The AI boom is becoming a materials challenge. As AI pushes computing into new territory, the materials behind that infrastructure are becoming just as crucial as the algorithms running on it. Semiconductors and data centers are approaching physical limits around performance, thermal management, electrical efficiency, and reliability, creating new demands for materials that can do more at once. At the same time, AI is giving materials scientists new ways to search the enormous universe of possible molecules and accelerate the development of solutions.<\/p>\n
For Mike Finelli, chief technology and innovation officer and chief North America officer at Syensqo, that convergence is transforming what advanced materials can enable. \u201cAI is now, from a material standpoint, really pushing semiconductors and the data centers to their physical limits,\u201d he says.<\/p>\n
As requirements accumulate, including high temperature, purity, electrical performance, chemical resistance, plasma resistance, and long-term stability, materials move toward what Finelli calls the \u201ctop of the pyramid.\u201d Beyond supporting AI innovation, he contends that advanced materials are \u201cactually increasingly defining what\u2019s going to be possible.\u201d<\/p>\n
That challenge is playing out across the infrastructure powering the AI surge. Syensqo is developing materials for high-voltage data center architectures, advanced sealing materials for semiconductor manufacturing, and thermal-management solutions including fluids for direct immersion cooling. Some of those innovations can also cross industry boundaries. Materials developed for electric vehicles, for example, can help address the higher voltage and energy-density demands that are emerging in data centers.<\/p>\n
The definition of performance is also changing. More customers are expecting materials to meet technical requirements while reducing environmental impact. \u201cOur goal is to remove the trade-off between performance and sustainability,\u201d Finelli says. That means considering sustainability at the beginning of the research process instead of treating it as an additional requirement once a material has been developed.<\/p>\n
AI is changing how those materials are discovered, too. Syensqo is using AI agents to digitally synthesize millions of potential molecular combinations, predict their performance and sustainability characteristics, and narrow them to a much smaller group for laboratory testing. The result, Finelli says, is the ability to go \u201cbroader, deeper, and faster\u201d while giving scientists more time to solve complex engineering problems.<\/p>\n
Looking to the future, Finelli sees the possibility of a reinforcing cycle: AI helps develop materials that improve AI infrastructure, which in turn enables better AI to accelerate materials discovery. That feedback loop could create a cycle of innovation and expand what future technologies can achieve.<\/p>\n
\u201cYou end up in this accelerated materials, innovative cycle of materials innovation,\u201d says Finelli. \u201cThat really excites me, and it gives us the opportunity to continue enabling technologies that will shape the future.\u201d<\/p>\n
This episode of Business Lab is produced in partnership with Syensqo.<\/em><\/p>\n Full Transcript:<\/strong><\/p>\n Megan Tatum:<\/em> From MIT Technology Review, I\u2019m Megan Tatum, and this is Business Lab, the show that helps business leaders make sense of new technologies coming out of the lab and into the marketplace.<\/p>\n This episode is produced in partnership with Syensqo.<\/p>\n Now asked to name the key enablers to AI advancement, many of us might list algorithms, data centers, or even computing power, but just as critical to the performance are the advanced materials that underpin each layer of that innovation. As AI continues to evolve, it\u2019s pushing the likes of semiconductors and data centers to new physical limits, putting new pressure on the advanced material sector to keep pace. But the relationship goes both ways. As the sector rises to this challenge, AI is also emerging as a powerful tool for accelerating materials discovery and development, significantly shortening development timelines for new solutions.<\/p>\n Two words for you: materials innovation.<\/p>\n My guest today is Mike Finelli, chief technology and innovation officer and chief North America officer at Syensqo.<\/p>\n Welcome, Mike.<\/p>\n Mike Finelli: <\/em>Thank you, Megan. Nice to be here.<\/p>\n Megan: <\/em>Thank you so much for joining us. Mike, can I start by asking you to tell us a little bit more about Syensqo and the role it plays in developing advanced materials?<\/p>\n Mike: <\/em>Yeah, absolutely. Syensqo is a global leader in specialty materials. Our job is to help customers solve their toughest technology challenges. We serve a lot of different markets, but the way I like to say it simply is if it flies, we\u2019re on it. If it drives, we\u2019re in it. In healthcare, our products literally are saving lives every day. And if you like your mobile devices, if you like AI, it\u2019s our products that are actually enabling the advanced semiconductor chips that are required to produce all of this. Our role is to enable innovation through advanced chemistry. We develop materials that deliver higher performances, greater reliability, and increasingly more sustainable solutions. The way I would say this, it\u2019s at the heart of our business. Actually, it\u2019s in our name, Syensqo. And to put some numbers around it, 20% of our annual revenues come from new products and applications that we\u2019ve launched in the last five years, which is really evidence of a really strong innovation engine.<\/p>\n Megan: <\/em>Yeah, absolutely. And as you sort of described there, you\u2019re in all sorts of different industries with an emphasis perhaps on electronics and semiconductors. Can you talk a bit more about that work and where those industries are headed perhaps?<\/p>\n Mike: <\/em>Sure. So look, electronics and semiconductors have been strategic markets for Syensqo for literally decades. I don\u2019t want to date myself, but 33 years ago when I started in the company, semiconductors were one of the first industries that I worked in. And we\u2019ve supported successive waves of innovation from enabling smaller, more powerful mobile devices, helping the industry get to the smaller and smaller profiles and the chips. We\u2019ve helped to advance hyperconnectivity, supporting increasingly sophisticated semiconductor manufacturing. And today we\u2019re helping to advance the AI era.<\/p>\n We have one of the industry\u2019s broadest portfolios of high performance polymers and advanced materials. We support applications across the entire electronics value chain from semiconductor fabrication, electronic components, to smart devices and telecommunications, even hyperconnectivity. And our materials are helping customers solve increasingly demanding challenges around miniaturization, thermal management, electrical performance, chemical resistance, higher and higher purities, and long-term reliability and sustainability. And today we work with leading semiconductor manufacturers and electronics companies all around the world.<\/p>\n Megan: <\/em>Fantastic. And as you alluded to there in the last 30 years, we\u2019ve seen huge evolutions in those sectors.<\/p>\n Mike: <\/em>Oh my God, yes.<\/p>\n Megan: <\/em>And now AI is putting these new demands on semiconductors and data centers. What does that mean for the materials they\u2019re built from and to what extent will AI innovation be constrained or enabled by materials science finding a solution?<\/p>\n Mike: <\/em>Yeah, so I mean, you\u2019re absolutely right. But AI is now, from a material standpoint, really pushing semiconductors and the data centers to their physical limits, and materials are becoming a key enabler of that continued progress.<\/p>\n The way I try to describe it, think of a pyramid, I call it the performance pyramid. You have commodity materials at the bottom of the pyramid and you have high performing specialty materials at the top of the pyramid. At Syensqo, all we do is we operate at the top of the pyramid and we\u2019re continually trying to raise the top of that pyramid by bringing newer and newer and more higher performing materials out.<\/p>\n Now you might say, okay, but why doesn\u2019t a data center or a semiconductor manufacturing fab need a specialty versus something in the commodity space? Well, I call it the and, and, and principle. If you just need a polymer or a material that can sit at the table at room temperature and stay there for 10 years and not change, well, there\u2019s a lot of commodity materials that will do that and you don\u2019t have a problem. The minute you start adding requirements, and I call it the and, and, and so if you need a polymer that can handle high temperature and have to have high purity and electrical performance and chemical resistance and plasma resistance and it\u2019s got to have long-term stability, all of these ands, you start moving to the top of the pyramid.<\/p>\n Now what AI is doing with semiconductors, because of the speed at which it\u2019s advancing, it\u2019s requiring semiconductor chips and data centers, the number of requirements are increasing the number of ands which is pushing the limits of the materials. That\u2019s where we come in. And I really believe that advanced materials, they\u2019re no longer just supporting AI innovation, we\u2019re actually increasingly defining what\u2019s going to be possible.<\/p>\n Megan: <\/em>Right. That\u2019s fascinating. And in terms of rising to that challenge of focusing on that top of the pyramid and that and, and, and principle you\u2019re talking about, could you talk us through perhaps an example or two of those top of the pyramid solutions you\u2019ve created or that you\u2019re working on at the moment?<\/p>\n Mike:<\/em> Like I said, our focus is enabling higher performance, but it\u2019s also without compromising on reliability or safety. We develop advanced polymers, elastomers, specialty fluids, fluids meaning lubricants and heat transfer fluids, and they\u2019re used throughout the semiconductor manufacturing process and also increasingly in AI data center infrastructure. One example of our work on specialty materials for next generation AI data centers is the work we\u2019re doing around high voltage architectures. Data centers are moving towards high voltage architectures because they can enable greater computing power while also improving energy efficiency. We know that\u2019s a big issue for that segment of the industry, and these high voltage architectures will help them reduce and improve energy efficiency because it reduces energy losses and they can ultimately help lower the environmental footprint of the data centers. And we\u2019re developing new materials that can help them get there.<\/p>\n Another example is our high performing sealing materials found inside semiconductor fabs and wafer tools. If you can picture, many people have seen what a semiconductor looks like during processing. It\u2019s a big, big silicon disc that\u2019s then later diced into the tiny little chips that go into the computer. But that wafer is put inside a giant chamber where it has a very extreme environment, aggressive plasmas, reactive chemicals, and they need higher and higher performing materials. And all of the seals that are around that chamber to keep those gases in the environment inside have to be able to withstand that environment. And that\u2019s what we\u2019re developing and we\u2019re pushing the limits. They\u2019re asking for higher temperatures, more aggressive environment with lower out gassing and purity. And that\u2019s what we\u2019re developing for this industry to allow that next chip to be developed and produced industrial.<\/p>\n Megan: <\/em>It\u2019s so fascinating that people wouldn\u2019t give much though necessarily to the seal in something like that. As you\u2019re outlining, it\u2019s just absolutely critical in terms of performance. And in developing those solutions, I understand you also looked across different markets to see what may be applicable perhaps in more than one space, and that includes an overlap between the automotive sector and data centers, I understand. Can you tell us a little bit more about that?<\/p>\n Mike: <\/em>As I mentioned just previously, the data centers are shifting to higher voltage architectures. This is the next generation data center, which can be more energy efficient, but it\u2019s got a higher energy density. The power density increases, which increases temperatures. And many of the material challenges that we will be facing there, we\u2019ve already developed for the automotive industry in electric vehicles. I\u2019ll give you an example of an application. I mean, think about an electric vehicle. The powerhouse in electric vehicle is no longer the motor, it\u2019s the battery. That\u2019s where all the energy sits. And when you\u2019re putting a hundred kilowatts of energy, driving that to the electric motor through wires and through what they call bus bars, you got to get that car up to 60 miles an hour pretty quick. You\u2019re driving massive amounts of energy that\u2019s increasing temperatures dramatically.<\/p>\n And all the electrical connections are in these bus bars that there\u2019s a polymer that\u2019s an insulating polymer with copper in between for all the connections. That\u2019s got to withstand that temperature increase, which could come pretty rapidly. We\u2019ve developed new materials there and those materials will be translatable over to these data centers where they\u2019re going to have the higher voltages with a higher energy density.<\/p>\n Another thing we\u2019ve been doing in automotive, we have a lot of knowledge in both automotive and semiconductor around fluid circulation and how to use dielectric materials to do direct immersion cooling. That\u2019s something that will be very valuable for data centers and server farms. Using air to cool semiconductors is really inefficient and energy intensive. If you could submerse them in a liquid, you have direct immersion cooling, that\u2019s extremely efficient, so that\u2019s another thing we\u2019re working on.<\/p>\n Another thing we developed in automotive that will be translated over is battery energy storage systems. Inside the battery, we\u2019ve developed a binder. It\u2019s the highest performing binder on the market, which is using the cathode of a lithium ion battery, and it keeps all the ingredients doing its job working together so that battery can actually last for 10 years and perform. Now that\u2019s moving over to the data centers because they\u2019re moving more towards renewables and they need to have these energy storage systems to smooth the peak loads and provide resilient backup power. That\u2019s one of the things that we\u2019re doing. By transferring our knowledge across the markets, we can accelerate new power and new thermal management solutions while supporting reliability required by next generation AI infrastructure.<\/p>\n Megan: <\/em>Fantastic. So many transferable applications there that necessarily wouldn\u2019t have sprung to mind. And it isn\u2019t only technical advancements that you need to contend with, of course. Companies today are also demanding the materials are developed and manufactured more responsibly too. So how is sustainability shaping your innovation process?<\/p>\n Mike: <\/em>Yeah, you\u2019re absolutely right. I will say performance is still the entry ticket. Our customers want performance. Now what\u2019s changing is that definition of performance is now broader and it is including sustainability targets and requirements. Our customers expect materials that deliver outstanding technical performance while also being developed and manufactured more responsibly.<\/p>\n At Syensqo, we believe that operating as a responsible company means we\u2019re providing true sustainable business solutions to our customers. And this is why we developed what we call the Sustainable Portfolio Management tool, SPM. It\u2019s a matrix, and it defines what a sustainable solution is. For us, it\u2019s a product that in a given application improves our product\u2019s social and environmental performance while also demonstrating a lower environmental impact in its production, creating values for our customers. In short, we want to develop products, and this is where it starts. Every one of our research projects before we even start them is assessed on whether it\u2019s going to be a sustainable product or not.<\/p>\n And 88% of our portfolio now is a sustainable product. We\u2019re developing materials that are better for the environment, lower environmental footprint when we produce it, but also they contribute to improvements for our customers as well so they could operate with a lower carbon footprint or they can operate in a safer way or less water consumption. There\u2019s a lot of different lists in there.<\/p>\n Another example is our longer-term development of next generation heat transfer fluids. Semiconductor manufacturing and data centers have become more powerful. I mentioned before the heat that they\u2019re generating, especially when they move to the higher voltage architectures. Managing that heat is increasingly important. And again, I talked about direct immersion cooling. We\u2019re developing those solutions because today there are fluids out there that will work, but they got high global warming. That\u2019s not good for the environment. We\u2019re developing the next generation heat transferred fluids that will reduce the potential environmental impact compared to the fluids today. In the end, our goal is to remove the trade-off between performance and sustainability. You notice that\u2019s another and, we can be performing and sustainable.<\/p>\n Megan: <\/em>That\u2019s so important, isn\u2019t it though, to think about sustainability in terms of performance? As you say, when we\u2019re thinking about commercially scaling up these solutions, it\u2019s such an important part of it. And as I talked about in the introduction, AI isn\u2019t only a challenge, but it\u2019s also an opportunity within the advanced material space. I\u2019d love to explore how you\u2019re using AI tools at Syensqo to inform and accelerate the development of solutions as well.<\/p>\n Mike:<\/em> Absolutely. We embarked on this journey about two years ago, where we\u2019re using AI in our research and development, and we\u2019ve partnered with Microsoft and their Microsoft discovery tool, and it\u2019s helping us to rapidly identify and evaluate promising molecular candidates.<\/p>\n Now, in the normal research approach, historically, you would design your experiment and you\u2019d look at all the potential combinations of materials and chemicals that you could make all these different molecules. And the combinations of potential and molecules that you could develop to solve a problem could be in the millions, but it\u2019s impossible to develop a million molecules or tens of millions of molecules in your laboratory and actually physically do that. But you have to select a small area based on your expertise and knowledge, based on the literature searches, based on the state of the art that\u2019s out there and looking at patents, et cetera. And you pick a small area and you go through the process, you develop the materials, you test them, you learn something, you go back to the drawing board, you start again. Eventually you find something that works, but it doesn\u2019t mean you found the best possible combination that\u2019s out there.<\/p>\n But what we\u2019re doing with AI is we have developed AI agents with Microsoft that are literally digitally synthesizing the entire millions and millions of combinations of potential molecules. And we have another AI agents that are using physics-based simulation to look at all those molecules and predict the performance of them, and not just performance on physical chemical properties, but also on toxicity, on sustainability, et cetera. Then we have another agent that takes all that information and ranks them all. In the end, we have explored all of the potential molecules out there. We understand roughly what the performance should be, and we end up with a priority list of maybe a hundred, instead of millions and millions, a hundred that we actually synthesize in the lab.<\/p>\n And at the end, you end up getting the solution faster, much, much faster. You\u2019ve explored the entire space. I basically say it allows us to go broader, deeper, and faster. And the important thing is it\u2019s not replacing our scientists, it\u2019s not replacing our scientific expertise. In a way, it\u2019s giving them superpowers. It\u2019s allowing them to spend less time searching and more time solving the industry\u2019s toughest engineering challenges.<\/p>\n Megan: <\/em>Amazing. It sounds like it\u2019s genuinely a really transformative tool by what you\u2019re explaining.<\/p>\n Mike: <\/em>Completely, completely.<\/p>\n Megan: <\/em>I mean, just to finish, Mike, it\u2019d be great to take a look ahead if we could, because there\u2019s so much activity in both AI and the advanced material space. I wonder what is coming down the pipeline that you are most excited about next?<\/p>\n Mike: <\/em>I\u2019ve talked a lot about AI and how we\u2019re using AI to develop new materials. I think to me, what\u2019s really exciting, and I\u2019m starting to see it actually happen, I\u2019m just curious how fast this is going to go, is that we\u2019re using AI to develop new materials that will enable AI to get better, and then that AI will use the new AI to develop new materials to get AI to go better. I see this loop of developing for AI, for AI to improve, and then we use that AI to improve ourselves. You end up in this accelerated materials, innovative cycle of materials innovation. That really excites me, and it gives us the opportunity to continue enabling technologies that will shape the future. That\u2019s what we do at Syensqo.<\/p>\n Megan: <\/em>Fantastic. Yeah, real sort of virtuous circle of innovation, it sounds like that. Amazing. Thank you so much, Mike.<\/p>\n Mike: <\/em>Thank you.<\/p>\n Megan: <\/em>Thank you so much. That was Mike Finelli, chief technology and innovation officer and chief North America officer at Syensqo, whom I spoke with from Brighton in England.<\/p>\n That\u2019s it for this episode of Business Lab. I\u2019m your host, Megan Tatum. I\u2019m a contributing editor and host for Insights, the custom publishing division of MIT Technology Review. We were founded in 1899 at the Massachusetts Institute of Technology, and you can find us in print on the web and at events each year around the world. For more information about us and the show, please check out our website at technologyreview.com.<\/p>\n This show is available wherever you get your podcasts, and if you enjoyed it, we hope you\u2019ll take a moment to rate and review us. Business Lab is a production of MIT Technology Review, and this episode was produced by Giro Studios. Thanks so much for listening. Goodbye.<\/p>\n This content was produced by Insights, MIT Technology Review\u2019s custom content arm, not its editorial staff. It was researched and written by humans, with any AI tools that may have been used limited to production processes under human oversight.<\/em><\/p>","protected":false},"excerpt":{"rendered":" The AI boom is becoming a materials challenge. As AI pushes computing into new territory, the materials behind that infrastructure are becoming just as crucial as the algorithms running on it. Semiconductors and data centers are approaching physical limits around performance, thermal management, electrical efficiency, and reliability, creating new demands for materials that can do more at once. At the same time, AI is giving materials scientists new ways to search the enormous universe of possible molecules and accelerate the development of solutions. For Mike Finelli, chief technology and innovation officer and chief North America officer at Syensqo, that convergence is transforming what advanced materials can enable. \u201cAI is now, from a material standpoint, really pushing semiconductors and the data centers to their physical limits,\u201d he says. As requirements accumulate, including high temperature, purity, electrical performance, chemical resistance, plasma resistance, and long-term stability, materials move toward what Finelli calls the \u201ctop of the pyramid.\u201d Beyond supporting AI innovation, he contends that advanced materials are \u201cactually increasingly defining what\u2019s going to be possible.\u201d That challenge is playing out across the infrastructure powering the AI surge. Syensqo is developing materials for high-voltage data center architectures, advanced sealing materials for semiconductor manufacturing, and thermal-management solutions including fluids for direct immersion cooling. Some of those innovations can also cross industry boundaries. Materials developed for electric vehicles, for example, can help address the higher voltage and energy-density demands that are emerging in data centers. The definition of performance is also changing. More customers are expecting materials to meet technical requirements while reducing environmental impact. \u201cOur goal is to remove the trade-off between performance and sustainability,\u201d Finelli says. That means considering sustainability at the beginning of the research process instead of treating it as an additional requirement once a material has been developed. AI is changing how those materials are discovered, too. Syensqo is using AI agents to digitally synthesize millions of potential molecular combinations, predict their performance and sustainability characteristics, and narrow them to a much smaller group for laboratory testing. The result, Finelli says, is the ability to go \u201cbroader, deeper, and faster\u201d while giving scientists more time to solve complex engineering problems. Looking to the future, Finelli sees the possibility of a reinforcing cycle: AI helps develop materials that improve AI infrastructure, which in turn enables better AI to accelerate materials discovery. That feedback loop could create a cycle of innovation and expand what future technologies can achieve. \u201cYou end up in this accelerated materials, innovative cycle of materials innovation,\u201d says Finelli. \u201cThat really excites me, and it gives us the opportunity to continue enabling technologies that will shape the future.\u201d This episode of Business Lab is produced in partnership with Syensqo. Full Transcript: Megan Tatum: From MIT Technology Review, I\u2019m Megan Tatum, and this is Business Lab, the show that helps business leaders make sense of new technologies coming out of the lab and into the marketplace. This episode is produced in partnership with Syensqo. Now asked to name the key enablers to AI advancement, many of us might list algorithms, data centers, or even computing power, but just as critical to the performance are the advanced materials that underpin each layer of that innovation. As AI continues to evolve, it\u2019s pushing the likes of semiconductors and data centers to new physical limits, putting new pressure on the advanced material sector to keep pace. But the relationship goes both ways. As the sector rises to this challenge, AI is also emerging as a powerful tool for accelerating materials discovery and development, significantly shortening development timelines for new solutions. Two words for you: materials innovation. My guest today is Mike Finelli, chief technology and innovation officer and chief North America officer at Syensqo. Welcome, Mike. Mike Finelli: Thank you, Megan. Nice to be here. Megan: Thank you so much for joining us. Mike, can I start by asking you to tell us a little bit more about Syensqo and the role it plays in developing advanced materials? Mike: Yeah, absolutely. Syensqo is a global leader in specialty materials. Our job is to help customers solve their toughest technology challenges. We serve a lot of different markets, but the way I like to say it simply is if it flies, we\u2019re on it. If it drives, we\u2019re in it. In healthcare, our products literally are saving lives every day. And if you like your mobile devices, if you like AI, it\u2019s our products that are actually enabling the advanced semiconductor chips that are required to produce all of this. Our role is to enable innovation through advanced chemistry. We develop materials that deliver higher performances, greater reliability, and increasingly more sustainable solutions. The way I would say this, it\u2019s at the heart of our business. Actually, it\u2019s in our name, Syensqo. And to put some numbers around it, 20% of our annual revenues come from new products and applications that we\u2019ve launched in the last five years, which is really evidence of a really strong innovation engine. Megan: Yeah, absolutely. And as you sort of described there, you\u2019re in all sorts of different industries with an emphasis perhaps on electronics and semiconductors. Can you talk a bit more about that work and where those industries are headed perhaps? Mike: Sure. So look, electronics and semiconductors have been strategic markets for Syensqo for literally decades. I don\u2019t want to date myself, but 33 years ago when I started in the company, semiconductors were one of the first industries that I worked in. And we\u2019ve supported successive waves of innovation from enabling smaller, more powerful mobile devices, helping the industry get to the smaller and smaller profiles and the chips. We\u2019ve helped to advance hyperconnectivity, supporting increasingly sophisticated semiconductor manufacturing. And today we\u2019re helping to advance the AI era. We have one of the industry\u2019s broadest portfolios of high performance polymers and advanced materials. We support applications across the entire electronics value chain from semiconductor fabrication, electronic components, to smart devices and telecommunications, even hyperconnectivity.<\/p>","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"pmpro_default_level":"","site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"_pvb_checkbox_block_on_post":false,"footnotes":""},"categories":[52,5,7,1],"tags":[],"class_list":["post-118316","post","type-post","status-publish","format-standard","hentry","category-ai-club","category-committee","category-news","category-uncategorized","pmpro-has-access"],"acf":[],"yoast_head":"\n