Carbon capture and storage technology (CCS) has tremendous potential to enable the use of fossil fuels while reducing the emissions of CO₂ into the atmosphere, and, consequently, combating climate change. CCS faces several challenges such as energy consumption, cost, low practical applications and environmental friendliness. In this work, a new approach to carbon capture that is not energy intensive is proposed.

鋰離子電池前幾個迴圈中，庫倫效率低（CE < 100％），表示電池負極中殘留了一些不可逆反應後的產物(Li+)，鋰離子電池的預鋰化可提高電池的庫倫效率，並降低不可逆產物的比例。本研究目的為探討預鋰化過程中加入的載劑種類對反應速率及活性鋰比例的影響，以找出最佳載劑供日後使用。文獻中提及具有苯環的載劑，其預鋰效果較佳，因此本研究比較biphenyl (聯苯)、naphthalene (萘)及benzophenone (二苯基甲酮)三種載劑對預鋰化過程與結果之影響。研究過程的部分，首先藉由實驗探討最佳測量活性鋰比例的方法，其次探討短時間內精準預鋰鋰離子電池陽極材料的方式，最後進行實驗比較不同載劑。實驗結果表示biphenyl (聯苯)的活性鋰比例最高且反應時間最短，naphthalene (萘)其次，benzophenone (二苯基甲酮)的預鋰效果最差，因此biphenyl (聯苯)為本研究中最佳的載劑。另外，透過實驗探討可得出將預鋰材料在氬氣環境中加水反應為最佳的活性鋰比例測量方式，且減少tetrahydrofuran(四氫呋喃)含水量及提高載劑濃度有助於短時間內精準預鋰。本研究未來可進一步探討鈍化與燒結對預鋰效果的影響，日後亦可應用於預鋰鋰離子電池的載劑選擇，進而運用在儲能市場及電動車產業。

因應氣候變遷碳，國際興起碳中和與循環經濟熱潮，而廢棄電子垃圾就像是一座城市礦山，蘊藏豐富的回收價值。本研究運用深度學習物件偵測來辨識廢棄PCB電路板上的有價值零件，以YOLO物件辨識方法建了一個AI影像辨識電子零件模型程式、常見PCB電子零件的金屬成分含量、紅外線影像處理，以及運用PID控制和圖像處理來控制傳送帶。實作出一個能估算廢棄電路板回收價值的原型機。其結果顯示對於鋁金屬和銅金屬有相顯著的回收效果。本研究希望讓大眾意識到廢棄家電的潛在價值，增進電子廢棄物意願，促進再生金屬產量，實現碳中和終極目標。

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.

Agro-guard aims to revolutionize disease identification and community-based projects in the field of agriculture. Integrating Machine learning, Computer vision, clustering, and community-based technology, this project helped farmers to detect their plant disease with their solution and for early warning of plant disease which was spreading in their community which helped in crop management. The research project is divided into three parts.First,Integrating Machine learning to detect and classify plant disease with their solutions.Second,Integrating Density-Based Spatial Clustering of Applications with Noise (DBSCAN),to identify disease and analyze the pattern within agricultural regions.Third,Establishing notification system to notify real-time alerts to farmers about disease spreading in particular region.The research is crucial because it solve one of the crucial problem of our community which is untimely detection of disease.The finding of the research highlight the effectiveness of Agro-Guard framework in early disease detection and community detection.The machine learning models achieved high accuracy in identifying common plant disease and clustering results the pattern in diseases that were very important for notifying the community.The significance of these findings is that it can build powerful system which will overall grow the production of crops and plants due to timely update of the disease prevailing in the community.It contributes in sustainability production of crops and plants which ultimately ensure the good livelihood of farmer.

無線傳電是非常新穎的創舉，但卻沒有人用簡單且便宜的方法來改善傳輸效率不佳的問題。在主線圈加入軟鐵後，輸入和輸出的功率比從52%增加到70%，大大減少無線傳輸能量損耗，且若次線圈再加入諧振線圈，則功率比可從52%再增加到80%，這是一個重大發現。在主線圈加入軟鐵實際無線充電之效果是：充電到10mAh時間從9分32秒縮短到5分23秒，減少3分51秒，效率提升40%。在次線圈加入諧振電路對實際無線充電之效果是：大大提升次線圈原本輸出功率約3.8倍，且主線圈加入軟鐵後，充電時間又再縮短20%。由實驗最佳數據，本組設計了無電池風扇並結合3d列印及雷射切割技術，使研究結果商品化，只需插上電源就可以使用。

Wind energy is one of the most promising and rapidly growing sources of renewable energy, although maximizing its efficiency while minimizing noise remains a challenge and limits its widespread adoption. The emergence of toroidal propellers, which have gained popularity for producing comparable thrust levels to traditional drone propellers while producing less noise, could mitigate this. This study aimed to develop a small-scale toroidal HAWT and compare its power and noise output to a conventional rotor design under similar wind velocity conditions. 15-centimeter diameter models of the toroidal and conventional rotors were created in Fusion 360 and simulated using Ansys Fluent to identify the significant aerodynamic characteristics that positively affect the blades’ power coefficient. The toroidal design with the greatest simulated power output at low tip speed ratios (TSRs) was then 3D printed and physically tested in a wind tunnel against the control rotor. The experimental results confirmed that the toroidal design had greater power coefficients at lower TSRs compared to the control rotor. The toroidal rotor started operating at a wind velocity of 3 m/s compared to the control rotor’s 6 m/s, which indicates superior start-up characteristics. While the toroidal rotor produced half the power output of the control at the highest tested wind speed of 7 m/s, it emitted 18 decibels less noise and showed a reduction in discernible noise between frequencies of two to five kilohertz. The results from this study show its potential in low-noise wind turbines within low-wind velocity environments.

As of 2021, there are 368 harmful algae blooms and over 6000 invasive species in the United States of America. Furthermore, it is reported that the United States spends more than 11.1 billion dollars per year on clean-up methods for marine debris. However, there currently isn’t a method to monitor aquatic problems simultaneously, autonomously, and efficiently, creating a capability in the aquatic biosecurity sector. To combat this, we have created an autonomous vehicle that can conduct long-term monitoring of freshwater bodies for up to 60 hours.

The economy around the world is changing rapidly, with new ways of industrial production being introduced all the time. This is due to the Fourth Industrial Revolution. The main objectives of Industry 4.0 [5] are digitalization and full automation of production processes, which increase productivity and worker safety.

Due to global population growth and industrialization, excessive inflow of causative nitrogen into rivers, and the increase in water temperature due to global warming, the occurrence of harmful algal blooms (HABs) is increasing. HABs can cause not only ecological destruction but also various social and economic problems. Additionally, consuming water from lakes with abundant toxic cyanobacteria can lead to liver damage, vomiting, abdominal pain, and even death if consumed over a long period. The first recorded occurrence of animal mortality due to HABs was in Australia in 1878, and since then, livestock and wildlife have suffered damages from HABs worldwide. Furthermore, the United States' Lake Erie has experienced frequent HAB occurrences since 2011, and in 2007, China faced social disruption when a massive HAB outbreak in Lake Tai, one of the freshwater lakes, resulted in a suspension of the water supply. In order to address these HAB occurrence issues and assess the severity of HAB events, several systems have already been established and potential solutions have been proposed. However, these systems have limitations such as being highly systematic and advanced in terms of equipment and configuration. They are often located only in periodically affected areas, and they involve substantial costs. Therefore, we aim to overcome these limitations and design a system that can effectively manage HABs.