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A family of kinetic distributions for interpretation of experimental fluctuations in kinetic problems

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1016/j.apsusc.2011.07.051openalex-snapshot
A chemical kinetic model for chemical vapor deposition of carbon nanotubes
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A detailed kinetic model for the hydrothermal decomposition process of sewage sludge
inferredcites →https://doi.org/10.1016/j.cej.2009.08.012openalex-snapshot
Experimental errors in kinetic tests and its influence on the precision of estimated parameters. Part I—Analysis of first-order reactions
inferredcites →https://doi.org/10.1016/j.cej.2012.05.039openalex-snapshot
A Monte Carlo modeling methodology for the simulation of hydrotreating processes
inferredcites →https://doi.org/10.1016/j.ces.2007.02.020openalex-snapshot
Optimum reference temperature for reparameterization of the Arrhenius equation. Part 1: Problems involving one kinetic constant
inferredcites →https://doi.org/10.1016/j.ces.2007.11.024openalex-snapshot
Nonlinear parameter estimation through particle swarm optimization
inferredcites →https://doi.org/10.1016/j.ces.2008.03.010openalex-snapshot
Optimum reference temperature for reparameterization of the Arrhenius equation. Part 2: Problems involving multiple reparameterizations
inferredcites →https://doi.org/10.1016/j.ces.2008.07.005openalex-snapshot
Optimum reparameterization of power function models
inferredcites →https://doi.org/10.1021/ac049885dopenalex-snapshot
A Theoretical Approach to Some Analytical Properties of Heterogeneous Enzymatic Assays
inferredcites →https://doi.org/10.1039/c2cp41194fopenalex-snapshot
Kinetic Monte Carlo modeling of silicate oligomerization and early gelation†
inferredcites →https://doi.org/10.1063/1.2877443openalex-snapshot
Kinetic Monte Carlo modeling of chemical reactions coupled with heat transfer
inferredcites →https://doi.org/10.1198/tech.2002.s671openalex-snapshot
Applied Statistics and Probability for Engineers
inferredcites →https://doi.org/10.2307/1269738openalex-snapshot
Applied Statistics and Probability for Engineers

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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A Bayesian Approach for Estimation of Model Parameters Near Hard Physical Boundaries
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Ru Promoted MgO and Al-Modified MgO for Ethanol Upgrading
inferred← citeshttps://doi.org/10.1016/j.biortech.2025.132746openalex-snapshot
Machine learning framework to predict product distribution of lignocellulosic biomass pyrolysis
inferred← citeshttps://doi.org/10.1016/j.cej.2022.136676openalex-snapshot
Use of the Taylor theorem to predict kinetic curves in an arbitrary mechanism
inferred← citeshttps://doi.org/10.1016/j.jcat.2023.01.033openalex-snapshot
How Many Data Points and How Large an R-Squared Value is Essential for Arrhenius Plots?
inferred← citeshttps://doi.org/10.1021/acs.cgd.3c01027openalex-snapshot
Statistical Analyses of a Population Balance Model of a Batch Crystallization Process
inferred← citeshttps://doi.org/10.1021/acs.energyfuels.8b02571openalex-snapshot
Pyrolysis Characteristics and Kinetics of Typical Municipal Solid Waste Components and Their Mixture: Analytical TG-FTIR Study
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Organic reaction mechanism classification using machine learning
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