Multi-Repeated Projection Lithography for High-Precision Linear Scale Based on Average Homogenization Effect.

A multi-repeated photolithography method for manufacturing an incremental linear scale using projection lithography is presented. The method is based on the average homogenization effect that periodically superposes the light intensity of different locations of pitches in the mask to make a consistent energy distribution at a specific wavelength, from which the accuracy of a linear scale can be improved precisely using the average pitch with different step distances. The method's theoretical error is within 0.01 µm for a periodic mask with a 2-µm sine-wave error. The intensity error models in the focal plane include the rectangular grating error on the mask, static positioning error, and lithography lens focal plane alignment error, which affect pitch uniformity less than in the common linear scale projection lithography splicing process. It was analyzed and confirmed that increasing the repeat exposure number of a single stripe could improve accuracy, as could adjusting the exposure spacing to achieve a set proportion of black and white stripes. According to the experimental results, the effectiveness of the multi-repeated photolithography method is confirmed to easily realize a pitch accuracy of 43 nm in any 10 locations of 1 m, and the whole length accuracy of the linear scale is less than 1 µm/m.

Multifunctional cross-sensitive magnetic alginate-chitosan-polyethylene oxide nanofiber sensor for human-machine interaction.

Flexible nanofiber membranes are compelling materials for the development of functional multi-mode sensors; however, their essential features such as high cross-sensitivity, reliable stability and signal discrimination capability have rarely been realized simultaneously in one sensor. Here, a novel multi-mode sensor with a nanofiber membrane structure based on multiple interpenetrating networks of bidisperse magnetic particles, sodium alginate (SA), chitosan (CHI) in conjunction with polyethylene oxide hydrogels was prepared in a controllable electrospinning technology. Specifically, the morphology distributions of nanofibers could be regulated by the crosslinking degree of the interpenetrating networks and the spinning process parameters. The incorporation of SA and CHI endowed the sensor with desirable flexibility, ideal biocompatibility and skin-friendly property. Besides, the assembled sensors not only displayed preferable magnetic sensitivity of 0.34 T-1 and reliable stability, but also exhibited favorable cross-sensitivity, quick response time, and long-term durability for over 5000 cycles under various mechanical stimuli. Importantly, the multi-mode stimuli could be discriminated via producing opposite electrical signals. Furthermore, based on the signal distinguishability of the sensor, a wearable Morse code translation system assisted by the machine learning algorithm was demonstrated, enabling a high recognizing accuracy (>99.1 %) for input letters and numbers information. Due to the excellent multifunctional sensing characteristics, we believe that the sensor will have a high potential in wearable soft electronics and human-machine interactions.

Flexible Multimodal Magnetoresistive Sensors Based on Alginate/Poly(vinyl alcohol) Foam with Stimulus Discriminability for Soft Electronics Using Machine Learning.

Flexible foam-based sensors have attracted substantial interest due to their high specific surface area, light weight, superior deformability, and ease of manufacture. However, it is still a challenge to integrate multimodal stimuli-responsiveness, high sensitivity, reliable stability, and good biocompatibility into a single foam sensor. To achieve this, a magnetoresistive foam sensor was fabricated by an in situ freezing-polymerization strategy based on the interpenetrating networks of sodium alginate, poly(vinyl alcohol) in conjunction with glycerol, and physical reinforcement of core-shell bidisperse magnetic particles. The assembled sensor exhibited preferable magnetic/strain-sensing capability (GF ≈ 0.41 T-1 for magnetic field, 4.305 for tension, -0.735 for bending, and -1.345 for pressing), quick response time, and reliable durability up to 6000 cycles under external stimuli. Importantly, a machine learning algorithm was developed to identify the encryption information, enabling high recognition accuracies of 99.22% and 99.34%. Moreover, they could be employed as health systems to detect human physiological motion and integrated as smart sensor arrays to perceive external pressure/magnetic field distributions. This work provides a simple and ecofriendly strategy to fabricate biocompatible foam-based multimodal sensors with potential applications in next-generation soft electronics.