Young Choon Park

Ambient Carbon Dioxide Capture by Boron-Rich Boron Nitride Nanotube

Carbon dioxides (CO(2)) emitted from large-scale coal-fired power stations or industrial manufacturing plants have to be properly captured to minimize environmental side effects. From results of ab initio calculations using plane waves [PAW-PBE] and localized atomic orbitals [ONIOM(wB97X-D/6-31G*:AM1)], we report strong CO(2) adsorption on boron antisite (B(N)) in boron-rich boron nitride nanotube (BNNT). We have identified two adsorption states: (1) A linear CO(2) molecule is physically adsorbed on the B(N), showing electron donation from the CO(2) lone-pair states to the B(N) double-acceptor state, and (2) the physisorbed CO(2) undergoes a carboxylate-like structural distortion and C═O π-bond breaking due to electron back-donation from B(N) to CO(2). The CO(2) chemisorption energy on B(N) is almost independent of tube diameter and, more importantly, higher than the standard free energy of gaseous CO(2) at room temperature. This implies that boron-rich BNNT could capture CO(2) effectively at ambient conditions.

Automatic thesaurus construction using Bayesian networks

Automatic thesaurus construction is accomplished by extracting term relations mechanically. A popular method uses statistical analysis to discover the term relations. For low-frequency terms, however, the statistical information of the terms cannot be reliably used for deciding the relationship of terms. This problem is generally referred to as the data-sparseness problem. Unfortunately, many studies have shown that low-frequency terms are of most use in thesaurus construction. This paper characterizes the statistical behavior of terms by using an inference network. A formal approach for the data-sparseness problem, which is crucial in constructing a thesaurus, is developed. The validity of this approach is shown by experiments.

Rapid Dye Regeneration Mechanism of Dye-Sensitized Solar Cells

During the light-harvesting process of dye-sensitized solar cells (DSSCs), the hole localized on the dye after the charge separation yields an oxidized dye, D(+). The fast regeneration of D(+) using the redox pair (typically the I(-)/I3(-) couple) is critical for the efficient DSSCs. However, the kinetic processes of dye regeneration remain uncertain, still promoting vigorous debates. Here, we use molecular dynamics simulations to determine that the inner-sphere electron-transfer pathway provides a rapid dye regeneration route of ∼4 ps, where penetration of I(-) next to D(+) enables an immediate electron transfer, forming a kinetic barrier. This explains the recently reported ultrafast dye regeneration rate of a few picoseconds determined experimentally. We expect that our MD based comprehensive understanding of the dye regeneration mechanism will provide a helpful guideline in designing TiO2-dye-electrolyte interfacial systems for better performing DSSCs.

Markov random field based English part-of-speech tagging system

Probabilistic models have been widely used for natural language processing. Part-of-speech tagging, which assingns the most likely tag to each word in a given sentence, is one of the problems which can be solved by statistical approach. Many researchers have tried to solve the problem by hidden Markov model (HMM), which is well known as one of the statistical models. But it has many difficulties: integrating heterogeneous information, coping with data sparseness problem, and adapting to new environments. In this paper, we propose a Markov radom field (MRF) model based approach to the tagging problem. The MRF provides the base frame to combine various statistical information with maximum entropy (ME) method. As Gibbs distribution can be used to describe a posteriori probability of tagging, we use it in maximum a posteriori (MAP) estimation of optimizing process. Besides, several tagging models are developed to show the effect of adding information. Experimental results show that the performance of the tagger gets improved as we add more statistical information, and that MRF-based tagging model is better than HMM based tagging in data sparseness problem.

Nuclear motion captured by the slow electron velocity imaging technique in the tunnelling predissociation of the S1 methylamine

Predissociation dynamics of methylamines (CH(3)NH(2) and CH(3)ND(2)) on the first electronically excited states are studied using the slow-electron velocity imaging method to unravel the multi-dimensional nature of the N-H(D) chemical bond dissociation reaction which occurs via tunnelling. The nearly free internal rotation around the C-N bond axis is found to be strongly coupled to the reaction pathway, revealing nuclear motions actively involved in the tunnelling process on the S(1) potential energy surfaces. The vibrational state-resolved energy and angular distributions of photoelectron, ejected from the ionization mediated by the metastable intermediate S(1) state provide a unique way for mapping the predissociative potential energy surfaces.

Constricted variational density functional theory for spatially clearly separated charge-transfer excitations.

Constricted Variational Density Functional Theory (CV-DFT) is known to be one of the successful methods in predicting charge-transfer excitation energies. In this paper, we apply the CV-DFT method to the well-known model systems ethylene-tetrafluoroethylene (C2H4 × C2F4) and the zincbacteriochlorin-bacteriochlorin complex (ZnBC-BC). The analysis of the CV-DFT energies enables us to understand the -1/R charge-transfer behaviour in CV-DFT for large separation distances R. With this we discuss the importance of orbital relaxations using the relaxed version of CV(∞)-DFT, the R-CV(∞)-DFT method. Possible effects of the optimization of the transition matrix for the relaxed self-consistent field version of CV(∞)-DFT, RSCF-CV(∞)-DFT in the case of large fragment separations are shown and we introduce two possible gradient restrictions to avoid the unwanted admixing of other transitions.

Efficient inferencing for sigmoid Bayesian networks by reducing sampling space

A sigmoid Bayesian network is a Bayesian network in which a conditional probability is a sigmoid function of the weights of relevant arcs. Its application domain includes that of Boltzmann machine as well as traditional decision problems. In this paper we show that the node reduction method that is an inferencing algorithm for general Bayesian networks can also be used on sigmoid Bayesian networks, and we propose a hybrid inferencing method combining the node reduction and Gibbs sampling. The time efficiency of sampling after node reduction is demonstrated through experiments. The results of this paper bring sigmoid Bayesian networks closer to large scale applications.

Dynamic Symmetry Breaking Hidden in Fano Resonance of a Molecule: S1 State of Diazirine Using Quantum Wave Packet Propagation

Fano resonance in the predissociation of the S1 state of diazirine was studied by applying a time-dependent wave packet propagation method, and dynamic symmetry breaking (DSB) around the stationary structure of S1 was disclosed in a detailed analysis of this theoretical result. The DSB was found to originate in coupling between the asymmetric C-N2 stretching and CH2 wagging modes, suggesting that there is a slight time gap between ring opening and the concurrent dragging of two H atoms of the CH2 moiety. Although the depth of the double well due to DSB is just 0.011 eV, its presence noticeably affects the early time dynamics and observed spectrum.