Interpretable Machine Learning Models for Practical Antimonate Electrocatalyst Performance.

Computationally predicting the performance of catalysts under reaction conditions is a challenging task due to the complexity of catalytic surfaces and their evolution in situ, different reaction paths, and the presence of solid-liquid interfaces in the case of electrochemistry. We demonstrate here how relatively simple machine learning models can be found that enable prediction of experimentally observed onset potentials. Inputs to our model are comprised of data from the oxygen reduction reaction on non-precious transition-metal antimony oxide nanoparticulate catalysts with a combination of experimental conditions and computationally affordable bulk atomic and electronic structural descriptors from density functional theory simulations. From human-interpretable genetic programming models, we identify key experimental descriptors and key supplemental bulk electronic and atomic structural descriptors that govern trends in onset potentials for these oxides and deduce how these descriptors should be tuned to increase onset potentials. We finally validate these machine learning predictions by experimentally confirming that scandium as a dopant in nickel antimony oxide leads to a desired onset potential increase. Macroscopic experimental factors are found to be crucially important descriptors to be considered for models of catalytic performance, highlighting the important role machine learning can play here even in the presence of small datasets.

Understanding the Stability of Manganese Chromium Antimonate Electrocatalysts through Multimodal In Situ and Operando Measurements.

Improving electrocatalyst stability is critical for the development of electrocatalytic devices. Herein, we utilize an on-line electrochemical flow cell coupled with an inductively coupled plasma-mass spectrometer (ICP-MS) to characterize the impact of composition and reactant gas on the multielement dissolution of Mn(-Cr)-Sb-O electrocatalysts. Compared to Mn2O3 and Cr2O3 oxides, the antimonate framework stabilizes Mn at OER potentials and Cr at both ORR and OER potentials. Furthermore, dissolution of Mn and Cr from Mn(-Cr) -Sb-O is driven by the ORR reaction rate, with minimal dissolution under N2. We observe preferential dissolution of Cr totaling 13% over 10 min at 0.3, 0.6, and 0.9 V vs RHE, with only 1.5% loss of Mn, indicating an enrichment of Mn at the surface of the particles. Despite this asymmetric dissolution, operando X-ray absorption spectroscopy (XAS) showed no measurable changes in the Mn K-edge at comparable potentials. This could suggest that modification to the Mn oxidation state and/or phase in the surface layer is too small or that the layer is too thin to be measured with the bulk XAS measurement. Lastly, on-line ICP-MS was used to assess the effects of applied potential, scan rate, and current on Mn-Cr-Sb-O during cyclic voltammetry and accelerated stress tests. With this deeper understanding of the interplay between oxygen reduction and dissolution, testing procedures were identified to maximize both activity and stability. This work highlights the use of multimodal in situ characterization techniques in tandem to build a more complete model of stability and develop protocols for optimizing catalyst performance.

First-Row Transition Metal Antimonates for the Oxygen Reduction Reaction.

The development of inexpensive and abundant catalysts with high activity, selectivity, and stability for the oxygen reduction reaction (ORR) is imperative for the widespread implementation of fuel cell devices. Herein, we present a combined theoretical-experimental approach to discover and design first-row transition metal antimonates as excellent electrocatalytic materials for the ORR. Theoretically, we identify first-row transition metal antimonates─MSb2O6, where M = Mn, Fe, Co, and Ni─as nonprecious metal catalysts with good oxygen binding energetics, conductivity, thermodynamic phase stability, and aqueous stability. Among the considered antimonates, MnSb2O6 shows the highest theoretical ORR activity based on the 4e- ORR kinetic volcano. Experimentally, nanoparticulate transition metal antimonate catalysts are found to have a minimum of a 2.5-fold enhancement in intrinsic mass activity (on transition metal mass basis) relative to the corresponding transition metal oxide at 0.7 V vs RHE in 0.1 M KOH. MnSb2O6 is the most active catalyst under these conditions, with a 3.5-fold enhancement on a per Mn mass activity basis and 25-fold enhancement on a surface area basis over its antimony-free counterpart. Electrocatalytic and material stability are demonstrated over a 5 h chronopotentiometry experiment in the stability window identified by theoretical Pourbaix analysis. This study further highlights the stable and electrically conductive antimonate structure as a framework to tune the activity and selectivity of nonprecious metal oxide active sites for ORR catalysis.

Acid anion electrolyte effects on platinum for oxygen and hydrogen electrocatalysis.

Platinum is an important material with applications in oxygen and hydrogen electrocatalysis. To better understand how its activity can be modulated through electrolyte effects in the double layer microenvironment, herein we investigate the effects of different acid anions on platinum for the oxygen reduction/evolution reaction (ORR/OER) and hydrogen evolution/oxidation reaction (HER/HOR) in pH 1 electrolytes. Experimentally, we see the ORR activity trend of HClO4 > HNO3 > H2SO4, and the OER activity trend of HClO4 [Formula: see text] HNO3 ∼ H2SO4. HER/HOR performance is similar across all three electrolytes. Notably, we demonstrate that ORR performance can be improved 4-fold in nitric acid compared to in sulfuric acid. Assessing the potential-dependent role of relative anion competitive adsorption with density functional theory, we calculate unfavorable adsorption on Pt(111) for all the anions at HER/HOR conditions while under ORR/OER conditions [Formula: see text] binds the weakest followed by [Formula: see text] and [Formula: see text]. Our combined experimental-theoretical work highlights the importance of understanding the role of anions across a large potential range and reveals nitrate-like electrolyte microenvironments as interesting possible sulfonate alternatives to mitigate the catalyst poisoning effects of polymer membranes/ionomers in electrochemical systems. These findings help inform rational design approaches to further enhance catalyst activity via microenvironment engineering.

Tuning the electronic structure of Ag-Pd alloys to enhance performance for alkaline oxygen reduction.

Alloying is a powerful tool that can improve the electrocatalytic performance and viability of diverse electrochemical renewable energy technologies. Herein, we enhance the activity of Pd-based electrocatalysts via Ag-Pd alloying while simultaneously lowering precious metal content in a broad-range compositional study focusing on highly comparable Ag-Pd thin films synthesized systematically via electron-beam physical vapor co-deposition. Cyclic voltammetry in 0.1 M KOH shows enhancements across a wide range of alloys; even slight alloying with Ag (e.g. Ag0.1Pd0.9) leads to intrinsic activity enhancements up to 5-fold at 0.9 V vs. RHE compared to pure Pd. Based on density functional theory and x-ray absorption, we hypothesize that these enhancements arise mainly from ligand effects that optimize adsorbate-metal binding energies with enhanced Ag-Pd hybridization. This work shows the versatility of coupled experimental-theoretical methods in designing materials with specific and tunable properties and aids the development of highly active electrocatalysts with decreased precious-metal content.