Featured Content: Revolutionizing energy efficiency in chemical separations!

🚨 Revolutionizing Energy Efficiency in Chemical Separations! We’re thrilled to share our latest open access research 🔓, just out in Nature Energy!

Our paradigm-shifting hybrid modeling approach combines cutting-edge machine learning with mechanistic insights, unlocking the potential to…
🔋 slash energy use and CO₂ emissions by up to 90% in pharmaceutical purifications;
🌎 achieve 40% energy reduction overall in chemical separations; and
💡 enable membrane technology to enhance up to 74% of chemical separations.

We analyzed a staggering 7.1 million scenarios across a wide range of application domains to guide sustainable technology choices in the chemical industry.

👏 A huge congratulations to the team:
Gergő Ignácz 🤖📊 – The Robot, who trained the AI
Aron Kristof Beke ⚙️⚡ – The Energizer, who powered the separations
Viktor Toth ⚗️🥼 – The Chemist, who cooked the drugs

👨‍💻 Dive into the future of energy-efficient separations here if you want to read the article, or listen to an AI-generated podcast:

Read the story behind the article in the KAUST Discovery magazine published under ‘Slashing industrial emissions using a hybrid model approach‘.

Read the commentary by Professor Michael Tsapatsis from Johns Hopkins University under ‘Facilitating decision making‘ published in the News & Views section of Nature Energy.

🌐 Use our online open access tools to enhance your separations at the OSN Database: www.OSNdatabase.com