Thank you!

While the 2024 MRS Spring Meeting & Exhibit came to conclusion with the end of The Virtual Experience on May 9th, Meeting content will be available online to registered participants through June 15, 2024.

Our congratulations go to the 2024 MRS Spring Meeting Chairs David Cahill of the University of Illinois at Urbana-Champaign, Mmantsae Diale of the University of Pretoria, Kaining Ding of Forschungszentrum Jülich GmbH, Martin Kaltenbrunner of Johannes Kepler Universität, and Takao Mori of the National Institute for Materials Science for putting together an excellent technical program along with various special events. MRS would also like to thank all the Symposium Organizers and Session Chairs for their part in the success of this Meeting. A thank you goes to the Exhibitors, Symposium Support, and to the sponsors of the Meeting and of the special events and activities.

Contributors to news on the 2024 MRS Spring Meeting & Exhibit include Meeting Scene reporters Sophia Chen, Matthew Hauwiller, Kazi Zihan Hossain, Molly McDonough, Rahul Rao, and Swati Suman; bloggers Jiachen Feng, Edith Green, and Matt Nakamura; and graphic artist Stephanie Gabborin; with newsletter production by Jason Zimmerman.

Thank you for subscribing to the MRS Meeting Scene newsletters. We hope you enjoyed reading them and continue your subscription as we launch into the 2024 MRS Fall Meeting & Exhibit. The conversation already started at #F24MRS! We welcome your comments and feedback.

Symposium MT03: Machine Learning Methods, Data and Automation for Sustainable Electronics

Nathaniel Park, IBM Research

Using Domain Specific Language to Enable Artificial Intelligence for Polymer and Catalyst Design

Written by Kazi Zihan Hossain

Artificial intelligence (AI) is reshaping different domains, including materials science and polymer chemistry. However, new polymer development is being obstructed due to a lack of well-developed open-source data. Additional efforts are often required to represent the existing experimental data with available models properly. To overcome the issue, Nathaniel Park and colleagues from IBM research have developed and presented a domain-specific language, Chemical Markdown Language (CMDL), specifically for polymers. CMDL allows researchers to use simple syntax to document complex polymeric structures and experimental results through the IBM Materials Notebook. They demonstrated the capability of CMDL by creating new polymers and catalysts through generative AI and validating them through experimentation. The research team is also improving the CMDL to integrate more features to allow seamless integration of AI in materials development.

Symposium SB11: Bio-based and Biomimetic Polymers in Soft Robotics

John A Rogers, Northwestern University

Flexible Electronic Skin for Sensing and Haptic Actuation

Written by Kazi Zihan Hossain

John Rogers and his group from Northwestern University have been pioneering flexible electronics that can be attached to human skin for various medical purposes. Traditional rigid electronics can be made flexible through geometrical manipulation such as thinning, incorporating wavy structures, or materials innovation. Instead of limiting the technology’s development to sensing body activity, these epidermal electronics also possess therapeutic potential. Skin-like wireless electronics have been used to monitor the vital health signs of babies in developing countries where adequate resources were not available. Soft electronics attached to the throat can be used to monitor the swallowing and vocal activity of stroke patients instead of bulky and cumbersome wired instruments, which is crucial for personalized rehabilitation. Haptic feedback integrated into wearables can assist prosthetic control and sensory substitution for people with nerve problems. The latest efforts have been made to attach flexible electronics to different positions of the human body and expand the haptic feedback to incorporate thermal feedback and multimodal systems to contribute to better healthcare systems.

Symposium MT03: Machine Learning Methods, Data and Automation for Sustainable Electronics

Victor Fung, Georgia Institute of Technology

Physics-Informed Pre-training of Graph Neural Networks for Materials Property Predictions

Written by Kazi Zihan Hossain

Materials scientists have used machine learning models and artificial intelligence (AI) to predict the properties of different materials. Traditional approaches require individual models and re-training for every different system to predict different properties. A model trained to predict a specific property can not be used to predict another property of the materials. To overcome this issue and develop a single model that can be used to predict different properties of materials, Victor Fung from the Georgia Institute of Technology has presented a Graph Neural Network (GNN) approach. In this technique, the composition and structure of a material can be encoded into a graph so that the properties of the materials can be related to the structure. Traditionally, GNN approaches work well when enough data is available to train the model, which may not be feasible from a material science perspective. Therefore, Fung and colleagues used the transfer learning technique to pre-train the model from a large dataset and then used the model to tune the properties of the materials. Different models, such as Crystal Graph Convolutional Neural Networks (CGCNN) and TorchMD-Net, were used to predict the properties of the materials, where the latter performed well under denoising scenarios.

Symposium EN07: Thermal Transport and Energy Conversion

Takahiro Baba, National Institute of Materials Science (NIMS), Japan

Thermal Diffusivity Measurement of Thin Films by Fourier Transform Thermo-Reflectance Method under Front Heat-Front Detect Configuration

Written by Kazi Zihan Hossain

Thermal conductivity measurement is crucial for nanoscale materials. In the traditional approach, thermal diffusivity is determined from an analytical solution after single pulse heating to obtain a thermos-reflectance signal; however, the actual thermos-reflectance signal requires periodic pulse heating. Takahiro Baba from the National Institute of Materials Science, Japan, and colleagues have developed a novel technique using the Fourier series to solve an exact analytical solution to express temperature response after heat diffusion from periodic pulse heating. A laser beam is incident on a 100-nm thick platinum thin film and collected with the photodetector when a pulse pump beam is used on the rear side of the thin film. The proposed technique could measure the thermal diffusivity and also exclude the effect of time delay. The technique is expected to work for more complicated samples like multi-layered thin films.

Symposium SB11: Bio-Based and Biomimetic Polymers in Soft Robotics

Michael Dickey, North Carolina State University

Ultra-Tough Ionogels for Stretchable Ionic Devices 

Written by Kazi Zihan Hossain

Gels are crosslinked polymers swollen with liquid. They are considered soft and flexible materials that break easily when they experience significant stretching. Soft materials like rubber bands are very stretchy and soft, while glasses are stiff and rigid and can experience very high strain but are not stretchable. Tough materials fall between them, and they can survive both high stress and high strain. Gels need to have both elastic networks and energy-dissipating networks to become tough materials. Michael Dickey from North Carolina State University has presented several revolutionary examples of such tough gels. Polyacrylamide and polyacrylic acid were copolymerized by introducing ionic liquid to induce phase separation and fabricate ultra-tough ionic gel with a facile approach. Gels can be made tough by also introducing a double network or introducing noncovalent crosslinks. Lastly, they have also presented another facile technique where liquid metal (eutectic gallium indium) was introduced to initiate free radical polymerization without the use of a traditional molecular initiator.

Symposium MF01: Advances in Polymer-based Soft Matter for Additive Manufacturing

Shangsi Chen, Zhaohe Xu, and Min Wang, The University of Hong Kong

3D Printed core-shell structured scaffolds with NIR-Triggered dual release for cancer therapy and uterine tissue regeneration

Written by Swati Suman

Gynecologic cancers and uterine fibroids often result in uterus dysfunction and hence result in female infertility. The hydrogel/poly(L-lactide-co-trimethylene carbonate hormone estradiol (E2)-containing polydopamine (PTMC-PDA@E2) coating layer significantly enhanced mechanical properties and strengthened the uterus better than the native tissues. The chronological release of the E2 over a certain period (28 days) promotes uterine tissue regeneration. This novel Gel- anticancer drug doxorubicin hydrochloride (DOX)/PTMC-PDA@E2 core-shell scaffolds have shown their potential for postoperative management in female patients. The coating layer protected the Gel-DOX core from rapid biodegradation and thus inhibited the burst release of DOX.

Symposium EL02: Towards Atomically Precise Colloidal Materials for Conventional and Quantum Optoelectronics

Written by Swati Suman

Jennifer Hollingsworth and Eric Bowes, Los Alamos National Laboratory

Precision colloidal synthesis: How far can it take us in realizing advanced quantum light sources?

Core/shell heterostructure of semiconductor quantum dot provides a better way to explore the limits of controlling quantum optical properties. Control over heterostructure at the nanoscale level provides better band structure, which affects the photoluminescence. Introducing a hole trap at the LnP/CdSe QDS interface releases two color excitonic emissions. The different nano-heterostructure systems reveal the opportunity for achieving designed quantum optical properties.

Victor Klimov, Los Alamos National Laboratory

Engineered colloidal quantum dots as universal optical gain media for solution and solid-state lasers and laser diodes

Laser technologies can be enhanced using colloidal quantum dots. The scalability and inexpensive chemical techniques used to synthesize QDs make them more attractive and prominent to work on. Faster Auger recombination can be removed using meticulously engineered hetero-QDs. The researcher has also employed compositional grading of QDs interior to smoothen charge carrier confinement potential, thereby suppressing the strength of individual Auger transition. This engineered QD shows longer optical gain lifetimes while maintaining strong 3D confinement.

Symposium SB02: Charge Carrier Transport in Organic and Organic-Inorganic Hybrid Materials

John Anthony, Tanner Smith, Karl Thorney, and Dean Windemuller, University of Kentucky

Synthesis and functionalization of new chromophores for organic electronics and photonics

Written by Swati Suman

Organic electronics has gained much attention due to its viability and range of applications in optoelectronics. However, it requires optimization of both core chromophore and interaction in solid states. The research team explored the synthesis and implementation of large chromophores fused ring aromatic compounds to enhance photonics properties. The researchers have developed a process to grow large aromatic systems that have absorbance in the near-infrared region. With electronic coupling within chromophores, the researchers developed singlet fission chromophores that yield quintet states. Along with tuning the functional group on other systems, using C-H-π interaction reduces disorders and greatly impacts charge transport. The researchers have established long life by new reaction pathways that alter reaction with oxygen or dimerization.

Symposium SB11: Bio-Based and Biomimetic Polymers in Soft Robotics

Jie Xu, Argonne National Laboratory

The Synergy of Electronic Polymers and Self-Driving Laboratories for Innovations in Soft Robots

Written by Kazi Zihan Hossain

Humankind has lived through different ages, such as the Stone Age, Iron Age, Bronze Age, and Industrial Age, and relied on different tools to advance technology and quality of life. Currently, we are living in the artificial intelligence (AI) age, and AI is getting involved in different aspects of our lives to automate various repeated tasks. However, AI has yet to get fully involved in the R&D sector, such as materials discovery with a new functional property. The development of such materials relies on the tremendous manual labor of students and laboratory workers. To overcome these problems, Jie Xu and colleagues from Argonne National Laboratory have developed an innovative solution, Polybot. This AI workflow can learn from literature data to understand the recipe of different molecules, synthesize them automatically by controlling robots, and characterize them. This AI-based platform holds huge potential for discovering new types of functional materials. They have demonstrated the feasibility of synthesizing an electrochromic polymer, which changes color based on the applied electrical potential or current. By declaring the desired color of the polymer, Polybot could synthesize the exact polymer on a limited budget with high accuracy. Thus, Polybot shows potential for groundbreaking innovation in soft robotics and beyond harnessing the power of AI.