Improved reversibility in lithium-oxygen battery: Understanding elementary reactions and surface charge engineering of metal alloy catalyst

Most Li-O-2 batteries suffer from sluggish kinetics during oxygen evolution reactions (OERs). To overcome this drawback, we take the lesson from other catalysis researches that showed improved catalytic activities by employing metal alloy catalysts. Such research effort has led us to find Pt3Co nanoparticles as an effective OER catalyst in Li-O-2 batteries. The superior catalytic activity was reflected in the substantially decreased overpotentials and improved cycling/rate performance compared to those of other catalysts. Density functional theory calculations suggested that the low OER overpotentials are associated with the reduced adsorption strength of LiO2 on the outermost Pt catalytic sites. Also, the alloy catalyst generates amorphous Li2O2 conformally coated around the catalyst and thus facilitates easier decomposition and higher reversibility. This investigation conveys an important message that understanding elementary reactions and surface charge engineering of air-catalysts are one of the most effective approaches in resolving the chronic sluggish charging kinetics in Li-O-2 batteries.

Boosting hot electron flux and catalytic activity at metal-oxide interfaces of PtCo bimetallic nanoparticles

Despite numerous studies, the origin of the enhanced catalytic performance of bimetallic nanoparticles (NPs) remains elusive because of the ever-changing surface structures, compositions, and oxidation states of NPs under reaction conditions. An effective strategy for obtaining critical clues for the phenomenon is real-time quantitative detection of hot electrons induced by a chemical reaction on the catalysts. Here, we investigate hot electrons excited on PtCo bimetallic NPs during H-2 oxidation by measuring the chemicurrent on a catalytic nanodiode while changing the Pt composition of the NPs. We reveal that the presence of a CoO/Pt interface enables efficient transport of electrons and higher catalytic activity for PtCo NPs. These results are consistent with theoretical calculations suggesting that lower activation energy and higher exothermicity are required for the reaction at the CoO/Pt interfac

Highly selective oxygen reduction to hydrogen peroxide on transition metal single atom coordination.

Shifting electrochemical oxygen reduction towards 2e- pathway to hydrogen peroxide (H2O2), instead of the traditional 4e- to water, becomes increasingly important as a green method for H2O2 generation. Here, through a flexible control of oxygen reduction pathways on different transition metal single atom coordination in carbon nanotube, we discovered Fe-C-O as an efficient H2O2 catalyst, with an unprecedented onset of 0.822 V versus reversible hydrogen electrode in 0.1 M KOH to deliver 0.1 mA cm-2 H2O2 current, and a high H2O2 selectivity of above 95% in both alkaline and neutral pH. A wide range tuning of 2e-/4e- ORR pathways was achieved via different metal centers or neighboring metalloid coordination. Density functional theory calculations indicate that the Fe-C-O motifs, in a sharp contrast to the well-known Fe-C-N for 4e-, are responsible for the H2O2 pathway. This iron single atom catalyst demonstrated an effective water disinfection as a representative application

Precious Metal-Free Nickel Nitride Catalyst for the Oxygen Reduction Reaction.

With promising activity and stability for the oxygen reduction reaction (ORR), transition metal nitrides are an interesting class of non-platinum group catalysts for polymer electrolyte membrane fuel cells. Here, we report an active thin-film nickel nitride catalyst synthesized through a reactive sputtering method. In rotating disk electrode testing in a 0.1 M HClO4 electrolyte, the crystalline nickel nitride film achieved high activity and selectivity to four-electron ORR. It also exhibited good stability during 10 and 40 h chronoamperometry measurements in acid and alkaline electrolyte, respectively. A combined experiment-theory approach, with detailed ex situ materials characterization and density functional theory calculations, provides insight into the structure of the catalyst and its surface during catalysis. Design strategies for activity and stability improvement through alloying and nanostructuring are discussed

Accelerated chemical science with AI

In light of the pressing need for practical materials and molecular solutions to renewable energy and health problems, to name just two examples, one wonders how to accelerate research and development in the chemical sciences, so as to address the time it takes to bring materials from initial discovery to commercialization. Artificial intelligence (AI)-based techniques, in particular, are having a transformative and accelerating impact on many if not most, technological domains. To shed light on these questions, the authors and participants gathered in person for the ASLLA Symposium on the theme of ‘Accelerated Chemical Science with AI’ at Gangneung, Republic of Korea. We present the findings, ideas, comments, and often contentious opinions expressed during four panel discussions related to the respective general topics: ‘Data’, ‘New applications’, ‘Machine learning algorithms’, and ‘Education’. All discussions were recorded, transcribed into text using Open AI's Whisper, and summarized using LG AI Research's EXAONE LLM, followed by revision by all authors. For the broader benefit of current researchers, educators in higher education, and academic bodies such as associations, publishers, librarians, and companies, we provide chemistry-specific recommendations and summarize the resulting conclusions

Atomic Structure-free Representation of Active Motifs for Expedited Catalyst Discovery

For CO* and H* binding energy prediction, we develop new representation of catalyst surface which split surface into three types of site, first nearest neighbor of adsorbates and second nearest neighbor in same layer and sublayer. From this representation and machine learning regression model, we achieve reasonable accuracy (0.120 eV for CO* and 0.105 eV for H*) with quick training (~200 sec using CPU). Because our representation does not require density functional calculation and atomic structure modelling, it can predict binding energies of possible active motifs without time-consuming steps

Understanding Catalytic Activity Trends of Electrochemical Ammonia Oxidation Reaction using Density Functional Theory Calculations and Microkinetic Modeling

Electrochemical ammonia oxidation reaction (AOR) is promising as an alternative anodic reaction to oxygen evolution in water electrolysis system. Herein, we develop a microkinetic model based on density functional theory (DFT) calculations for all possible reaction pathways considering both thermochemical and electrochemical N-N bond formation processes. From the microkinetic analysis, we discover that Faradaic bond formation contributes to AOR more significantly than non-Faradaic counterpart and we observe good agreements with the experimental results. We then construct a kinetic volcano plot using binding energies of two reaction intermediates as descriptors, which suggests a catalyst design strategy. Following this strategy, we enumerate numerous alloy combinations and identify a few promising candidates with higher catalytic activity than the most active monometallic Pt catalyst

An Efficient Discovery of Active, Selective and Stable Catalysts for Electrochemical H2O2 Synthesis Through Active Motif Screening

Electrochemical reduction of O2 provides a clean and decentralized pathway to produce H2O2 compared to the current energy-intensive anthraquinone process. As the electrochemical reduction of O2 proceeds via either two-electron or four-electron path- way, it is thus essential to control the selectivity as well as to maximize the catalytic activity. Siahrostami et al. demonstrated a novel approach to control the reaction pathway by optimizing an adsorption ensemble to tune adsorption sites of reaction intermediates, and identified Pt-Hg catalysts from density functional theory (DFT) calculations and experimentally validated this catalyst (Nat. Mater. 2013, 12, 1137). Inspired by this concept, in this work, we apply a state-of-the-art high-throughput screening to develop O2 reduction catalyst for selective H2O2 production. Starting from Materials Project database, we evaluate activity, selectivity and electrochemical stability. To efficiently perform the screening, we introduce an active motif based approach which pre-screens unpromising materials and only performs DFT calculations for promising materials, which significantly reduce the number of the required calculations. We not only provide a list of promising candidates identified by DFT calculations, but also suggest element species to achieve high catalytic activity or H2O2 selectivity for future experimental attempts. Finally, we discuss a strategy for efficient future high-throughput screening using a machine learning pipeline consisting of a non-linear dimension reduction and a density-based clustering. </div

Discovery of Acid-Stable Oxygen Evolution Catalysts : High-throughput Computational Screening of Equimolar Bimetallic Oxides

Discovering acid-stable, cost-effective and active catalysts for oxygen evolution reaction (OER) is critical since this reaction is bottlenecking many electrochemical energy conversion systems. Current systems use extremely expensive iridium oxide catalysts. Identifying Ir-free or catalysts with reduced Ir-composition has been suggested as goals, but no systematic strategy to discover such catalysts has been reported. In this work, we performed high-throughput computational screening to investigate bimetalic oxide catalysts with space groups derived from those of IrO$_x$, identified promising OER catalysts predicted to satisfy all the desired properties: Co-Ir, Fe-Ir and Mo-Ir bimetallic oxides. We find that for the given crystal structures explored, it is essential to include noble metals to maintain the acid-stability, although one-to-one mixing of noble and non-noble metal oxides could keep the materials survive under the acidic conditions. Based on the calculated results, we provide insights to efficiently perform future high-throughput screening to discover catalysts with desirable properties