AI 中文总结
本综述以DLIP为模型系统，指出仅靠几何无法预测金属表面功能，需分离写入几何与形成的界面，明确跨领域变量可转移性，为DLIP表面工程提供预测框架。
AI 中文摘要
直接激光干涉图案化（DLIP）可生成周期性微纳尺度结构，兼具日益提升的精度与通量，但相似几何结构会产生本质不同的功能响应。本综述探究为何仅靠形貌无法预测摩擦、润湿性、冰附着力、细菌响应、细胞行为、光学性能或电化学与光伏功能。将DLIP视为模型系统，其中光学规定的几何可与加工过程中形成的界面区分开，写入周期与浮雕深度、纵横比、分级形貌、表面化学、老化及工艺历史相分离。功能响应被解读为两阶段过程：几何结构创造与外部介质相互作用的机会，而形成的界面决定该相互作用在特定界面状态下如何成为可测量的性能。以金属为核心的精选证据涵盖摩擦学、润湿性与防冰、抗菌及生物医学表面、光学、电化学、能源器件与制造，表明仅周期-深度坐标极少能跨领域预测功能。几何在光学系统与受控机械接触中仍具高度可转移性，而生物、状态依赖及器件级功能还取决于表面状态、运行条件与系统架构。该框架调和了矛盾观测结果，明确了哪些变量可转移、哪些仍依赖情境，因此预测性DLIP表面工程需识别相互作用介质、分离写入几何与形成的界面、分离控制界面耦合的变量，并通过直接的机制特异性终点进行评估。
英文摘要
Direct laser interference patterning (DLIP) generates periodic micro- and nanoscale structures with increasing precision and throughput, yet similar geometries can produce fundamentally different functional responses. This review examines why morphology alone cannot predict friction, wetting and ice adhesion, bacterial response, cell behaviour, optical performance or electrochemical and photovoltaic function. DLIP is treated as a model system in which the optically prescribed geometry can be distinguished from the interface realised during processing. The written period is separated from relief depth, aspect ratio, hierarchical topography, surface chemistry, ageing and process history. Functional response is interpreted as a two-stage process: geometry creates the opportunity for interaction with an external agent, while the realised interface determines how that interaction becomes measurable performance under a specific interfacial state. A curated, metals-centred evidence base spanning tribology, wetting and anti-icing, antibacterial and biomedical surfaces, optics, electrochemistry, energy devices and manufacturing shows that period-depth coordinates alone rarely predict function across domains. Geometry remains highly transferable in optical systems and controlled mechanical contacts, whereas biological, state-dependent and device-level functions also depend on surface state, operating conditions and system architecture. The framework reconciles conflicting observations and identifies which variables are transferable and which remain context-dependent. Predictive DLIP surface engineering therefore requires identification of the interacting agent, separation of written geometry from the realised interface, isolation of variables governing interfacial coupling and evaluation through direct, mechanism-specific endpoints.
CommentsReview article, 6 figures, 2 tables
