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

Photocatalysis Using Semiconductor Nanoclusters

We report on experiments using nanosize MoS{sub 2} to photo-oxidize organic pollutants in water using visible light as the energy source. We have demonstrated that we can vary the redox potentials and absorbance characteristics of these small semiconductors by adjusting their size, and our studies of the photooxidation of organic molecules have revealed that the rate of oxidation increases with increasing bandgap (i.e. more positive valence band and more negative conduction band potentials). Because these photocatalysis reactions can be performed with the nanoclusters fully dispersed and stable in solution, liquid chromatography can be used to determine both the intermediate reaction products and the state of the nanoclusters during the reaction. We have demonstrated that the MoS{sub 2} nanoclusters remain unchanged during the photooxidation process by this technique. We also report on studies of MoS{sub 2} nanoclusters deposited on TiO{sub 2} powder

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

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

SQG-Differential Evolution for difficult optimization problems under a tight function evaluation budget

In the context of industrial engineering, it is important to integrate efficient computational optimization methods in the product development process. Some of the most challenging simulation-based engineering design optimization problems are characterized by: a large number of design variables, the absence of analytical gradients, highly non-linear objectives and a limited function evaluation budget. Although a huge variety of different optimization algorithms is available, the development and selection of efficient algorithms for problems with these industrial relevant characteristics, remains a challenge. In this communication, a hybrid variant of Differential Evolution (DE) is introduced which combines aspects of Stochastic Quasi-Gradient (SQG) methods within the framework of DE, in order to improve optimization efficiency on problems with the previously mentioned characteristics. The performance of the resulting derivative-free algorithm is compared with other state-of-the-art DE variants on 25 commonly used benchmark functions, under tight function evaluation budget constraints of 1000 evaluations. The experimental results indicate that the new algorithm performs excellent on the 'difficult' (high dimensional, multi-modal, inseparable) test functions. The operations used in the proposed mutation scheme, are computationally inexpensive, and can be easily implemented in existing differential evolution variants or other population-based optimization algorithms by a few lines of program code as an non-invasive optional setting. Besides the applicability of the presented algorithm by itself, the described concepts can serve as a useful and interesting addition to the algorithmic operators in the frameworks of heuristics and evolutionary optimization and computing

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

Combination of linear classifiers using score function -- analysis of possible combination strategies

In this work, we addressed the issue of combining linear classifiers using their score functions. The value of the scoring function depends on the distance from the decision boundary. Two score functions have been tested and four different combination strategies were investigated. During the experimental study, the proposed approach was applied to the heterogeneous ensemble and it was compared to two reference methods -- majority voting and model averaging respectively. The comparison was made in terms of seven different quality criteria. The result shows that combination strategies based on simple average, and trimmed average are the best combination strategies of the geometrical combination

Randomized Reference Classifier with Gaussian Distribution and Soft Confusion Matrix Applied to the Improving Weak Classifiers

In this paper, an issue of building the RRC model using probability distributions other than beta distribution is addressed. More precisely, in this paper, we propose to build the RRR model using the truncated normal distribution. Heuristic procedures for expected value and the variance of the truncated-normal distribution are also proposed. The proposed approach is tested using SCM-based model for testing the consequences of applying the truncated normal distribution in the RRC model. The experimental evaluation is performed using four different base classifiers and seven quality measures. The results showed that the proposed approach is comparable to the RRC model built using beta distribution. What is more, for some base classifiers, the truncated-normal-based SCM algorithm turned out to be better at discovering objects coming from minority classes.Comment: arXiv admin note: text overlap with arXiv:1901.0882