ZEISS Microscopy

ZEISS FESEM : A Versatile Platform for Material and Biomaterial Imaging & Microanalysis

October 19, Monday 3 PM - 5 PM IST | October 20, Tuesday 3 PM - 5 PM IST

Overview:

ZEISS FESEMs (SIGMA 300 VP ) is a versatile tool for imaging and analytics in Materials and BioSciences.
The efficient 3 step workflow in ZEISS SIGMA 300 VP enables even a novice user to effectively produce repeatable and reliable results. With our GEMINI Technology of having a compound twin doublet objective lens, the low kV imaging of non-conductors and insulators takes the user to next level.
This emphasizes the superior imaging capabilities of ZEISS FESEMs even with a minimal amount of sample preparation. The Software enables you to combine multi-modal imaging in one single platform, to navigate seamlessly to respective ROIs, and combine the functionalities of both an EM and LM.


Key Takeaways

  • A complete tool for a core facility with the possibility of integrating with more analytics and in situ applications
  • Understanding the importance of multimodal imaging
  • How to minimize artifacts and inducing false structures during sample preparation for beam sensitive samples

Schedule

Session 1

  • Date: October 19, 2020
  • Time: 3 PM to 5 PM IST
  • Speaker: Mr. Arul Maximus Rabel - Product and Application Sales Specialist, EM, ZEISS APAC

Session 2

  • Date: October 20, 2020
  • Time: 3 PM to 5 PM IST
  • Speaker: EDAX Product Specialist

Register for the session

Single registration will suffice for both sessions

Speaker Profile: Mr. Arul Maximus Rabel

Product and Application Sales Specialist, EM, ZEISS APAC

Areas of Specialisation:

  • Field Emission Scanning Electron Microscopy – Material Science
  • Applications Crossbeam+ Fs LASER – Materials 
  • Bio Sample Preparation for high resolution SEM, TEM Imaging
  • Nanoscience and Nanotechnology, Magnetic Nanomaterial applications for Biomedical Applications 
  • Correlative Microscopy between Light and Electron Microscopes
  • Drug delivery and use of imaging in clinical diagnostics
  • Machine learning and AI for microscopy