Evaluation of TiO2 nanotubes array ordering in tree-graph representation

2017 ◽  
Author(s):  
P. L. Titov ◽  
S. A. Schegoleva ◽  
N. B. Kondrikov
Keyword(s):  
Tio2 Nanotubes ◽  
Graph Representation ◽  
Tree Graph

Theoretical and Experimental Exploration of the Energy Landscape of LiI

2005 ◽  
Vol 494 ◽  
pp. 61-66 ◽  
Author(s):  
Ž.P. Čančarević ◽  
J.C. Schön ◽  
D. Fischer ◽  
Martin Jansen
Keyword(s):  
Rock Salt ◽  
Energy Landscape ◽  
Type Structure ◽  
Graph Representation ◽  
Chemical System ◽  
Stable Phase ◽  
Cubic Phase ◽  
Tree Graph ◽  
Existence And Stability ◽  
Atomic Beam Deposition

The prediction of the existence and stability of (meta)-stable phases in a chemical system is realized via a two-step process: identification of structure candidates through global exploration of the classical empirical energy landscape, followed by a local optimization of the candidates on ab-initio level employing a heuristic algorithm. From the computed energy/volume curves, one can then calculate the thermodynamically stable phase at a given pressure and the transition pressures among the phases. In order to gain insight into the kinetic stability of the structure candidates, one computes estimates of the energy and enthalpy barriers around the structures with the so-called threshold algorithm, yielding a tree graph representation of the chemical system. In this work we perform a theoretical and experimental study of the LiI energy landscape. We determine the structure candidates, construct the tree graph representation and compute the abinitio energy/volume curves for the hypothetical structures. We find that the thermodynamically preferred modifications at standard pressure should exhibit the rock salt and the wurtzite structure, respectively. In order to validate our predictions by experiments, we have employed the newly developed ´Low-Temperature - Atomic Beam Deposition` (LT-ABD) technique, which allows to disperse the components of the desired product at an atomic level and in an appropriate ratio. After depositing LiI at T = 77 K, the first crystallization occurs at T » 173 K in the wurtzite-type structure followed by a transition to the more stable rock salt-type structure at T » 273 K. At room temperature only the cubic phase remains.

Graph Neural Networks for Prediction of Fuel Ignition Quality

2020 ◽  
Author(s):  
Artur Schweidtmann ◽  
Jan Rittig ◽  
Andrea König ◽  
Martin Grohe ◽  
Alexander Mitsos ◽  
...  
Keyword(s):  
Neural Networks ◽  
Octane Number ◽  
Molecular Graph ◽  
Chemical Properties ◽  
Graph Representation ◽  
Structure Property ◽  
Oxygenated Hydrocarbons ◽  
Physico Chemical ◽  
Ignition Quality ◽  
Graph Neural Networks

<div>Prediction of combustion-related properties of (oxygenated) hydrocarbons is an important and challenging task for which quantitative structure-property relationship (QSPR) models are frequently employed. Recently, a machine learning method, graph neural networks (GNNs), has shown promising results for the prediction of structure-property relationships. GNNs utilize a graph representation of molecules, where atoms correspond to nodes and bonds to edges containing information about the molecular structure. More specifically, GNNs learn physico-chemical properties as a function of the molecular graph in a supervised learning setup using a backpropagation algorithm. This end-to-end learning approach eliminates the need for selection of molecular descriptors or structural groups, as it learns optimal fingerprints through graph convolutions and maps the fingerprints to the physico-chemical properties by deep learning. We develop GNN models for predicting three fuel ignition quality indicators, i.e., the derived cetane number (DCN), the research octane number (RON), and the motor octane number (MON), of oxygenated and non-oxygenated hydrocarbons. In light of limited experimental data in the order of hundreds, we propose a combination of multi-task learning, transfer learning, and ensemble learning. The results show competitive performance of the proposed GNN approach compared to state-of-the-art QSPR models making it a promising field for future research. The prediction tool is available via a web front-end at www.avt.rwth-aachen.de/gnn.</div>

Drug Delivery Systems Based on Titania Nanotubes and Active Agents for Enhanced Osseointegration of Bone Implants

2020 ◽  
Vol 27 (6) ◽  
pp. 854-902 ◽  
Author(s):  
Raluca Ion ◽  
Madalina Georgiana Necula ◽  
Anca Mazare ◽  
Valentina Mitran ◽  
Patricia Neacsu ◽  
...  
Keyword(s):  
Drug Delivery ◽  
Tio2 Nanotubes ◽  
Therapeutic Agent ◽  
Bone Defects ◽  
Delivery Systems ◽  
Antimicrobial Compounds ◽  
Bone Implant ◽  
Bioactive Agents ◽  
Localized Drug Delivery ◽  
Regeneration Of Bone

TiO2 nanotubes (TNTs) are attractive nanostructures for localized drug delivery. Owing to their excellent biocompatibility and physicochemical properties, numerous functionalizations of TNTs have been attempted for their use as therapeutic agent delivery platforms. In this review, we discuss the current advances in the applications of TNT-based delivery systems with an emphasis on the various functionalizations of TNTs for enhancing osteogenesis at the bone-implant interface and for preventing implant-related infection. Innovation of therapies for enhancing osteogenesis still represents a critical challenge in regeneration of bone defects. The overall concept focuses on the use of osteoconductive materials in combination with the use of osteoinductive or osteopromotive factors. In this context, we highlight the strategies for improving the functionality of TNTs, using five classes of bioactive agents: growth factors (GFs), statins, plant derived molecules, inorganic therapeutic ions/nanoparticles (NPs) and antimicrobial compounds.

scholarly journals TiO2 nanotubes regulate histone acetylation through F-actin to induce the osteogenic differentiation of BMSCs

2021 ◽  
Vol 49 (1) ◽  
pp. 398-406
Author(s):  
Yanchang Liu ◽  
Zhicheng Tong ◽  
Chen Wang ◽  
Runzhi Xia ◽  
Huiwu Li ◽  
...  
Keyword(s):  
Osteogenic Differentiation ◽  
Histone Acetylation ◽  
Tio2 Nanotubes

scholarly journals Graph Representation Ensemble Learning

2020 ◽  
Author(s):  
Palash Goyal ◽  
Sachin Raja ◽  
Di Huang ◽  
Sujit Rokka Chhetri ◽  
Arquimedes Canedo ◽  
...  
Keyword(s):  
Ensemble Learning ◽  
Graph Representation

scholarly journals A Survey of Information Cascade Analysis

2021 ◽  
Vol 54 (2) ◽  
pp. 1-36
Author(s):  
Fan Zhou ◽  
Xovee Xu ◽  
Goce Trajcevski ◽  
Kunpeng Zhang
Keyword(s):  
Information Diffusion ◽  
Research Work ◽  
Viral Marketing ◽  
Graph Representation ◽  
Digital Information ◽  
Information Cascades ◽  
Challenges And Opportunities ◽  
Scientific Papers ◽  
Popularity Prediction ◽  
Types Of Information

The deluge of digital information in our daily life—from user-generated content, such as microblogs and scientific papers, to online business, such as viral marketing and advertising—offers unprecedented opportunities to explore and exploit the trajectories and structures of the evolution of information cascades. Abundant research efforts, both academic and industrial, have aimed to reach a better understanding of the mechanisms driving the spread of information and quantifying the outcome of information diffusion. This article presents a comprehensive review and categorization of information popularity prediction methods, from feature engineering and stochastic processes , through graph representation , to deep learning-based approaches . Specifically, we first formally define different types of information cascades and summarize the perspectives of existing studies. We then present a taxonomy that categorizes existing works into the aforementioned three main groups as well as the main subclasses in each group, and we systematically review cutting-edge research work. Finally, we summarize the pros and cons of existing research efforts and outline the open challenges and opportunities in this field.

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