RESUMEN
In current research on the synthesis of colloidal nanostructures, the size and morphology of nanoparticles still exhibit certain dispersion and variation from batch to batch. Characterization of size distribution and morphology distribution of nanoparticles often requires techniques such as scanning electron microscopy or transmission electron microscopy, which involve high vacuum environments, are time-consuming, and costly. Experienced researchers can roughly estimate the size and distribution of nanostructure from spectra for a given synthetic route, but the accuracy is often limited. This paper reports the potential of using neural networks to accurately predict the composition of colloidal nanostructures from spectra. We address several fundamental issues in neural network prediction of colloidal composition. We first demonstrate the prediction of the composition of a colloidal binary mixture of gold nanoparticles using a gated recurrent neural network (GRU). The evolution of prediction errors for scattering, absorption, and extinction spectra of nanostructures with sizes ranging from 5 to 120 nm are analyzed. Furthermore, we demonstrate that the neural network model operates robustly under white noise in experimental testing scenarios. Compared to fully connected neural networks, the gated recurrent unit exhibits better testing accuracy in spectral prediction. When confronted with experimental data that deviates from simulation outputs, minor adjustments to the training set can allow the predictions to align closely with the experimental spectra, paving the way for the characterization of complex colloidal compositions with artificial intelligence.
RESUMEN
Nanotransfer printing of colloidal nanoparticles is a promising technique for the fabrication of functional materials and devices. However, patterning nonplanar nanostructures pose a challenge due to weak adhesion from the extremely small nanostructure-substrate contact area. Here, the study proposes a thermal-assisted nonplanar nanostructure transfer printing (NP-NTP) strategy for multiscale patterning of polystyrene (PS) nanospheres. The printing efficiency is significantly improved from ≈3.1% at low temperatures to ≈97.2% under the glass transition temperature of PS. Additionally, the arrangement of PS nanospheres transitioned from disorder to long-range order. The mechanism of printing efficiency enhancement is the drastic drop of Young's modulus of nanospheres, giving rise to an increased contact area, self-adhesive effect, and inter-particle necking. To demonstrate the versatility of the NP-NTP strategy, it is combined with the intaglio transfer printing technique, and multiple patterns are created at both micro and macro scales at a 4-inch scale with a resolution of ≈2757 pixels per inch (PPI). Furthermore, a multi-modal anti-counterfeiting concept based on structural patterns at hierarchical length scales is proposed, providing a new paradigm of imparting multiscale nanostructure patterning into macroscale functional devices.
