Photonically active bowtie nanoassemblies with chirality continuum

Kumar, Prashant; Vo, Thi; Cha, Minjeong; Visheratina, Anastasia; Kim, Ji-Young; Xu, Wenqian; Schwartz, Jonathan; Simon, Alexander; Katz, Daniel; Nicu, Valentin Paul; Marino, Emanuele; Choi, Won Jin; Veksler, M. A.; Chen, Si; Murray, Christopher B.; Hovden, Robert; Glotzer, Sharon C.; Kotov, Nicholas A.

doi:10.1038/s41586-023-05733-1

Cited by 62 publications

(60 citation statements)

References 41 publications

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“…Here, we want to understand the potential of developed models trained on particular chiral structures for the detection and classification of other chiral particles with potentially different shapes. The structures we selected included chiral Au–S ‘twisted bundle’ particles, achiral Au–S ‘kayak’ particles, and achiral ‘pancake stack’ particles made similar to bowties but using amino acid racemate . The classifier based on the Siamese learning model detected these chiral objects previously unseen by the neural network with 87.5% accuracy.…”

Section: Resultsmentioning

confidence: 99%

“…The structures we selected included chiral Au− S 'twisted bundle' particles, 1 achiral Au−S 'kayak' particles, 1 and achiral 'pancake stack' particles made similar to bowties but using amino acid racemate. 45 The classifier based on the Siamese learning model detected these chiral objects previously unseen by the neural network with 87.5% accuracy. As discussed in Section 2.1, the distinct shape of such hierarchically organized particles is one of the most critical factors for the model to detect and classify them.…”

Section: Detection and Classification Of Model-unknownmentioning

confidence: 99%

“…We used nanostructured particles with bowtie shapes because they can be synthesized with right-/left-handed twists, and all of them can be made with different but controllable lengths, widths, thicknesses, and twist angles. 45 Also, using the same synthesis method, achiral particles with no twist (aka stacked pancakes) can be obtained. A database with as few as 30 SEM images is sufficient for achieving an accuracy of classification of bowtie particles as high as 99%.…”

mentioning

confidence: 99%

“…We also show that it is possible to recognize diverse chiral nanostructures in SEM images and classify them based on their handedness using EfficientNet , YOLOv5 , and SegFormer DL neural networks. We used nanostructured particles with bowtie shapes because they can be synthesized with right-/left-handed twists, and all of them can be made with different but controllable lengths, widths, thicknesses, and twist angles . Also, using the same synthesis method, achiral particles with no twist (aka stacked pancakes) can be obtained.…”

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confidence: 99%

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Chirality Analysis of Complex Microparticles using Deep Learning on Realistic Sets of Microscopy Images

Visheratina

Visheratin²,

Kumar

et al. 2023

ACS Nano

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Nanoscale chirality is an actively growing research field spurred by the giant chiroptical activity, enantioselective biological activity, and asymmetric catalytic activity of chiral nanostructures. Compared to chiral molecules, the handedness of chiral nano- and microstructures can be directly established via electron microscopy, which can be utilized for the automatic analysis of chiral nanostructures and prediction of their properties. However, chirality in complex materials may have multiple geometric forms and scales. Computational identification of chirality from electron microscopy images rather than optical measurements is convenient but is fundamentally challenging, too, because (1) image features differentiating left- and right-handed particles can be ambiguous and (2) three-dimensional structure essential for chirality is ‘flattened’ into two-dimensional projections. Here, we show that deep learning algorithms can identify twisted bowtie-shaped microparticles with nearly 100% accuracy and classify them as left- and right-handed with as high as 99% accuracy. Importantly, such accuracy was achieved with as few as 30 original electron microscopy images of bowties. Furthermore, after training on bowtie particles with complex nanostructured features, the model can recognize other chiral shapes with different geometries without retraining for their specific chiral geometry with 93% accuracy, indicating the true learning abilities of the employed neural networks. These findings indicate that our algorithm trained on a practically feasible set of experimental data enables automated analysis of microscopy data for the accelerated discovery of chiral particles and their complex systems for multiple applications.

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Section: Resultsmentioning

confidence: 99%

Section: Detection and Classification Of Model-unknownmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Chirality Analysis of Complex Microparticles using Deep Learning on Realistic Sets of Microscopy Images

Visheratina

Visheratin²,

Kumar

et al. 2023

ACS Nano

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“…CM: A portion of this research was recently highlighted as a cover article in Nature. 4 CM: What inspired you to study and/or conduct research in this area?…”

Section: ■ David Hardingmentioning

confidence: 99%

35 Voices from Chemistry of Materials: Creativity, Collaboration, and Persistence

Toro¹,

Skrabalak²

2023

Chem. Mater.

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Voltage‐Modulated Untwist Deformations and Multispectral Optical Effects from Ion Intercalation into Chiral Ceramic Nanoparticles

et al. 2023

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Reconfiguration of chiral ceramic nanostructures after ion intercalation should favor specific nanoscale twists leading to strong chiroptical effects. In this work, V2O3 nanoparticles are shown to have “built‐in” chiral distortions caused by binding of tartaric acid enantiomers to the nanoparticle surface. As evidenced by spectroscopy/microscopy techniques and calculations of nanoscale chirality measures, the intercalation of Zn2+ ions into the V2O3 lattice results in particle expansion, untwist deformations, and chirality reduction. Coherent deformations in the particle ensemble manifest as changes in sign and positions of circular polarization bands at ultraviolet, visible, mid‐infrared (IR), near‐IR (NIR), and IR wavelengths. The g‐factors observed for IR and NIR spectral diapasons are ≈100–400 times higher than those for previously reported dielectric, semiconductor, and plasmonic nanoparticles. Nanocomposite films layer‐by‐layer assembled (LBL) from V2O3 nanoparticles reveal cyclic‐voltage‐driven modulation of optical activity. Device prototypes for IR and NIR range problematic for liquid crystals and other organic materials are demonstrated. High optical activity, synthetic simplicity, sustainable processability, and environmental robustness of the chiral LBL nanocomposites provide a versatile platform for photonic devices. Similar reconfigurations of particle shapes are expected for multiple chiral ceramic nanostructures, leading to unique optical, electrical, and magnetic properties.

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Photonically active bowtie nanoassemblies with chirality continuum

Cited by 62 publications

References 41 publications

Chirality Analysis of Complex Microparticles using Deep Learning on Realistic Sets of Microscopy Images

Chirality Analysis of Complex Microparticles using Deep Learning on Realistic Sets of Microscopy Images

35 Voices from Chemistry of Materials: Creativity, Collaboration, and Persistence

Voltage‐Modulated Untwist Deformations and Multispectral Optical Effects from Ion Intercalation into Chiral Ceramic Nanoparticles

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