Many chemical reactions occurring at aqueous interfaces show different kinetics and thermodynamics than the same reactions occurring in the bulk. The nature of these chemical reactions is central in understanding environmental, industrial, and biological processes; but remains incompletely understood due to its complexity and experimental difficulties in tuning and characterizing reactions at aqueous interfaces. In this dissertation, different experimental approaches are utilized to generate large, well-characterized aqueous interfaces for kinetic studies of chemical reactions. Chapter 1 introduces deviations of chemistry at aqueous interfaces that can alter physiochemical properties of chemical processes. In chapter 2, I study mechanistic rate accelerations of organic reactions at the organic-water interface and find that free OH groups of interfacial water molecules play an essential role in catalysis. In chapter 3, I revisit the effects of electric fields at the air-water interface of water microdroplets on directly converting water into hydrogen peroxide which is thermodynamically unfavorable in solution. Contrast to previous reports, no hydrogen peroxide production is observed in water microdroplets when tuning the electric fields at droplet surfaces. In chapter 4, I discuss claims of spontaneous hydrogen peroxide formation at the air-water interface and pinpoint potential experiments that can help to clarify them. Chapter 5 is the conclusion of the work presented in this dissertation.

Due to the proliferation of mobile devices and services, the scale of multiple-input-multiple-output (MIMO) communication systems is getting larger and larger and can be massive in future wireless networks. This results in significant increases in hardware cost and power consumption. Recently, low-resolution analog-to-digital converters (ADCs) have been considered as a practical solution for reducing hardware cost and power consumption in MIMO systems. This is because low-resolution ADCs have simple hardware architectures as well as very low power consumption. However, the severe nonlinearity of low-resolution ADCs causes significant distortions in the received signals and therefore makes signal processing tasks such as channel estimation and data detection much more challenging compared to those in high-resolution systems. Motivated by the fact that machine learning is very powerful in solving non-linear problems, this dissertation exploits machine learning to develop low-complexity yet efficient and robust algorithms for channel estimation and data detection in MIMO systems with low-resolution ADCs.

As more mobile devices are becoming connected everyday, we face an unprecedented demand and growth for bandwidth in wireless networks. The gradual shift from personal computers (PCs) to tablets and mobile phones brings new opportunities as well as challenges. Users tend to be mobile and demand instant access to their contents. Bandwidth usage ranges from simple browsing of web pages to audio and video streaming. Due to the limited and scarce resources, care must be taken in order to utilize the spectrum more efficiently, to mediate access to a shared common medium, and, especially, to disseminate information. This dissertation focuses on designing a new channel access scheme as well as using its channel access time more efficiently through multiple channels and dynamic rate adaptation for high-throughput wireless networks.

First, it provides a new multi-channel medium access control (MAC) for bandwidth exploitation. Current IEEE 802.11 uses one common channel for both control and data packets because it is simple even though many other adjacent channels are available and left intact. We can improve the throughput significantly by simply allowing other channels to be used. The added complexity includes channel switching and additional control packets to ensure that the neighbors are aware of the channel selection. Collision freedom is difficult in wireless environment, but I show that it can be achieved for a single transceiver without requiring temporal synchronization among nodes through an asynchronous split phase together with an observation phase as well as a unique handshake.

Second, it provides an in-depth study and analysis of rate adaptation for single and multiple antenna systems, together with a new throughput enabled approach. Throughput-based approach eliminates complexity of sender-initiated rate adaptation by incorporating errors and multiple access interference (MAI) implicitly. Its main job is to simply find the best attainable throughput at the sender and keep using it.

My contributions include providing a collision-free multiple channels access MAC with no temporal synchronization and an efficient, robust, yet simple, throughput enabled rate adaptation for wireless networks. Each proposed approach to multi-channel MAC and multi-rate adaptation is validated through analysis and extensive simulations and prototype implementations. My work provides a better understanding on the current limitation of the wireless systems and new insights for further improvements, from the time node accessing the medium through multi-channel MAC to making the best use of its access time through an efficient and robust rate adaptation.