This project aims to investigate and develop an energy storage system utilizing solar energy sources through the integration of solar cell technology and Graphene Quantum Dot Battery, representing a novel approach to enhancing energy storage efficiency and prolonging the lifespan of renewable energy systems. The selection of graphene and quantum dots as materials for battery development is attributed to their exceptional properties, including high electrical conductivity, charge storage capacity, efficient energy transfer, and enhanced stability.
การใช้พลังงานทดแทนจากแหล่งพลังงานแสงอาทิตย์ได้รับความสนใจเนื่องจากเป็นพลังงานสะอาดและยั่งยืน แต่การเก็บพลังงานที่ผลิตได้ในระหว่างวันยังคงมีข้อจำกัดในการใช้งานในช่วงกลางคืนหรือเมื่อพลังงานผลิตต่ำ การพัฒนาระบบกักเก็บพลังงานที่มีประสิทธิภาพสูงจึงมีความสำคัญเพื่อให้การใช้พลังงานแสงอาทิตย์เป็นไปอย่างยั่งยืนและมีประสิทธิภาพสูงสุด แบตเตอรี่กราฟีนควันตัมดอทมีคุณสมบัติเด่นในการเก็บพลังงานและการถ่ายโอนพลังงานอย่างรวดเร็ว โดยสามารถเพิ่มประสิทธิภาพและยืดอายุการใช้งานของแบตเตอรี่ได้ จึงเป็นทางเลือกที่ดีในการพัฒนาระบบกักเก็บพลังงานจากแสงอาทิตย์
คณะวิทยาศาสตร์
Eco Grow Pellets are high-porosity plant-growing clay pellets made from ceramic industrial sediment, blended with ground chicken bone to enhance calcium and essential minerals, promoting strong and healthy plant growth. They are suitable for all types of plants, especially those requiring well-aerated soil with good water drainage. Eco Grow Pellets are an innovative clay-based growing medium designed to optimize plant cultivation efficiency. Their high porosity structure allows for excellent air and water circulation, reducing soil compaction and waterlogging—common causes of root rot and stunted growth. Additionally, the pellets are enriched with calcium and essential minerals from ground chicken bones, reinforcing plant structure and enhancing root strength, enabling better nutrient absorption. This product is made from 100% recycled ceramic industrial sediment, aligning with the principles of Zero Waste and the BCG Economy Model. It helps minimize industrial waste while transforming discarded materials into high-value, eco-friendly growing media. Eco Grow Pellets are ideal for vegetables, flowers, and potted plants, offering ease of use, cleanliness, and safety. They contribute to sustainable agriculture by improving both crop productivity and environmental health.
คณะแพทยศาสตร์
Migraine, a prevalent neurological disorder, is the third most common disease globally, causing significant health and financial burdens. It has four stages: prodrome, aura, headache, and postdrome. The prodrome (also known as premonitory) stage is crucial as it precedes the headache by up to 72 hours. Taking medication during the premonitory peroid has shown to prevent the headache phase . However, the symptoms of premonitory period lack specificity, making it difficult for patients to know if they’re experiencing premonitory symptoms. Calcitonin-gene related peptide (cGRP),is a protein that plays a key role in migraine pathogenesis and studies found that salivary cGRP levels increase during the premonitory stage. This study aims to develop and evaluate a lateral flow immunoassay kit for detecting salivary cGRP levels in migraine patients during the prodrome stage. It can serve as a confirmation tool for premonitory symptoms.
คณะเทคโนโลยีการเกษตร
Durian is a crucial economic crop of Thailand and one of the most exported agricultural products in the world. However, producing high-quality durian requires maintaining the health of durian trees, ensuring they remain strong and disease-free to optimize productivity and minimize potential damage to both the tree and its fruit. Among the various diseases affecting durian, foliar diseases are among the most common and rapidly spreading, directly impacting tree growth and fruit quality. Therefore, monitoring and controlling leaf diseases is essential for preserving durian quality. This study aims to apply image analysis technology combined with artificial intelligence (AI) to classify diseases in durian leaves, enabling farmers to diagnose diseases independently without relying on experts. The classification includes three categories: healthy leaves (H), leaves infected with anthracnose (A), and leaves affected by algal spot (S). To develop the classification model, convolutional neural network (CNN) algorithms—ResNet-50, GoogleNet, and AlexNet—were employed. Experimental results indicate that the classification accuracy of ResNet-50, GoogleNet, and AlexNet is 93.57%, 93.95%, and 68.69%, respectively.