Tailorable, visible light emission from silicon nanocrystals

J. P. Wilcoxon and G. A. Samara Crystalline, size-selected Si nanocrystals in the size range 1.8-10 nm grown in inverse micellar cages exhibit highly structured optical absorption and photoluminescence (PL) across the visible range of the spectrum. The most intense PL for the smallest nanocrystals produced This report was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, make any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. to induce a useful level of visible photoluminescence (PL) from silicon (Si). The approaches understood. Visible PL has been observed from Si nanocrystals, or quantum dots, produced by a variety of techniques including aerosols,2 colloids,3 and ion implantation.4 However, all of The optical absorption spectra of our nanocrystals are much richer in spectral features spectrum of bulk Si where the spectral features reflect the details of the band structure shown in nanocrystals estimated to have a Si core diameter of 1-2 nm. These measured quantum those in the spectrum of bulk Si in Fig. 1 are striking indicating that nanocrystals of this size 8-Room temperature PL results on an HPLC size-selected, purified 2 nm nanocrystals but blue shifted by -0.4 eV due to quantum confinement. Excitation at 245 nm yields the PL shows the PL spectrum for a similar sample excited at 490 nm (2.53 eV) trapped excitons at the surface of Si nanocrystals. The excitons are obtained for dimer bonds 1.8- 10 nm. These nanocrystals retain bulk-like optical absorption and an indirect bandgap Figure 1. The absorption spectrum of d = 2 nm Si nanocrystals compared to that of bulk7 Si. Figure 2. The extinction and PL (excitation at 490 nm) spectra ford= 8-10 nm Si nanocrystals

Size Distributions of Gold Nanoclusters Studied by Liquid Chromatography

The authors report high pressure liquid chromatography, (HPLC), and transmission electron microscopy, (TEM), studies of the size distributions of nanosize gold clusters dispersed in organic solvents. These metal clusters are synthesized in inverse micelles at room temperature and those investigated range in diameter from 1--10 nm. HPLC is sensitive enough to discern changes in hydrodynamic volume corresponding to only 2 carbon atoms of the passivating agent or metal core size changes of less than 4 {angstrom}. The authors have determined for the first time how the total cluster volume (metal core + passivating organic shell) changes with the size of the passivating agent

A Layer-Wise Information Reinforcement Approach to Improve Learning in Deep Belief Networks

With the advent of deep learning, the number of works proposing new methods or improving existent ones has grown exponentially in the last years. In this scenario, "very deep" models were emerging, once they were expected to extract more intrinsic and abstract features while supporting a better performance. However, such models suffer from the gradient vanishing problem, i.e., backpropagation values become too close to zero in their shallower layers, ultimately causing learning to stagnate. Such an issue was overcome in the context of convolution neural networks by creating "shortcut connections" between layers, in a so-called deep residual learning framework. Nonetheless, a very popular deep learning technique called Deep Belief Network still suffers from gradient vanishing when dealing with discriminative tasks. Therefore, this paper proposes the Residual Deep Belief Network, which considers the information reinforcement layer-by-layer to improve the feature extraction and knowledge retaining, that support better discriminative performance. Experiments conducted over three public datasets demonstrate its robustness concerning the task of binary image classification

Studies of photoredox reactions on nanosize semiconductors

Light induced electron transfer (ET) from nanosize semiconductors Of MoS{sub 2} to organic electron acceptors such as 2,2`-bipyridine (bpy) and methyl substituted 4,4`,5,5`-tetramethyl- 2,2`-bipyridine (tmb) was studied by static and time resolved photoluminescence spectroscopy. The kinetics of ET were varied by changing the nanocluster size (the band gap), the electron acceptor, and the polarity of the solvent. MoS{sub 2} is an especially interesting semiconductor material as it is an indirect semiconductor in bulk form, and has a layered covalent bonding arrangement which is highly resistant to photocorrosion

Determining appropriate approaches for using data in feature selection

Feature selection is increasingly important in data analysis and machine learning in big data era. However, how to use the data in feature selection, i.e. using either ALL or PART of a dataset, has become a serious and tricky issue. Whilst the conventional practice of using all the data in feature selection may lead to selection bias, using part of the data may, on the other hand, lead to underestimating the relevant features under some conditions. This paper investigates these two strategies systematically in terms of reliability and effectiveness, and then determines their suitability for datasets with different characteristics. The reliability is measured by the Average Tanimoto Index and the Inter-method Average Tanimoto Index, and the effectiveness is measured by the mean generalisation accuracy of classification. The computational experiments are carried out on ten real-world benchmark datasets and fourteen synthetic datasets. The synthetic datasets are generated with a pre-set number of relevant features and varied numbers of irrelevant features and instances, and added with different levels of noise. The results indicate that the PART approach is more effective in reducing the bias when the size of a dataset is small but starts to lose its advantage as the dataset size increases

Novel statistical approaches for non-normal censored immunological data: analysis of cytokine and gene expression data

Background: For several immune-mediated diseases, immunological analysis will become more complex in the future with datasets in which cytokine and gene expression data play a major role. These data have certain characteristics that require sophisticated statistical analysis such as strategies for non-normal distribution and censoring. Additionally, complex and multiple immunological relationships need to be adjusted for potential confounding and interaction effects. Objective: We aimed to introduce and apply different methods for statistical analysis of non-normal censored cytokine and gene expression data. Furthermore, we assessed the performance and accuracy of a novel regression approach in order to allow adjusting for covariates and potential confounding. Methods: For non-normally distributed censored data traditional means such as the Kaplan-Meier method or the generalized Wilcoxon test are described. In order to adjust for covariates the novel approach named Tobit regression on ranks was introduced. Its performance and accuracy for analysis of non-normal censored cytokine/gene expression data was evaluated by a simulation study and a statistical experiment applying permutation and bootstrapping. Results: If adjustment for covariates is not necessary traditional statistical methods are adequate for non-normal censored data. Comparable with these and appropriate if additional adjustment is required, Tobit regression on ranks is a valid method. Its power, type-I error rate and accuracy were comparable to the classical Tobit regression. Conclusion: Non-normally distributed censored immunological data require appropriate statistical methods. Tobit regression on ranks meets these requirements and can be used for adjustment for covariates and potential confounding in large and complex immunological datasets

Atomistic simulations of self-trapped exciton formation in silicon nanostructures: The transition from quantum dots to nanowires

Using an approximate time-dependent density functional theory method, we calculate the absorption and luminescence spectra for hydrogen passivated silicon nanoscale structures with large aspect ratio. The effect of electron confinement in axial and radial directions is systematically investigated. Excited state relaxation leads to significant Stokes shifts for short nanorods with lengths less than 2 nm, but has little effect on the luminescence intensity. The formation of self-trapped excitons is likewise observed for short nanostructures only; longer wires exhibit fully delocalized excitons with neglible geometrical distortion at the excited state minimum.Comment: 10 pages, 4 figure

Mechanical and Electronic Properties of MoS2_2 Nanoribbons and Their Defects

We present our study on atomic, electronic, magnetic and phonon properties of one dimensional honeycomb structure of molybdenum disulfide (MoS$_2$) using first-principles plane wave method. Calculated phonon frequencies of bare armchair nanoribbon reveal the fourth acoustic branch and indicate the stability. Force constant and in-plane stiffness calculated in the harmonic elastic deformation range signify that the MoS$_2$ nanoribbons are stiff quasi one dimensional structures, but not as strong as graphene and BN nanoribbons. Bare MoS$_2$ armchair nanoribbons are nonmagnetic, direct band gap semiconductors. Bare zigzag MoS$_2$ nanoribbons become half-metallic as a result of the (2x1) reconstruction of edge atoms and are semiconductor for minority spins, but metallic for the majority spins. Their magnetic moments and spin-polarizations at the Fermi level are reduced as a result of the passivation of edge atoms by hydrogen. The functionalization of MoS$_2$ nanoribbons by adatom adsorption and vacancy defect creation are also studied. The nonmagnetic armchair nanoribbons attain net magnetic moment depending on where the foreign atoms are adsorbed and what kind of vacancy defect is created. The magnetization of zigzag nanoribbons due to the edge states is suppressed in the presence of vacancy defects.Comment: 11 pages, 5 figures, first submitted at November 23th, 200