Yanliang Zhang

Replicating the intricate hierarchical architecture of natural vascular networks, especially at capillary-scale resolution, remains a pivotal challenge in organ fabrication. Here we present a machine learning-enhanced hybrid bioprinting strategy that combines high-resolution aerosol jet printing of sacrificial materials and high-throughput extrusion printing of tissue matrices. This integrated approach enables sub-10-µm resolution, achieving capillary-like channels and allowing on-demand modulation of vessel diameters in real time. Constrained Bayesian optimization rapidly identifies optimal printing parameters, ensuring reliable, high-fidelity attainment of target channel sizes without exhaustive trial and error. This streamlined workflow supports the fabrication from one-dimensional conduits to three-dimensional multibranch hierarchical networks with tunable geometries. Endothelial cells seeded into these channels form continuous, functional monolayers, substantially reducing permeability while maintaining high cell viability and proliferation. By transcending the resolution limits of conventional sacrificial printing, this bioprinting method establishes a new route for producing biomimetic vasculature. The combination of rapid optimization, real-time tunability and microcapillary-scale precision holds promise for tissue engineering, regenerative medicine and drug discovery. The alternative text for this image may have been generated using AI.

Scalable ink-based processing of Ag ₂ Se thermoelectric ink produces dense structures with enhanced transport properties, high power factor, and high power density thermoelectric generator.

A high-throughput strategy converts elemental powders into Ag ₂ Se thermoelectric alloys and devices within a minute, achieving a high power factor of 2.35 mW m ⁻¹ K ⁻² and a peak zT of 1.1, offering a scalable pathway for thermoelectric manufacturing.

Recent advances in scalable, high-throughput, and cost-effective printing methods have enabled the rapid development of printed sensors for a broad range of emerging applications. This article reviews recent developments in printed sensors, emphasizing innovative fabrication techniques such as extrusion printing, screen printing, inkjet printing, and aerosol jet printing. These methods enable the rapid production of sensors with intricate designs, high spatial resolution, and exceptional mechanical flexibility, surpassing the capabilities of traditional manufacturing processes. Printed sensors enable the measurement of a diverse range of physical parameters such as temperature, pressure, strain, and chemical or biological signals for structural health, human health, and environmental monitoring. Notably, thermoelectric-based sensors offer energy-efficient solutions for temperature and heat flux monitoring, while thermoelectric generators enable self-powered sensing systems by harnessing waste heat, body heat, or ambient thermal gradients. This review also addresses critical challenges in improving sensor resolution, sensitivity, and durability while offering emerging trends and perspectives on advancing high-performance sensing solutions.

Additive manufacturing of metallic materials holds the potential to revolutionize the fabrication of functional devices unattainable via traditional methods. Despite recent advancements, printing metallic materials typically requires thermal processing at elevated temperatures to form dense structures with desired properties, which presents a major challenge for direct printing and integration with temperature‐sensitive materials. Herein, a unique co‐jet printing (CJP) method is reported integrating an aerosol jet and a non‐thermal, atmospheric pressure plasma jet to enable concurrent aerosol deposition of metal nanoparticle inks and in situ sintering at ambient temperature. A machine learning algorithm is integrated with the CJP to perform real‐time defect detection and autonomous correction, enhancing the yield of printed films with high electrical conductivity from 44% to 94%. Concurrent printing and sintering eliminate the need for post‐printing processing, reducing the overall manufacturing time by multiple folds depending on product size. CJP enables direct printing of functional devices on a variety of temperature‐sensitive materials including biological materials. Direct printing of hydration sensors on living plant leaves is demonstrated for long‐duration monitoring of hydration level in the plant. The versatile CJP method opens tremendous opportunities to harmoniously integrate abiotic and biotic materials for emerging applications in wearable/implantable devices and biohybrid systems.

Stretchable electronics capable of conforming to nonplanar and dynamic human body surfaces are central for creating implantable and on‐skin devices for high‐fidelity monitoring of diverse physiological signals. While various strategies have been developed to produce stretchable devices, the signals collected from such devices are often highly sensitive to local strain, resulting in inevitable convolution with surface strain‐induced motion artifacts that are difficult to distinguish from intrinsic physiological signals. Here all‐printed super stretchable strain‐insensitive bioelectronics using a unique universal gradient interface (UGI) are reported to bridge the gap between soft biomaterials and stiff electronic materials. Leveraging a versatile aerosol‐based multi‐materials printing technique that allows precise spatial control over the local stiffnesses with submicron resolution, the UGI enables strain‐insensitive electronic devices with negligible resistivity changes under a 180% uniaxial stretch ratio. Various stretchable devices are directly printed on the UGI for on‐skin health monitoring with high signal quality and near‐perfect immunity to motion artifacts, including semiconductor‐based photodetectors for sensing blood oxygen saturation levels and metal‐based temperature sensors. The concept in this work will significantly simplify the fabrication and accelerate the development of a broad range of wearable and implantable bioelectronics for real‐time health monitoring and personalized therapeutics.

In response to the escalating demand for flexible devices in applications such as wearables, sensors, and touch panels, there is a need for innovative fabrication approaches for devices made from nanomaterial-based inks. Subsequent to ink deposition, a pivotal stage in device manufacturing typically involves high-temperature sintering, posing challenges for heat-sensitive substrates. Nonthermal plasma jet sintering utilizing an atmospheric pressure dielectric barrier discharge (DBD) plasma jet enables sintering at room temperature and standard pressure, facilitating the sintering of printed nanoparticle films without compromising substrate or film surface integrity. However, determining optimal plasma jet sintering conditions can be challenging due to multiple processing variables with intricate interrelationships. This work employed Bayesian optimization (BO) and machine learning (ML) to identify optimal values for seven primary plasma jet sintering variables. Optimization yielded a 99.2% increase in the measured electrical conductivity for plasma jet-sintered indium tin oxide (ITO) films after five rounds of experiments. Moreover, the optimal sintering conditions achieved an electrical conductivity that was 81.4% of conventional furnace sintering at 300 °C, but was three times faster and with a peak substrate temperature below 47 °C. This result demonstrates the prospect of applying BO to optimize processing techniques for emerging low-temperature requirements.

Optimized via machine learning, extrusion printed thermoelectric materials (BiSbTe) achieve an ultrahigh zT of 1.3 at room temperature.