index.json

[{"authors":["samueldy"],"categories":null,"content":"I am a postdoctoral researcher in the Goldsmith Computational Modeling Laboratory at the University of Michigan. I completed my doctoral degree at University of Michigan in August 2023. My graduate research centered on using computational catalysis and machine learning for environmental sciences, specifically water remediation. I am also a 2022–2023 J. Robert Beyster Computational Innovation Graduate Fellow and a member of the Michigan Catalysis Science and Technology Institute.\nIn my free time, I enjoy swimming, music composition, and organ performance.\nCheck out my CV!\n","date":1697130000,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1697130000,"objectID":"55bc91470fadcf396828f307f47dde0a","permalink":"","publishdate":"0001-01-01T00:00:00Z","relpermalink":"","section":"authors","summary":"I am a postdoctoral researcher in the Goldsmith Computational Modeling Laboratory at the University of Michigan. I completed my doctoral degree at University of Michigan in August 2023. My graduate research centered on using computational catalysis and machine learning for environmental sciences, specifically water remediation.","tags":null,"title":"Samuel D. Young","type":"authors"},{"authors":["Samuel D. Young"],"categories":["umich"],"content":"Humans contribute over 108 tonnes of reactive nitrogen to the environment each year, largely from fertilizer runoff and industrial processes.1 A major consequence is that aqueous nitrate (NO3–) is one of the most widespread water pollutants.2,3 Consuming nitrate has been linked to infant methemoglobinemia (“blue baby syndrome”), childbirth complications, and ovarian cancer.2,4 High nitrate levels in lakes and oceans also cause mass death of aquatic life through eutrophication.5 Finding an effective strategy to balance the nitrogen cycle is a National of Academy of Engineering grand challenge.6 Concentrated nitrate also exists in low-level nuclear waste,7 and could facilitate spent fuel recovery if removed from the waste stream.8\nComparison of denitrification approaches The electrocatalytic nitrate reduction reaction (NO3RR) is a promising strategy for aqueous denitrification. It converts nitrates to benign or valuable products, such as nitrogen gas (N2) or ammonia (NH3). Unlike thermocatalytic, physical, or biological denitrification, the NO3RR does not require additional chemicals or generate a secondary waste stream.9 This makes NO3RR approaches desirable for modular, decentralized applications. A technoeconomic analysis of low-level nuclear waste found that using the NO3RR to convert nitrate to NH3 and N2 gas could be cost-competitive if a sufficiently stable, active, and selective catalyst exists.10 However, researchers have yet to identify such an electrocatalyst.\nMy research focuses on discovering active, selective, and stable electrocatalysts to promote NO3RR. I am currently studying bimetallic alloys of earth-abundant metals as well as metal sulfides for this reaction, in collaboration with experimental researchers in the Nirala Singh lab at University of Michigan. Recent projects include an examination of how PtRu alloy composition affects catalyst activity as well as whether metal sulfides are resistant to halide poisoning.\nPtRu Alloys Basic schematic of NO3RR reactions and species The electrocatalytic nitrate reduction reaction (NO3RR) converts nitrate (NO3–) ions in water to benign or valuable products, such as nitrogen (N2) gas, ammonia (NH3), hydroxylamine (NH2OH), or ammonium nitrate (NH4NO3).11 The rate of conversion and selectivity towards each product depends on a complex reaction mechanism and operating conditions such as solution pH and applied cell voltage. On transition metal surfaces at moderately low (\u0026lt; 1 M) nitrate concentrations and, NO3RR follows a reaction mechanism involving dissociation of surface-bound NO3– to other surface-bound nitrogen intermediates, as well as surface reaction between these intermediates and surface-bound H.12 This mechanism appears below:13\nSimplified NO3RR mechanism and transition state intermdiates A major challenge in commercializing aqueous-phase electrocatalytic denitrification is the discovery of a sufficiently active, selective, and stable electrocatalyst. Such an electrocatalyst must also attain high activity and selectivity at relatively low (\u0026lt; 1 V) overpotentials.\nOf the pure transition metals, Rh is the most active towards NO3RR, as the proposed rate-limiting step (dissociation of NO3* to NO2* and O*) requires a high coverage of NO3, and Rh is able to bind NO3 to its surface more strongly than other metals do. However, Rh is very expensive (~$8,300/oz) and rare, so it is not a practical choice for widespread denitrification applications.\nPrevious and recent studies have investigated the performance of bimetallic alloys for nitrate reduction and have demonstrated that alloy catalysts can often achieve performance that is better than either pure metal alone. This is accomplished through ligand, strain, and ensemble effects.14 For example, alloying Pt and Sn helps increase the rate of the NO3RR rate-limiting step (nitrate dissociation) and also makes the reaction more selective toward producing hydroxylamine.15 Additionally, a Cu50Ni50 alloy was shown to be six times as active as pure Cu at 0 V vs. RHE.16 It was also shown that the NO3RR activity on the CuNi alloy is a function of the alloy composition, and that there exists an optimal composition that maximizes activity.\nOur previous computational work13 predicted that Pt3Ru is a highly active alloy for NO3RR. Our research around PtRu alloys explores whether this is true, and whether NO3RR activity depends on PtRu alloy composition, as it does for CuNi alloys. Preliminary experimental and computational studies suggest that NO3RR activity on PtxRuy alloys is indeed a function of alloy composition, and is maximized at an approximate composition of Pt78Ru22. We are also interested in investigating\nhow PtRu alloy composition impacts the selectivity of the reaction towards one or more products, which step in the reaction is rate-determining at a variety of alloy compositions and potentials, the stability of PtRu alloys in acidic media during long periods of operation, and the denitrification cost per …","date":1600717750,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1600717750,"objectID":"6aa44fe8c24c9b54150a29fe35bde2b5","permalink":"https://samueldy.github.io/project/no3rr/","publishdate":"2020-09-21T15:49:10-04:00","relpermalink":"/project/no3rr/","section":"project","summary":"Electrocatalysis is a promising approach to remediating nitrate-contaminated water.","tags":["catalysis","environment"],"title":"Electrocatalytic Nitrate Reduction","type":"project"},{"authors":["Samuel D. Young"],"categories":["umich"],"content":"My larger vision for alloy research is to use machine learning (ML) and microkinetic knowledge to discover highly active and selective electrocatalysts that are presently unknown. Of the potentially millions of materials (such as intermetallics,1 low- and high-entropy metal alloys,2 metal sulfides,3 and single atoms4) that might be active and selective for the nitrate reduction reaction, it is impossible to know ahead of time which of them are the most performant. Traditional evaluation of catalyst figures of merit (e.g., intuition-guided experiments or DFT calculations on a few catalysts at a time) are far too slow to screen a catalyst space of this size. However, ML promises to accelerate this process by providing ways to more cheaply evaluate a potential catalyst’s figures of merit.5,6\nComparison of experimental, DFT-only, and ML-assisted pathways for estimating catalyst performance. Once trained with active learning, the ML model can exploit adsorbate scaling relations, BEP (Brønsted-Evans-Polanyi) relations, and volcano surface calculations to cheaply calculate binding energies, reaction activation barriers, and catalyst activity, respectively. ML prediction is usually at least O(103) times faster than DFT prediction. Supervised ML learns correlations between sets of input and output training data to gain the ability to predict what output should result from a new input. My research focuses on training supervised ML models to act as surrogate DFT calculators, a scheme which can estimate binding energies approximately 103 times faster than analogous DFT calculation. In this scheme, a supervised ML model predicts a binding energy given only the geometry and atomic identities of an adsorbed slab. If trained on enough data, such a model could screen a large catalyst space in a more reasonable amount of time by rapidly predicting whether a binding energy falls within a window known to lead to high catalyst activity. This would greatly accelerate the search for a performant catalyst.\nSeveral software packages implement some of the features useful for constructing these models. The GASpy software package7 automates the combinatorial calculation of potentially thousands of adsorption energies of common monodentate adsorbates across multiple facets on bielemental crystal structures. It uses the Atomic Simulation Environment8 to carry out atomic transformations and has been used to calculate CO and H binding energies on bimetallic alloys7 and, more recently, energies on Cu alloys for nitrate-to-ammonia reduction.9 The Atomate10 and Rocketsled11 packages automate many of the same tasks for workflows built on the Pymatgen12 library.\nSeveral machine learning (ML) models have been developed to predict binding energy from the geometry and identity of the atoms of an adsorbed slab model. A number of featurization algorithms have emerged to encode this atomic geometry into translation- and rotation-invariant ML features, such as the Smooth Overlap of Atomic Positions (SOAP) representation13, the moment tensor potential (MTP) representation14, and the many-body tensor representation (MBTR)15. There is also a class of exciting models called crystal graph convolutional neural networks (CGCNNs), which function by encoding information about each atom and chemical bond in a topological graph representing a bulk crystal structure. This allows the convolutional and pooling layers to extract features relevant to that crystal16. This model was later adapted for surface catalysis by additionally encoding information about the local atomic geometry around each slab and adsorbate atom17 (see figure below) and further improved by including information about each atom’s electron configuration18.\nActive learning is another important technology relevant to catalyst ML problems. Active learning is a strategy used when a training data set is small and obtaining more training data is costly. The high cost of DFT calculations means that most DFT-based catalysis data sets are small. Active learning can simultaneously refine the accuracy of a ML model and build a training data set by strategically selecting new training data to evaluate with DFT This approach helps minimize the number of expensive DFT calculations that must occur. The figure below illustrates pool-based sampling,19 one way of implementing active learning.\nActive learning workflows obtain new data based on an acquisition function, which is an algorithmic approach for selecting data in a manner that constructs the model as cheaply as possible. One major acquisition function is the expected improvement acquisition function,20 which provides a good balance between improving model accuracy and exploring unstudied catalyst structures that may have desirable figures of merit.21\nOur insights about which acquisition functions and featurization protocols create the most accurate and generalizable surrogate DFT models will enable many more researchers in catalysis to exploit the newest …","date":1599949043,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1599949043,"objectID":"948c9e5941437631ffc52f0bda9501b5","permalink":"https://samueldy.github.io/project/alloy-ml/","publishdate":"2020-09-12T18:17:23-04:00","relpermalink":"/project/alloy-ml/","section":"project","summary":"Active machine learning can accelerate the search for highly active, stable, and selective catalysts.","tags":["catalysis","ml"],"title":"Machine Learning for Alloy Catalyst Discovery","type":"project"},{"authors":["Samuel D. Young"],"categories":null,"content":"","date":1697130000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1697130000,"objectID":"8bf92bb42b443340472a6af27d334695","permalink":"https://samueldy.github.io/talk/heterogeneous-electrocatalysts-for-aqueous-nitrate-reduction-and-nitrogen-chemistry/","publishdate":"2023-10-17T12:11:43-04:00","relpermalink":"/talk/heterogeneous-electrocatalysts-for-aqueous-nitrate-reduction-and-nitrogen-chemistry/","section":"event","summary":"U.S. Army Research Lab Distinguished Postdoctoral Fellowship Candidate Seminar","tags":["umich"],"title":"Heterogeneous Electrocatalysts for Aqueous Nitrate Reduction and Nitrogen Chemistry","type":"event"},{"authors":["Samuel D. Young"],"categories":[],"content":"Abstract Humans contribute more fixed nitrogen than can be reduced naturally. Understanding nitrogen chemistry is essential to balancing the global nitrogen cycle. An imbalanced nitrogen cycle raises levels of nitrate (NO3–) in water. Nitrate-laden water is linked to infant methemoglobinemia and ovarian cancer in humans, and to eutrophication in water reservoirs. To denitrify water, we propose using the electrocatalytic nitrate reduction reaction (NO3RR). NO3RR sustainably removes nitrate from water and generates benign or value-added products, such as NH3 or N2. However, understanding the interconversion of NO3–, NH3, and N2 and developing new catalytic materials are critical to enabling this process. In this thesis, we explore new NO3RR electrocatalysts, including metal alloys, metal sulfides, and metal oxynitrides. Chapters II–IV focus on original research, Chapter I provides an introduction to nitrate reduction, and Chapter V provides conclusions and a future outlook.\nIn Chapter II, we study the NO3RR mechanism on Pt–Ru catalysts. We hypothesized that tuning the Pt–Ru alloy composition will maximize the NO3RR rate by changing the NO3– and H adsorption strengths. We find Pt78Ru22/C in particular to be six times as active as Pt/C at 0.1 V vs. RHE. This maximum in activity arises from a transition in rate-determining step from nitrate dissociation to a different step at higher Ru content. This study demonstrates how electrocatalyst performance is tunable by changing the adsorption strength of reacting species through alloying.\nIn Chapter III, we study halide poisoning, a serious problem for many NO3RR electrocatalysts. Here we compare the NO3RR activity of rhodium sulfide (RhxSy) against Pt/C and Rh/C in the presence of chloride. We find that RhxSy is 1.6 to 5.6 times more active than Rh/C (the most active transition metal electrocatalyst) and 10 to 24 times more active than Pt/C over a potential range of 0 to 0.2 V vs RHE. In addition to being more active than Pt/C, RhxSy retains 63% of its activity in the presence of chloride. Sulfur vacancies in Rh3S4 terraces are predicted to be active for nitrate reduction via an H-assisted nitrate dissociation mechanism, but also bind chloride strongly. Our findings rationalize the experimentally observed high NO3RR activity but moderate chloride poison resistance of RhxSy/C.\nIn Chapter IV, we investigate the thermodynamic stability of perovskite oxynitrides (PONs), a promising class of ammonia synthesis electrocatalysts. We determine a prototypical stable anion ordering for both ABO2N and ABON2 stoichiometries containing a high degree of cis ordering between B cations and minority-composition anions. We predict 85 stable and 109 metastable PON compounds, with A = {La, Pb, Nd, Sr, Ba, Ca} and B = {Re, Os, Nb, Ta} forming PONs of less than 10 meV/atom above the thermodynamic convex hull. Computational Pourbaix diagrams for two stable candidates, CaReO2N and LaTaON2, suggest that not all compounds with zero energy above the thermodynamic convex hull can be easily synthesized.\nChapter V reviews the major findings of Chapters II–IV and discusses future research. We propose how machine learning studies can extend this dissertation’s work and accelerate discovery of new NO3RR electrocatalysts, including high-entropy and defected alloys, defected metal chalcogenides, and complex perovskites. Highly active, selective, and stable NO3RR electrocatalysts will help mitigate the ecological and health risks from the nitrogen cycle imbalance in an energy-efficient and economically viable way.\n","date":1693146204,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1693146204,"objectID":"a391b7ede3551fbdb15a08bbddeb727d","permalink":"https://samueldy.github.io/publication/written-dissertation/","publishdate":"2023-08-27T10:23:24-04:00","relpermalink":"/publication/written-dissertation/","section":"publication","summary":"Abstract Humans contribute more fixed nitrogen than can be reduced naturally. Understanding nitrogen chemistry is essential to balancing the global nitrogen cycle. An imbalanced nitrogen cycle raises levels of nitrate (NO3–) in water.","tags":["umich"],"title":"Written Dissertation","type":"publication"},{"authors":["Samuel D. Young"],"categories":null,"content":"","date":1691413200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1691413200,"objectID":"a5b7da8679f433afaf444a1688171849","permalink":"https://samueldy.github.io/talk/oral-dissertation-defense/","publishdate":"2023-08-27T10:23:16-04:00","relpermalink":"/talk/oral-dissertation-defense/","section":"event","summary":"Oral defense of the dissertation of Samuel D. Young","tags":["umich"],"title":"Oral Dissertation Defense","type":"event"},{"authors":["Samuel D. Young","Jiadong Chen","Wenhao Sun","Bryan R. Goldsmith","Ghanshyam Pilania"],"categories":[],"content":"The version of record is located here. Supporting information is available for free here.\n","date":1689739200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1689739200,"objectID":"ee88f6a2f1472f17ea39aece1b4c3d37","permalink":"https://samueldy.github.io/publication/chemater-pon-article/","publishdate":"2023-06-28T01:17:11-04:00","relpermalink":"/publication/chemater-pon-article/","section":"publication","summary":"Perovskite oxynitrides (PONs) are a promising class of materials for applications ranging from catalysis to photovoltaics. However, the vast space of single PON materials (ABO3-*x*N*x*) has yet to be fully explored. Additionally, the community needs guidelines that relate PON chemistry and anion ordering to stability to better understand how to design PON materials that resist corrosion and decomposition under operating conditions. Screening this materials space requires identifying candidate PON materials that will be stable under operating conditions, which in turn requires methods to evaluate each material's stability. Here we predict the stability of single PON materials using a four-step approach based on density functional theory modeling: (i) enumerate viable cation pairs, (ii) select an energetically favorable prototypical anion ordering, (iii) compute each PON's energy above the thermodynamic convex hull, and (iv) generate computational Pourbaix diagrams to determine allowable ranges of electrochemical operating conditions. A critical part of our approach is determining a prototypical stable anion ordering for both ABO2N and ABON2 stoichiometries across a variety of A- and B-site cations. We demonstrate a stable anion ordering containing a high degree of *cis* ordering between B cations and minority-composition anions. We predict 85 stable and 109 metastable PON compounds, with A = {La, Pb, Nd, Sr, Ba, Ca} and B = {Re, Os, Nb, Ta} forming cation pairs that lead to stable PONs less than 10 meV/atom above the thermodynamic convex hull. Computational Pourbaix diagrams for two stable candidates, CaReO2N and LaTaON2, suggest that not all compounds with zero energy above the thermodynamic convex hull can be easily synthesized.","tags":["umich"],"title":"Thermodynamic Stability and Anion Ordering of Perovskite Oxynitrides","type":"publication"},{"authors":["Samuel D. Young","Bianca Ceballos","Amitava Banerjee","Ghanshyam Pilania","Bryan Goldsmith"],"categories":null,"content":"","date":1668787920,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1668787920,"objectID":"ec8fd181b773d4682f1050e9b4e99fa3","permalink":"https://samueldy.github.io/talk/aiche-2022-thermodynamic-stability-and-anion-ordering-in-abo2n-and-abon2-perovskite-oxynitrides/","publishdate":"2022-11-21T21:35:14-05:00","relpermalink":"/talk/aiche-2022-thermodynamic-stability-and-anion-ordering-in-abo2n-and-abon2-perovskite-oxynitrides/","section":"event","summary":"Here we report a DFT screening study of 295 ABO2N and ABON2 PON structures composed of combinations of 55 A and B cations. Our workflow enumerates PON structures and discovers trends related to stability under reaction conditions (Figure 1). For a given pair of A-B cation oxidation states, we analyze relative stability of different anion orderings to understand composition- and configuration-dependent design rules in this chemical space. Ab initio analysis both confirms the stability of cis ordering of N and O atoms around the B-site octahedra and predicts many previously unknown PONs to be thermodynamically stable. In particular, PONs with B = {Re, Os} and a variety of A-site chemistries are predicted to be stable (i.e., a decomposition free energy of 10 meV/atom or less). Other PONs with A = {La, Nd} and B = {Nb, Ta} are also possibly stable. Theoretical Pourbaix diagrams of promising compositions are generated to understand their aqueous thermodynamic (meta)stability under varying pH and applied potentials, which has relevance to electrocatalytic applications. Generally, we find that the thermodynamic stability of PONs depends heavily on cation composition and anion ordering, leading to design guidelines to select potentially stable and synthesizable PON materials. Additionally, only a few PONs seem to be stable or metastable under acidic electrochemical conditions, suggesting that PON catalysts may function better in non-aqueous solvents for many electrochemical systems.","tags":["catalysis","umich"],"title":"AIChE 2022: Thermodynamic Stability and Anion Ordering in ABO2N and ABON2 Perovskite Oxynitrides","type":"event"},{"authors":["Samuel D. Young","Amitava Banerjee","Bryan R. Goldsmith","Ghanshyam Pilania"],"categories":null,"content":"","date":1661269200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1661269200,"objectID":"aff526faad228caa8b45f6f9d1e2ed27","permalink":"https://samueldy.github.io/talk/acs-fall-2022-thermodynamic-stability-and-anion-ordering-in-abo2n-and-abon2-perovskite-oxynitrides/","publishdate":"2022-08-29T12:26:36-05:00","relpermalink":"/talk/acs-fall-2022-thermodynamic-stability-and-anion-ordering-in-abo2n-and-abon2-perovskite-oxynitrides/","section":"event","summary":"Perovskite oxynitrides (PONs), derivatives of the perovskite oxide structure with both N and O anions, show great promise as catalysts in electrochemical and photochemical reactions. Their versatility arises largely from a high degree of geometric and electronic structure tunability, achievable laregly through anion and cation ordering and composition. There are potentially tens of thousands of PON structures. However, little is known about which are synthesizable or stable under electrochemical reaction conditions, or the trends in anion or cation choice that could predict this stability. Here we conduct a high-throughput DFT screening study of 295 ABO2N and ABON2 PON structures composed of combinations of 55 cations. We hypothesized that anion orderings with cis ordering of the anions and particular cations in a PON structure would correspond to a low calculated decomposition energy. We identified 32 possible symmetrically distinct anion orderings and found that orderings with both a high fraction of M-B-M cis bonds (M = N for ABO2N and O for ABON2) and a high distribution of these bonds throughout the structure generally lead to stable PON structures. We found that PONs with B = {Re, Os} are predicted to be especially stable, with some A = {La, Nd} compounds also predicted to have high stability. Our work suggests that formability of PONs indeed relies on cation and anion engineering and establishes design guidelines for selecting and synthesizing stable PON materials. Our methodology also provides an impotant filtering step to focus future high-throughput materials studies towards promising and stable candidates.","tags":["umich"],"title":"ACS Fall 2022: Thermodynamic Stability and Anion Ordering in ABO2N and ABON2 Perovskite Oxynitrides","type":"event"},{"authors":["Samuel D. Young","Bianca Ceballos","Amitava Banerjee","Rangachary Mukundan","Ghanshyam Pilania","Bryan R. Goldsmith"],"categories":["umich","lanl"],"content":"Selected as an ACS Editors’ Choice article! Publicly available to read for free through the end of Jan 2023 at https://pubs.acs.org/doi/full/10.1021/acs.jpcc.2c02816.\n","date":1659555075,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1659555075,"objectID":"fce863014c7d4861061f9fb4096d01a5","permalink":"https://samueldy.github.io/publication/jpcc-perovskite-nrr/","publishdate":"2022-08-03T15:31:15-04:00","relpermalink":"/publication/jpcc-perovskite-nrr/","section":"publication","summary":"The successful deployment of technologies for the electrocatalytic nitrogen reduction reaction (e-NRR) to synthesize ammonia would enable distributed ammonia production with lower greenhouse gas emissions compared to the Haber–Bosch process. However, electrocatalysts that can readily activate N2, promote selective ammonia formation over the competing hydrogen evolution reaction, and maintain stability under reaction conditions are needed to enable this technology. Herein, we give our perspective on metal oxynitrides (A*x*B*y*O*w*N*z*) as an emerging and underexplored materials class for e-NRR. We contrast the activity, selectivity, and stability of metal oxynitrides with those of their metal nitride and metal oxide counterparts. We discuss the different possible e-NRR reaction mechanisms on metal oxynitrides, emphasize challenges related to using metal oxynitrides for e-NRR, and provide an outlook for future research. Ultimately, the huge design space of metal oxynitrides is ripe for exploration to find catalyst formulations that overcome some of the limitations of traditional metal oxides and metal nitrides for e-NRR.","tags":["catalysis"],"title":"Metal Oxynitrides for the Electrocatalytic Reduction of Nitrogen to Ammonia","type":"publication"},{"authors":["Samuel D. Young","Zixuan Wang","Nirala Singh","Bryan Goldsmith"],"categories":null,"content":" ","date":1637098200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1637098200,"objectID":"312e8f98c5dc413bd699eb8102f4fe04","permalink":"https://samueldy.github.io/talk/aiche-2021-platinum-ruthenium-alloys-as-electrocatalysts-for-efficient-aqueous-nitrate-reduction/","publishdate":"2021-11-16T20:35:47-05:00","relpermalink":"/talk/aiche-2021-platinum-ruthenium-alloys-as-electrocatalysts-for-efficient-aqueous-nitrate-reduction/","section":"event","summary":"To be held in-person at the Hynes Convention Center, Sheraton Boston and Marriott Boston Copley Place over the dates of Nov. 7-11 and virtually from Nov. 15-19, the AIChE Annual Meeting is the forum for ChEs interested in innovation and professional growth. Experts will cover wide range of topics relevant to cutting-edge research, new technologies, and emerging areas in the field.","tags":[],"title":"AIChE 2021: Platinum-Ruthenium Alloys As Electrocatalysts for Efficient Aqueous Nitrate Reduction","type":"event"},{"authors":["Danielle Richards","Samuel D. Young","Bryan R. Goldsmith","Nirala Singh"],"categories":[],"content":"","date":1634169600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1634169600,"objectID":"94b1994cc94bb6362e662f9ba9dd0a6e","permalink":"https://samueldy.github.io/publication/rh-sulfides/","publishdate":"2021-10-22T21:29:44-04:00","relpermalink":"/publication/rh-sulfides/","section":"publication","summary":"Chloride poisoning is a serious problem for the electrocatalytic reduction of aqueous nitrate (NO3−) and improved electrocatalysts are needed. Here we study the electrocatalytic activity of rhodium sulfide supported on carbon (RhxSy/C) for the reduction of nitrate and compare it against Pt/C and Rh/C in the presence of chloride. Between 0.05–0.15 V vs. RHE, RhxSy/C has a steady-state nitrate reduction current density in 1 M H2SO4 + 1 M NaNO3 that is 1.6–5.6 times greater than Rh/C (the most active metal electrocatalyst) and 10–24 times greater than Pt/C. Current densities are decreased by 37% for RhxSy/C, 62% for Rh/C, and 40% for Pt/C at 0.1 V vs. RHE in the presence of 1 mM chloride. The decrease in nitrate reduction activity for Pt, Rh, and RhxSy is due to the competitive adsorption of chloride and nitrate on the surface. Density functional theory (DFT) modeling predicts that chloride poisoning will persistently inhibit nitrate reduction on metals due to linear adsorbate scaling relations between nitrate and chloride. DFT calculations and microkinetic modeling of our experimental measurements predict that nitrate converts to nitrite via an H-assisted dissociation mechanism on Pt and direct nitrate dissociation on Rh and RhxSy. Pristine RhxSy (i.e., Rh3S4, Rh2S3, and Rh17S15) terraces are predicted to be inactive toward nitrate reduction. In contrast, sulfur vacancies in Rh3S4 terraces are predicted to be active for nitrate reduction, but also bind chloride strongly. Thus, sulfur-defected Rh3S4 rationalize the experimentally observed high activity but moderate chloride poison-resistance of RhxSy/C for nitrate reduction.","tags":["catalysis","nitrate"],"title":"Electrocatalytic nitrate reduction on rhodium sulfide compared to Pt and Rh in the presence of chloride","type":"publication"},{"authors":["Danielle Richards","Samuel D. Young","Nirala Singh","Bryan Goldsmith"],"categories":null,"content":"","date":1630004400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1630004400,"objectID":"ad4e56d2a6dd4b2b827d6899571a6e86","permalink":"https://samueldy.github.io/talk/acs-fall-2021-rhodium-sulfide-electrocatalysts-for-electrocatalytic-nitrate-reduction/","publishdate":"2021-09-10T15:57:25-04:00","relpermalink":"/talk/acs-fall-2021-rhodium-sulfide-electrocatalysts-for-electrocatalytic-nitrate-reduction/","section":"event","summary":"Chloride poisoning of catalysts is a serious and largely understudied issue for the electrocatalytic reduction of aqueous nitrate (NO3−). Without poison-resistant electrocatalysts, nitrate reduction processes require increased capital cost for upstream removal of poisons. Here we study electrocatalytic activity of rhodium sulfide supported on carbon (RhxSy/C) for NO3− reduction and compare it against Pt/C and Rh/C in the presence of chloride. We hypothesized that RhxSy/C would combine the high nitrate reduction activity of Rh with the halide resistance observed for other reactions, such as hydrogen evolution. RhxSy/C has a steady-state nitrate reduction current density (−0.12 mA cm−2) at 0.1 V vs. RHE and pH 0 with 1 M nitrate that is five times higher than Rh/C (the most active pure metal electrocatalyst) and two orders of magnitude higher than Pt/C. Current densities are decreased by 37% for RhxSy/C, 62% for Rh/C, and 40% for Pt/C at 0.1 V vs. RHE in the presence of chloride. Decreased nitrate reduction activity for Pt, Rh, and RhxSy is due to the competitive adsorption of chloride and nitrate on the surface. Density functional theory (DFT) modeling predicts that chloride poisoning persistently inhibits nitrate reduction on metals due to linear adsorbate scaling relations between nitrate and chloride. DFT calculations and microkinetic modeling of our experimental measurements predict that nitrate converts to nitrite via an H-assisted dissociation mechanism. Pristine RhxSy terraces are predicted to be inactive toward nitrate reduction. In contrast, sulfur vacancies in RhxSy terraces are active for nitrate reduction, but also bind chloride strongly. Thus, sulfur-defected RhxSy surfaces rationalize the experimentally observed high activity but moderate chloride poison resistance of RhxSy/C for nitrate reduction. Our work highlights metal sulfides as a material class that helps mediate the effects of chloride poisoning and provides design guidelines for the discovery and engineering of other poison-resistant electrocatalysts.","tags":[],"title":"ACS Fall 2021: Rhodium Sulfide Electrocatalysts for Electrocatalytic Nitrate Reduction","type":"event"},{"authors":["Samuel D. Young","Amitava Banerjee","Ghanshyam Pilania","Bryan R. Goldsmith"],"categories":["umich","lanl"],"content":"Now available online. You can read this article for free until 15 Sep 2021.\n","date":1627167223,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1627167223,"objectID":"0aeee419d8c44d72394be99503c722db","permalink":"https://samueldy.github.io/publication/trchem-perovskites-tunable/","publishdate":"2020-09-16T00:00:00Z","relpermalink":"/publication/trchem-perovskites-tunable/","section":"publication","summary":"Perovskite oxynitrides are a tunable class of materials with unique chemistry for electrocatalytic nitrogen reduction.","tags":["catalysis"],"title":"Perovskite Oxynitrides as Tunable Materials for Electrocatalytic Nitrogen Reduction to Ammonia","type":"publication"},{"authors":["Samuel D. Young","Zixuan Wang","Nirala Singh","Bryan Goldsmith"],"categories":null,"content":"","date":1618346400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1618346400,"objectID":"f888a6b77c4b5b13e74f4d9fe06762f2","permalink":"https://samueldy.github.io/talk/acs-spring-2021-platinum-ruthenium-alloys-as-electrocatalysts-for-efficient-aqueous-nitrate-reduction/","publishdate":"2021-04-02T05:05:41-06:00","relpermalink":"/talk/acs-spring-2021-platinum-ruthenium-alloys-as-electrocatalysts-for-efficient-aqueous-nitrate-reduction/","section":"event","summary":"Nitrate ions produced from industrial and agricultural processes have deeply imbalanced the global nitrogen cycle. Electrocatalytic reduction is a sustainable route to remediate nitrate while generating useful products such as NH3 or N2. Here we report density functional theory predictions of intrinsic nitrate reduction rates on PtRu random surface alloy models of compositions from Pt100 through Pt50Ru50. Density functional theory calculations predict increasing nitrate binding strength with increasing Ru composition and a maximum in nitrate reduction activity at 25 at% Ru surface composition. The calculations are consistent with experimental activity measurements of nitrate reduction on synthesized platinum-ruthenium catalysts (PtxRuy/C, x = 48--100%). These experiments show that PtxRuy/C alloys are more active than Pt/C, with Pt78Ru22/C the most active of them all (six times more active than Pt/C at 0.1 V vs. RHE). This maximum in activity arises from a transition from nitrate dissociation as the rate-determining step to a new rate-determining step at higher Ru content. Linear adsorbate scaling and Brønsted-Evans-Polanyi relationships exist on the model alloys with N and O binding energies as the descriptors. That such relationships exist with the same descriptors as used in our previous computational study of nitrate reduction on pure metals suggests that prior nitrated reduction microkinetic models developed for pure metals can be extended to alloys. This finding will greatly accelerate the search for performant nitrate reduction electrocatalysts through high-throughput screening and machine learning. Our work demonstrates that electrocatalyst performance for the nitrate reduction reaction is tunable by changing the adsorption strength of the reacting species through alloying. Beyond catalysis, our findings can also be applied to accelerate the discovery of other energy-relevant materials, such as photovoltaics, superconductors, and thermoelectrics.","tags":[],"title":"ACS Spring 2021: Platinum-Ruthenium Alloys as Electrocatalysts for Efficient Aqueous Nitrate Reduction","type":"event"},{"authors":["Zixuan Wang","Samuel D. Young","Bryan Goldsmith","Nirala Singh"],"categories":["umich"],"content":"Supporting information available here.\n","date":1597160478,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1597160478,"objectID":"ff4d1bb751e63a583302cff84768cf2e","permalink":"https://samueldy.github.io/publication/pt-ru-alloying-study/","publishdate":"2020-08-11T11:41:18-04:00","relpermalink":"/publication/pt-ru-alloying-study/","section":"publication","summary":"PtRu alloy catalysts have higher nitrate reduction activity than Pt or Ru alone.","tags":["catalysis","no3rr"],"title":"Increasing Electrocatalytic Nitrate Reduction Activity by Controlling Adsorption through PtRu Alloying","type":"publication"},{"authors":["James R. O’Dea","Megan E. Holtz","Anna E. Legard","Samuel D. Young","Raymond G. Burns","Abigail R. Van Wassen","David A. Muller","Héctor D. Abruña","Francis J. DiSalvo","R. Bruce van Dover","John A. Marohn"],"categories":["cornell"],"content":"Supporting information available here.\n","date":1433305712,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1433305712,"objectID":"88ffa9111aacbe90deac25561c0135fe","permalink":"https://samueldy.github.io/publication/combi-nitrides/","publishdate":"2020-09-29T00:28:32-04:00","relpermalink":"/publication/combi-nitrides/","section":"publication","summary":"Ta-Ti-Al nitride thin films with only ~20 at.% Ti may enable PEM fuel cell catalyst supports that are both conductive and durable.","tags":["nitrides","catalyst supports"],"title":"Conductivity and Microstructure of Combinatorially Sputter-Deposited Ta–Ti–Al Nitride Thin Films","type":"publication"}]