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Three-dimensional (3D) printing of metal components through powder bed fusion, material extrusion, and vat photopolymerization, has attracted interest continuously. Particularly, extrusion-based and photopolymerization-based processes employ metal particle-reinforced polymer matrix composites (PMCs) as raw materials. However, the resolution for extrusion-based printing is limited by the speed-accuracy tradeoff. In contrast, photopolymerization-based processes can significantly improve the printing resolution, but the filler loading of the PMC is typically low due to the critical requirement on raw materials’ rheological properties. Herein, we develop a new metal 3D printing strategy by utilizing micro-continuous liquid interface printing (μCLIP) to print PMC resins comprising nanoporous copper (NP-Cu) powders. By balancing the need for higher filler loading and the requirements on rheological properties to enable printability for the μCLIP, the compositions of PMC resin were optimized. In detail, the concentration of the NP-Cu powders in the resins can reach up to 40 wt% without sacrificing the printability and printing speed (10 μm·s−1). After sintering, 3D copper structures with microscale features (470 ± 140 μm in diameter) manifesting an average resistivity of 150 kΩ·mm can be realized. In summary, this new strategy potentially benefits the rapid prototyping of metal components with higher resolution at faster speeds.

 

Additive manufacturing (AM), also known as three-dimensional (3D) printing, is thriving as an effective and robust method in fabricating architected piezoelectric structures, yet most of the commonly adopted printing techniques often face the inherent speed-accuracy trade-off, limiting their speed in manufacturing sophisticated parts containing micro-/nanoscale features. Herein, stabilized, photo-curable resins comprising chemically functionalized piezoelectric nanoparticles (PiezoNPs) were formulated, from which microscale architected 3D piezoelectric structures were printed continuously via micro continuous liquid interface production ( μ CLIP) at speeds of up to ~60  μ m s -1 , which are more than 10 times faster than the previously reported stereolithography-based works. The 3D-printed functionalized barium titanate (f-BTO) composites reveal a bulk piezoelectric charge constant d 33 of 27.70 pC N -1 with the 30 wt% f-BTO. Moreover, rationally designed lattice structures that manifested enhanced, tailorable piezoelectric sensing performance as well as mechanical flexibility were tested and explored in diverse flexible and wearable self-powered sensing applications, e.g., motion recognition and respiratory monitoring. 

The emerging Internet of Things (IoTs) invokes increasing security demands that require robust encryption or anti‐counterfeiting technologies. Albeit being acknowledged as efficacious solutions in processing elaborate graphical information via multiple degrees of freedom, optical data encryption and anti‐counterfeiting techniques are typically inept in delivering satisfactory performance without compromising the desired ease‐of‐processibility or compatibility, thus leading to the exploration of novel materials and devices that are competent. Here, a robust optical data encryption technique is demonstrated utilizing polymer‐stabilized‐liquid‐crystals (PSLCs) combined with projection photoalignment and photopatterning methods. The PSLCs possess implicit optical patterns encoded via photoalignment, as well as explicit geometries produced via photopatterning. Furthermore, the PSLCs demonstrate improved robustness against harsh chemical environments and thermal stability and can be directly deployed onto various rigid and flexible substrates. Based on this, it is demonstrated that a single PSLC is apt to carry intricate information or serve as an exclusive watermark with both implicit features and explicit geometries. Moreover, a novel, generalized design strategy is developed, for the first time, to encode intricate and exclusive information with enhanced security by spatially programming the photoalignment patterns of a pair of cascade PSLCs, which further illustrates the promising capabilities of PSLCs in optical data encryption and anti‐counterfeiting.

 

Abstract Privacy protection is paramount in conducting health research. However, studies often rely on data stored in a centralized repository, where analysis is done with full access to the sensitive underlying content. Recent advances in federated learning enable building complex machine-learned models that are trained in a distributed fashion. These techniques facilitate the calculation of research study endpoints such that private data never leaves a given device or healthcare system. We show—on a diverse set of single and multi-site health studies—that federated models can achieve similar accuracy, precision, and generalizability, and lead to the same interpretation as standard centralized statistical models while achieving considerably stronger privacy protections and without significantly raising computational costs. This work is the first to apply modern and general federated learning methods that explicitly incorporate differential privacy to clinical and epidemiological research—across a spectrum of units of federation, model architectures, complexity of learning tasks and diseases. As a result, it enables health research participants to remain in control of their data and still contribute to advancing science—aspects that used to be at odds with each other.