Sustainable ammonia production using water and air through the coupling of plasma-driven intermediary NOx generation and their electrocatalytic conversion.

Photoreforming is a process that harnesses the redox ability of photocatalysts upon illumination, to simultaneously drive the reduction of H+ into hydrogen gas and oxidation of organic compounds. Over the...

Renewable‐electricity‐powered electrocatalytic CO2 reduction reactions (CO2RR) have been identified as an emerging technology to address the issue of rising CO2 emissions in the atmosphere. While the CO2RR has been demonstrated to be technically feasible, further improvements in catalyst performance through active sites engineering are a prerequisite to accelerate its commercial feasibility for utilization in large CO2‐emitting industrial sources. Over the years, the improved understanding of the interaction of CO2 with the active sites has allowed superior catalyst design and subsequent attainment of prominent CO2RR activity in literature. This review tracks the evolution of the understanding of CO2RR active sites on different electrocatalysts such as metals, metal‐oxides, single atoms, metal‐carbon, and subsequently metal‐free carbon‐based catalysts. Despite the tremendous research efforts in the field, many scientific questions on the role of various active sites in governing CO2RR activity, selectivity, stability, and pathways are still unanswered. These gaps in knowledge are highlighted and a discussion is set forth on the merits of utilizing advanced in‐situ and operando characterization techniques and machine learning (ML). Using this technique, the underlying mechanisms can be discerned, and as a result new strategies for designing active sites may be uncovered. Finally, this review advocates an interdisciplinary approach to discover and design CO2RR active sites (rather than focusing merely on catalyst activity) in a bid to stimulate practical research for industrial application.

Robust screening of materials on the basis of structure–property–activity relationships to discover active photocatalysts is a highly sought out aspect of photocatalysis research. Recent advancements in machine learning offer considerable opportunities to evolve photocatalysts discovery practices. Machine learning has largely facilitated various areas of science and engineering, including heterogeneous catalysis, but adaptation of it in photocatalysis research is still at an elementary stage. The scarcity of consistent training data is a major bottleneck, and we foresee the integration of photocatalysis domain knowledge in mainstream machine learning protocols as a viable solution. Here, we present a holistic framework incorporating machine learning and domain knowledge to set directions toward accelerated discovery of solar photocatalysts. This Perspective begins with a discussion on domain knowledge available in photocatalysis which could potentially be leveraged to liaise with machine learning methods. Subsequently, we present prevalent machine learning practices in heterogeneous catalysis tailored to assist discovery of photocatalysts in a purely data-driven fashion. Lastly, we conceptualize various strategies for complementing data-driven machine learning with photocatalysis domain knowledge. The strategies involve the following: (i) integration of theoretical and prior empirical knowledge during the training of machine learning models; (ii) embedding the knowledge in feature space; and (iii) utilizing existing material databases to constrain machine learning predictions. The aforementioned human-in-loop framework (leveraging both human and machine intelligence) could possibly mitigate the lack of interpretability and reliability associated with data-driven machine learning and reinforce complex model architectures irrespective of data scarcity. The concept could also offer substantial benefits to photocatalysis informatics by promoting a paradigm shift away from the Edisonian approach.

Thermodynamic and kinetic limitations can restrict the feasibility and scalability of conventional catalytic processes for CO 2 methanation at the industrial level. Due to its nonequilibrium nature, nonthermal plasma (NTP) promises to reduce reaction barriers and make this gas conversion approach viable even at low temperatures. However, the current understanding of the fundamental chemical and physical behaviors in the hybrid plasma catalytic interactions is insufficient. This study demonstrates plasma-driven CO 2 conversions approaching the reaction equilibrium with high methane yields even at low temperature (150 °C). It was observed that the addition of plasma to the catalytic bed enhanced the CO 2 conversion around 20 times relative to thermal activity, whereas the CH 4 selectivity increased around 5 times by introducing the nickel catalyst into plasma discharge compared to plasma only (at 150 °C). Moreover, the findings provide new insights into the gas phase activation of reactants (CO 2 and H 2 ) and the reaction over Ni 0 to decouple the plasma and catalyst synergy. The catalyst did not undergo significant structural changes under plasma discharge, apart from a slight decrease in Ni crystallite size, while an enhanced metal dispersion was evident (24% to 42%, from CO pulse chemisorption). The optimized system achieved a CO 2 conversion of 60% with a CH 4 selectivity of over 97% at 150 °C, which required much higher temperatures (320−330 °C) to achieve equivalent conversion in thermal catalysis. This study is a step toward an understanding and effective control of the plasma enhanced catalytic CO 2 transformation via low energy reaction pathways that utilize the NTP for low-temperature CO 2 methanation with high conversion, selectivity, stability, and controllability.