{"id":9283,"date":"2026-08-20T18:45:00","date_gmt":"2026-08-20T18:45:00","guid":{"rendered":"https:\/\/www.aiproblog.com\/index.php\/2026\/08\/20\/paving-the-way-for-greener-ammonia-production\/"},"modified":"2026-08-20T18:45:00","modified_gmt":"2026-08-20T18:45:00","slug":"paving-the-way-for-greener-ammonia-production","status":"publish","type":"post","link":"https:\/\/www.aiproblog.com\/index.php\/2026\/08\/20\/paving-the-way-for-greener-ammonia-production\/","title":{"rendered":"Paving the way for greener ammonia production"},"content":{"rendered":"<p>Author: David L. Chandler | Department of Materials Science and Engineering<\/p>\n<div>\n<p>Ammonia is one of the most important chemicals produced in the world, ranking second only to sulfuric acid in the total volume produced each year. It is used mostly to make fertilizer, which is essential to feeding the world\u2019s population. Yet its production accounts for up to 2 percent of the world\u2019s energy consumption and about 1.5 percent of greenhouse gas emissions, so the search has been underway for ways to produce ammonia more sustainably.<\/p>\n<p>The traditional way of making ammonia, in use for more than a century and accounting for the vast majority of production, is the Haber-Bosch process, which relies on fossil fuels to provide the needed heat. Hydrogen used in the process is also largely produced from fossil fuels.<\/p>\n<p>There is another way, using electrochemistry instead of heat and pressure, but so far this method has not been anywhere near economically competitive at the scales needed.<\/p>\n<p>Now, researchers at MIT have developed a way to predict which materials could be most promising as catalysts in electrochemical ammonia production.\u00a0Catalysts help drive chemical reactions, and their properties determine how efficiently those reactions proceed. Rather than using trial and error to test each possible combination out of the millions of possible alloys \u2014 which can take years \u2014 the new approach could greatly speed up the search for materials that could make this low-emissions method competitive with the Haber-Bosch process.\u00a0<\/p>\n<p>\u201cOur approach identifies the key physical properties that drive catalytic activity in ammonia production,\u201d says Bilge Yildiz, the\u00a0Breen M. Kerr Professor in the departments of Nuclear Science and Engineering and Materials Science and Engineering (DMSE). The results can guide the search for new and more effective catalyst compounds.<\/p>\n<p>The open-access <a href=\"https:\/\/pubs.rsc.org\/ey\/article\/doi\/10.1039\/d6ey00138f\/1289470\/Nitrogen-2p-metal-d-band-hybridization-governs\">findings were published<\/a> Aug. 11 in the Royal Society of Chemistry journal <em>EES Catalysis<\/em>, in a paper by Yildiz and doctoral students Constantine Athanitis of DMSE and Filip Grajkowski of the Department of Chemistry.\u00a0<\/p>\n<p><strong>The challenge of greener ammonia<\/strong><\/p>\n<p>As the world\u2019s population grows, Athanitis says, \u201cwe\u2019re just going to need more and more food, and the only reason why we\u2019re able to sustain so many people is because of fertilizer.\u201d But more than 90 percent of the ammonia needed for fertilizer is still made by that energy-intensive Haber-Bosch process, which \u201chas been hyper-optimized since it first came out more than a century ago,\u201d he says.<\/p>\n<p>\u201cIf we\u2019re trying to keep in line with society\u2019s sustainability and energy targets and climate change targets, we really need to come up with another alternative,\u201d he explains. The world currently uses about 200 million metric tons of ammonia each year, \u201cso ideally we want to be able to find a way to produce the same amount of ammonia, or even more, but in a more energy-efficient way and also with lower CO<sub>2<\/sub> emissions,\u201d he says.<\/p>\n<p>Using electricity to produce ammonia is not a new idea. \u201cIt\u2019s really just the electrochemical reaction between proton-electron pairs and nitrogen gas. And these technologies exist,\u201d he says.\u00a0The approach uses the same basic principles as electrolyzers, which use electricity to drive chemical reactions in devices.<\/p>\n<p>But while the process works, it\u2019s not efficient enough for industrial-scale production. \u201cProduction rates and yields are still too low,\u201d Athanitis says. \u201cEven though a technology might be better for the world or for the climate, companies and capitalism won\u2019t really allow it unless it\u2019s cost competitive.\u201d<\/p>\n<p>How to make it more competitive? The key ingredient in the electrochemical process is a metallic catalyst, whose properties govern the reaction that takes place on its surface. \u201cIf we can somehow find a catalyst that reduces the energy needed and is more selective for ammonia production,\u201d Athanitis says, \u201cthen we could essentially hit the jackpot.\u201d\u00a0A more selective catalyst would produce more ammonia while reducing unwanted side reactions.<\/p>\n<p><strong>Finding better catalysts<\/strong><\/p>\n<p>But finding that ideal catalyst is not simply a matter of identifying one perfect material. Different materials can improve different parts of the reaction, and researchers are seeking combinations that can make ammonia production efficient, affordable, and practical at large scale.<\/p>\n<p>\u201cMetal nitride compounds make an ideal material system for this reaction and for identifying the electronic, chemical, and structural properties that determine reactivity in nitrogen reduction and ammonia electrosynthesis,\u201d Yildiz says.\u00a0<\/p>\n<p>Transition metals could form promising nitride alloys for this purpose, and historically, \u201cmaterials research has been pretty much trial and error,\u201d Athanitis says.<\/p>\n<p>The usual process is to take some existing material and \u201ctweak it in some way,\u201d he says. \u201cIt\u2019s all somewhat guided by scientific and chemical intuition.\u201d\u00a0<\/p>\n<p>Now, increasingly, computational tools are being used to model the physical interactions and predict outcomes. A method called density functional theory uses quantum mechanics to simulate the properties and behavior of materials, allowing researchers to predict how different atomic arrangements may perform before making them in the lab. Rather than searching randomly through every possible alloy combination, Yildiz says, \u201cwe first assessed what microscopic properties of the material make them tick for nitrogen reduction.\u201d\u00a0<\/p>\n<p>For ammonia-producing catalysts, \u201cwe\u2019re looking at transition metal nitrides,\u201d Athanitis says, because they have been found to be effective in these electrochemical nitrogen reactions. They are especially effective because \u201cthe nitrogen inherent to the catalyst itself becomes part of the reaction.\u201d<\/p>\n<p>This produces a series of chemical steps in which one step provides part of the energy needed to drive the next, reducing the amount of input energy needed. This helps solve one of the major bottlenecks in the nitrogen reduction reaction:\u00a0the high energy required to break the strong bonds in nitrogen molecules, he says.<\/p>\n<p>But the process is far from perfect, Athanitis says. It is \u201cstill limited by certain steps throughout the reaction pathway, including nitrogen dissociation and hydrogen transfer.\u201d The study attempted to identify those bottlenecks and, with the help of machine learning, determine which alloys of these metals might overcome them.<\/p>\n<p>With that understanding, \u201cit can give us insights and open up potential strategies for how we can tune these materials to create next-generation better nitride catalysts,\u201d Athanitis says.<\/p>\n<p><strong>Pushing past theory<\/strong><\/p>\n<p>The approach is \u201cexciting work\u201d that could help develop a foundation for designing new catalysts for ammonia production,\u00a0says Dane Morgan, a professor of engineering at the University of Wisconsin who was not involved in this study.<\/p>\n<p>\u201cThis work helps clarify how fundamental electronic properties of a material relate to its role as a catalyst in making ammonia,\u201d Morgan says. \u201cSuch understanding can help guide researchers in designing new catalysts, both through better qualitative understanding and by accelerating computational screening.\u201d\u00a0<\/p>\n<p>So far, the study is purely theoretical: The researchers have used computer models to identify promising alloys, but those materials still need to be made and tested.\u00a0Morgan notes that \u201ctranslating these calculations into practical catalysts will require many additional steps, so meaningful real-world impact is likely still some distance away.\u201d<\/p>\n<p>The next step will be to build a working reaction cell, a laboratory device that uses the catalyst to produce ammonia and test its performance under real operating conditions. \u201cFor this to really make an impact in society, we need to bring it to the experimental lab,\u201d Athanitis says.<\/p>\n<p>\u201cThere have always been pushes at the frontiers of what\u2019s possible,\u201d he adds. \u201cWe like to think we\u2019ve pushed the boundary of candidate materials here beyond what was thought of before, and hopefully we\u2019re almost there. But even if we\u2019re not almost there, we\u2019re still pushing in the right direction.\u201d<\/p>\n<\/div>\n<p><a href=\"https:\/\/news.mit.edu\/2026\/paving-way-for-greener-ammonia-production-0820\">Go to Source<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Author: David L. Chandler | Department of Materials Science and Engineering Ammonia is one of the most important chemicals produced in the world, ranking second [&hellip;] <span class=\"read-more-link\"><a class=\"read-more\" href=\"https:\/\/www.aiproblog.com\/index.php\/2026\/08\/20\/paving-the-way-for-greener-ammonia-production\/\">Read More<\/a><\/span><\/p>\n","protected":false},"author":1,"featured_media":457,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_bbp_topic_count":0,"_bbp_reply_count":0,"_bbp_total_topic_count":0,"_bbp_total_reply_count":0,"_bbp_voice_count":0,"_bbp_anonymous_reply_count":0,"_bbp_topic_count_hidden":0,"_bbp_reply_count_hidden":0,"_bbp_forum_subforum_count":0,"footnotes":""},"categories":[24],"tags":[],"_links":{"self":[{"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/posts\/9283"}],"collection":[{"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/comments?post=9283"}],"version-history":[{"count":0,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/posts\/9283\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/media\/467"}],"wp:attachment":[{"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/media?parent=9283"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/categories?post=9283"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/tags?post=9283"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}