NeoCity Academy

Project Highlight #1 - Autonomous Vacuum Pump Control and Cyrostat Hoist Systems

Diego Perez Pijuan, imec USA

Auto Vacuum Pump Control System

This project focused on addressing the limitations of the Agilent TPS Vacuum System, with the aim of enhancing its functionality and scalability for use with multiple cryostats in future superconducting experiments. It also involved the development of a programmatic control strategy utilizing a Raspberry Pi and an Analog to Digital Converter for precise pressure monitoring of the main chamber. Additionally, the development of a voltage divider circuit and created a PCB layout using KiCad, significantly improving system efficiency and streamlining the assembly process. These enhancements enabled 24/7 operation across cryostats, increasing valve bleed flow efficiency by over 35% and reducing vacuum operation time from 2 hours to 45 minutes.

Cryostat Hoist System

This project aimed to streamline the loading, preparation, cooldown, and measurement processes for the cryostat, addressing the imec Superconducting team’s need for more efficient access to internal hardware. The system comprised pulley components, motor components, and control surfaces. Collaboration with engineering firms sourced the necessary hardware, while the pulley design was optimized using Desmos to reduce the required effort. Motor components were developed, gaining hands-on experience in welding, CNC milling, and the use of power tools. SolidWorks was utilized to model over 30 key components in CAD, and Lockout-Tagout (LOTO) procedures were implemented for safety. This project improved hoist personal efficiency by 50%, allowing for more streamlined laboratory operations and enhancing my skills in software integration, 3D modeling, and workplace optimization practices.

Project Highlight #2 - Blue Origin's New Shepard rocket to test the effects of gravity on ultrasonic sound waves 

TBD

In Development

VIDEO: Positively Osceola Spotlight

ARTICLE: Osceola News-Gazette

ELISA
In this lab, we got to work with antibodies and antigens as well as learning about them in a didactic way. This was the first lab we recorded in our scientific notebook which was used more frequently later on. Through this lab, we learned about many laboratory skills such as how to dilute with buffer, and how to calculate the dilution and working with wells. With the data collected, we then analyzed the quantitative results and determined an initial positive or negative diagnosis for the patients of the sample. In this lab in particular, we where trying to find among college students, which had been infected with meningitis and which of the students had most likely been the one to infect the others.



WOLBACHIA PROJECT + PCR - TAS2R38
Scientists can target a specific gene or region of the DNA in a PCR reaction by running different samples (positive and negative controls) in order to test it, they add primers, dNTPs, buffers and Taq polymerase in the solution then the test tubes with the mixture are added to the PCR machine, after various cycles, scientists can determine if the reaction worked by running it through aragose gel. Based on the information that we knew, I concluded that the reagents included in the PCR bead that was used in the experiment was the nucleotides, enzymes and Taq polymerase it is necessary to run a control in this experiment in order to compare the samples and identify if it matches with either the TAS2R38 gene or not. In the WOLBACHIA project, we got to work with real samples of various insects in order to find if they contained a specific DNA strand called WOLBACHIA that battles the chikungunya viral disease that is transmitted via mosquitoes. This project was directed by the University of Pensilvania which conducted field releases of male Wolbachia-carrying Aedes aegypti (Wolbachia-Aedes) mosquitoes to suppress urban Aedes aegypti mosquito populations, the main vector for dengue, in parts of Singapore. This lab was important to me since I once got infected by a chikungunya mosquito

 myself and also because I knew that this was something that was going to help people in Singapore as well. I felt that my research findings actually had a purpose for the first time.

Project Highlight #1 - TucanSpeak 

Julian Dominguez, Qianyu Huang, Max Castel, Shohruh Ismatulla 

"TucanSpeak" is an AI-driven language-learning web application developed to enhance English proficiency among Spanish speakers through interactive games. The project integrates several mini games that target different aspects of language learning, such as vocabulary, translation, pronunciation, and comprehension. By leveraging AI models, including those from OpenAI, the app personalizes the learning experience to suit individual user progress. 

The platform underwent rigorous testing to address security vulnerabilities, ensuring data protection through measures like session-based authentication, SSL encryption, email verification, password hashing and salting, and DDoS protection. Deployment was managed on AWS Lightsail, with continuous improvements facilitated through a GitHub development environment.

The project has received national recognition, including a first-place award in the 2024 National TSA Software Development competition and AP with WE Service Recognition, highlighting the innovative approach and impact of the "TucanSpeak" platform. 

Project Highlight #2 - Using AI to Classify IvCurve Experiments

Julian Dominguez, imec USA

This project focused on enhancing data filtering efficiency for superconducting experiments at imec USA by leveraging machine learning techniques. A graphical interface built with PyQt6 allowed for the labeling of over 2,000 experiments. A custom resampling and normalization algorithm was developed to prepare IV-curve (current vs. voltage) data for input into a neural network.

The machine learning model, designed using TensorFlow, utilized a combination of convolutional, LSTM RNN, and fully connected layers, achieving a 96% test accuracy in distinguishing “good” data from “garbage” data. The system was integrated into the production workflow, leading to the classification of over 60,000 past experiments. This solution significantly improved research efficiency, halving the time needed to filter through experiment data.