In Situ (S)TEM Solutions
Study the impact of structure on material function and performance with Protochips highly published in situ TEM solutions.
WHY PROTOCHIPS IN SITU SOLUTIONS?
All our in situ TEM solutions are designed with 3 major goals in mind:
- Scaling bulk scale to nanoscale with relevance
- Accelerating productivity
- Fostering collaboration and discovery
Accomplishing these goals requires attention to all aspects of the scientific workflow, and Protochips is the only one to provide solutions that support you every step of the way:
- Sample preparation
- Data collection
- In situ analysis
- Publication
Each solution extends beyond the microscope, including tools for sample preparation, data collection, and data management and processing to support rigor, reproducibility, and efficiency.
Want to learn more about why in situ TEM is such a valuable technique? Read more background here.
IN SITU MICROSCOPY AND Artificial intelligence
FUSION AX WITH AI (DEEP LEARNING)
Deep learning, combined with the Fusion AX heating capabilities, enabled automated analysis of large in situ TEM datasets to identify interlayer stacking changes in bilayer phosphorene. Applied across extensive time-series data, the model captured how these displacements evolved both across the sample and over time, particularly during edge reconstruction. This allowed the researchers to directly see how the structure changes during heating, highlighting localized distortions that quickly fade into the material.
POSEIDON AX WITH AI (AQUADENOISING)
Here, deep learning is used in the Poseidon AX system to enhance contrast and resolution when analyzing nanoparticles forming in liquid environments. Using a simulation-trained model, noisy LP-STEM videos were significantly improved, with image clarity increased up to fifteen-fold. This enabled automatic detection and tracking of nanoparticle nucleation and growth across large datasets, achieving expert-level precision at much higher speed.
FUSION WITH AXON
In this research, Fusion, AXON and deep learning algorithms were combined to analyze advanced orientation and grain boundary characterization. Automated analysis of thin films revealed which grain boundaries appeared most frequently. Thin films provided a robust platform for studying grain growth, enabling seamless correlative TEM imaging with crystal orientation data, high spatial and temporal resolution, and large-scale automated data analysis.
Barmak, K., Rickman, (2024) JOM, 76,3622–3636
Patrick, M.J. et al. (2023) Microsc. Microanal. 0, 1–12
RDM AND FAIR
Publishing research data using Findable, Accessible, Interoperable, and Reusable (FAIR) principles is becoming increasingly important particular when applying for funding. Application of the FAIR principles using machine-vision software, can enhance the ability to automatically find and use microscopy data, as well as improve data reuse and reproducibility. Our data types are not proprietary, which in turn enables more robust analysis, data mining and review of in-situ experiments by outside researchers, increasing productivity and ultimately elevating the field of electron microscopy.
Current user?
Find our preparation guides, applications notes and other support material on our Success Community.
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