Numerous metabolic processes in nature are governed by extrinsic stimuli such as light and pH variations, which afford opportunities for synthetic and biological applications. In developing a multisensor apparatus, we have integrated submicrometer purple membrane patches, each harboring bacteriorhodopsin, onto a surface. Bacteriorhodopsin is a light-driven proton pump. We conducted monitoring of the interactions between this system and a pH-responsive supramolecular hydrogel to evaluate fibrous matrix growth. Initial photostimulation induced localized reductions in pH at the membrane surface, thereby catalyzing fibrogenesis within the hydrogel. Utilizing liquid atomic force microscopy alongside confocal laser scanning microscopy, we observed the hydrogel’s morphogenesis and structural adaptations in real time. The system adeptly modulated microscale pH environments, fostering targeted fibrous development within the hydrogel matrix. This elucidates the potential for engineering responsive materials that emulate natural bioprocesses.

This PhD thesis, titled "Reaction Cascades Coupled to Surface-Chemical Nanoscale Patterns," aims to develop strategies for spatiotemporal control of chemical reaction networks (CRNs) at the micro- and nanoscale. By controlling the size and shape of nanostructures, the research facilitates the creation of unique material properties and applications. The study integrates micro/nanoscale lithography with two types of CRNs: the first, driven by a synthetic self-assembled system featuring a supramolecular hydrogelator catalyzed by protons, and the second, the Belousov-Zhabotinsky (BZ) reaction, a non-linear chemical oscillatory CRN. Both approaches seek to control local and transient CRNs using micro/nanoscale patterns.

Chapter 1 provides an overview of current methods for spatiotemporal CRN control. Chapter 2 details the top-down and bottom-up fabrication techniques and outlines the CRN and analytical methods used in the thesis.

Chapter 3, titled "Quantification of Proton Pumping in Biological Membrane Patches," focuses on measuring localized proton gradients from Purple Membranes (PMs), a lightdriven proton pump. It describes the design and fabrication of an optically triggered device and uses fluorescence microscopy to document and control proton pumping. Potential applications include managing fuel density and production rates in proton-catalyzed CRNs.

Chapter 4, titled "Control of a Gel-Forming Chemical Reaction Network Using Light- Triggered Proton Pumps," combines an acid catalyst-assisted self-assembly CRN with PMs. It aims to create a localized CRN that can be switched on and off with an optical trigger. The chapter details a system for measuring pH increases through irreversible fiber growth accelerated by protons and demonstrates the influence of PM pumping on microscale hydrogel formation using liquid atomic force microscopy and confocal laser scanning microscopy. The system is designed to develop a pH-responsive hydrogel that responds to external stimuli.

Chapter 5, titled "Network of Light-Triggered Proton Pumps," explores manipulating proton flux for spatiotemporal control of CRNs. It involves fabricating a device that combines nanochannels with locally controlled PM deposition for nanoscale fuel transport. The chapter covers the fabrication of nanochannels on a Si/SiO2/Al2O3 substrate using thermal scanning probe lithography (t-SPL), atomic layer deposition, plasma-enhanced chemical vapor deposition, and photolithography. It also discusses localized PM deposition in the Tunable Nanofluidic Confinement Apparatus (TNCA) and the development of a pH sensor using a pH-sensitive dye.

Chapter 6, titled "Networks of Microscale Chemical Oscillators: Toward Chemical Computing," aims to miniaturize and couple microscale chemical reactors (MCRs) to create a network of communicating chemical oscillators. The chapter demonstrates chemical communication (coupling and synchronization) within complex MCR networks driven by the BZ reaction, aiming to mimic signaling and regulate BZ reactions at specific locations and times. The study proposes new methods for diversifying and optimizing information processing.

Overall, this thesis presents the development and study of CRN-driven devices for spatiotemporal control, advancing applications in sensing, material property studies, and computation. The research is expected to enhance emerging technologies and deepen the understanding of chemistry in relation to biology, materials science, physics, and computing.