Machine Learning‐Assisted Microfluidic Synthesis of Perovskite Quantum Dots

Chen, Gaoyu; Zhu, Xia; Xing, Chenyu; Wang, Yongkai; Xu, Xiangxing; Bao, Jianchun; Huang, Jian; Zhao, Yurong; Wang, Xuan; Zhou, Xiuqing; Du, Xin; Wang, Xun

doi:10.1002/adpr.202200230

Cited by 10 publications

(2 citation statements)

References 68 publications

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“…Krishnadasan et al adapted a noise-tolerant global search algorithm for a blackbox optimization of injection rate and temperature for synthesizing CdSe nanoparticles on a Y-shaped microfluidic reactor from CdO and Se precursor solutions [107]. Similarly, ANN can be used to extract the condition-property relationship from the combinatorial synthesis data and provide conditions to synthesize nanoparticles [108][109][110][111][112][113], nanotubes [114], nanoformulation of pharmaceuticals [115], quantum dots [116,117], liposomes [118], or polymeric microparticles [119,120] (Figure 3b). Diamiati et al trained an ANN to predict particle size of poly(Lactide-co-glycolide) (PLGA) microparticles, a biocompatible drug delivery polymer, based on the conditions of the flow focusing synthesis experiments on multiple different synthesis platforms.…”

Section: Nanoparticle Synthesismentioning

confidence: 99%

Microsystem Advances through Integration with Artificial Intelligence

2023

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Microfluidics is a rapidly growing discipline that involves studying and manipulating fluids at reduced length scale and volume, typically on the scale of micro- or nanoliters. Under the reduced length scale and larger surface-to-volume ratio, advantages of low reagent consumption, faster reaction kinetics, and more compact systems are evident in microfluidics. However, miniaturization of microfluidic chips and systems introduces challenges of stricter tolerances in designing and controlling them for interdisciplinary applications. Recent advances in artificial intelligence (AI) have brought innovation to microfluidics from design, simulation, automation, and optimization to bioanalysis and data analytics. In microfluidics, the Navier–Stokes equations, which are partial differential equations describing viscous fluid motion that in complete form are known to not have a general analytical solution, can be simplified and have fair performance through numerical approximation due to low inertia and laminar flow. Approximation using neural networks trained by rules of physical knowledge introduces a new possibility to predict the physicochemical nature. The combination of microfluidics and automation can produce large amounts of data, where features and patterns that are difficult to discern by a human can be extracted by machine learning. Therefore, integration with AI introduces the potential to revolutionize the microfluidic workflow by enabling the precision control and automation of data analysis. Deployment of smart microfluidics may be tremendously beneficial in various applications in the future, including high-throughput drug discovery, rapid point-of-care-testing (POCT), and personalized medicine. In this review, we summarize key microfluidic advances integrated with AI and discuss the outlook and possibilities of combining AI and microfluidics.

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Section: Nanoparticle Synthesismentioning

confidence: 99%

Microsystem Advances through Integration with Artificial Intelligence

2023

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“…[3] Furthermore, in the process of creating nanohybrids, the usual synthetic variables as well as additional material variables can be considered, [4,5] which open up possibilities for exploring a broader range of new scientific phenomena. Diverse nanoscale materials with specific functionalities, such as exceptional electrical and optical properties, [6][7][8] have been designed or discovered for both academic research and industrial applications. [9][10][11] Inorganic lead halide nanocrystals with exceptional optical and electronic properties are garnering interest as key components in a variety of optical, optoelectronic, catalytic, and imaging applications.…”

Section: Introductionmentioning

confidence: 99%

Two‐Step Sequential Process for Fabricating Graphene Nanosheets Decorated with Copper Halide Nanocrystals and Their Optoelectronic Applications

Kim

Lee

et al. 2023

Advanced Optical Materials

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Nanocrystals of Cs3Cu2I5, a metal halide with promising potential as a perovskite substitute, are successfully grown on graphene nanosheets. An intermediate stage of physically exfoliated graphene nanosheets, concurrent with the formation of copper‐based seeds on graphene surface, is developed, for the first time, allowing for the subsequent growth of Cs3Cu2I5 nanocrystals (CsNCs) to obtain CsNCs‐decorated graphene nanohybrids (CsGNHs). The morphology and properties of the CsGNHs depend on the input amount of graphene nanosheets during the reaction. The photoluminescence intensity of the CsGNHs decreases with increasing graphene content, indicating that graphene facilitates nonradiative energy transfer from the CsNCs in the CsGNHs. The inherent self‐trapping exciton emission of the CsNCs is also affected by the graphene substrate. As the graphene content increases, the emission peaks of the CsGNHs broaden and diminish. The CsGNHs emit a stable solid‐state luminescence with a large Stokes shift (zero self‐absorption) under a high‐energy ultraviolet (UV) light. Consequently, the CsGNHs can act as optoelectronic transducers and selectively generate a turn‐on photocurrent response under specific‐wavelength UV light. Additionally, a new anticounterfeiting strategy is developed using the CsGNHs as a luminescent black ink to fabricate luminescent quick‐response codes with fake pixels.

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Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures

Kuznetsova,

Coogan,

Botov

et al. 2024

Advanced Materials

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Machine learning holds significant research potential in the field of nanotechnology, enabling nanomaterial structure and property predictions, facilitating the design of novel materials, and reducing the need for time‐consuming and labour‐intensive experiments and simulations. In contrast to their achiral counterparts, the application of machine learning for chiral nanomaterials and nanostructures is still in its infancy, with a limited number of publications to date. This is despite the great potential of machine learning to advance the development of new sustainable chiral materials with high values of optical activity, circularly polarised luminescence, and enantioselectivity, as well as for the analysis of structural chirality by electron microscopy. In this review, we provide an analysis of machine learning methods used in studying achiral nanomaterials, subsequently offering guidance on adapting and extending this work to chiral nanomaterials. We present an overview of chiral nanomaterials within the framework of synthesis‐structure‐property‐application relationships and provide insights on how to leverage machine learning for the study of these highly complex relationships. We also review and discuss some key recent publications on the application of machine learning for chiral nanomaterials. Finally, the review captures the key achievements, ongoing challenges, and the prospective outlook for this very important research field.This article is protected by copyright. All rights reserved

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Machine Learning‐Assisted Microfluidic Synthesis of Perovskite Quantum Dots

Cited by 10 publications

References 68 publications

Microsystem Advances through Integration with Artificial Intelligence

Microsystem Advances through Integration with Artificial Intelligence

Two‐Step Sequential Process for Fabricating Graphene Nanosheets Decorated with Copper Halide Nanocrystals and Their Optoelectronic Applications

Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures

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