綠色推進研究以低毒性推進劑、凝膠燃料、電解／觸媒引燃與小尺度推進測試為核心，重點在分解、點火與反應傳播機制。Green-propulsion research focuses on lower-toxicity propellants, gel fuels, electrolytic/catalytic ignition, and small-scale propulsion tests, with emphasis on decomposition, ignition, and reaction-propagation mechanisms.
以硝酸羥胺（hydroxylammonium nitrate, HAN）水溶液與凝膠、過氧化氫—煤油凝膠等系統為主要範例，研究熱、觸媒與電解三類分解途徑與自燃式點火的反應路徑及時間尺度，並系統比較液態、凝膠與雙凝膠等相態及凝膠化學（含水量、凝膠骨架、膠化方式）對霧化、點火與反應傳播的影響，建立配方—流變—燃燒的對應關係。本方向並發展電解點火微推進器、離子液電解／催化複合點火裝置與凝膠推進劑測試平台，朝可應用於衛星與在軌任務的綠色推進系統推進。Using hydroxylammonium nitrate (HAN) solutions and gels and hydrogen peroxide–kerosene gels as primary systems, the work studies thermal, catalytic, and electrolytic decomposition pathways and the reaction pathways and time scales of hypergolic ignition, and compares how phase form (liquid, gel, bi-gel) and gel chemistry (water content, gellant skeleton, gellation method) affect atomization, ignition, and reaction propagation, building formulation–rheology–combustion correspondences. It also develops electrolytic-ignition microthrusters, electrolytic/catalytic hybrid igniters for ionic-liquid propellants, and gel-propellant test rigs, progressing toward green propulsion systems for satellite and in-orbit missions.
以離子液（HAN、ADN，水溶液或凝膠態）與雙凝膠煤油—過氧化氫為主要系統，系統改變 HAN 濃度、膠化劑與凝膠骨架、燃料—氧化劑比例、點火方式（熱、觸媒、電解、自燃接觸）及操作壓力與容器幾何。推進劑以化學合成、凝膠化與觸媒裝填製備，測試平台包含高壓容器、電解槽、線性燃速量測台、霧化噴注台與小型推進器測試台，並整合 FTIR 產物氣體分析、高速影像、壓力感測器與流變儀，萃取分解溫度、產物組成、燃速、點火延遲、壓力歷程與噴霧粒徑等指標以判讀限制步驟；目前並結合機器學習，從配方與操作條件直接預測分解與點火行為。With ionic liquids (HAN, ADN, as solutions or gels) and bi-gel kerosene–hydrogen peroxide as primary systems, the work varies HAN concentration, gelling agent and skeleton, fuel-to-oxidizer ratio, ignition mode (thermal, catalytic, electrolytic, hypergolic contact), operating pressure, and chamber geometry. Propellants are prepared by chemical synthesis, gellation, and catalyst loading; platforms include high-pressure vessels, electrolytic cells, linear-burn-rate rigs, atomization rigs, and small thruster stands, with FTIR product-gas analysis, high-speed imaging, pressure transducers, and rheometers yielding decomposition temperature, product composition, burn rate, ignition delay, pressure history, and droplet size to identify rate-limiting steps. Machine-learning models are being added to predict decomposition and ignition directly from formulation and operating conditions.