Out-of-Plane Ionic Conductivity Measurement Configuration for High-Throughput Experiments

2018 ◽  
Vol 20 (7) ◽  
pp. 443-450
Author(s):  
Ruiyun Huang ◽  
Chris J. Kucharczyk ◽  
Yangang Liang ◽  
Xiaohang Zhang ◽  
Ichiro Takeuchi ◽  
...  
Keyword(s):  
Ionic Conductivity ◽  
High Throughput ◽  
Conductivity Measurement ◽  
Out Of Plane ◽  
Ionic Conductivity Measurement ◽  
High Throughput Experiments

Local Ionic Conductivity Measurement That Reveals the Heterogeneity of Porous Electrodes

2021 ◽  
Vol MA2021-02 (4) ◽  
pp. 444-444
Author(s):  
Baichuan Liu ◽  
Kayci Prugue ◽  
Brain Mazzeo ◽  
Dean Wheeler
Keyword(s):  
Ionic Conductivity ◽  
Conductivity Measurement ◽  
Porous Electrodes ◽  
Ionic Conductivity Measurement

Ionic Conductivity of β"-Alumina Solid Electrolyte Prepared by Liquid Phase Sintering Method

2010 ◽  
Vol 93-94 ◽  
pp. 513-516 ◽  
Author(s):  
Kanita Srisurat ◽  
Anuson Niyompan ◽  
Rungnapa Tipakontitikul
Keyword(s):  
Liquid Phase ◽  
Ionic Conductivity ◽  
Solid Electrolyte ◽  
Conductivity Measurement ◽  
Secondary Phase ◽  
Liquid Phase Sintering ◽  
Alumina Powder ◽  
Dry Pressing ◽  
Ionic Conductivity Measurement ◽  
Sintering Method

Na- β"-alumina solid electrolyte proposed for electric vehicle battery system application was prepared using liquid phase sintering method. Firstly, the Na- β"-alumina powder was prepared according to the formular Na1-xMg2xAl5-xO8 with x = 0.175, calcinations temperature was at 1200 C for 10 h. Calcined powder was milled and mixed with Bi2O3 or CuO in approximate concentration 1, 3 and 5 percent by mole respectively. The uniaxial dry-pressing was employed for green body forming. The green pellets were then sintered at different temperature and dwell time were kept constant for 4 h during the sintering process. Ionic conductivity measurement was performed by impedance analyzer. The XRD characterization on both powder and ceramic show that β"-alumina form as a major phase with tiny amount of the secondary phase β-alumina. The β"/β concentration proportion slightly decrease after sintering. Addition with Bi2O3 or CuO do not lead to phase change and high densification ceramic is obtained. Ionic conductivity of β"-alumina ceramic added with Bi2O3 is higher than that of ceramic with CuO addition. The relative calculated activation energy of the Na+ migration in the former composition is also lower. The highest ionic conductivity measured at 300 C is found in ceramic sample sintered at 1450 C and with 1 mol% of Bi2O3.

scholarly journals Discovering collectively informative descriptors from high-throughput experiments

2009 ◽  
Vol 10 (1) ◽  
Author(s):  
Clark D Jeffries ◽  
William O Ward ◽  
Diana O Perkins ◽  
Fred A Wright
Keyword(s):  
High Throughput ◽  
High Throughput Experiments

INFERRING PROTEIN-PROTEIN INTERACTIONS FROM MESSENGER RNA EXPRESSION PROFILES WITH SVM

2005 ◽  
Vol 13 (03) ◽  
pp. 287-298 ◽  
Author(s):  
JUN CAI ◽  
YING HUANG ◽  
LIANG JI ◽  
YANDA LI
Keyword(s):  
High Throughput ◽  
Protein Interactions ◽  
Messenger Rna ◽  
Expression Profiles ◽  
Support Vector ◽  
Svm Classifier ◽  
Good Prediction ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
High Throughput Experiments

In post-genomic biology, researchers in the field of proteome focus their attention on the networks of protein interactions that control the lives of cells and organisms. Protein-protein interactions play a useful role in dynamic cellular machinery. In this paper, we developed a method to infer protein-protein interactions based on the theory of support vector machine (SVM). For a given pair of proteins, a new strategy of calculating cross-correlation function of mRNA expression profiles was used to encode SVM vectors. We compared the performance with other methods of inferring protein-protein interaction. Results suggested that, through five-fold cross validation, our SVM model achieved a good prediction. It enables us to show that expression profiles in transcription level can be used to distinguish physical or functional interactions of proteins as well as sequence contents. Lastly, we applied our SVM classifier to evaluate data quality of interaction data sets from four high-throughput experiments. The results show that high-throughput experiments sacrifice some accuracy in determination of interactions because of limitation of experiment technologies.

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