Conventional techniques for audio monitoring, including Passive Audio Monitoring (PAM), often call for the use of environmentally unsustainable and challenging equipment. This among other limitations can disrupt the very environment that is meant to be studied and may also limit the ability to observe an ecosystem in its natural state.

This thesis explores the possibility of a passive wireless biodegradable microphone for the purpose of audio monitoring in fragile environments. This project introduces the "The BioThing - A biodegradable microphone for monitoring biodiversity".
The BioThing uses a biodegradable material, to address the current challenges of state-of-art monitoring techniques. The design utilises a resonant cavity to function as a listening device through the implementation of the backscatter wave modulation technique and with a primary working resonant frequency of around 1.7 GHz.

This report presents a detailed analysis of the device's design and function to optimize its geometry for various frequencies, operational ranges, and functionalities by investigating the effect of critical parameters such as the variable capacitances namely the distance between the diaphragm and the tuning post, and the distance between the tuning post and the antenna. Additionally, it also analyses the effect of device geometry on resonance properties by investigating the impact of parameters such as tuning post height, membrane thickness, and cavity height. The report also investigates the possibilities for device enlargement for long-distance operation and device miniaturisation to facilitate faster degradation. This optimization process aims to achieve improved performance, ultimately leading to a more effective listening device. A prototype was fabricated using commercially available Zinc and plant-based resin.

This research works towards a new generation of ecologically sustainable audio monitoring equipment that will allow a deeper insight into the soundscapes of fragile environments without disturbing the ecosystems.

Organ-on-Chip (OoC) is a technology that aims to increase the efficiency of drug development processes and organ models by engineering well-defined cell culture environments. Physiological relevant mechanical, chemical, or electrical cues provide in vivo-like microenvironments for realistic cell maturation. Biodegradable technologies have gained attention for the development of novel OoCs by integrating transient features to the culture platforms imitating the ever-changing environment inside the human body. Current efforts to replicate durable brain tissue models from organoids are limited by the lack of sufficient vascularisation introducing cell necrosis inside the 3D cell culture.
This report presents the design and fabrication of a 3D-printed OoC-platform that combines two independent cell protocols for a Vessel-on-Chip and a cortical brain organoid. The microfluidic chip is embedded with a biodegradable membrane, that separates the two cell cultures for a strictly defined time period. The membrane, composed of bayberry wax, lanolin, and carbonyl iron particles, enables the controlled opening via alternating magnetic field exposure. The thermal behaviour of the membrane is analysed with DSC and the magnetic particles with a SQUID magnetometer. Inductive heating experiments determine the optimal composite composition and exposure profile to facilitate membrane opening and subsequent communication between neural and vascular cells. The integrated membrane proved to be successful during the injection and evacuation phase. This positive result paves the way for co-culturing two inherently different cell protocols on a single chip. This master project lays the foundation for collaborative efforts towards vascularised brain organoids-on-chip and showcases the potential of additive manufacturing and biodegradable materials in OoC technology.

Drug efficacy and side effects are significantly impacted by the first-pass metabolism, a sequence of events that occurs in the stomach and the liver after oral ingestion. Accurate prediction of this mechanism is crucial for a faster and more cost-efficient drug development process. Replicating the first-pass metabolism in vitro using standard cell culture techniques is challenging due to its complexity involving simultaneous transport and organ-organ interactions in both the gut and liver tissue. A more precise in vitro to in vivo translation of drug distribution, efficacy, and toxicity may be provided by organ-on-a-chip (OoC) models, thereby creating the ability to partially replace currently-used animal models. More specifically, gut-liver-on-a-chip models can aid with in vitro predictions of oral drug administration and the first-pass metabolism. Therefore, the aim of this thesis was to develop an OoC system that could accommodate both intestinal and hepatic cell sources. A hollow fiber membrane (HFM) as a cell scaffold was a critical component of the chip design in order to more accurately reproduce the three-dimensional native environment of the cells. Additionally, the system would need to exhibit the ability to be interconnected quickly and easily, thereby creating the possibility to develop a MOoC system in a plug-and-play manner in a later stage of development, enabling the ultimate realization of organ-organ interactions.

Cardiorenal Syndrome (CRS) is a multi-organ disease characterized by a reciprocal pathological interaction between the heart and kidneys during acute or chronic injury. Yet, a complete understanding of the underlying bi-directional pathophysiological mechanisms is lacking, hence obscuring effective diagnostic strategies, patient stratification and adequate administering of intervention therapies. Thus far, CRS has only been modelled in in vivo animal models, which are inherently limited in terms of experimental control and translatability of results to humans physiology. This underlines the need to develop more advanced in vitro models that accurately recapitulate the complex inter-organ cross talk responsible for the onset and progression of CRS. However, to date no such disease model system has been reported.

This project set out to design, develop and validate a microfluidic set-up, which would then enable the investigation of CRS in a three-dimensional environment. To that end, a theoretical framework covering all aspects related to Multi-Organ-On-Chip (MOoC) design was developed. This framework then formed the foundation for an extensive list of requirements to establish an ideal microfluidic set-up for a heart-kidney disease model. Various conceptual circuit designs were proposed and evaluated on the basis of the set requirements and their associated priority.

By executing feasibility studies, simulating computational fluid dynamics, designing specific organoid inserts and eventually setting-up the proposed microfluidic circuits, a better understanding of the technological and biological challenges to be addressed was obtained. Proof-of-principle experiments were promising, but more experimental iterations with a focus on in-depth biological tissue characterization are required to further evolve and fully validate the
proposed cardiorenal model. Pressing issues entail establishing physiologically relevant scaling ratios, reducing required medium volume and identifying the exact disease models to incorporate once the physiological organoid system is functional and has been validated.

This report entails one of two subsystems in a joint project to provide a web-based platform for smartwatch data acquisition, for applications in healthcare. In this work, we design and implement algorithms for human activity recognition using various machine learning approaches and test them on data acquired online as well as using our own developed platform. Together with the web-based platform, this provides a solid base for more research using data gathered from smartwatches. The human activity recognition is implemented first using a classical machine learning approach
with feature extraction and a random forest classifier. Next, both convolutional neural network and a recurrent neural network are implemented using Tensorflow [1]. We further perform several tests to investigate: (i) the optimal segment size with respect to classification accuracy, (ii) the effect of filtering and preprocessing on the classification results, and (iii) the best classifier for activity detection.

Engineered Heart Tissues (EHTs) are a valuable approach enabled by Organ-on-Chip (OoC) technology to model human cardiac tissue. These small microfluidic devices allow the culture and development of living cardiac cells in 3D structures, to reproduce tissue dynamics and functionality in-vitro, thus fostering the development of new and more precise models for human diseases and organs’ physiological response.
One of the most important gaps in this recent technology is the lack of integration of electronic devices in the platforms, such as electrodes for tissue stimulation or sensors for real-time monitoring of the tissue.

To cover this gap, a polymer-based platform with microwell and micropillars for culturing EHTs and measuring their contractile properties was developed in ECTM group at TU Delft. The contraction force exerted by the beating cardiac tissue, self-assembled around the micropillars, is quantified by measuring the displacement of the micropillars with spiral capacitive sensors embedded in the substrate. The force generated by the tissue corresponds to a capacitance change which is simulated to be in the aF range, requiring a high-precision, sensitive, and portable read-out circuitry.

The investigation and development of this readout electronics is the final goal of this Master Thesis. A literature survey about possible capacitive readout techniques was conducted to identify suitable architectures for measuring such small dynamic changes in capacitance. Two solutions available on the market (Smartec UTI and Analog Devices AD7746) implementing two of these architectures were chosen, tested, and characterised. Benchmark measurements with accurate laboratory instrumentation were performed, and noise figures of the two solutions were evaluated.
To allow the readout, EHT platforms with embedded sensors need to be transferred and assembled on a custom Printed Circuit Board (PCB): the viability of this challenging assembly process was evaluated. The multiple constraints deriving from such a complex project determined the development of a non-standard assembly process, which proved to be delicate and gave origin to multiple failure modes. Those were documented and analysed, to identify alternative or improved assembly procedures which need to be developed to fabricate reliable samples, since these weaknesses were identified as the most critical aspect at this point of the project.

The results obtained showed how the two solutions for the readout provided results in good agreement with more precise non-portable laboratory instrumentation, and promising noise figures. The platforms with embedded sensors were successfully transferred to the developed PCBs, and measurements showed good agreement with the simulated static behaviour of the sensors, thus providing a valuable proof-of-concept for the whole project.
The dynamic behaviour of the sensors was preliminarily investigated and characterized using nanoindentation tests, indicating the possibility to measure a linear relationship between the force applied and the difference in the sensed capacitance. Despite this good result, further tests need to be performed to verify it and to address some criticalities that were identified.
Cardiac tissue was cultured on platforms with integrated sensors and the biocompatibility of the entire system was proved. The behaviour of the sensors during biological measurement with cardiac cells is left to future investigation.

Silicon accelerometers used in the automotive industry should be improved in resolution and accuracy. Exploiting quantum effects in diamond to improve sensing accuracy is a popular technique for nanoscale sensing applications. This thesis will present a new design concept for an accelerometer using these quantum effects for sensing on a macroscopic scale. This sensor can sense these forces with a resolution of 6g and a range of 120g. In measurement time it is slower than current sensors, but our sensor can still serve as a prototype to be improved in the future.