Brian Langone

"I want it all... and I want it now" means spatial mapping during operando studies of real cell formats with the latest cathode engineered materials. Below shows the the size of the beam spot compared to the cell size, which is tiny in comparison. Does that mean you are getting representative statistics? To answer this question researchers at WMG, University of Warwick and FINDEN Ltd worked on developing both instrumentation and data processing to perform XRD at every point on the cell fast enough to get full measurements to get real time XRD maps. The measurements were able to pull out spatial chemical variations of the cathode, anode and electrolyte! Read more on the work of Ashok S. Menon and Steve Price on demonstrating operando XRD spatial mapping of prototype LNO//graphite pouch cells at A. S. Menon, S. W. Price, M. Ans, N. Shah, N. S. Kaur, P. Sallés Perramon, I. McClelland, A. M. Beale, S. Jacques, S. Cussen and L. F. J. Piper, Chem. Commun., 2025, DOI: 10.1039/D5CC02935J. https://lnkd.in/e83t8jWx

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🚀 Bridging AI and Thermodynamics: Accelerating Alloy Design with the HIENet Universal Potential 🔬 The prediction of phase diagrams is central to materials discovery, enabling the design of next-generation materials with tailored properties. While CALPHAD methods have long been the gold standard for thermodynamic modeling, their reliance on first-principles calculations and experimental assessments makes them computationally expensive and slow to expand into compositionally complex spaces. Our recent work (https://lnkd.in/esNTVucP) explored the potential of machine learning interatomic potentials (MLIPs) to accelerate this process by reducing the dependence on density functional theory (DFT) while preserving high fidelity in phase stability predictions. Our results suggested that some potentials indeed can be used as foundation models for the development of CALPHAD databases. As part of this broader effort, we are excited to share our contributions to HIENet, a novel hybrid invariant-equivariant foundation model that brings unparalleled efficiency to the prediction of energies, forces, and stresses of crystals, as well as important downstream tasks such as the prediction of phase diagrams and other key thermodynamic properties. HIENet uniquely integrates E(3)-invariant and O(3)-equivariant message passing layers, achieving: ✅ More than double the inference speed compared to existing foundation models ⚡ ✅ State-of-the-art accuracy for alloy phase diagram predictions 📊 ✅ Improved force and stress consistency 💡 📌 Why does this matter for CALPHAD and alloy design? HIENet’s ability to predict phase diagrams, phonon band structures, elasticity tensors, and bulk moduli aligns directly with our broader vision: 🔹 Leveraging accurate and efficient MLIPs to replace expensive DFT calculations in CALPHAD workflows 🔹 Enabling rapid assessment of new alloy compositions, including high-entropy alloys and refractory systems 🔹 Expanding CALPHAD models beyond experimentally assessed systems, unlocking new phase stability predictions The accuracy and efficiency of HIENet make it an ideal foundation model for the integration of AI and thermodynamic modeling, providing an accelerated pathway for high-throughput alloy discovery. Our preprint and model checkpoints are available now, with the training code coming soon! 📄 Preprint: https://lnkd.in/eJcUfBeM 💻 Code: https://lnkd.in/eQvBs3Sy 🔬 I am really grateful for having the opportunity to contribute to this team: Keqiang Yan, Montgomery Bohde, Andrii Kryvenko, Ziyu Xiang, Kaiji Zhao, Siya Zhu, Saagar Kolachina, Doğuhan Sarıtürk, Jianwen Xie, Raymundo Arroyave, Xiaoning Qian, Xiaofeng Qian, Shuiwang Ji

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🔋 Learn techniques and best practices for building detailed models of battery cells and packs by attending the Battery Design minicourse at the COMSOL Conference 2025 Boston. During this session, Niloofar Kamyab, Ph.D. will share how to use COMSOL Multiphysics to model batteries at various scales — from the microscale to the pack scale — and predict how they will perform under real-world operating conditions. Watch the video below to see what you can expect to learn in the minicourse! Then, get more info on this year’s conference and its highlights here: https://lnkd.in/eWAG6MJE

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🚀 Exciting Research Alert! 🚀 I'm thrilled to share my latest research publication on battery thermal management systems (BTMS) for lithium-ion batteries, now available in AIAA Journal of Thermophysics and Heat Transfer! 🔥🔋 📄 Title: Aero-Fin-Based Air-Cooled Battery Thermal Management System for Lithium-Ion Batteries 📌 Link: https://lnkd.in/gkY4z29n ✅ Key Highlights: 🔹 We introduce aero-fin configurations (Straight, Inclined, Alternate Inclination, and Gradually Inclined) for air-cooled BTMS. 🔹 The Alternate Inclination Aero-Fin (AIAF) at 40° achieved the best thermal performance with minimal pressure drop. 🔹 Our model demonstrates a 3°C reduction in average temperature compared to conventional designs. 🔹 Results emphasize the trade-off between temperature reduction and pressure drop in fin-based cooling strategies. This study provides insights into efficient and scalable cooling strategies for air-cooled battery packs, contributing to the development of safer and more reliable energy storage systems for EVs and beyond. Would love to hear your thoughts and discuss potential collaborations! 🤝 #BatteryThermalManagement #LithiumIonBatteries #EV #ThermalEngineering #CFD #EnergyStorage #AIAA

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A group of researchers led by the Zhejiang University in China has fabricated a perovskite-TOPCon tandem solar cell with a top inverted perovskite cell utilizing a self-assembled monolayer (SAM) that can reportedly enhance the cell's 𝘁𝗵𝗲𝗿𝗺𝗮𝗹 𝘀𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆. “The results highlight the possibility of the rational design of SAMs by making use of the inductive effect to achieve favored interfacial energetics with wide-bandgap perovskite,” they emphasized. “We believe this strategy is promising in contributing to the commercialization of perovskite and perovskite-TOPCon tandem solar cells.” Read more in the 𝗮𝗿𝘁𝗶𝗰𝗹𝗲 in the comments 👇 #Photovoltaics #EnergyStorage #RenewableEnergy

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Our recent study on Anode-free solid-state Li batteries has been published in Advanced Materials. Anode-free sulfide-based all-solid-state lithium metal batteries (ASSLMBs), which eliminate the need for a lithium metal anode during fabrication, offer superior energy density, enhanced safety, and simplified manufacturing. Their performance is largely influenced by the interfacial properties of the current collectors. Although previous studies have investigated the degradation of sulfide electrolytes on commonly used copper (Cu) and stainless steel (SS) current collectors, the impact of spontaneously formed surface oxides, such as copper oxide (Cu2O/CuO) and chromium oxide (Cr2O3), on interfacial stability remains underexplored. This study systematically evaluates the neglected role of passivation layers of both Cu and SS. Results demonstrate that Cu facilitates more stable lithium deposition. Electrochemical impedance spectroscopy (EIS) reveals that interfacial resistance on SS is consistently higher than on Cu during cycling. In-situ X-ray absorption spectroscopy (XAS) and computational modelling confirm the formation of phosphate (PO43−) and sulfate (SO42−) species at both interfaces, attributed to reactions between the sulfide electrolyte and surface oxides. On SS, partial reversible formation of transition metal chlorides is also detected. Based on these findings, an artificial interface is engineered on Cu, significantly improving lithium plating/stripping efficiency. These insights contribute to solid-solid interface engineering strategies and advance the fundamental understanding of anode-free ASSLMBs. Especially, we developed in situ X-ray absorption spectroscopy measurements for the anode-free cell at the SXRMB beamline of Canadian Light Source Inc. / Centre canadien de rayonnement synchrotron with the strong support from the beamline scientist of Drs. Mohsen Shakouri and Qunfeng Xiao. The work is supported by @Flex-Ion Battery Innovation Center; Natural Sciences and Engineering Research Council of Canada (NSERC); Canada Foundation for Innovation/Fondation canadienne pour l'innovation; Mitacs; Canadian Light Source Inc. / Centre canadien de rayonnement synchrotron. The work is a collaboration with Ka Ho (Nick) Chan, PhD; Jack Bekou; Payam Kaghazchi and Lijia Liu. The work is led by PhD student Yijia (Erica) Wang Here is the link: https://lnkd.in/g7zn6uKD

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