Stirling engine is the external heated engine that heat is sup-plied externally to the heater part of the engine. Thus, Stirling cycle engine can be employed with various sources of renewable energy such as biomass, biofuel, solar energy, geothermal energy, recovery heat, and waste. The integration of gasifier, burner, and heat engine as a power system offers more fuel choices of each local area with potential resources resulting independent from shortage and cost fluctuation of fossil fuel. This research aims to investigate the integration of the Stirling engine with a wood pellet gasifier for electric power generation. Biomass can be controlled to have continuously combustion with ultra-low toxic emission. Stirling engine, therefore, is a promising alternative in small-scale-electricity production. Even though many biomass-powered Stirling engines were successfully constructed and marketed but these engines and the use of biomass resources as fuel for power generation are quite new concepts in some developing countries. Especially, the capital cost of this engine is high and unaffordable for installation compared to other power systems. Therefore, this research aims to the study attractive and feasibility of the compact Stirling engine with green energy.
เนื่องจากความต้องการพลังงานที่มีมากขึ้น แต่เชื้อเพลิงฟอสซิลซึ่งเป็นแหล่งพลังงานหลักมีอยู่อย่างจำกัดและเป็นสาเหตุหนึ่งของมลพิษและภาวะโลกร้อน ดังนั้นพลังงานทางเลือกจึงเป็นกุญแจสำคัญเพื่อความยั่งยืนด้านพลังงาน ประเทศไทยมีศักยภาพของพลังงานชีวมวลจากเกษตรกรรม ดังนั้นการพัฒนาระบบผลิตไฟฟ้าที่มลพิษต่ำและสามารถใช้ได้กับแหล่งพลังงานทดแทนจึงจำเป็นอย่างยิ่ง โดยเฉพาะเครื่องยนต์สเตอร์ลิงซึ่งมีโครงสร้างชิ้นส่วนไม่ซับซ้อน ปราศจากการสันดาปภายในเครื่องยนต์จึงเป็นเครื่องยนต์ที่มีศักยภาพผลิตไฟฟ้าด้วยพลังงานสะอาดและเป็นมิตรกับสิ่งแวดล้อมและความสำเร็จของโรงไฟฟ้าเครื่องยนต์สเตอร์ลิง ในประเทศไทย เพื่อคนไทย
คณะวิทยาศาสตร์
With the development of space technology, wide-field sky surveys using telescopes have expanded the range of new data available for time-domain astronomical research. Traditional data analysis methods can no longer respond quickly and accurately enough to the growing volume of data. Thus, classifying time-series data, such as light curves, has become a significant challenge in the era of big data. In modern times, analyzing light curves has become essential for using machine learning techniques to handle and filter through massive amounts of data. Machine learning algorithms can be divided into two categories: shallow learning and deep learning. Numerous researchers have proposed and developed a variety of algorithms for light curve classification. In this study, we experimented with Support Vector Machine (SVM) and XGBoost, which are shallow machine learning algorithms, as well as 1D-CNN and Long Short-Term Memory (LSTM), which are deep learning algorithms, which are branches of deep machine learning, to classify variable stars. The training and testing data used in this study were from the Optical Gravitational Lensing Experiment-III (OGLE-III), consisting of variable star data from the Large Magellanic Cloud (LMC), categorized into five main classes: Classical Cepheids, δ Scutis, eclipsing binaries, RR Lyrae stars, and Long-period variables. The results demonstrate the performance analysis of each machine learning algorithm type applied to light curve data, while also highlighting the accuracy and statistical metrics of the algorithms used in the experiments.
คณะอุตสาหกรรมอาหาร
This study aims to investigate the co-encapsulation technique of vitamin C and coenzyme Q10 within liposomes to enhance their stability and encapsulation efficiency and evaluate their antioxidant activity and release behavior under simulated gastrointestinal conditions. Liposomes were prepared using the High-Speed Homogenization Method, and their characteristics, including particle size, zeta potential, encapsulation efficiency, and antioxidant activity, were analyzed using DPPH, ABTS, and FRAP assays. The results demonstrated that co-encapsulation significantly improved the stability of vitamin C and coenzyme Q10 compared to single encapsulation. The liposomes exhibited high encapsulation efficiency and maintained strong antioxidant activity. The release profile under simulated gastrointestinal conditions also indicated a sustained and controlled release. These findings highlight the potential of the co-encapsulation technique in enhancing the efficacy of functional bioactive compounds, making it applicable to the food and nutraceutical industries.
คณะวิทยาศาสตร์
With the urgent need for rapid screening of Aflatoxin B1 (AFB1) due to its association with increased liver cirrhosis and hepatocellular carcinoma cases from contaminated agricultural foods, we propose a novel electrochemical aptasensor. This aptasensor is based on trimetallic nanoparticles AuPt-Ru supported by reduced graphene oxide (AuPt-Ru/RGO) modified on a low-cost and disposable goldleaf electrode (GLEAuPt-Ru/RGO) for detection of AFB1. The trimetallic nanoparticle AuPt-Ru was synthesized using an ultrasonic-driven chemical reduction method. The synthesized AuPt-Ru exhibited a waxberry-like appearance, with AuPt core-shell structure and ruthenium dispersed over the particles. The average particle size was 57.35 ± 8.24 nm. The AuPt-Ru was integrated into RGO sheets (inner diameter of 0.5 to 1.6 µm) in order to enhance electron transfer efficiency and increase the specific immobilizing surface area of the thiol-5’-terminated modified aptamer (Apt) to target AFB1. With a large electrochemical surface area and low electrochemical impedance, GLEAuPt-Ru/RGO displays ultra-high sensitivity for AFB1 detection. Differential pulse voltammetry (DPV) measurements revealed a linear range for AFB1 detection range from 0.3 to 30.0 pg mL-1 (R2 = 0.9972), with a limit of detection (LOD, S/N = 3) and a limit of quantification (LOQ, S/N = 10) of 0.009 pg mL-1 and 0.031 pg mL-1, respectively. The developed aptasensor also demonstrated excellent accuracy in real agricultural products, including dried red chili, garlic, peanut, pepper, and Thai jasmine rice, achieving recovery rates between 94.6 and 107.9%. The fabricated aptamer-based GLEAuPt-Ru/RGO performance is comparable to that of a modified commercial electrode, which has great potential application prospects for detecting AFB1 in agricultural products.