Environmental Footprint Assessment of Methylene Blue Photodegradation using Graphene-based Titanium Dioxide

To date, photocatalysis has received much attention in terms of the degradation of organic pollutants in wastewater. Various studies have shown that graphene-based photocatalysts are one of the impressive options owing to their intriguing features, including high surface area, good conductivity, low recombination rate of electron-hole pair, and fast charge separation and transfer. However, the environmental impacts of the photocatalysts synthesis and their photodegradation activity remain unclear. Thus, this report aims to identify the environmental impacts associated with the photodegradation of methylene blue (MB) over reduced graphene oxide/titanium oxide photocatalyst (TiO2/rGO) using Life Cycle Assessment (LCA). The life cycle impacts were assessed using ReCiPe 2016 v1.1 midpoint method, Hierachist version in Gabi software. A cradle-to-gate approach and a functional unit of 1 kg TiO2/rGOwere adopted in the study. Several important parameters, such as the solvent type (ultrapure water, ethanol, and isopropanol), with/without silver ion doping, and visible light power consumption (150, 300, and 500 W) were evaluated in this study. In terms of the selection of solvent, ultrapure water is certainly a better choice since it contributed the least negative impact on the environment. Furthermore, it is not advisable to dope the photocatalyst with silver ions since the increment in performance is insufficient to offset the environmental impact that it caused. The results of different power of visible light for MB degradation showed that the minimum power level, 150 W, could give a comparable photodegradation efficiency and better environmental impacts compared to higher power light sources. Copyright © 2023 by Authors, Published by BCREC Group. This is an open access article under the CC BY-SA License (https://creativecommons.org/licenses/by-sa/4.0).

Ontology Language XOL Used for Cross-Application Communication

The 2000s may be the flourishing time of the topic of ontology. Specialists and scholars concentrated to define ontology effectively and formulated uniform ontology protocol. Ontology language can be classified into SHOE, OML, XOL, OIL, OWL, and RDFs by different protocols and syntaxes. As for effective exchange of the different ontology messages in different applications, US bioinformatic community and researcher develop a XML-based ontology language. With the simplified OKBC-Lite protocol and flexible XML syntax, XOL offers the ways to define an ontology with the human-readable XML, simplified protocol, and compatible interface. In this chapter, we will introduce its motivation from history, orientation in development, semantic usage, and interpreted example in detail

Neural-network solutions to stochastic reaction networks

The stochastic reaction network is widely used to model stochastic processes in physics, chemistry and biology. However, the size of the state space increases exponentially with the number of species, making it challenging to investigate the time evolution of the chemical master equation for the reaction network. Here, we propose a machine-learning approach using the variational autoregressive network to solve the chemical master equation. The approach is based on the reinforcement learning framework and does not require any data simulated in prior by another method. Different from simulating single trajectories, the proposed approach tracks the time evolution of the joint probability distribution in the state space of species counts, and supports direct sampling on configurations and computing their normalized joint probabilities. We apply the approach to various systems in physics and biology, and demonstrate that it accurately generates the probability distribution over time in the genetic toggle switch, the early life self-replicator, the epidemic model and the intracellular signaling cascade. The variational autoregressive network exhibits a plasticity in representing the multi-modal distribution by feedback regulations, cooperates with the conservation law, enables time-dependent reaction rates, and is efficient for high-dimensional reaction networks with allowing a flexible upper count limit. The results suggest a general approach to investigate stochastic reaction networks based on modern machine learning

Development and Application of Highly Accurate and Reproducible Inkjet Sample Introduction Method for Capillary Electrophoresis

首都大学東京, 2013-09-30, 博士（工学）, 甲第425号首都大学東

2,2′-[Ethyl­enebis(azanediyl­methyl­ene)]diphenol

In the title compound, C16H20N2O4, the mol­ecule features a zigzag –CH2–NH–CH2–CH2–NH–CH2– chain whose ends are connected to the hydroxy­phenyl rings. The mol­ecules lies about a center of inversion. The imino group is a hydrogen-bond donor for the hydr­oxy group, which is a hydrogen-bond donor for the imino group of an adjacent mol­ecule. This latter inter­molecular hydrogen bonding leads to a layer structure

Bis(4-hydroxy­pyridinium) sulfate monohydrate

In the crystal structure of the title salt, 2C5H6NO+·SO4 2−·H2O, one planar (r.m.s. deviation = 0.01 Å) cation is stacked approximately over the other [dihedral angle between planes = 8.6 (1)°]. The pyridinium and hydr­oxy H atoms are hydrogen-bond donor atoms to the O atoms of the sulfate anion; the cations, anions and water mol­ecules are consolidated into a three-dimensional network through O—H⋯O and N—H⋯O hydrogen bonds

Tris(4-hydroxy­pyridinium) hydrogen sulfate–sulfate monohydrate

In the crystal structure of the title salt, 3C5H6NO+·HSO4 −·SO4 2−·H2O, the hydrogen sulfate ion is linked to the sulfate ion by an O—H⋯O hydrogen bond. The hydrogen sulfate–sulfate anion is a hydrogen-bond acceptor for the three independent cations and the uncoordinated water mol­ecule, the hydrogen-bonding inter­actions giving rise to a three-dimensional hydrogen-bonded network. In the hydrogen sulfate–sulfate species, one of the sulfate groups is disordered in respect of its O atoms in a 2:1 ratio