Year 3 Study- Applications of Antimicrobial Bioplastics Engineered from Invasive Algae and Waste Corn Cobs

Overview

Plastics in the market are produced from fossil sources, like natural gases and coal, which contribute largely to the increase of greenhouse gases and eventually worsen global warming. Thus, there is considerable interest in biodegradable plastics. Current biodegradable plastics pose harm to the environment too; therefore, the purpose of this project is to produce a biodegradable bioplastic from invasive algae and waste corn cobs. To produce the bioplastic, I extracted the starch from the waste corn cobs, created sodium alginate from Undaria Pinnatifida, and combined the ingredients to finally produce the bioplastic. Then, I completed a trial-and-error process to find the right combination of materials that could make the best spoon and straw and measured the bioplastic’s qualities through different analyses. After weighing the bioplastic and the conventional plastic, my bioplastic was able to withhold beyond 2000g, however, the conventional plastic broke at 200g, meaning that the novel bioplastic is stronger. I used a melting station to melt the sodium alginat e I extracted and a commercial one and found that the one I extracted melted at 95oC, and the commercial one at 99oC. After weighing and comparing both plastics, I found that the novel bioplastic exhibited 84% biodegradation rate. With the Image Jprogram, I measured the zone of inhibition and found that 80 ml of bioplastic killed 94% of E.coli. In conclusion, the novel bioplastic was more efficient and environmentally friendlier than conventional plastics. A Rigid-Elastic Hybrid Finger Exoskeleton Rehabilitation System (FERS) for Stroke Patients with Motor Impairment Brad Wu Arizona College Preparatory High School, Chandler, AZ Rehabilitation needs for stroke patients with motor impairment have garnered great attention worldwide. Addressing the limitations observed in existing hand rehabilitation devices, particularly in aspects like Finger Precision, Fine Motor Coordination, and Isolated Finger Movement, as well as mitigating the risk of accidental pain and injury caused by the exoskeleton itself, a novel hybrid finger exoskeleton rehabilitation system supporting the index finger and thumb has been designed and implemented, incor porating the exoskeleton structure and a versatile user interface. Its advantages include precise control of each finger joint, more Degree of Freedom (DOF) and Range of Motion (ROM) movements, pain and injury protection, user-friendly interface, cheaper, and lighter. Noteworthy features include an optimal Multi -bar Serial Linkage with compact Z -shape structure and specialized palm components to reduce size, and elastic elements to alleviate excess force. The hybrid materials used offer advantages from both rigid and elastic components. The 3D-printed exoskeleton structure, inclusive of 6 motors, weighs a mere 300g. Versatile user interface methods, such as GUI Phone App, Mechanical Switch, AI Voice Control, and Computer Vision with Machine Learning which en ables preliminary autonomous grasping, have been integrated. Experimental results confirm the successful achievement of all design objectives, showcasing all 7 DOF movements and precise control on each joint and phalanx during rehabilitation training. Perf ormance in practical tests, demonstrating the ability to grasp, pinch, type, and touch, plus 100% repeatability rate and 99.8% accuracy, proves the rehabilitation system’s significance in aiding individuals with finger impairment due to stroke and spinal cord injuries. Arkansas GlaucoScreen: A Novel Deep Learning Based System for Glaucoma Detection and Progression Monitoring Siddhartha Milkuri Bentonville High School, Bentonville, AR Glaucoma, an eye disease that causes damage to the optic nerve, is the second leading cause of blindness worldwide. Swift diagnosis and treatment of glaucoma is crucial to prevent any glaucoma induced vision loss, however this is not usually achievable in developing countries due to their lack of medical resources. This research aims to solve this by creating GlaucoScreen, an inexpensive, accessible system for both reliable glaucoma diagnosis and accurate real time progression monitoring. GlaucoScreen is a system that uses three deep learning models working in tandem with one another to make predictions based on a retinal fundus image. The system first, using U -Net, segments the optic disk from a retinal fundus image. It then analyzes the segmented optic dis k with both the glaucoma detection model, which has an accuracy of 97.26%, and progression categorization model, which has an accuracy of 95.88%. These two models utilize InceptionV3. Although there already exist models for optic disk segmentation and glau coma detection, GlaucoScreen is unique in that it also monitors the progression state of glaucoma which is crucial for its treatment. Additionally, an open -source mobile application and an attachment, which would allow a smartphone to take retinal fundus i mages, are being developed to be used alongside the deep learning models. In doing so, this research hopes to break down barriers for glaucoma diagnosis and treatment globally.

Competition history

• JSHS 2024 Category not listed

Resources

• National JSHS abstract book ↗

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