One way to tackle the rise in antibiotic resistance, is to develop new techniques for testing the susceptibility of cells to antibiotics. In this paper, a comparison is made between a novel bacterial susceptibility testing method and a modification on this method. Both methods rely on bacterial samples deposited on graphene cavities, where the bacteria will stick to the graphene by the addition of APTES. By making use of a 632.8 nm He-Ne laser, the sample is probed, and the cavities then serve as ultrasenstive sensors for determining bacterial nanomotion. The existing method (single spot readout) is based on focusing a laser on graphene drums, where the drums are read out one at a time by the use of a photodiode. The modified method (parallel readout) makes, by the addition of one lens, use of an expanded laser, and a CMOS camera. At 100 frames per second, four drums are read out simultaneously. This technique hypothetically makes very high throughput possible for antimicrobial testing.

Both methods rely on converting a signal based on the intensity of the incoming light to the membrane deflection in nanometer, and the bacterial motility is found by taking the variance. The comparison of the two methods is done by performing multiple experiments, in order to relate the quality of the signal by finding the standard deviation (noted as S) of the variance of the deflection σ_z² . 

From an analysis of S, statistical quantities describing the distances between probability distributions have been conceived, and a criterion is proposed to differentiate between the noise levels of the two techniques. One such quantity is the normalized distance between the signals of two types of experiments, the one being an experiment without bacteria as a reference and the other being an experiment with living bacteria.
In the case of hypermotile bacteria, parallel readout has an average variance of deflection of 5.95 nm², it is substantially higher than the method of single spot readout, having average 2.92 nm². The unitless metric D for the distance between two signals however shows that both methods score similarly in probing nonmotile (∆-MotAB) E. Coli, as for the parallel readout the measure has for ∆-MotAB a value 0.34 and for single spot readout it has the value 0.31. In the case of hypermotile (7740) E. Coli, parallel readout scores again better with a value of 2.35 versus 0.39, which is the value obtained for single spot readout.

Finally an outlook is given where interesting findings have been summarized. With the use of power spectral densities and heatmaps of either average intensity or variance of the signal, interesting phenomena are noted and are topics for future research.

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.

With the incline of multidrug resistant bacteria due to the widespread use of antibiotics, there has been an ongoing search for a faster and more effective method to test the effect of antibiotics on bacteria. With current techniques for investigation of bacterial resistance requiring a minimum of 24 hours to complete, a new technique is essential to reduce the widespread misuse of antibiotics worldwide. In the last decade, new methods of this so-called Antibiotic Susceptibility Testing have been on the rise, with an emphasis on the use of nanomechanical oscillators as highly sensitive sensors. In this thesis, a novel method based on cavities in silicon substrates is introduced to detect the motion of a single motile bacterium and is compared to a highly sensitive sensor based on graphene membranes. A sensor with bacteria attached will exhibit random oscillations and these oscillations can be monitored with a laser. The rigid silicon substrates are intuitively not showing any oscillations. Nonetheless, the motility of a bacterium can still be observed by the changes in the laser path. By using an interferometric setup and monitoring the fluctuation in the voltage signal, coming from bacterial biophysical processes, the Signal-to-Noise-Ratio is determined. The amplitude increase in the fluctuation of the signal is evidently correlated with the motility of the bacteria and is demonstrated to show an increasing trend when presented with a deterministically known topographical location of the bacteria. Subsequently, the ability to detect motile cells enables the capabilities of Antibiotic Susceptibility Testing on these substrates, by monitoring the change in motion of the bacteria prior and post exposure to an effective antibiotic. The motion change, due to antibiotic exposure, is observed to show a significant difference even for a single bacterium, which opens up opportunities for an elementary non-invasive monitoring tool based on silicon.