This article presents an effective approach for patterned growth of vertically aligned ZnO nanowire (NW) arrays with high throughput and low cost at wafer scale without using cleanroom technology. Periodic hole patterns are generated using laser interference lithography on substrates coated with the photoresist SU-8. ZnO NWs are selectively grown through the holes via a low-temperature hydrothermal method without using a catalyst and with a superior control over orientation, location/density, and as-synthesized morphology. The development of textured ZnO seed layers for replacing single crystalline GaN and ZnO substrates extends the large-scale fabrication of vertically aligned ZnO NW arrays on substrates of other materials, such as polymers, Si, and glass. This combined approach demonstrates a novel method of manufacturing large-scale patterned one-dimensional nanostructures on various substrates for applications in energy harvesting, sensing, optoelectronics, and electronic devices.
A simple two‐step method of fabricating vertically aligned and periodically distributed ZnO nanowires on gallium nitride (GaN) substrates is described. The method combines laser interference ablation (LIA) and low temperature hydrothermal decomposition. The ZnO nanowires grow heteroepitaxially on unablated regions of GaN over areas spanning 1 cm2, with a high degree of control over size, orientation, uniformity, and periodicity. High resolution transmission electron microscopy and scanning electron microscopy are utilized to study the structural characteristics of the LIA‐patterned GaN substrate in detail. These studies reveal the possible mechanism for the preferential, site‐selective growth of the ZnO nanowires. The method demonstrates high application potential for wafer‐scale integration into sensor arrays, piezoelectric devices, and optoelectronic devices.
The prediction of earth pressure balance (EPB) shield performance is an essential part of project scheduling and cost estimation of tunneling projects. This paper establishes an efficient multi-objective optimization model to predict the shield performance during the tunneling process. This model integrates the adaptive neuro-fuzzy inference system (ANFIS) with the genetic algorithm (GA). The hybrid model uses shield operational parameters as inputs and computes the advance rate as output. GA enhances the accuracy of ANFIS for runtime parameters tuning by multi-objective fitness function. Prior to modeling, datasets were established, and critical operating parameters were identified through principal component analysis. Then, the tunneling case for Guangzhou metro line number 9 was adopted to verify the applicability of the proposed model. Results were then compared with those of the ANFIS model. The comparison showed that the multi-objective ANFIS-GA model is more successful than the ANFIS model in predicting the advance rate with a high accuracy, which can be used to guide the tunnel performance in the field.
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