Theoretical modeling and machine learning-based data processing workflows in comprehensive two-dimensional gas chromatography—A review

M Gaida, PH Stefanuto, JF Focant - Journal of Chromatography A, 2023 - Elsevier
In recent years, comprehensive two-dimensional gas chromatography (GC× GC) has been
gradually gaining prominence as a preferred method for the analysis of complex samples …

Perspective on Fuel Property Blending Rules for Design and Qualification of Aviation Fuels: A Review

RC Boehm, Z Yang, DC Bell, C Faulhaber… - Energy & …, 2024 - ACS Publications
The push toward sustainable aviation fuels (SAFs) is intensifying in response to global
decarbonization efforts. This review discusses the development and assessment of …

Comprehensive two-dimensional gas chromatography: a universal method for composition-based prediction of emission characteristics of complex fuels

J Melder, J Zinsmeister, T Grein, S Jürgens… - Energy & …, 2023 - ACS Publications
Liquid fuels such as gasoline, kerosene, or diesel exhibit a very complex chemical
composition. The transition toward a sustainable world requires the development of novel …

Quantifying isomeric effects: A key factor in aviation fuel assessment and design

C Hall, DC Bell, J Feldhausen, B Rauch, J Heyne - Fuel, 2024 - Elsevier
Isomeric structural differences can profoundly influence hydrocarbon properties of
importance to aviation turbine fuels. This is particularly true for alternative fuels that often …

Numerical Approaches to Determine Cetane Number of Hydrocarbons and Oxygenated Compounds, Mixtures, and their Blends

B Creton, N Brassart, A Herbaut, M Matrat - Energy & Fuels, 2024 - ACS Publications
In the present work, we report the development and use of models to predict the cetane
number of hydrocarbons and oxygenated compounds, mixtures, and their blends. The study …

Prediction of critical micelle concentration for per-and polyfluoroalkyl substances

B Creton, E Barraud, C Nieto-Draghi - SAR and QSAR in …, 2024 - Taylor & Francis
In this study, we focus on the development of Quantitative Structure-Property Relationship
(QSPR) models to predict the critical micelle concentration (CMC) for per-and polyfluoroalkyl …

Comprehensive accurate prediction of critical jet fuel properties with multiple machine learning models

Y Shao, M Yu, M Zhao, K Xue, X Zhang, JJ Zou… - Chemical Engineering …, 2025 - Elsevier
Quantitative structure–property relationship (QSPR) model development driven by emerging
machine learning (ML) shows promise for accelerating design and preparation of jet fuels …

Prediction of hydrocarbons ignition performances using machine learning modeling

G Flora, F Karimzadeh, MSP Kahandawala, MJ DeWitt… - Fuel, 2024 - Elsevier
This study presents a computational methodology for determining the Derived Cetane
Number (DCN) of practical aviation fuels. The proposed approach integrates a novel …

Explainable machine learning assisted design of tailor-made fuels using conjoint fingerprints

Y Chen, Z Lu, Z Yao, B Li, X Zhang, H Wang… - Energy Conversion and …, 2024 - Elsevier
This work presents an advanced computer-aided molecular design framework by
considering the molecular structure of fuels as the fundamental design degree of freedom …