PacSurf2024 Session BI-MoP: Biomaterial Surfaces & Interfaces Poster Session

Due to the high biocompatibility, facile chemical modification and excellent responsiveness, hydrogel materials have received great deal of attention as wearable or implantable biosensor substrates. To fabricate a hydrogel-based biosensor, a stable bond at the interface of hydrogel and a functional sensing material is essential. In this study, we demonstrated fabrication and application of hydrogel-based optical sensor with a biocompatible micro-grating pattern for implantable medical devices.

To fabricate a functional micro-grating pattern, photolithographic patterning of a photoresist (PR) was performed to define the micro-scale line and spacing pattern. Gold (Au) nanoparticles spin-coated on the PR pattern were patterned by ligand exchange and lift-off process, resulting in an Au micro-grating pattern on a silicon (Si) wafer. The as-fabricated Au micro-grating pattern showed a rabbit ear morphology by controlling the thickness of the PR pattern. Subsequently, molding of a hydrogel precursor into the Au micro-grating pattern on a Si wafer was conducted to transfer the Au micro-grating pattern to the target hydrogel substrate.

The rabbit ear morphology and porous structure of the Au pattern enabled large interfacial contact area between hydrogel precursor and Au nanoparticles, resulting in stable bonding at the interface of Au micro-grating pattern and hydrogel substrate. Due to the biocompatibility of Au and hydrogel, this hydrogel-based biosensor can be used as for implantable medical devices.

As a case study, we demonstrated the application of hydrogel-based optical sensor composed of Au micro-grating pattern for smart intraocular lens (IOL). A pH-responsive hydrogel sensor with Au grating pattern was attached to an IOL to measure the micro-displacement of reactive hydrogel in response to pH changes by optical Moiré pattern detection. With the optical Moiré pattern detection scheme, the proposed hydrogel-based biosensor provides novel implantable optical sensor without external battery, highlightening its potential as a versatile tool for detecting various disease-specific biomarkers.

Antibiotic resistance can rapidly spread through bacterial populations via bacterial conjugation. The bacterial membrane has an important role in facilitating conjugation, thus investigating the effects on the bacterial membrane caused by conjugative plasmids, antibiotic resistance, and genes involved in conjugation is of interest. Analysis of bacterial membranes was conducted using gas cluster ion beam–secondary ion mass spectrometry (GCIB-SIMS). The complexity of the data means that data analysis is important for the identification of changes in the membrane composition. Pre-processing of data and several analytical methods for identification of changes in bacterial membranes have been investigated. GCIB-SIMS data from Escherichia coli samples were subjected to principal components analysis (PCA), principal components–canonical variate analysis (PC-CVA), and Random Forests (RF) data analysis with the aim of extracting the maximum biological information. The influence of increasing replicate data was assessed, and the effect of diminishing biological variation was studied. Optimized m/z region-specific scaling provided improved clustering, with an increase in biologically significant peaks contributing to the loadings. PC-CVA improved clustering, provided clearer loadings, and benefited from larger data sets collected over several months. RF required larger sample numbers and while showing overlap with the PC-CVA, produced additional peaks of interest. The combination of PC-CVA and RF allowed very subtle differences between bacterial strains and growth conditions to be elucidated for the first time. Specifically, comparative analysis of an E. coli strain with and without the F-plasmid revealed changes in cyclopropanation of fatty acids, where the addition of the F-plasmid led to a reduction in cyclopropanation.