The paper provides an overview of high-resolution electrohydrodynamic (EHD) printing processes for general applications in high-precision micro/nanoscale fabrication and manufacturing. Compared with other printing approaches, EHD printing offers many unique advantages and opportunities in the printing resolution, tunable printing modes, and wide material applicability, which has been successfully applied in numerous applications that include additive manufacturing, printed electronics, biomedical sensors and devices, and optical and photonic devices. In this review, the EHDs-based printing mechanism and the resulting printing modes are described, from which various EHD printing processes were developed. The material applicability and ink printability are discussed to establish the critical factors of the printable inks in EHD printing. A number of EHD printing processes and printing systems that are suitable for micro/nanomanufacturing applications are described in this paper. The recent progresses, opportunities, and challenges of EHD printing are reviewed for a range of potential application areas.

Electrohydrodynamic (EHD) printing has been used in various applications (e.g., sensors, batteries, photonic crystals). Currently, research on studying the relationships between EHD jetting behaviors, material properties, and processing conditions is still challenging due to a large number of parameters, cost, time, and the complex nature of experiments. In this research, we investigated EHD printing behavior using a machine learning (ML)-guided approach to overcome limitations in the experiments. Specifically, we investigated two jetting modes and the size of printed material with a broader range of material properties and processing parameters. We used samples from both literature and our own experiment results with different type of materials. Different ML models have been developed and applied to the data. Our results have shown that ML can navigate a vast parameter search space to predict printing behavior with an accuracy of higher than 95% during EHD printing. Moreover, the results showed that ML models can be used to predict the printing behavior and feather size for new materials. The ML models can guide the investigation of EHD printing and helped us understand the printing behavior in a systematic manner with reduced time, cost, and required experiments.

Selective laser sintering (SLS) is one of the most commonly used methods in additive manufacturing, due to its high prototyping speed and applicability to various materials. In the present work, molecular dynamics (MD) simulations were performed to study the thermodynamic behaviors of the planar stacked nanoparticles (NPs) model and explore the potential capability of the SLS process with nano-sized metal powders in the zero-gravity space environment. A multi-particle model of titanium–aluminum (Ti–Al) core–shell NP with a particle radius of 50 Å was constructed to investigate the characteristics of the melted pattern during sintering. Two patterns with different spatial densities were considered to study the influence of particle stacking on the melting process. Various core volume fractions and heating rates were examined to investigate their effects on the quality of the final sintered product. The stacked-NPs models with core volume fractions (CVFs) of 3%, 12%, and 30% were linearly heated up to 1100 K from room temperature (298 K) with heating rates of 0.04, 0.2, 0.5, and 1.0 K ps⁻¹. The initial fusion temperature and final sintering temperature for each stacking pattern were obtained via the validation from the radial distribution function, mean squared displacement, and the radius of the gyration analysis. The 30% CVF yields the largest neck size before the melting point, while beyond the melting point, a larger core helps delay the formation of the fully-melted products. It is observed that using the close-packed stacked-NPs model under a slow heating rate (long melting duration) would help form a stable, completely sintered product with a relatively low final sintering temperature.

Temperature control is crucial for live cell imaging, particularly in studies involving plant responses to high ambient temperatures and thermal stress. This study presents the design, development, and testing of two cost-effective heating devices tailored for confocal microscopy applications: an aluminum heat plate and a wireless mini-heater. The aluminum heat plate, engineered to integrate seamlessly with the standard 160 mm × 110 mm microscope stage, supports temperatures up to 36°C, suitable for studies in the range of non-stressful warm temperatures (e.g., 25-27°C for Arabidopsis thaliana) and moderate heat stress (e.g., 30-36°C for A. thaliana). We also developed a wireless mini-heater that offers rapid, precise heating directly at the sample slide, with a temperature increase rate over 30 times faster than the heat plate. The wireless heater effectively maintained target temperatures up to 50°C, ideal for investigating severe heat stress and heat shock responses in plants. Both devices performed well in controlled studies, including the real-time analysis of heat shock protein accumulation and stress granule formation in A. thaliana. Our designs are effective and affordable, with total construction costs lower than $300. This accessibility makes them particularly valuable for small laboratories with limited funding. Future improvements could include enhanced heat uniformity, humidity control to mitigate evaporation, and more robust thermal management to minimize focus drift during extended imaging sessions. These modifications would further solidify the utility of our heating devices in live cell imaging, offering researchers reliable, budget-friendly tools for exploring plant thermal biology.

Additive manufacturing (AM) offers great design flexibility but remains constrained by the complexity of tuning processing parameters from a vast parameter search space either by traditional trial-and-error experimental approach or expensive computational fluid dynamics simulations. Further, AM processes involve critical transient phenomena at very small timescales ranging from microsecond to millisecond, which are difficult to observe and characterize experimentally. This work presents a novel approach that combines high-speed photography with machine learning (ML) to model and predict dynamic printing behavior. We used jet printing data obtained from Electrohydrodynamic (EHD) jet printing as a representative AM process. A Gaussian Process (GP) classifier is trained on 123 high-speed video sequences captured at 15,000 frames per second accurately predicts five printing regimes, including cone-jetting, micro-dripping, dripping, unstable, and no-print, across a wide range of parameters. Further, a deep learning model combining a 3D convolutional encoder and transformer-based temporal predictor provided highly accurate quantitative estimation of frame-by-frame droplet dynamics at a computational cost orders of magnitude less than equivalent computational fluid dynamics simulations. These results establish a data-driven framework for the simulation, predictive control, and rapid optimization of EHD printing dynamics, demonstrating the potential of high-speed imaging-informed machine learning as a computationally efficient surrogate for complex AM processes.