Application of Carbon Aerogels in Lithium-Air Batteries
One of the main challenges with today’s batteries is their relatively low volumetric and specific capacities. The highest specific capacity can be achieved with lithium-air batteries, which use metallic lithium as the anode and typically some form of porous carbon as the cathode. To enhance performance, aerogels—among the world’s lightest solid materials—are ideal candidates for cathodes. Resorcinol-formaldehyde (RF)-based carbon aerogels, for example, serve this purpose well. In my work, I utilized two types of carbon aerogels as cathode materials: one derived from pyrolyzed resorcinol-formaldehyde polymer and the other a graphene-oxide-modified version of this carbon gel. I integrated the carbon aerogels I had pyrolyzed into lithium-air batteries to improve the cell’s performance, energy density, and capacity compared to cells using activated carbon. In my research, I examined the pore structure and surface properties of these materials in aqueous media using NMR (nuclear magnetic resonance) relaxometry and cryoporometry, exploring their impact on battery efficiency. I found that the graphene-oxide-containing sample's pores filled with water in a layered manner, indicating a more hydrophilic surface, which suggests a denser arrangement of oxygen-containing functional groups compared to the unmodified carbon aerogel. The pore sizes were reduced after adding graphene oxide, resulting in an increased specific surface area for the sample. Incorporating the reduced graphene-oxide-containing carbon aerogel enabled the creation of a more efficient, higher-capacity battery than with the RF carbon aerogel. This improved performance is likely due to the aerogel’s higher oxygen content and altered morphology. The increased oxygen content provides more active sites for oxygen reduction, meaning that a greater specific power output can be obtained from the battery.
A Humanoid Robot on the Basis of Modules Controlled Through a Serial Half-Duplex UART Bus
This thesis presents the design and construction of a small-scale humanoid robot, covering all aspects from 3D modeling to electronics design and programming. The robot is built entirely from custom 3D-printed components, with a new servomotor developed specifically to meet the project’s requirements. During the robot’s development, custom electronics were also designed, leading to a modular platform that enables easy interaction with diverse modules like servomotors and inertial measurement unit (IMU) modules. This modular approach allows these components to be programmed and controlled with minimal adjustments, as well as making development of potential future modules straightforward. The robot is operated via a computer application that includes a graphical user interface for displaying real-time data from the robot.
DIVE&CLEAN - Intervention Possible
The DIVE&CLEAN project is an educational and innovative initiative aimed at addressing a significant environmental challenge: marine pollution. With oceans covering over 70% of the Earth’s surface and providing a home to 50–80% of life on the planet, their health is critical. However, marine ecosystems are under threat due to plastic pollution, which impacts wildlife, coastal communities, and global biodiversity. This project centers around the idea of introducing underwater trash bins, especially in areas frequented by recreational divers. While most divers explore the seas without specific tools to collect trash, they could contribute significantly with the right infrastructure. The vision of DIVE&CLEAN is to inspire behavioral change, encourage collaboration, and promote actionable solutions to reduce ocean pollution. Using interactive robotics and storytelling, the project tells the story of divers rescuing animals entangled in plastic and collecting trash from the ocean floor using underwater bins. Through creative performances, it seeks to educate and motivate individuals, resorts, and authorities to adopt sustainable practices.
AI-Based Customer Sentiments Dashboard
In this fast-paced digital economy, customers' judgment is based on their experience with a company’s products and services. Customer reviews become a vital source of information for companies because this information can be used to enhance their products, understand customer wants and needs, improve brand reputation, and provide a competitor’s advantage. A company can understand customer needs and wants by going through reviews. Customers are encouraged to leave not only their opinion but also their ideas for the development of the product or service. By understanding these reviews, a company can actively respond and engage with a reviewer or problem. Failure of companies who don't answer customer queries may negatively impact customer loyalty. Customers will feel neglected by these companies and will choose competing companies to handle their needs. Additionally, customers may speak negatively about a company that does not respond to reviews. The AI-based customer sentiment dashboard can help gain a company's competitive advantage by identifying weaknesses in themselves and others. Companies will be enabled to understand where they succeed and where improvement is needed compared to their competitors, leveraging businesses to address strengths and weaknesses before competitors do. Through AI-based customer sentiment dashboards, a company can analyze its competitor’s reviews and use that information as leverage to make improvements to its products and services. Customers are increasingly leaving reviews on popular apps like Google Play, Stamped.io, Yapto, and Judge.me, Loox, Qualaroo, and Yelp. The reviews are rich in customer sentiments offering valuable insights into user satisfaction and pointing out the areas for improvement that are crucial to every company no matter how big or small. Despite their value, manually processing these reviews is a challenging task due to the large volume of unstructured data. Manual processing is also vulnerable to bias and human error, leading to inaccurate information. Traditional methods such as surveys have been proven to be ineffective if the main focus is targeted feedback and have low responses compared to reviews. The advances in artificial intelligence like Natural Language Processing (NLP) help us interpret and analyze human language and generate outputs like predicting what type of sentiments are in reviews. This project proposes developing an AI-based sentiment analysis model to evaluate customer feedback on two widely used taxi applications. Natural Language Processing libraries, such as the Valence Aware Dictionary and Sentiment Reasoner (. The model aims to categorize customer reviews into positive, negative, and neutral sentiments.
SUSTUNI - SOFTWARE FOR SMART AND SUSTAINABLE DESIGN OF INDUSTRIAL ELECTRICAL CIRCUITS
The theme of this project is to develop software to facilitate and innovate the design of low-voltage industrial electrical circuits. The goal is to develop a program that makes projects more efficient in terms of time, accuracy, and sustainability, automating dimensions such as calculating conductor cross-sections, protections, single-line diagrams, and analyzing with AI at which points industrial electrical circuits can be more sustainable. The 2023 Electric Energy Yearbook of the Energy Research Company describes that electricity consumption increases 2% per year in Brazil, and industrial installations represent the largest part of the national electrical sector (36.2%). As stated in standard NBR 5410/2004, when developing an installation project, an electrical professional works with several processes, depending on several criteria and calculations to present a reliable electrical installation. Minimal errors in calculations can cause damage to equipment, conductors, and individuals present in the installation. Using software to model these circuits optimizes time and brings more confidence to the project. This work aims to differentiate itself in this field by filling in the gaps in existing solutions for the industry, providing support for Brazilian standards, automatically generating single-line diagrams and presenting suggestions for sustainability in the circuits. The program is developed in Python, based on NBR 5410/2004 and engineering works. The software developed allows the user to size different distribution boards, motors and circuits, calculating the cross-section of the conductors/electrical protections, a particular transformer, and generating a single-line diagram in CAD. The program also presents suggestions aimed at sustainability to reduce material/energy costs. Tests were carried out with electrical engineering companies and students in the technical area, where the software presented high precision and very positive feedback from the interviewees, and it can be said that the work achieved its objectives.