Structure and nanostructure in ionic liquids.

This review gives a survey on the latest most representative developments and progress concerning ionic liquids, from their fundamental properties to their applications in catalytic processes. It also highlights their emerging use for biomass treatment and transformation.

Ionic liquids and catalysis: Recent progress from knowledge to applications

Until recently, “room-temperature” (<100–150 °C) liquid-state electrochemistry was mostly electrochemistry of diluted electrolytes(1)–(4) where dissolved salt ions were surrounded by a considerable amount of solvent molecules. Highly concentrated liquid electrolytes were mostly considered in the narrow (albeit important) niche of high-temperature electrochemistry of molten inorganic salts(5-9) and in the even narrower niche of “first-generation” room temperature ionic liquids, RTILs (such as chloro-aluminates and alkylammonium nitrates).(10-14) The situation has changed dramatically in the 2000s after the discovery of new moisture- and temperature-stable RTILs.(15, 16) These days, the “later generation” RTILs attracted wide attention within the electrochemical community.(17-31) Indeed, RTILs, as a class of compounds, possess a unique combination of properties (high charge density, electrochemical stability, low/negligible volatility, tunable polarity, etc.) that make them very attractive substances from fundamental and application points of view.(32-38) Most importantly, they can mix with each other in “cocktails” of one’s choice to acquire the desired properties (e.g., wider temperature range of the liquid phase(39, 40)) and can serve as almost “universal” solvents.(37, 41, 42) It is worth noting here one of the advantages of RTILs as compared to their high-temperature molten salt (HTMS)(43) “sister-systems”.(44) In RTILs the dissolved molecules are not imbedded in a harsh high temperature environment which could be destructive for many classes of fragile (organic) molecules.

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Ionic Liquids at Electrified Interfaces

Ionic liquids are remarkable chemical compounds, which find applications in many areas of modern science. Because of their highly tunable nature and exceptional properties, ionic liquids have become essential players in the fields of synthesis and catalysis, extraction, electrochemistry, analytics, biotechnology, etc. Apart from physical and chemical features of ionic liquids, their high biological activity has been attracting significant attention from biochemists, ecologists, and medical scientists. This Review is dedicated to biological activities of ionic liquids, with a special emphasis on their potential employment in pharmaceutics and medicine. The accumulated data on the biological activity of ionic liquids, including their antimicrobial and cytotoxic properties, are discussed in view of possible applications in drug synthesis and drug delivery systems. Dedicated attention is given to a novel active pharmaceutical ingredient-ionic liquid (API-IL) concept, which suggests using traditional drugs in ...

Biological Activity of Ionic Liquids and Their Application in Pharmaceutics and Medicine.

https://www.cs.usask.ca/~croy/papers/2009/RCK_SCP_Clones.pdf

Comparison and evaluation of code clone detection techniques and tools: A qualitative approach

2.1. UNIFAC Models 1421 2.1.1. History of Group Contribution Methods 1422 2.1.2. The Modified UNIFAC Model 1422 2.1.3. The γ∞-Based UNIFAC Model 1423 2.2. MOSCED and SPACE Models 1423 2.3. CAMD 1424 2.3.1. CAMD Program 1425 2.3.2. Case Study 1426 2.4. DISQUAC Model 1427 2.5. Pierotti−Deal−Derr Model 1428 2.6. Parachor Model 1428 2.7. Weimer−Prausnitz Model 1428 2.8. Prausnitz and Anderson Theory 1428 2.8.1. Physical Force 1429 2.8.2. Chemical Force 1429 2.9. Group Contribution Equations of State 1430 3. Solvent−Solid Salt Systems 1431 3.1. The Extended UNIFAC Models 1431 3.1.1. Model of Kikic et al. 1431 3.1.2. Model of Achard et al. 1432 3.1.3. Model of Yan et al. 1432 3.2. Scaled Particle Theory 1433 3.2.1. Description of Theory 1433 3.2.2. Salt Effect on Relative Volatility at Infinite Dilution 1434

Predictive Molecular Thermodynamic Models for Liquid Solvents, Solid Salts, Polymers, and Ionic Liquids

Bad smells are signs of potential problems in code. Detecting and resolving bad smells, however, remain time-consuming for software engineers despite proposals on bad smell detection and refactoring tools. Numerous bad smells have been recognized, yet the sequences in which the detection and resolution of different kinds of bad smells are performed are rarely discussed because software engineers do not know how to optimize sequences or determine the benefits of an optimal sequence. To this end, we propose a detection and resolution sequence for different kinds of bad smells to simplify their detection and resolution. We highlight the necessity of managing bad smell resolution sequences with a motivating example, and recommend a suitable sequence for commonly occurring bad smells. We evaluate this recommendation on two nontrivial open source applications, and the evaluation results suggest that a significant reduction in effort ranging from 17.64 to 20 percent can be achieved when bad smells are detected and resolved using the proposed sequence.

Schedule of Bad Smell Detection and Resolution: A New Way to Save Effort

With the popularity of UML and MDA, models are re- placing source code as core artifacts of software devel- opment and maintenance. But duplications in models re- duce models? maintainability and reusability. To address the problem, we should detect duplications first. As an ini- tial step to address the problem, we propose an approach to detect duplications in sequence diagrams. With special pre- processing, we convert 2-dimensional sequence diagrams into a 1-dimensional array. Then we construct a suffix tree of the array. We revise the traditional construction algo- rithm of suffix trees by proposing a special algorithm to detect common prefixes of suffixes. The algorithm ensures that every duplication detected with the suffix tree can be extracted into a separate reusable sequence diagram. With the suffix tree, duplications are found as refactoring candi- dates. With tool support, the proposed approach has been applied to real industrial projects, and the evaluation re- sults suggest that the approach is effective.

Detecting Duplications in Sequence Diagrams Based on Suffix Trees

Structural Characterization of Highly Stable Ni/SBA-15 Catalyst and Its Catalytic Performance for Methane Reforming with CO2

Software refactoring is widely employed to improve software quality. A key step in software refactoring is to identify which part of the software should be refactored. To facilitate the identification, a number of approaches have been proposed to identify certain structures in the code (called code smells) that suggest the possibility of refactoring. Most of such approaches rely on manually designed heuristics to map manually selected source code metrics to predictions. However, it is challenging to manually select the best features, especially textual features. It is also difficult to manually construct the optimal heuristics. To this end, in this paper we propose a deep learning based novel approach to detecting feature envy, one of the most common code smells. The key insight is that deep neural networks and advanced deep learning techniques could automatically select features (especially textual features) of source code for feature envy detection, and could automatically build the complex mapping between such features and predictions. We also propose an automatic approach to generating labeled training data for the neural network based classifier, which does not require any human intervention. Evaluation results on open-source applications suggest that the proposed approach significantly improves the state-of-the-art in both detecting feature envy smells and recommending destinations for identified smelly methods.

Hui Liu

Papers

Predictive Molecular Thermodynamic Models for Liquid Solvents, Solid Salts, Polymers, and Ionic Liquids

Schedule of Bad Smell Detection and Resolution: A New Way to Save Effort

Detecting Duplications in Sequence Diagrams Based on Suffix Trees

Structural Characterization of Highly Stable Ni/SBA-15 Catalyst and Its Catalytic Performance for Methane Reforming with CO2

Deep learning based feature envy detection