Bacterial identification is crucial for addressing infectious diseases and enabling effective treatment strategies. Conventional bacteria identification methods like MALDI-TOF, while efficient, lack the capability for screening the effectiveness of antibiotics. On the other hand, existing antimicrobial resistance (AMR) tests, despite being reliable, suffer from time inefficiency and lack concurrent identification capabilities. In response to these challenges, the present study employs the recent advancements in single-cell nanomotion detection using graphene drums to address these limitations. We integrate nanomotion detection with Machine Learning (ML) algorithms which enables us to simultaneously identify bacteria phenotype and their resistance to antibiotics. Bacterial time signals are transformed into time-frequency spectrograms, which then serve as inputs for machine learning algorithms. Through pattern recognition, these algorithms identify features within the images, facilitating the development of robust classification models. Utilizing single-cell nanomotion signals, differentiation is achieved between the species Escherichia coli, Staphylococcus aureus, and Klebsiella pneumoniae, as well as in detecting antibiotic-resistant and -susceptible strains, achieving an accuracy of 98.57% in the latter. This research marks the first instance of ML integrated with nanomotion detection for bacterial species identification and antibiotic susceptibility testing. It provides a basis for advanced diagnostic tools, expediting the provision of vital data regarding bacterial identification and antibiotic susceptibility, contributing significantly to medical diagnostics.

Life at low-Reynolds numbers regime is intriguing. Single motile bacteria are known to exhibit erratic behaviour; they swim in what is known as a random walk, alternating between periods of swimming straight and abrupt moments of re-orientation. Yet in dense populations, cells of E.coli have been reported to exhibit weak synchronization and self-organize into collective oscillatory motion patterns. However, the origin of this rhythmic behaviour remains largely unknown. Here, we present a method of inducing self-sustained oscillations over minute timescales in single E.coli cells by trapping them in circular microcavities. By engineering the size of these microwells, we show that the velocity of single E.coli can be tuned between speeds ranging from a few to 20 um/s. Furthermore, we show that by connecting the microwells via on-chip channels, E.coli tend to coordinate their motion through hydrodynamic interaction and exhibit collective dynamics. Using analytical modeling, we extract the coupling strength and design the channels to mediate synchronized oscillation between two bacterial oscillators. Our work not only advances our understanding of the collective dynamics of swarming bacteria but also provides the first evidence for single-cell bacterial oscillators, paving the way to using micro-organism-inspired oscillations in applications as varied as antibiotic screening, scientific computing and micro-swimmers.

Antibiotic susceptibility test (AST) shows the efficacy of antibiotics against different strains and is frequently applied in finding new antibiotics, bacteria identification, and clinical analysis. The tests provide scientific evidence for finding pathogens and avoiding the misuse of antibiotics.

The first AST was invented by Alexander Fleming in the 1920s, right after the invention of antibiotics. Since then, methods of antibiotic testing have developed and improved. The nanomotion method is one of the auspicious methods in antibiotic susceptibility tests (AST). It features detecting the vibration of single living bacteria on the graphene drum before and after being treated with antibiotics. However, such a method is restricted in laboratory conditions currently. The aim of this thesis is to make the nanomotion method compatible with clinical requirements. As a result, a new set of designs including chips and their cartridge is fabricated and validated in this thesis.

In this research, a review including the traditional and up-to-date AST methods shows that in the nanomotion method, only one measurement is carried out simultaneously. As the research question, this novel design is supposed to carry out a few measurements with different parameters including concentration and antibiotic category.
To realize this purpose, concept designs analyzing the requirements are carried out on three components, fluid system, ventilation and sealing. More parameters regarding the material and fabrication are discussed in order to ensure its requirements including durability, printing quality and more.
In the end, the design is validated and tested in the setup. The result is compared to the original nanomotion method and the traditional AST method. We hope the thesis provides the possible direction of improvements for the nanomotion method.