UCLA

Understanding electrical activity of neural network is of great interest. Although electrodes and optical voltage sensors have been evolved over the past few decades, there is no such probe satisfying not only large enough sensitivity but also fast enough time response in large scale yet. Therefore, the reverse engineering on brain circuitry such that `every action potential in every neuron in large field of view' is still far-off.

In this thesis, I introduce the inorganic voltage sensor for neural electricity imaging, based on semiconductor nanoparticles (NPs) via quantum-confined Stark effect (QCSE). Firstly, I validate NPs' QCSE at room temperature (RT) at single molecule level. Based on the experimental results and theoretical investigation, I optimize the NP structure displaying the largest QCSE. Besides, undiscovered physical phenomena including wavelength blue-shift, linear energy-field dependency, and field dependent Auger recombination were revealed from the QCSE experiment for the first time. Secondly, I predict the performance of membrane

inserted NPs by self-consistent Schr�dinger-Poisson calculation, and propose voltage imaging strategies.

As a delivery method, peptide-based surface coating is developed which successfully guides NPs to the membrane. Lastly, delivered NPs are tested under voltage oscillating HEK

cells and they generate voltage dependent emission fluorescence. This voltage information is finally captured and imaged by charge coupled device and analyzed. This result demonstrates the inorganic voltage sensor's high throughput simultaneous multisite voltage imaging.