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.

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.

The need for diversification and optimization of information processing systems is increasing. Current computers based on micro-electronic chips struggle with the demand of energy-efficient processing of continuously increasing amounts of data. Unconventional computers, such as chemistry-based devices, also known as a ‘chemical computers’(CCs), are a potential answer. Chemical computing can be based on information processing by a non-linear oscillatory chemical reaction network (CRN). Other than current computation architectures, a CC can process information by exploiting the enormous parallelism of collective networks even in the presence of large amounts of noise. Likewise, it provides energy efficiency as the processing unit and memory reside in the same space. Also, many CRNs do not require any external energy for their operation [1].
Towards the development of a CC, we built a diffusion driven network for information processing. The network comprises a set of micro-scaled chemical reactors (MCRs) connected by nanofluidic channels. The MCRs contain reagents of a non-linear oscillatory CRN, the Belousov–Zhabotinsky (BZ) reaction. This reaction is based on the oxidation of an organic compound by bromic acid (HBrO3) [2] mediated by a transition-metal catalyst in acidic aqueous solution. During oscillations, the catalyst switches between two oxidation states that offer several ways of readout (color, fluorescence, redox potential). In our case, the catalyst of the BZ reaction is a ruthenium bipyridyl complex, Ru(bpy)32+, which enables visualizing oscillations by fluorescence microscopy. The Ru complex fluoresces only in the Ru²⁺ state and is dark in the Ru³⁺ state.
Previous efforts regarding the use of BZ reaction for information processing have been described: at macro-scale, a language-recognizing Turing machine based on the addition of aliquots of BZ reagents in a ‘one pot reactor’ was shown [3]. Another example features a programmable chemical processor in a reaction array of cells fluidically connected [4]. For higher-speed communication among reactors, micro-scaled systems were proposed [5]. However, in these cases, complex architectures of reactors presenting directional diffusive coupling of MCRs were not explored.
To harvest the energy efficiency and nanoscale dimensions of molecules, chemical computing units, we will have to be scaled to dimensions in the micrometer range or below. Considering that, as a first step, we successfully fabricated miniaturized, complex architectures of BZ-MCR networks in silicon by photolithography, as presented in Figure 1. The advantages of silicon-based fabrication of MCRs are the use of a chemically inert material and the scalability and freedom of design inherent with silicon based micro-fabrication. On our chips, the MCRs’ diameters range from 5 to 30 μm, their depth is 20 μm. In Figure 2, the proof-of-concept of communication in a MCR network is presented. We visualize the branching of a BZ reaction wave in a couple of 22.5μm diameter MCRs. When the wave arrives at the branching point (figure 2-d), it is transmitted to both directions (figure 2-e). In Figure 3, one example of the synchronization of different MCR sizes is presented. Two equally sized MCRs (reactor 10 and 12) are both connected to a MCR with a 4x larger diameter (reactor 11). The coupling mediates synchronization of the slower oscillations in the large reactors with the faster oscillation in the smaller reactors. After a few oscillation cycles, reactor 11 synchronizes to twice the period of reactors 10 and 12 (figure 3-d).
In conclusion, we demonstrated uniform device behavior in large arrays of silicon based MCRs. Connecting channels mediate a coupling between compartments which leads to synchronization, an important ingredient for data processing. These steps are important first results towards scalable chemical computing architectures based on simple molecules.
We thank European Union’s Horizon 2020 - Marie Skłodowska-Curie grant agreement No 812868.

[1] E. Bergh et al. Advances in Unconventional Computing. Springer, Cham (2017) 677-709.
[2] R.J. Field et al. Journal of the American Chemical Society 94.25 (1972) 8649-8664.
[3] M. Dueñas-Díez, and J. Pérez-Mercader, iScience 19 (2019) 514-526.
[4] J.M. Parrilla-Gutierrez et al. Nature communications 11.1 (2020) 1442.
[5] I.L. Mallphanov and V. K. Vanag. Russian Chemical Reviews 90.10 (2021) 1263.

This paper presents a maskless method to manufacture fused silica chips for low-noise resistive-pulse sensing. The fabrication includes wafer-scale density modification of fused silica with a femtosecond-pulsed laser, low-pressure chemical vapor deposition (LPVCD) of silicon nitride (SiNx) and accelerated chemical wet etching of the laser-exposed regions. This procedure leads to a freestanding SiNx window, which is permanently attached to a fused silica support chip and the resulting chips are robust towards Piranha cleaning at ∼80 °C. After parallel chip manufacturing, we created a single nanopore in each chip by focused helium-ion beam or by controlled breakdown. Compared to silicon chips, the resulting fused silica nanopore chips resulted in a four-fold improvement of both the signal-to-noise ratio and the capture rate for signals from the translocation of IgG1 proteins at a recording bandwidth of 50 kHz. At a bandwidth of ∼1 MHz, the noise from the fused silica nanopore chips was three- to six-fold reduced compared to silicon chips. In contrast to silicon chips, fused silica chips showed no laser-induced current noise—a significant benefit for experiments that strive to combine nanopore-based electrical and optical measurements.