Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures

Advanced Materials, Journal Year: 2024, Volume and Issue: 36(18)

Published: Jan. 19, 2024

Abstract Machine learning holds significant research potential in the field of nanotechnology, enabling nanomaterial structure and property predictions, facilitating materials design discovery, reducing need for time‐consuming labor‐intensive experiments simulations. In contrast to their achiral counterparts, application machine chiral nanomaterials is still its infancy, with a limited number publications date. This despite great advance development new sustainable high values optical activity, circularly polarized luminescence, enantioselectivity, as well analysis structural chirality by electron microscopy. this review, an methods used studying provided, subsequently offering guidance on adapting extending work nanomaterials. An overview within framework synthesis–structure–property–application relationships presented insights how leverage study these highly complex are provided. Some key recent reviewed discussed Finally, review captures achievements, ongoing challenges, prospective outlook very important field.

Language: Английский

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Racemic dielectric metasurfaces for arbitrary terahertz polarization rotation and wavefront manipulation

Opto-Electronic Advances, Journal Year: 2024, Volume and Issue: 7(10), P. 240075 - 240075

Published: Jan. 1, 2024

Language: Английский

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Integrating machine learning and biosensors in microfluidic devices: A review

Biosensors and Bioelectronics, Journal Year: 2024, Volume and Issue: 263, P. 116632 - 116632

Published: Aug. 3, 2024

Microfluidic devices are increasingly widespread in the literature, being applied to numerous exciting applications, from chemical research Point-of-Care devices, passing through drug development and clinical scenarios. Setting up these microenvironments, however, introduces necessity of locally controlling variables involved phenomena under investigation. For this reason, literature has deeply explored possibility introducing sensing elements investigate physical quantities biochemical concentration inside microfluidic devices. Biosensors, particularly, well known for their high accuracy, selectivity, responsiveness. However, signals could be challenging interpret must carefully analysed carry out correct information. In addition, proper data analysis been demonstrated even increase biosensors' mentioned qualities. To regard, machine learning algorithms undoubtedly among most suitable approaches undertake job, automatically highlighting biosensor signals' characteristics at best. Interestingly, it was also benefit themselves, a new paradigm that is starting name "intelligent microfluidics", ideally closing benefic interaction disciplines. This review aims demonstrate advantages triad microfluidics-biosensors-machine learning, which still little used but great perspective. After briefly describing single entities, different sections will benefits dual interactions, applications where reviewed employed.

Language: Английский

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Data-driven polarimetric imaging: a review

Opto-Electronic Science, Journal Year: 2024, Volume and Issue: 3(2), P. 230042 - 230042

Published: Jan. 1, 2024

This study reviews the recent advances in data-driven polarimetric imaging technologies based on a wide range of practical applications. The widespread international research and activity techniques demonstrate their broad applications interest. Polarization information is increasingly incorporated into convolutional neural networks (CNN) as supplemental feature objects to improve performance computer vision task Polarimetric deep learning can extract abundant address various challenges. Therefore, this article briefly developments imaging, including descattering, 3D reflection removal, target detection, biomedical imaging. Furthermore, we synthetically analyze input, datasets, loss functions list existing datasets with an evaluation advantages disadvantages. We also highlight significance future development.

Language: Английский

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Enhanced Structure-Based Prediction of Chiral Stationary Phases for Chromatographic Enantioseparation from 3D Molecular Conformations

Analytical Chemistry, Journal Year: 2024, Volume and Issue: 96(6), P. 2351 - 2359

Published: Feb. 3, 2024

The accurate prediction of suitable chiral stationary phases (CSPs) for resolving the enantiomers a given compound poses significant challenge in chromatography. Previous attempts at developing machine learning models structure-based CSP have primarily relied on 1D SMILES strings [the simplified molecular-input line-entry system (SMILES) is specification form line notation describing structure chemical species using short ASCII strings] or 2D graphical representations molecular structures and met with only limited success. In this study, we apply recently developed 3D conformation representation algorithm, which uses rapid conformational analysis point clouds atom positions space, enabling efficient learning. By harnessing power data set comprising over 300,000 chromatographic enantioseparation records sourced from literature, our afford notable improvements choice appropriate enantioseparation, paving way more informed decision-making field

Language: Английский

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Review for Micro‐Nano Processing Technology of Microstructures and Metadevices

Advanced Functional Materials, Journal Year: 2025, Volume and Issue: unknown

Published: March 5, 2025

Abstract As a popular artificial composite material emerging in recent years, metasurfaces are one of the most likely devices to break through volume limitation conventional optical components due their compact structure, flexible materials, and high modulation resolution beam. With unique arrangement units or made special metasurface can effectively modulate incident light's amplitude, phase, polarization, frequency, thus realizing applications such as communication, imaging, sensing, beam steering. The interaction high‐resolution periodic arrangement, constituent materials makes it possible realize these applications, so researchers should choose appropriate micro‐nano processing technologies when designing preparing metasurface. This review will present related preparation metasurfaces, electron lithography (EBL), femtosecond laser processing, focused ion (FIB), additive manufacturing, nanoimprinting, self‐assembly, respectively. In addition, classical techniques wet lithography, plasma deep reactive etching (DRIE), photolithography be introduced. Their development history functions described detail, examples micro‐nano‐structures different branches presented, well some using techniques. this paper has produced several tables describing technologies, outlining resolution, advantages disadvantages, on. Hopefully, provide with options ideas for metasurfaces.

Language: Английский

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Optimized design for absorption metasurface based on autoencoder (AE) and BiLSTM-Attention-FCN-Net

Physica Scripta, Journal Year: 2024, Volume and Issue: 99(3), P. 036002 - 036002

Published: Jan. 19, 2024

Abstract In order to speed up the process of optimizing design metasurface absorbers, an improved model for absorbers based on autoencoder (AE) and BiLSTM-Attention-FCN-Net (including bidirectional long-short-term memory network, attention mechanism, fully-connection layer network) is proposed. The structural parameters can be input into forward prediction network predict corresponding absorption spectra. Meantime, obtained by inputting spectra inverse network. Specially, in (BiLSTM) effectively capture context relationship between spectral sequence data, mechanism enhance BiLSTM output features, which highlight critical feature information. After training, mean square error (MSE) value validation set reverse converges 0.0046, R 2 reaches 0.975, our accurately structure within 1.5 s with a maximum 0.03 mm. Moreover, this achieve optimal multi-band including single-band, dual-band, three-band absorptions. proposed method also extended other types optimization design.

Language: Английский

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Near‐Field Coupling Induced Less Chiral Responses in Chiral Metamaterials for Surface‐Enhanced Vibrational Circular Dichroism

Advanced Functional Materials, Journal Year: 2023, Volume and Issue: 34(13)

Published: Dec. 18, 2023

Abstract Chiral metamaterials play vital roles in manipulating the circular polarization of electromagnetic waves. Although planar chiral are believed to have no true/intrinsic chirality, design structural anisotropy can still create enormous dichroism, while mechanism is fully explored. Here, for first time, it observed that strong near‐field coupling induces less response metamaterials. Selective exposure methods manipulate strength, and experimentally validate dichroism difference from tailored effect leveraged, which provides evidence assumption be utilized framework. Besides, using enhanced (over 750‐fold), surface‐enhanced vibrational (SEVCD) glucose enantiomers, shows a larger‐than‐one normalized sensitivity compared with demonstrated. Furthermore, The potential SEVCD by detecting broadband signal arrayed These findings pave way toward chiroptical nanophotonic designs biomedical healthcare applications.

Language: Английский

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Chiral composite metamaterials with gradient phase index for vortex electromagnetic−wave generation and attenuation

Journal of Alloys and Compounds, Journal Year: 2023, Volume and Issue: 976, P. 173256 - 173256

Published: Dec. 22, 2023

Language: Английский

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Novel View Synthesis Based on Similar Perspective

Computer Animation and Virtual Worlds, Journal Year: 2025, Volume and Issue: 36(1)

Published: Jan. 1, 2025

ABSTRACT Neural radiance fields (NeRF) technology has garnered significant attention due to its exceptional performance in generating high‐quality novel view images. In this study, we propose an innovative method that leverages the similarity between views enhance quality of image generation. Initially, a pre‐trained NeRF model generates initial image, which is subsequently compared and subjected feature transfer with most similar reference from training dataset. Following this, selected We designed texture module employs strategy progressing coarse‐to‐fine, effectively integrating salient features into thus producing more realistic By using views, approach not only improves perspective images but also incorporates dataset as dynamic information pool integration process. This allows for continuous acquisition utilization useful data throughout synthesis Extensive experimental validation shows provide scene significantly outperforms existing neural rendering techniques enhancing realism accuracy

Language: Английский

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