Fiber Optic Pathogen Detection based on Dynamic Light Scattering

This research project, conducted by Michael Raum under the supervision of Professor Lingze Duan, introduces an innovative approach for pathogen detection in water using dynamic light scattering (DLS) through a fiber-optic laser setup. The study's primary objective was to utilize a duplex fiber-optic probe to transmit and collect light within a water sample, enabling the identification of pathogens based on particle size analysis through backscattered signals. The experimental configuration encompassed the operation of a fiber-optic laser at approximately 0.24 A, followed by division through a 10:90 splitter. The predominant branch guided the laser through the fiber-optic probe into the solution, where backscattered light was directed back into the probe. This backscattered signal combined with the minor branch via a 50:50 splitter and was subsequently recorded by a photodetector. To simulate particles, cornstarch was introduced to the solution at various concentrations. Optimal outcomes materialized at a concentration of approximately 1 gram per liter, effectively balancing particle backscattering and signal noise. Results showcased a substantial signal amplitude increase in the cornstarch mixture compared to the control, which exhibited a baseline signal of approximately 15 mV attributed to photodetector noise. Conversely, the cornstarch solution yielded an average signal of 55 mV. The utilization of an autocorrelation function on this dataset facilitated the calculation of the mean decay constant, pivotal for particle size determination. The research's potential implications are profound, particularly within the agricultural sector. Further refinement of this technique could yield substantial cost reductions in routine chemical testing of water supplies. The continuous monitoring capabilities enabled by the fiber-optic setup could identify particles akin in size to known pathogens, prompting supplementary chemical tests. Ultimately, this approach holds the promise of mitigating product recalls and enhancing global food safety standards, signifying a substantial advancement in pathogen detection methodologies.