Zhibin Yu

This study explored the use of boron nitride nanotubes (BNNTs) as reinforcing fillers to enhance the mechanical properties of polymer-derived carbon matrix composites. BNNT-reinforced carbon matrix composites containing 0.5–5 wt% BNNTs were fabricated with pyrolysis conducted at different temperatures. X-ray diffraction and Raman spectroscopy revealed enhanced crystallinity and reduced defects in carbon matrix composites with BNNT addition. At 1200 °C pyrolysis temperature, sample shrinkage decreased from 28% in the control sample without BNNT addition to 12% with 5 wt% BNNTs, demonstrating BNNTs’ significant influence on the matrix. The density increased by 20.1% with 5 wt% BNNTs. Mechanical testing demonstrated an enhancement in the failure strain from 0.7% to 0.8% and an 87.8% increase in the work of fracture with 5 wt% BNNTs. Furthermore, the flexural strength and modulus improved by 68.7% and 55.6%, respectively, at this BNNT concentration. Increasing the pyrolysis temperature to 1500 °C further boosted the mechanical properties, with the flexural strength increasing by 283.7% and the flexural modulus by 528.6% when comparing samples containing 5 wt% BNNTs to those without BNNT reinforcement. Samples processed at 1500 °C with 5 wt% BNNT composition exhibited optimal performance.

The scarcity of measured data for defect identification often challenges the development and certification of additive manufacturing processes. Knowledge transfer and sharing have become emerging solutions to small-data challenges in quality control to improve machine learning with limited data, but this strategy raises concerns regarding privacy protection. Existing zero-shot learning and federated learning methods are insufficient to represent, select, and mask data to share and control privacy loss quantification. This study integrates differential privacy in cybersecurity with federated learning to investigate sharing strategies of manufacturing defect ontology. The method first proposes using multilevel attributes masked by noise in defect ontology as the sharing data structure to characterize manufacturing defects. Information leaks due to sharing ontology branches and data are estimated by epsilon differential privacy (DP). Under federated learning, the proposed method optimizes sharing defect ontology and image data strategies to improve zero-shot defect classification given privacy budget limits. The proposed framework includes (1) developing a sharing strategy based on multilevel attributes in defect ontology with controllable privacy leaks, (2) optimizing joint decisions in differential privacy, zero-shot defect classification, and federated learning, and (3) developing a two-stage algorithm to solve the joint optimization, combining stochastic gradient descent search for classification models and an evolutionary algorithm for exploring data-sharing strategies. A case study on zero-shot learning of additive manufacturing defects demonstrated the effectiveness of the proposed method in data-sharing strategies, such as ontology sharing, defect classification, and cloud information use.

X-ray detectors are demonstrated using composite films of lead-free Cs₂AgBiBr₆ halide double perovskite embedded in a polymer matrix as the X-ray photoconductors.

Intrinsically stretchable light‐emitting diodes (LEDs) are demonstrated using organometal‐halide‐perovskite/polymer composite emitters. The polymer matrix serves as a microscale elastic connector for the rigid and brittle perovskite and induces stretchability to the composite emissive layers. The stretchable LEDs consist of poly(ethylene oxide)‐modified poly(3,4‐ethylenedioxythiophene) polystyrene sulfonate as a transparent and stretchable anode, a perovskite/polymer composite emissive layer, and eutectic indium–gallium as the cathode. The devices exhibit a turn‐on voltage of 2.4 V, and a maximum luminance intensity of 15 960 cd m⁻² at 8.5 V. Such performance far exceeds all reported intrinsically stretchable LEDs based on electroluminescent polymers. The stretchable perovskite LEDs are mechanically robust and can be reversibly stretched up to 40% strain for 100 cycles without failure.

Thermoplastic poly(tert-butyl acrylate) (PTBA) is reported as an electroactive polymer that is rigid at ambient conditions and turns into a dielectric elastomer above a transition temperature. In the rubbery state, a PTBA thin film can be electrically actuated to strains up to 335% in area expansion. The calculated actuation pressure is 3.2 MPa. The actuation is made bistable by cooling to below glass transition temperature. The PTBA represents the bistable electroactive polymer (BSEP) that can be actuated to various largely strained, rigid shapes. The application of the BSEP for refreshable Braille display, an active tactile display, is also demonstrated.