| dc.contributor.advisor | Christopher E. Carr. | en_US |
| dc.contributor.author | Lee, Sam S.,M. Eng.(Sam Seunghun)Massachusetts Institute of Technology. | en_US |
| dc.contributor.other | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. | en_US |
| dc.date.accessioned | 2021-05-24T19:52:19Z | |
| dc.date.available | 2021-05-24T19:52:19Z | |
| dc.date.copyright | 2021 | en_US |
| dc.date.issued | 2021 | en_US |
| dc.identifier.uri | https://hdl.handle.net/1721.1/130699 | |
| dc.description | Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, February, 2021 | en_US |
| dc.description | Cataloged from the official PDF of thesis. | en_US |
| dc.description | Includes bibliographical references (pages 49-51). | en_US |
| dc.description.abstract | Finding life beyond Earth has been a major focus of space exploration since the dawn of the space age. There have been a number of instruments developed for the purposes of life detection, but there are lmited options for detecting informational polymers like nucleic acids or proteins in situ. Single molecule detection systems using nanogaps like the AXN system developed by Taniguchi Lab in Osaka University [25] are able to detect and potentially sequence informational polymers like DNA, RNA and proteins, and they might be able to detect alternative informational polymers if they exist. In this project, we developed a prototype Electronic Life-detection Instrument for Enceladus/Europa (ELIE) based on the AXN system. ELIE has been designed as a nanogap single molecule detection system with the goal of making a life detection instrument capable of detecting informational polymers in situ. Alongside newly designed hardware system, we developed a control software designed specifically for ELIE as well as a new analysis pipeline that is capable of handling data from both ELIE and the original nanogap system, AXN. ELIE, although currently in early stages of development, seems to be able to detect molecules that AXN can in a similar fashion, and the analysis pipeline was effective on data from both systems. | en_US |
| dc.description.statementofresponsibility | by Sam S. Lee. | en_US |
| dc.format.extent | 51 pages | en_US |
| dc.language.iso | eng | en_US |
| dc.publisher | Massachusetts Institute of Technology | en_US |
| dc.rights | MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided. | en_US |
| dc.rights.uri | http://dspace.mit.edu/handle/1721.1/7582 | en_US |
| dc.subject | Electrical Engineering and Computer Science. | en_US |
| dc.title | Single molecule detection and classification using nanogaps | en_US |
| dc.type | Thesis | en_US |
| dc.description.degree | M. Eng. | en_US |
| dc.contributor.department | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science | en_US |
| dc.identifier.oclc | 1251800297 | en_US |
| dc.description.collection | M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science | en_US |
| dspace.imported | 2021-05-24T19:52:19Z | en_US |
| mit.thesis.degree | Master | en_US |
| mit.thesis.department | EECS | en_US |