A Machine Learning Model for Predicting Composition of Catalytic Coprocessing Products from Molecular Beam Mass Spectra-Reference-Cited by-同舟云学术

A Machine Learning Model for Predicting Composition of Catalytic Coprocessing Products from Molecular Beam Mass Spectra

Published:2023-08-03 Issue:32 Volume:11 Page:11912-11923
ISSN:2168-0485
Container-title:ACS Sustainable Chemistry & Engineering
language:en
Short-container-title:ACS Sustainable Chem. Eng.

Author:

Jabed Mohammed A.¹^ORCID,Kim Yeonjoon¹^ORCID,Yarbrough Clark²,Harman-Ware Anne E.³^ORCID,Olstad Jessica²,Seiser Reinhard²,Paeper Cheyenne²,Starace Anne K.²^ORCID,Kim Seonah¹^ORCID

Affiliation:

1. Chemistry Department, Colorado State University, 1301 Center Ave, Fort Collins, Colorado 80523, United States

2. Catalytic Carbon Transformation and Scale-up Center, National Renewable Energy Laboratory, Golden, Colorado 80401, United States

3. Renewable Resources and Enabling Sciences Center, National Renewable Energy Laboratory, Golden, Colorado 80401, United States

Funder

Bioenergy Technologies Office

Publisher

American Chemical Society (ACS)

Subject

Renewable Energy, Sustainability and the Environment,General Chemical Engineering,Environmental Chemistry,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acssuschemeng.3c01821

Reference70 articles.

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4. FT-IR imaging and pyrolysis-molecular beam mass spectrometry: new tools to investigate wood tissues

5. Characterization of the water-insoluble fraction from pyrolysis oil (pyrolytic lignin). Part I. PY–GC/MS, FTIR, and functional groups

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