scholarly journals Bayesian data analysis reveals no preference for cardinal Tafel slopes in CO2 reduction electrocatalysis

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Aditya M. Limaye ◽  
Joy S. Zeng ◽  
Adam P. Willard ◽  
Karthish Manthiram
Keyword(s):  
Data Analysis ◽  
Synthetic Data ◽  
Co2 Reduction ◽  
Tafel Slope ◽  
Bayesian Data Analysis ◽  
Electrochemical Catalyst ◽  
Current Voltage ◽  
Uncertainty Estimates ◽  
Tafel Slopes ◽  
Kinetic Investigations

AbstractThe Tafel slope is a key parameter often quoted to characterize the efficacy of an electrochemical catalyst. In this paper, we develop a Bayesian data analysis approach to estimate the Tafel slope from experimentally-measured current-voltage data. Our approach obviates the human intervention required by current literature practice for Tafel estimation, and provides robust, distributional uncertainty estimates. Using synthetic data, we illustrate how data insufficiency can unknowingly influence current fitting approaches, and how our approach allays these concerns. We apply our approach to conduct a comprehensive re-analysis of data from the CO2 reduction literature. This analysis reveals no systematic preference for Tafel slopes to cluster around certain "cardinal values” (e.g. 60 or 120 mV/decade). We hypothesize several plausible physical explanations for this observation, and discuss the implications of our finding for mechanistic analysis in electrochemical kinetic investigations.

scholarly journals Bayesian Data Analysis Reveals No Preference for Cardinal Tafel Slopes in CO2 Reduction Electrocatalysis

2020 ◽  
Author(s):  
Aditya Limaye ◽  
Joy S. Zeng ◽  
Adam Willard ◽  
Karthish Manthiram
Keyword(s):  
Data Analysis ◽  
Synthetic Data ◽  
Co2 Reduction ◽  
Human Intervention ◽  
Bayesian Data Analysis ◽  
Current Voltage ◽  
Mechanistic Analysis ◽  
Uncertainty Estimates ◽  
Tafel Slopes ◽  
Kinetic Investigations

In this paper, we develop a Bayesian data analysis approach to estimate the Tafel slope from experimentally-measured current-voltage data. Our approach obviates the human intervention required by current literature practice for Tafel estimation, and provides robust, distributional uncertainty estimates. Using synthetic data, we illustrate how data insufficiency can unknowingly influence current fitting approaches, and how our approach allays these concerns. We apply our approach to conduct a comprehensive re-analysis of data from the CO<sub>2</sub> reduction literature. This analysis reveals no systematic preference for Tafel slopes to cluster around certain "cardinal values" (e.g. 60 or 120 mV/decade). We hypothesize several plausible physical explanations for this observation, and discuss the implications of our finding for mechanistic analysis in electrochemical kinetic investigations.

scholarly journals Bayesian Data Analysis Reveals No Preference for Cardinal Tafel Slopes in CO2 Reduction Electrocatalysis

2020 ◽  
Author(s):  
Aditya Limaye ◽  
Joy S. Zeng ◽  
Adam Willard ◽  
Karthish Manthiram
Keyword(s):  
Data Analysis ◽  
Synthetic Data ◽  
Co2 Reduction ◽  
Human Intervention ◽  
Bayesian Data Analysis ◽  
Current Voltage ◽  
Mechanistic Analysis ◽  
Uncertainty Estimates ◽  
Tafel Slopes ◽  
Kinetic Investigations

In this paper, we develop a Bayesian data analysis approach to estimate the Tafel slope from experimentally-measured current-voltage data. Our approach obviates the human intervention required by current literature practice for Tafel estimation, and provides robust, distributional uncertainty estimates. Using synthetic data, we illustrate how data insufficiency can unknowingly influence current fitting approaches, and how our approach allays these concerns. We apply our approach to conduct a comprehensive re-analysis of data from the CO<sub>2</sub> reduction literature. This analysis reveals no systematic preference for Tafel slopes to cluster around certain "cardinal values" (e.g. 60 or 120 mV/decade). We hypothesize several plausible physical explanations for this observation, and discuss the implications of our finding for mechanistic analysis in electrochemical kinetic investigations.

scholarly journals Methods for estimating uncertainty in factor analytic solutions

2014 ◽  
Vol 7 (3) ◽  
pp. 781-797 ◽  
Author(s):  
P. Paatero ◽  
S. Eberly ◽  
S. G. Brown ◽  
G. A. Norris
Keyword(s):  
Environmental Protection Agency ◽  
Synthetic Data ◽  
Analytic Solutions ◽  
Data Sets ◽  
Random Errors ◽  
Data Set ◽  
Factor Analytic ◽  
Uncertainty Estimates ◽  
Multilinear Engine ◽  
Analytic Models

Abstract. The EPA PMF (Environmental Protection Agency positive matrix factorization) version 5.0 and the underlying multilinear engine-executable ME-2 contain three methods for estimating uncertainty in factor analytic models: classical bootstrap (BS), displacement of factor elements (DISP), and bootstrap enhanced by displacement of factor elements (BS-DISP). The goal of these methods is to capture the uncertainty of PMF analyses due to random errors and rotational ambiguity. It is shown that the three methods complement each other: depending on characteristics of the data set, one method may provide better results than the other two. Results are presented using synthetic data sets, including interpretation of diagnostics, and recommendations are given for parameters to report when documenting uncertainty estimates from EPA PMF or ME-2 applications.

A Bayesian Data Analysis System for the Evaluation of Social Programs

1977 ◽  
Vol 72 (360) ◽  
pp. 711 ◽  
Author(s):  
Ming-Mei Wang ◽  
Melvin R. Novick ◽  
Gerald L. Isaacs ◽  
Dan Ozenne
Keyword(s):  
Data Analysis ◽  
Social Programs ◽  
Bayesian Data Analysis ◽  
Analysis System ◽  
Data Analysis System

scholarly journals Distinguishing different modes of growth using single-cell data

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Prathitha Kar ◽  
Sriram Tiruvadi-Krishnan ◽  
Jaana Männik ◽  
Jaan Männik ◽  
Ariel Amir
Keyword(s):  
Data Analysis ◽  
Single Cell ◽  
Statistical Methods ◽  
Synthetic Data ◽  
Cellular Growth ◽  
Biological Mechanisms ◽  
E Coli ◽  
High Throughput Data ◽  
Consistent Method ◽  
Cell Data

Collection of high-throughput data has become prevalent in biology. Large datasets allow the use of statistical constructs such as binning and linear regression to quantify relationships between variables and hypothesize underlying biological mechanisms based on it. We discuss several such examples in relation to single-cell data and cellular growth. In particular, we show instances where what appears to be ordinary use of these statistical methods leads to incorrect conclusions such as growth being non-exponential as opposed to exponential and vice versa. We propose that the data analysis and its interpretation should be done in the context of a generative model, if possible. In this way, the statistical methods can be validated either analytically or against synthetic data generated via the use of the model, leading to a consistent method for inferring biological mechanisms from data. On applying the validated methods of data analysis to infer cellular growth on our experimental data, we find the growth of length in E. coli to be non-exponential. Our analysis shows that in the later stages of the cell cycle the growth rate is faster than exponential.

scholarly journals Bayesian data analysis in the phonetic sciences: A tutorial introduction

2018 ◽  
Vol 71 ◽  
pp. 147-161 ◽  
Author(s):  
Shravan Vasishth ◽  
Bruno Nicenboim ◽  
Mary E. Beckman ◽  
Fangfang Li ◽  
Eun Jong Kong
Keyword(s):  
Data Analysis ◽  
Bayesian Data Analysis

Mathematics and Statistics in Anesthesiology

2018 ◽  
Author(s):  
Daniel Mortlock
Keyword(s):  
Probability Theory ◽  
Data Analysis ◽  
Survey Design ◽  
Probability Distributions ◽  
Statistical Tests ◽  
Real World Data ◽  
Mathematical Functions ◽  
P Values ◽  
Bayesian Data Analysis ◽  
Probability And Statistics

Mathematics is the language of quantitative science, and probability and statistics are the extension of classical logic to real world data analysis and experimental design. The basics of mathematical functions and probability theory are summarized here, providing the tools for statistical modeling and assessment of experimental results. There is a focus on the Bayesian approach to such problems (ie, Bayesian data analysis); therefore, the basic laws of probability are stated, along with several standard probability distributions (eg, binomial, Poisson, Gaussian). A number of standard classical tests (eg, p values, the t-test) are also defined and, to the degree possible, linked to the underlying principles of probability theory. This review contains 5 figures, 1 table, and 15 references. Keywords: Bayesian data analysis, mathematical models, power analysis, probability, p values, statistical tests, statistics, survey design

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