Benchmarking Unsupervised Clustering Algorithms for Atomic Force Microscopy Data on Polyhydroxyalkanoate Films.

Surface of polyhydroxyalkanoate (PHA) films of varying monomer compositions are analyzed using atomic force microscopy (AFM) and unsupervised machine learning (ML) algorithms to investigate and classify films based on global attributes such as the scan size, film thickness, and monomer type. The experiment provides benchmarked results for 12 of the most widely used clustering algorithms via a hybrid investigation approach while highlighting the impact of using the Fourier transform (FT) on high-dimensional vectorized data for classification on various pools of data. Our findings indicate that the use of a one-dimensional (1D) FT of vectorized data produces the most accurate outcome. The experiment also provides insights into case-by-case investigations of algorithm performances and the impact of various data pools. Lastly, we show an early version of our tool aimed at investigating surfaces using ML approaches and discuss the results of our current experiment to configure future improvements.

Universal Method Based on Layer-by-Layer Assembly for Aptamer-Based Sensors for Small-Molecule Detection.

Development of a fast and accurate pesticide analysis system is a challenging task, as a large amount of commonly used pesticide has negative effects on humans' health. Detection of pesticide residues is crucial for food safety management and environmental protection. Aptamers─short single-stranded oligonucleotides (RNA or DNA) selected by aptamer selection method SELEX─can selectively bind to their target pesticide molecules with high affinity. Thus, in the present study, we developed an electrochemical biosensor based on aptamers to detect the commonly used pesticide, glyphosate. Carbon fibers were used as the platform to assemble polyelectrolyte layers with the incorporated aptamers selectively binding with glyphosate molecules for electrochemical detection. The best limit of detection of 0.3 µM was achieved at open-circuit potential measurements, which is comparable to the current need in detection of glyphosate. The developed method can be implemented into existing systems for the determination of pesticides on farms to control residual concentrations of glyphosate in soil and water.