TY - JOUR A1 - Jablonka, Kevin Maik A1 - Ai, Qianxiang A1 - Al-Feghali, Alexander A1 - Badhwar, Shruti A1 - Bocarsly, Joshua D. A1 - Bran, Andres M. A1 - Bringuier, Stefan A1 - Brinson, L. Catherine A1 - Choudhary, Kamal A1 - Circi, Defne A1 - Cox, Sam A1 - de Jong, Wibe A. A1 - Evans, Matthew L. A1 - Gastellu, Nicolas A1 - Genzling, Jerome A1 - Gil, María Victoria A1 - Gupta, Ankur K. A1 - Hong, Zhi A1 - Imran, Alishba A1 - Kruschwitz, Sabine A1 - Labarre, Anne A1 - Lála, Jakub A1 - Liu, Tao A1 - Ma, Steven A1 - Majumdar, Sauradeep A1 - Merz, Garrett W. A1 - Moitessier, Nicolas A1 - Moubarak, Elias A1 - Mouriño, Beatriz A1 - Pelkie, Brenden A1 - Pieler, Michael A1 - Ramos, Mayk Caldas A1 - Ranković, Bojana A1 - Rodriques, Samuel G. A1 - Sanders, Jacob N. A1 - Schwaller, Philippe A1 - Schwarting, Marcus A1 - Shi, Jiale A1 - Smit, Berend A1 - Smith, Ben E. A1 - Van Herck, Joren A1 - Völker, Christoph A1 - Ward, Logan A1 - Warren, Sean A1 - Weiser, Benjamin A1 - Zhang, Sylvester A1 - Zhang, Xiaoqi A1 - Zia, Ghezal Ahmad Jan A1 - Scourtas, Aristana A1 - Schmidt, K. J. A1 - Foster, Ian A1 - White, Andrew D. A1 - Blaiszik, Ben T1 - 14 examples of how LLMs can transform materials science and chemistry: a reflection on a large language model hackathon N2 - Large-language models (LLMs) such as GPT-4 caught the interest of many scientists. Recent studies suggested that these models could be useful in chemistry and materials science. To explore these possibilities, we organized a hackathon. This article chronicles the projects built as part of this hackathon. Participants employed LLMs for various applications, including predicting properties of molecules and materials, designing novel interfaces for tools, extracting knowledge from unstructured data, and developing new educational applications. The diverse topics and the fact that working prototypes could be generated in less than two days highlight that LLMs will profoundly impact the future of our fields. The rich collection of ideas and projects also indicates that the applications of LLMs are not limited to materials science and chemistry but offer potential benefits to a wide range of scientific disciplines. KW - Large Language model KW - Hackathon KW - Concrete KW - Prediction KW - Inverse Design KW - Orchestration PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-589961 DO - https://doi.org/10.1039/d3dd00113j VL - 2 IS - 5 SP - 1233 EP - 1250 PB - Royal Society of Chemistry (RSC) AN - OPUS4-58996 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Jamieson, O. D. A1 - Bell, Jérémy A1 - Hudson, A. A1 - Saczek, J. A1 - Perez-Padilla, Victor A1 - Kaiya, G. A1 - Novakovic, K. A1 - Davies, M. A1 - Foster, E. A1 - Gruber, J. A1 - Rurack, Knut A1 - Peeters, M. T1 - Design and Application of an Imprinted Polymer Sensor for the Dual Detection of Antibiotic Contaminants in Aqueous Samples and Food Matrices N2 - An innovative polymer-based dual detection microfluidic platform has been developed for the accurate and reliable sensing of trace amounts of antibiotic tetracycline in environmental and food samples. This was achieved through the production of a bespoke polymeric material formed via an imprinting technique using a fluorescent dye. Thus, this enables dual detection of tetracycline, both thermally, via analyzing the heat-transfer resistance at the solid−liquid interface, and optically, through the inner filter effect. The combination of these two methods achieved a nanomolar limit of detection for tetracycline while also providing rapid, selective, and cost-effective sensing. Additionally, this method successfully detected tetracycline levels of 0.56 μM in blank egg samples which was significantly lower than the maximum residual level of 400 μg L−1 (0.9 μM). Our work shows that this approach can be used for the efficient detection of trace antibiotics in complex environmental and food samples, offering enhanced reliability through the integration of two complementary analysis techniques. This sensor has the potential to identify sources of antimicrobial resistance, which is crucial for targeted efforts to combat this pressing global health challenge. KW - Molecularly imprinted polymers KW - Antibiotics monitoring KW - Orthogonal detection KW - Sensors PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-626497 DO - https://doi.org/10.1021/acsapm.4c03218 SN - 2637-6105 VL - 7 IS - 4 SP - 1 EP - 9 PB - American Chemical Society CY - Washington, D.C. AN - OPUS4-62649 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -