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Title: Active Learning-driven Quantitative Synthesis-Structure-Property Relations for Improving Performance and Revealing Active Sites of Nitrogen-Doped Carbon for the Hydrogen Evolution Reaction

Abstract

While quantitative structure-properties relations (QSPRs) have been developed successfully in multiple fields, catalyst synthesis affects structure and in turn performance, making simple QSPRs inadequate. Furthermore, catalysts often have multiple active sites preventing one from obtaining insights into structure-property relations. Here, we develop a data-driven quantitative synthesis-structure-property relation (QS2PRs) methodology to elucidate correlations between catalyst synthesis conditions, structural properties as well as observed performance and to provide fundamental insights into active sites and a systematic way to optimize practical catalysts. Here, we demonstrate the approach to the synthesis of nitrogen-doped catalysts (NDC) made via pyrolysis for the performance of the electrochemical hydrogen evolution reaction (HER), quantified by the onset potential and the current density. We determine crystallinity, nitrogen species type and fraction, surface area, and pore structure of the NDC’s using XRD, XPS, and BET characterization. We demonstrated that an active learning-based optimization combined with various elementary machine learning tools (regression, principal component analysis, partial least squares) can efficiently identify optimum pyrolysis conditions to tune structural characteristics and performance with concomitant savings in materials and experimental time. Unlike previous reports on the importance of pyridinic or graphitic nitrogen, we discover that the electrochemical performance is not driven by a single catalystmore » property; rather, it arises from a multivariate influence of nitrogen dopants, pore structure and disorder in the NDC materials. Identification of active sites can help mechanistic understanding and further catalyst improvement.« less

Authors:
 [1];  [1];  [1];  [1];  [1]
  1. University of Delaware, Newark, DE (United States)
Publication Date:
Research Org.:
Catalysis Center for Energy Innovation, Newark, DE (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES)
Contributing Org.:
Delaware Environmental Institute Fellows Program from the University of Delaware
OSTI Identifier:
1657667
Alternate Identifier(s):
OSTI ID: 1659341
Grant/Contract Number:  
SC0001004
Resource Type:
Accepted Manuscript
Journal Name:
Reaction Chemistry & Engineering
Additional Journal Information:
Journal Name: Reaction Chemistry & Engineering; Journal ID: ISSN 2058-9883
Publisher:
Royal Society of Chemistry
Country of Publication:
United States
Language:
English
Subject:
42 ENGINEERING

Citation Formats

Ebikade, Osamudiamhen Elvis, Wang, Yifan, Samulewicz, Nicholas, Hasa, Bjorn, and Vlachos, Dion. Active Learning-driven Quantitative Synthesis-Structure-Property Relations for Improving Performance and Revealing Active Sites of Nitrogen-Doped Carbon for the Hydrogen Evolution Reaction. United States: N. p., 2020. Web. https://doi.org/10.1039/d0re00243g.
Ebikade, Osamudiamhen Elvis, Wang, Yifan, Samulewicz, Nicholas, Hasa, Bjorn, & Vlachos, Dion. Active Learning-driven Quantitative Synthesis-Structure-Property Relations for Improving Performance and Revealing Active Sites of Nitrogen-Doped Carbon for the Hydrogen Evolution Reaction. United States. https://doi.org/10.1039/d0re00243g
Ebikade, Osamudiamhen Elvis, Wang, Yifan, Samulewicz, Nicholas, Hasa, Bjorn, and Vlachos, Dion. Fri . "Active Learning-driven Quantitative Synthesis-Structure-Property Relations for Improving Performance and Revealing Active Sites of Nitrogen-Doped Carbon for the Hydrogen Evolution Reaction". United States. https://doi.org/10.1039/d0re00243g.
@article{osti_1657667,
title = {Active Learning-driven Quantitative Synthesis-Structure-Property Relations for Improving Performance and Revealing Active Sites of Nitrogen-Doped Carbon for the Hydrogen Evolution Reaction},
author = {Ebikade, Osamudiamhen Elvis and Wang, Yifan and Samulewicz, Nicholas and Hasa, Bjorn and Vlachos, Dion},
abstractNote = {While quantitative structure-properties relations (QSPRs) have been developed successfully in multiple fields, catalyst synthesis affects structure and in turn performance, making simple QSPRs inadequate. Furthermore, catalysts often have multiple active sites preventing one from obtaining insights into structure-property relations. Here, we develop a data-driven quantitative synthesis-structure-property relation (QS2PRs) methodology to elucidate correlations between catalyst synthesis conditions, structural properties as well as observed performance and to provide fundamental insights into active sites and a systematic way to optimize practical catalysts. Here, we demonstrate the approach to the synthesis of nitrogen-doped catalysts (NDC) made via pyrolysis for the performance of the electrochemical hydrogen evolution reaction (HER), quantified by the onset potential and the current density. We determine crystallinity, nitrogen species type and fraction, surface area, and pore structure of the NDC’s using XRD, XPS, and BET characterization. We demonstrated that an active learning-based optimization combined with various elementary machine learning tools (regression, principal component analysis, partial least squares) can efficiently identify optimum pyrolysis conditions to tune structural characteristics and performance with concomitant savings in materials and experimental time. Unlike previous reports on the importance of pyridinic or graphitic nitrogen, we discover that the electrochemical performance is not driven by a single catalyst property; rather, it arises from a multivariate influence of nitrogen dopants, pore structure and disorder in the NDC materials. Identification of active sites can help mechanistic understanding and further catalyst improvement.},
doi = {10.1039/d0re00243g},
journal = {Reaction Chemistry & Engineering},
number = ,
volume = ,
place = {United States},
year = {2020},
month = {9}
}

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