Interfacial characterizations and analytical applications of chemically-modified surfaces

This dissertation explores several new strategies and approaches to the surface modifications for applications in environmental monitoring, and the characterizations of interfaces at the microscopic level. The first of the four papers included in this dissertation describes the development of optical pH sensors based on the immobilization of fluoresceinamine at a base-hydrolyzed cellulose acetate film. The advantages of the sensors include a rapid response time (7 pH units), and exceptional long-term stability. The ionic strength and temperature effects, metal-ion interference, and fluorescence properties of the sensors were examined;The second paper demonstrates the in situ monitoring of the base-hydrolysis of a dithio-bis(succinimidylundecanoate) (DSU) monolayer chemisorbed at a Au(111) surface using scanning force microscopy (SFM). The experiment is based on the dependence of the frictional interactions of the chemical functional groups at the outermost few angstroms of the two surfaces that form the microcontact. The conversion of the ester functionality of DSU to a carboxylate functionality results in an increase in the friction at the tip-sample interface. The tip-assisted hydrolysis of DSU monolayer is reported in the third paper. It was found that contact imaging accelerates the base-hydrolysis of the DSU monolayer relative to the surrounding unimaged area. The proposed mechanism and potential implications to nanotechnology are discussed;An electrochemical approach to the minimization of chloride interference in the determination of chemical oxygen demand (COD) is described in the fourth paper. It is based on the electrochemical deposition of Cl- at silver electrodes. The performance of two types of silver electrodes were evaluated and characterized. Chloride removal to levels below 3 ppm with analysis times of \u3c15 min and COD precision \u3c±20% were demonstrated;An overview of the development in the above research areas was given in the General Introduction section, and a summary of the research results and possible future work were included in the General Conclusions

Thermostat-assisted continuously-tempered Hamiltonian Monte Carlo for Bayesian learning

We propose a new sampling method, the thermostat-assisted continuously-tempered Hamiltonian Monte Carlo, for Bayesian learning on large datasets and multimodal distributions. It simulates the Nos\'e-Hoover dynamics of a continuously-tempered Hamiltonian system built on the distribution of interest. A significant advantage of this method is that it is not only able to efficiently draw representative i.i.d. samples when the distribution contains multiple isolated modes, but capable of adaptively neutralising the noise arising from mini-batches and maintaining accurate sampling. While the properties of this method have been studied using synthetic distributions, experiments on three real datasets also demonstrated the gain of performance over several strong baselines with various types of neural networks plunged in