Indoor Air Pollutant (Toluene) Reduction Based on Ultraviolet-A Irradiance and Changes in the Reactor Volume in a TiO2 Photocatalyst Reactor.

This study experimentally confirmed the effect of TiO2 photocatalysts on the removal of indoor air pollutants. In the experiment, toluene, a representative indoor air pollutant, was removed using a coating agent containing TiO2 photocatalysts. Conditions proposed by the International Organization for Standardization (ISO) were applied mutatis mutandis, and a photoreactor for an experiment was manufactured. The experiment was divided into two categories. The first experiment was conducted under ISO conditions using the TiO2 photocatalyst coating agent. In the second experiment, the amount of ultraviolet-A (UV-A) light was varied depending on the lamp's service life, and the volume of the reactor was varied depending on the number of contaminants. The results showed that the TiO2 photocatalytic coating agent reduced the effect of toluene. This reduction effect can be increased as a primary function depending on the changes in the amount of UV-A light and reactor volume. However, because toluene is decomposed in this study, additional organic pollutants such as benzene and butadiene can be produced. Because these pollutants are decomposed by the TiO2 photocatalysts, the overall reduction performance may change. Nonetheless, TiO2 photocatalysts can be used to examine the effect of indoor pollutant reduction in indoor ventilation systems and building materials.

Demonstration of Arithmetic Calculations by DNA Tile-Based Algorithmic Self-Assembly.

Owing to its high information density, energy efficiency, and massive parallelism, DNA computing has undergone several advances and made significant contributions to nanotechnology. Notably, arithmetic calculations implemented by multiple logic gates such as adders and subtractors have received much attention because of their well-established logic algorithms and feasibility of experimental implementation. Although small molecules have been used to implement these computations, a DNA tile-based calculator has been rarely addressed owing to complexity of rule design and experimental challenges for direct verification. Here, we construct a DNA-based calculator with three types of building blocks (propagator, connector, and solution tiles) to perform addition and subtraction operations through algorithmic self-assembly. An atomic force microscope is used to verify the solutions. Our method provides a potential platform for the construction of various types of DNA algorithmic crystals (such as flip-flops, encoders, and multiplexers) by embedding multiple logic gate operations in the DNA base sequences.

Calculation of π and Classification of Self-avoiding Lattices via DNA Configuration.

Numerical simulation (e.g. Monte Carlo simulation) is an efficient computational algorithm establishing an integral part in science to understand complex physical and biological phenomena related with stochastic problems. Aside from the typical numerical simulation applications, studies calculating numerical constants in mathematics, and estimation of growth behavior via a non-conventional self-assembly in connection with DNA nanotechnology, open a novel perspective to DNA related to computational physics. Here, a method to calculate the numerical value of π, and way to evaluate possible paths of self-avoiding walk with the aid of Monte Carlo simulation, are addressed. Additionally, experimentally obtained variation of the π as functions of DNA concentration and the total number of trials, and the behaviour of self-avoiding random DNA lattice growth evaluated through number of growth steps, are discussed. From observing experimental calculations of π (πexp) obtained by double crossover DNA lattices and DNA rings, fluctuation of πexp tends to decrease as either DNA concentration or the number of trials increases. Based upon experimental data of self-avoiding random lattices grown by the three-point star DNA motifs, various lattice configurations are examined and analyzed. This new kind of study inculcates a novel perspective for DNA nanostructures related to computational physics and provides clues to solve analytically intractable problems.

3-Input/1-Output Logic Implementation Demonstrated by DNA Algorithmic Self-Assembly.

Although structural DNA nanotechnology is a well-established field, computations performed using DNA algorithmic self-assembly is still in the primitive stages in terms of its adaptability of rule implementation and experimental complexity. Here, we discuss the feasibility of constructing an M-input/ N-output logic gate implemented into simple DNA building blocks. To date, no experimental demonstrations have been reported with M > 2 owing to the difficulty of tile design. To overcome this problem, we introduce a special tile referred to as an operator. We design appropriate binding domains in DNA tiles, and we demonstrate the growth of DNA algorithmic lattices generated by eight different rules from among 256 rules in a 3-input/1-output logic. The DNA lattices show simple, linelike, random, and mixed patterns, which we analyze to obtain errors and sorting factors. The errors vary from 0.8% to 12.8% depending upon the pattern complexity, and sorting factors obtained from the experiment are in good agreement with simulation results within a range of 1-18%.