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.

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.

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.

本研究將不同比例的硫.與二氯化錫，在固定溫度200℃、加熱時間9小時，所合成出的壓電材料SnS2用來降解羅丹明染料，實驗結果發現以莫耳比1：4為最佳，降解率可達到96%。接著固定莫耳比1：4及溫度200℃下，發現在不同水熱時間時以水熱九小時的SnS2觸媒降解效果最好。最後本實驗以固定比1：4及水熱時間9小時在不同水熱溫度所合成的SnS2觸媒，以水熱溫度200℃時的降解效率最好，可達到96%。接著我們將最佳合成條件的觸媒對不同濃度的羅丹明進行降解，可發現當羅丹明濃度達到50ppm時，只需要2秒降解率即可達到99%，即使濃度達到70ppm時濃度降解效果仍可達到86%。最後對SnS.觸媒進行SEM分析發現顆粒大小為微米等級，而在PL分析發現本觸媒具有活性的能力且符合實驗結果。

水凝膠被廣泛應用於生醫材料方面，其特性可用來吸收大量溶劑、生物流體，或是吸附水樣中的物質，其中應用於吸附金屬離子以淨水為最具發展潛力。本計畫將N-異丙基丙烯醯胺與幾丁聚醣反應形成共聚物NIPAAm-g-chitosan水凝膠，此合成水凝膠具有 生物可降解性、溫度敏感性，探討不同嫁接程度之共聚物對金屬離子之吸附效率。目前研究從FTIR確認已成功合成出NIPAAm-g-chitosan水凝膠，且膨潤率可高達約900%。此NC水凝膠比幾丁聚醣更具廣泛的pH溶脹範圍，且在150℃高溫下不會熱裂解，顯示其穩定性佳。對於金屬離子的吸附，NC水凝膠的吸附率皆較幾丁聚醣 為佳，對於鈷離子的吸附率更可高達75%，應用在工業廢水的檢測會是一大效益。

本研究旨在探討如何利用地衣共生藻類與共生真菌天然的互利性來建構長效的微生物電池，此實驗將培養出的地衣共生真菌與藻類利用海藻酸鈉（SA）進行固化，並進一步製成不須添加質子交換膜的晶球地衣電池，並觀察其發電量。經觀察，本研究之地衣電池電壓高峰為0.497 V，且目前已維持運作1038小時，電壓仍有0.3 V。由上述可知，利用海藻酸鈉固化之方式能製作出穩定且高效能的地衣電池；而地衣取自於自然環境，亦不需添加質子交換膜，故對成本低廉且環境友善成本低廉，符合永續發展目標（SDGs）中的目標七：確保所有的人都可取得負擔的起、可靠、永續及現代的能源。期許未來能夠發展為具備實用性且低成本的綠色能源。

植物進化成陸生植物的過程中，氣孔和角質層是關鍵特徵。氣孔由保衛細胞調控，負責二氧化碳進入和水分蒸發；角質層則保護植物免受水分流失及環境壓力。調控這些特徵的基因尚不明確。本研究利用全基因組關聯分析（GWAS）探討阿拉伯芥的角質層和氣孔發育。GWAS結果顯示，與角質層厚度相關的基因位於第二條染色體，而與二氧化碳吸收效率相關的基因位於第五條染色體。氣孔導度和水分吸收效率的調控基因可能在第一、三、四條染色體上。角質層變薄時，氣孔密度下降，導致氣孔導度和水分蒸散率上升；而當角質層通透性增加到一定程度時，二氧化碳固定效率達飽和。此外，透過反向遺傳學篩選候選基因，研究特定基因對角質層合成及光合作用效率的影響。突變株分析顯示，抑制控制角質層或氣孔的基因會促進另一性狀的表現，未來可進一步探討自然族群中相關基因的功能。

研究旨在檢測牛樟芝菌絲萃取物4-Acetylantroquinonol B和Antrodin C對口腔癌幹細胞的影響。過去研究發現細胞膜蛋白CD44的表現與癌幹性有密切關係，因此本實驗著重於追蹤CD44的表現情況。透過3D懸浮培養獲得腫瘤球來擴增癌幹細胞群並用流式細胞儀分析。隨著兩種牛樟芝萃取物的濃度增加，CD44表現量下降，顯示此二化合物可能可以抑制其表現。實驗顯示牛樟芝萃取物不僅抑制癌幹細胞的存活率，且在低濃度下顯著抑制成球效率，還能促進癌幹細胞的凋亡。研究結果說明牛樟芝萃取物對癌幹細胞有影響，而這個發現可能可以提供潛在的治療靶點，有益未來口腔癌治療發展。

本實驗研究常見除草劑「固殺草」的降解與檢驗，同時利用各種物質與方法嘗試消除農藥，並尋找消除農藥「固殺草」的最佳方法。 本研究發現：藉由產生「親核取代反應」(Nucleophilic substitution) 能有較佳的消除農藥效果，並且當環境物質含有越多量的胺基酸與維生素時，其消除農藥效果也越好。 根據實驗結果，我們利用環境中易取得的物質，自製簡單、便宜的農藥消除劑，用來協助農民與一般民眾消除農作物上殘存的農藥，並根據實驗結果可以在極短時間內去除99%以上的農藥殘留，期望幫助民眾遠離農藥的毒害。

可再現醫療效益之腸道益生菌關鍵分子是重要蛋白質，蛋白質純化極重要但傳統方法費時耗力，導致發展腸道益生菌保健食品的成本很高。本研究取用助於改善糖尿病和血脂異常的腸道益生菌 Akkermansia muciniphila 外膜蛋白Amuc_1100為樣品，先以傳統方法開發純化製程，利用親和性層析和離子交換層析找出純化臺灣本土菌株isolate 02和國際標準菌株BAA-835之條件及其差異，確認純化蛋白質可維持3日穩定。本研究進行AlphaFold 電腦模型預測蛋白質結構並進行蛋白質序列分析，發現蛋白質本身結構和電性不同才導致isolate 02和BAA-835純化製程條件有所差異。本研究提供全新展望，透過人工智慧將蛋白質結構對應到合適製程，大幅減少研發純化條件的時程，完善蛋白質純化方法學，並得到可量產且品質穩定的純化蛋白質。未來將選用其他臺灣本土 Amuc_1100樣品以擴大驗證及建立蛋白質結構與純化製程資料庫。

本作品在探討2023年IMO問題5中所提到的關於日式三角形(Japanese triangle)之問題，日式三角形是將1+2+...+n個圓排成正三角形的形狀，使得對所有i=1,2,...,n，由上往下數的第i列有i個圓，且每一列都有一個圓塗成紅色。日式三角形中的忍者路徑是一串由最上列到最下列的n個圓，其中每個圓連到其下一列與之相鄰的兩圓之一。我們分成兩個研究方向：一、找出k的最大值，保證在每一個日式三角形中，有一條包含至少k個紅色圓的忍者路徑。二、找出k的最小值，保證在每一個日式三角形中，有一條包含至多k個紅色圓的忍者路徑。 研究中，我們一般化每列的紅圓數為任意自然數𝓵(若該列總圓數不足𝓵則以該列總圓數塗色)，並將問題推廣至空間三角垛的情形。最後，我們將𝓵=l的情形推廣至高維空間。

在超大質量黑洞周圍，偶爾能觀測到潮汐破壞事件的發生，而這也是一個能夠探測黑洞的手段。潮汐破壞事件是一種特殊的現象，當一個天體進入到所謂的「洛希極限」半徑範圍內時，因為受到的潮汐力超越了自身的重力而遭到撕裂。當這個事件發生時，會因為黑洞在吸積的過程中產生明顯的亮度變化，因此可以透過一系列的亮度變化觀測潮汐破壞事件，並可以推算黑洞的各項參數，因此潮汐破壞事件在天文學的發展上有其重要性。 因此，我們想要嘗試模擬潮汐破壞事件的演化過程。我們學習Linux語言以及如何使用Mcluster和PeTar等模擬軟體，並透過Python分析模擬結果，然後與理論預測值進行比較，以了解我們有那些地方需要修正。