharpening. Fitting structural equation models (SEM) from quantitative genetics, we evaluated genetic influences on the Jensen-Shannon divergence (JSD), a novel measure of fiber spatial coherence, and on the generalized fiber anisotropy (GFA) a measure of fiber integrity. With random-effects regression, we mapped regions where diffusion profiles were highly correlated with subjects' intelligence quotient (IQ). Fiber complexity was predominantly under genetic control, and higher in more highly anisotropic regions; the proportion of genetic versus environmental control varied spatially. Our methods show promise for discovering genes affecting fiber connectivity in the brain.

Mapping genetic influences on brain fiber architecture with high angular resolution diffusion imaging (HARDI)

We report the first 3D maps of genetic effects on brain fiber complexity. We analyzed HARDI brain imaging data from 90 young adult twins using an information-theoretic measure, the Jensen-Shannon divergence (JSD), to gauge the regional complexity of the white matter fiber orientation distribution functions (ODF). HARDI data were fluidly registered using Karcher means and ODF square-roots for interpol ation; each subject's JSD map was computed from the spatial coherence of the ODFs in each voxel's neighborhood. We evaluated the genetic influences on generalized fiber anisotropy (GFA) and complexity (JSD) using structural equation models (SEM). At each voxel, genetic and environmental components of data variation were estimated, and their goodness of fit tested by permutation. Color-coded maps revealed that the optimal models varied for different brain regions. Fiber complexity was predominantly under genetic control, and was higher in more highly anisotropic regions. These methods show promise for discovering factors affecting fiber connectivity in the brain.

Genetics of brain fiber architecture and intellectual performance

The study is the first to analyze genetic and environmental factors that affect brain fiber architecture and its genetic linkage with cognitive function. We assessed white matter integrity voxelwise using diffusion tensor imaging at high magnetic field (4 Tesla), in 92 identical and fraternal twins. White matter integrity, quantified using fractional anisotropy (FA), was used to fit structural equation models (SEM) at each point in the brain, generating three-dimensional maps of heritability. We visualized the anatomical profile of correlations between white matter integrity and full-scale, verbal, and performance intelligence quotients (FIQ, VIQ, and PIQ). White matter integrity (FA) was under strong genetic control and was highly heritable in bilateral frontal (a 2 = 0.55, p = 0.04, left; a 2 = 0.74, p = 0.006, right), bilateral parietal (a 2 = 0.85, p < 0.001, left; a 2 = 0.84, p < 0.001, right), and left occipital (a 2 = 0.76, p = 0.003) lobes, and was correlated with FIQ and PIQ in the cingulum, optic radiations, superior fronto- occipital fasciculus, internal capsule, callosal isthmus, and the corona radiata (p = 0.04 for FIQ and p = 0.01 for PIQ, corrected for multiple comparisons). In a cross-trait mapping approach, common genetic factors mediated the correlation between IQ and white matter integrity, suggesting a common physiological mechanism for both, and common genetic determination. These genetic brain maps reveal heritable aspects of white matter integrity and should expedite the discovery of single-nucleotide polymorphisms affecting fiber connectivity and cognition.

Information-theoretic analysis of brain white matter fiber orientation distribution functions

We propose a new information-theoretic metric, the symmetric Kullback-Leibler divergence (sKL-divergence), to measure the difference between two water diffusivity profiles in high angular resolution diffusion imaging (HARDI). Water diffusivity profiles are modeled as probability density functions on the unit sphere, and the sKL-divergence is computed from a spherical harmonic series, which greatly reduces computational complexity. Adjustment of the orientation of diffusivity functions is essential when the image is being warped, so we propose a fast algorithm to determine the principal direction of diffusivity functions using principal component analysis (PCA). We compare sKL-divergence with other inner-product based cost functions using synthetic samples and real HARDI data, and show that the sKL-divergence is highly sensitive in detecting small differences between two diffusivity profiles and therefore shows promise for applications in the nonlinear registration and multisubject statistical analysis of HARDI data.

Heritability of white matter fiber tract shapes: A HARDI study of 198 twins

Genetic analysis of diffusion tensor images (DTI) shows great promise in revealing specific genetic variants that affect brain integrity and connectivity. Most genetic studies of DTI analyze voxel-based diffusivity indices in the image space (such as 3D maps of fractional anisotropy) and overlook tract geometry. Here we propose an automated workflow to cluster fibers using a white matter probabilistic atlas and perform genetic analysis on the shape characteristics of fiber tracts. We apply our approach to large study of 4-Tesla high angular resolution diffusion imaging (HARDI) data from 198 healthy, young adult twins (age: 20-30). Illustrative results show heritability for the shapes of several major tracts, as color-coded maps.

The multivariate A/C/E model and the genetics of fiber architecture

We present a new algorithm to compute the voxel-wise genetic contribution to brain fiber microstructure using diffusion tensor imaging (DTI) in a dataset of 25 monozygotic (MZ) twins and 25 dizygotic (DZ) twin pairs (100 subjects total). First, the structural and DT scans were linearly co-registered. Structural MR scans were nonlinearly mapped via a 3D fluid transformation to a geometrically centered mean template, and the deformation fields were applied to the DTI volumes. After tensor re-orientation to realign them to the anatomy, we computed several scalar and multivariate DT-derived measures including the geodesic anisotropy (GA), the tensor eigenvalues and the full diffusion tensors. A covariance-weighted distance was measured between twins in the Log-Euclidean framework [2], and used as input to a maximum-likelihood based algorithm to compute the contributions from genetics (A), common environmental factors (C) and unique environmental ones (E) to fiber architecture. Quanititative genetic studies can take advantage of the full information in the diffusion tensor, using covariance weighted distances and statistics on the tensor manifold.