Self-assembly in micro- and nanofluidic devices has been the focus of much attention in recent years. This is not only due to their advantages of self-assembling with fine temporal and spatial control in addition to continuous processing that is not easily accessible in conventional batch procedures, but they have evolved to become indispensable tools to localize and assimilate micro- and nanocomponents into numerous applications, such as bioelectronics, drug delivery, photonics, novel microelectronic architectures, building blocks for tissue engineering and metamaterials, and nanomedicine. This review aims to focus on the most recent advancements and characteristic investigations on the self-assembly of micro- and nanoscopic objects in micro- and nanofluidic devices. Emphasis is placed on the salient aspects of this technology in terms of the types of micro- and nanomaterials being assembled, the principles and methodologies, as well as their novel applications.

To address the high experimental costs and data scarcity inherent in Directed Energy Deposition (DED), this study proposes a data-efficient hybrid optimization framework for the precision manufacturing of Inconel 718 aerospace components. The framework leverages a two-stage strategy to bridge traditional experimental design with advanced machine learning, ensuring robust process optimization even with limited datasets. In the first stage, the Taguchi method (L16 orthogonal array) was employed for coarse-grained screening to identify influential control factors. In the second stage, a Fully Connected Neural Network (FNN) coupled with Bayesian Optimization (BO) was deployed. Crucially, this machine learning component functions as an optimization-oriented trend surrogate rather than a global regressor, successfully guiding the optimization under extreme data scarcity. The optimized process window yielded exceptional structural integrity, achieving a porosity as low as 0.03%. To thoroughly validate its practical efficacy, tensile testing (ASTM E8/E8M) and Rockwell hardness measurements (ASTM E18) were systematically conducted on the optimized specimens. The mechanical characterization demonstrated an average tensile strength of approximately 1358 MPa and a hardness of ~40 HRC. Finally, the framework was successfully validated through the robotic DED fabrication of a complex-geometry aerospace engine combustion chamber casing, bridging laboratory-scale optimization with authentic industrial applications.