Graduate Student Seminar Scheduled for April 6th, 2011

The Spring 2011 Beckman Institute Graduate Student Seminar Series continues on Wednesday, April 6th. The seminar will feature three short talks from students Chao Ma, Marta Baginska, and Eamon Caddigan. The seminar will be held in Beckman Institute Room 1005 and a pizza lunch will be served to those attending the talks.

Towards A Mobile NMR System 
Chao Ma
Beckman Institute Graduate Fellow. Department of Electrical and Computer Engineering.

Nuclear magnetic resonance (NMR) is a unique physical phenomenon that can reveal the chemical, physical, and structural properties of samples. It has been widely utilized for studying chemical structures of materials (using NMR spectroscopy) and for clinical diagnosis of various diseases (using MR imaging), revolutionizing biology and medicine over the last three decades.  Conventional NMR and MRI systems, which are designed and built for either large samples (such as animals and humans) or applications requiring high spectral resolution (at field strength from 7 to 14 Tesla), are expensive and incapable for on-site applications. We report our work towards developing a low cost, mobile NMR system with full spectroscopy capability, which includes whole system design, NMR microcoil fabrication, and development of a complete compact NMR electronics system for NMR signal excitation, detection and processing. The mobile NMR system will enable new NMR applications that are difficult or impossible to perform using conventional NMR systems, such as on-site detection of toxic materials (in military, homeland security or environmental sensing and applications), detection of bacteria/biomarker for point-of-care diagnosis in global healthcare, and large-scale, parallel NMR analysis of biochemical samples for drug discovery.

Thermoresponsive Microcapsules for Autonomic Shutdown of Lithium-Ion Batteries
Marta Baginska
Department of Aerospace Engineering

Lithium-ion (Li-ion) batteries are used in a variety of applications ranging from consumer electronics such as cellular phones and computers to hybrid vehicles. However, safety remains an important issue when lithium-ion batteries undergo external heating, over-charging, high current charging, or physical damage. Li-ion batteries must be safe and damage tolerant for full market penetration. Functionalization of battery electrodes with thermoresponsive microcapsules is proposed as a fail-safe mechanism for autonomic shutdown of Li-ion batteries. In the event that the internal cell temperature exceeds a design threshold, the capsules melt and coat the electrodes with a polymeric film that prevents conduction of ionic lithium, effectively shutting down the battery cell. Thermally responsive capsules must satisfy a rigorous set of requirements for autonomic shutdown including (1) long term stability in the electrochemical environment typical for Li-ion batteries, (2) survival of battery processing conditions, and (3) thermal triggering of capsules at the target temperature. The design and preparation of microcapsules for thermal shutdown and cycling results for battery coin cells demonstrating autonomic shutdown are presented.

Detecting and Categorizing Natural Scenes
Eamon Caddigan
Beckman Institute, Department of Psychology

Examples of the images used. Phase scrambled versions appear below each intact image.

Human observers are remarkably adept at extracting information from briefly presented images of natural scenes. This is demonstrated by rapid scene categorization tasks; a photo can appear for only 20 ms before it’s replaced by a visual mask, and many observers would still be able to determine whether it was a picture of a beach or a city. Images that have previously been rated as bad examples of their category (e.g., rocky beaches) are categorized less accurately. Here we present recent evidence that suggests that poor category exemplars are actually more difficult to “see”, based on the results of a two-alternative, intact vs. scrambled “image detection” task. Moreover, we discuss the results of an experiment which uncovers striking similarities between our ability to detect and categorize natural scenes. Together these results suggest that simply detecting a visual scene may be more related to categorization than previously believed.