Snapshot Polarization-Sensitive Holography for Detecting Microplastics in Turbid Water

Huang, Jianqing; Zhu, Yanmin; Li, Yuxing; Lam, Edmund Y.

doi:10.1021/acsphotonics.3c01350

Cited by 10 publications

(5 citation statements)

References 56 publications

(99 reference statements)

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“…It has been primarily demonstrated that our imaging system is capable of seeing through scattering media and obtaining multimodal information about the object. The results suggest that polarization features can substantially improve image contrast even in highly turbid water 31 . In addition, an objective lens will be introduced into the system to increase the detection of full-scale samples, such as those on the micrometer scale.…”

Section: Discussionmentioning

confidence: 85%

“…Concurrently, computational methods are gradually being incorporated into MP assessment, promoting rapid and accurate approaches. Zhu et al and Huang et al are also integrating polarization-related computational imaging techniques with more compact configurations 31 – 34 . In their works, they consider several complex natural environments, such as turbid water.…”

Section: Introductionmentioning

confidence: 99%

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High-throughput microplastic assessment using polarization holographic imaging

Li,

Zhu,

Huang

et al. 2024

Sci Rep

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Microplastic (MP) pollution has emerged as a global environmental concern due to its ubiquity and harmful impacts on ecosystems and human health. MP assessment has therefore become increasingly necessary and common in environmental and experimental samples. Microscopy and spectroscopy are widely employed for the physical and chemical characterization of MPs. However, these analytical methods often require time-consuming pretreatments of samples or expensive instrumentation. In this work, we develop a portable and cost-effective polarization holographic imaging system that prominently incorporates deep learning techniques, enabling efficient, high-throughput detection and dynamic analysis of MPs in aqueous environments. The integration enhances the identification and classification of MPs, eliminating the need for extensive sample preparation. The system simultaneously captures holographic interference patterns and polarization states, allowing for multimodal information acquisition to facilitate rapid MP detection. The characteristics of light waves are registered, and birefringence features are leveraged to classify the material composition and structures of MPs. Furthermore, the system automates real-time counting and morphological measurements of various materials, including MP sheets and additional natural substances. This innovative approach significantly improves the dynamic monitoring of MPs and provides valuable information for their effective filtration and management.

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Section: Discussionmentioning

confidence: 85%

Section: Introductionmentioning

confidence: 99%

High-throughput microplastic assessment using polarization holographic imaging

Li,

Zhu,

Huang

et al. 2024

Sci Rep

Self Cite

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show abstract

“…Future works could aim at developing high-throughput microfluidic 46 systems for non-contact, quick MP quantification 47 , 48 . Low-cost, compact, and portable devices are also of interest to researchers in the field and underwater MP detection 49 . Last but not least, the salient discriminative capability of SPLASH can be applied to industrial and medical material analysis, facilitating the identification of medicinal components and aiding in tissue diagnosis 50 , 51 .…”

Section: Discussionmentioning

confidence: 99%

Smart polarization and spectroscopic holography for real-time microplastics identification

Zhu,

Li,

Huang

et al. 2024

Commun Eng

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Optical microscopy technologies as prominent imaging methods can offer rapid, non-destructive, non-invasive detection, quantification, and characterization of tiny particles. However, optical systems generally incorporate spectroscopy and chromatography for precise material determination, which are usually time-consuming and labor-intensive. Here, we design a polarization and spectroscopic holography to automatically analyze the molecular structure and composition, namely smart polarization and spectroscopic holography (SPLASH). This smart approach improves the evaluation performance by integrating multi-dimensional features, thereby enabling highly accurate and efficient identification. It simultaneously captures the polarization states-related, holographic, and texture features as spectroscopy, without the physical implementation of a spectroscopic system. By leveraging a Stokes polarization mask (SPM), SPLASH achieves simultaneous imaging of four polarization states. Its effectiveness has been demonstrated in the application of microplastics (MP) identification. With machine learning methods, such as ensemble subspace discriminant classifier, k-nearest neighbors classifier, and support vector machine, SPLASH depicts MPs with anisotropy, interference fringes, refractive index, and morphological characteristics and performs explicit discrimination with over 0.8 in value of area under the curve and less than 0.05 variance. This technique is a promising tool for addressing the increasing public concerning issues in MP pollution assessment, MP source identification, and long-term water pollution monitoring.

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“…Conventional computers are organized around a centralized processing architecture with a central processor and separated memory, which is suited to running sequential, digital, procedure-based programs. However, such an architecture is inefficient for computational models that are distributed, massively parallel, and adaptive, most notably those used for neural networks in machine learning (ML). − ML is an attempt to achieve a human-level approach to tasks that are challenging for traditional computers but easy for humans. Furthermore, the human brain, which could be abstracted as a neural network, is a dynamic system characterized by high parallelism and adaptability. − Reservoir computing (RC), as a ML approach based on the principles of dynamic system theory, attempts to mimic these neural network characteristics, although its structure is relatively simpler compared to the human brain. − Compared with those of other ML models, the input and reservoir weights of RC are randomly generated and fixed.…”

Section: Introductionmentioning

confidence: 99%

Photonic Reservoir Computing System for Pattern Recognition Based on an Array of Four Distributed Feedback Lasers

Guo,

Zhou,

Xiang

et al. 2024

ACS Photonics

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Reservoir computing (RC), especially photonic RC based on a single semiconductor laser with a feedback loop, as a method of machine learning, shows excellent performance in time series prediction and classification tasks. The faster the processing speed, the shorter the feedback loop should be employed. However, the performance of the system is not ideal enough due to the limited reservoir nodes caused by the short feedback loop. To overcome this drawback, it is necessary and challenging to develop integrated photonic RC. In this paper, we propose an integrated neuromorphic photonic time-delayed RC scheme based on an array of four distributed feedback lasers (F-DFBs) with optical feedback and injection. Here, we investigate the feasibility of using larger laser arrays and shorter external feedback cavities to provide the RC system with more virtual nodes at the same processing speed, enabling it to handle more complex tasks. Additionally, we compare the performance of the systems with a single DFB (the S-DFB RC) and the F-DFBs RC through simulations and experiments involving iris recognition tasks. Furthermore, the minimum symbol error rate values can reach 0.052 in experiments (and 0 in simulations).

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Snapshot Polarization-Sensitive Holography for Detecting Microplastics in Turbid Water

Cited by 10 publications

References 56 publications

High-throughput microplastic assessment using polarization holographic imaging

High-throughput microplastic assessment using polarization holographic imaging

Smart polarization and spectroscopic holography for real-time microplastics identification

Photonic Reservoir Computing System for Pattern Recognition Based on an Array of Four Distributed Feedback Lasers

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