Prediction and Inverse Design of Structural Colors of Nanoparticle Systems via Deep Neural Network

Ma, Longhua; Hu, Kaixiang; Wang, Chengchao; Yang, Jia‐Yue

doi:10.3390/nano11123339

Cited by 10 publications

(6 citation statements)

References 46 publications

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“…Finally, the color spectrum observed in AlxOy-coated CFFs shares a conceptual link with the structural colors of gold nanoparticle systems described by Ma et al [32], i.e., the size of the particles profoundly affects the scattering and absorption of light, with larger particles exhibiting shifts in resonance peaks and stronger scattering, leading to a spectrum of observed colors from red to purple as particle size increases. Just as the colors in nanoparticle systems are shaped by factors like particle size, volume fraction, and layer thickness, the hues seen in AlxOy-coated fabrics result from alterations in film granularity and thickness due to varying ALD cycles.…”

Section: Thermal Analysis Of the Al X O Y -Coated Cffsupporting

confidence: 69%

Enhancing Carbon Fiber Fabrics with ALD AlxOy Coatings: An Investigation of Thickness Effects on Weight, Morphology, Coloration, and Thermal Properties

Dias,

Galvão,

Miranda

et al. 2024

Coatings

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This study explores the impact of non-stoichiometric aluminum oxide (AlxOy) coatings applied via thermal atomic layer deposition (ALD) on carbon fiber fabrics (CFFs), emphasizing volume per cycle, FESEM analyses, color transitions, and thermal stability enhancements. Using trimethylaluminum and water at 100 °C, AlxOy was deposited across a range of 1000 to 5000 ALD cycles, with film thicknesses extending up to 500 nm. This notable increase in the volume of material deposited per cycle was observed for the 3D CFFs, highlighting ALD’s capability to coat complex structures effectively. FESEM analyses revealed the morphological evolution of CFF surfaces post-coating, showing a transition from individual grains to a dense, continuous layer as ALD cycles increased. This morphological transformation led to significant color shifts from green to red to blue, attributed to structural coloration effects arising from variations in film thickness and surface morphology. Thermogravimetric analyses (TGA and dTG) indicated that the AlxOy coatings enhanced the thermal stability of CFFs, with a postponement in degradation onset observed in samples subjected to more ALD cycles. In essence, this research highlights the nuanced relationship between ALD processing parameters and their collective influence on both the aesthetic and functional properties of CFFs. This study illustrates ALD’s potential in customizing CFFs for applications requiring specific color and thermal resilience, balancing the discussion between the surface morphological changes and their implications for color and thermal behavior.

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Section: Thermal Analysis Of the Al X O Y -Coated Cffsupporting

confidence: 69%

Enhancing Carbon Fiber Fabrics with ALD AlxOy Coatings: An Investigation of Thickness Effects on Weight, Morphology, Coloration, and Thermal Properties

Dias,

Galvão,

Miranda

et al. 2024

Coatings

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“…The phenomenon of radiative (light) transfer in nanoparticle systems is ubiquitous and plays a vital role in numerous applications, including climate science, ocean optics, remote sensing, biomedicine, color paints, solar energy utilization and radiative cooling [1][2][3][4][5][6][7][8][9][10][11][12]. Accurate prediction of the radiative properties of nanoparticle systems is central to such applications.…”

Section: Introductionmentioning

confidence: 99%

Extinction and Independent Scattering Criterion for Clusters of Spherical Particles Embedded in Absorbing Host Media

Zhai

Zhang

2023

Photonics

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In practical applications, the independent scattering approximation (ISA) is widely used to analyze light transfer in nanoparticle systems. However, the traditional independent scattering criterion is obtained under the assumption that the host medium surrounding particles is nonabsorbing, and thus may be invalid in certain circumstances. In this work, to explore the applicability of the ISA for small particles in absorbing host media, we calculate the extinction efficiency of particle clusters by direct solutions of macroscopic Maxwell equations. Using the far-field and distance-independent definitions of extinction, the computational efficiency multi-sphere method is applied for particle clusters in absorbing host, and its accuracy is verified with the discrete dipole approximation method. It is well known that for small particles, the dependent scattering in transparent host always enhances the extinction of the cluster and the criterion for the ISA is nearly independent of the particle refractive index and particle size. We show, however, that when the host medium is absorbing, the dependent scattering between particles can lead to a decreased or even negative extinction, and thus the ISA criterion depends on the particle refractive index, size, and host medium absorption index. In this result, the generalized criteria for absorbing host media may differ significantly from the conventional ones for transparent host media. The results can provide guidance in solving problems related to light transfer in nanoparticle systems, particularly in the presence of absorption in the host medium.

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“…Fortunately, the unprecedented development of machine learning has made it a powerful tool for solving complex computing and inverse design problems. Several studies have reported machine learning methods to facilitate the design of structural colors and inverse design of nanostructures and materials to achieve the desired optical response [ 27 , 28 , 29 , 30 , 31 , 32 , 33 ]. So et al [ 28 ] achieved the simultaneous inverse design of materials and structural parameters of core–shell nanoparticles by using neural networks.…”

Section: Introductionmentioning

confidence: 99%

Evaluation and Design of Colored Silicon Nanoparticle Systems Using a Bidirectional Deep Neural Network

Zhou

Wang

et al. 2022

Nanomaterials

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Silicon nanoparticles (SiNPs) with lowest-order Mie resonance produce non-iridescent and non-fading vivid structural colors in the visible range. However, the strong wavelength dependence of the radiation pattern and dielectric function makes it very difficult to design nanoparticle systems with the desired colors. Most existing studies focus on monodisperse nanoparticle systems, which are unsuitable for practical applications. This study combined the Lorentz–Mie theory, Monte Carlo, and deep neural networks to evaluate and design colored SiNP systems. The effects of the host medium and particle size distribution on the optical and color properties of the SiNP systems were investigated. A bidirectional deep neural network achieved accurate prediction and inverse design of structural colors. The results demonstrated that the particle size distribution flattened the Mie resonance peak and influenced the reflectance and brightness of the SiNP system. The SiNPs generated vivid colors in all three of the host media. Meanwhile, our proposed neural network model achieved a near-perfect prediction of colors with high accuracy of the designed geometric parameters. This work accurately and efficiently evaluates and designs the optical and color properties of SiNP systems, thus accelerating the design process and contributing to the practical production design of color inks, decoration, and printing.

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Prediction and Inverse Design of Structural Colors of Nanoparticle Systems via Deep Neural Network

Cited by 10 publications

References 46 publications

Enhancing Carbon Fiber Fabrics with ALD AlxOy Coatings: An Investigation of Thickness Effects on Weight, Morphology, Coloration, and Thermal Properties

Enhancing Carbon Fiber Fabrics with ALD AlxOy Coatings: An Investigation of Thickness Effects on Weight, Morphology, Coloration, and Thermal Properties

Extinction and Independent Scattering Criterion for Clusters of Spherical Particles Embedded in Absorbing Host Media

Evaluation and Design of Colored Silicon Nanoparticle Systems Using a Bidirectional Deep Neural Network

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