Carbon nanodots: Chemiluminescence, fluorescence and photoluminescence properties

Khatokar, J Aditya; Vinay, N; Sanjay, B R; Bhargava, S; Bale, Ajay Sudhir; Kolekar, Tejeswini R.; Singh, Shantanu; Umarani, Sunkireddy; Huddar, Shivashankar

doi:10.1016/j.matpr.2021.02.582

Cited by 8 publications

(2 citation statements)

References 21 publications

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“…In general, luminescence is defined as the radiation released by an atom due to energy absorption and an excited state. Depending on the excitation source (incident radiation, electrons, or particles) different types of phenomena (luminescence) can be distinguished: (i) photoluminescence (fluorescence or phosphorescence) when the source is an electromagnetic radiation; (ii) chemiluminescence when the source is a chemical reaction; (iii) electroluminescence, when the source is an electrical field; (iv) thermoluminescence, in which the luminescence is thermally activated; and (v) mechanoluminescence due to a mechanical action [24][25][26].…”

Section: Fluorescencementioning

confidence: 99%

Fluorescence in Smart Textiles

Patti

Acierno²

2023

Encyclopedia

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Fluorescence has been identified as an advantageous feature in smart fabrics, notably for the protection of humans during outdoor athletic activities, as well as for preventing counterfeiting and determining authenticity. Fluorescence in smart fabrics is achieved using dendrimers, rare earth metal compounds, and fluorescent dye. The principal method for producing fluorescent fabrics is to immerse the sample in a solution containing fluorescent agents. However, covalent connections between fluorophores and textile substates should be established to improve the stability and intensity of the fluorescent characteristics. Fabric can be fluorescent throughout, or fluorescent fibers can be woven directly into the textile structures, made of natural (cotton, silk) or synthetic (polyamide- and polyester-based) fibers, into a precise pathway that becomes visible under ultraviolet irradiation.

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Section: Fluorescencementioning

confidence: 99%

Fluorescence in Smart Textiles

Patti

Acierno²

2023

Encyclopedia

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“…With the development of science and technology, non-contact image acquisition of silicon wafers has been realized using photoluminescence technology [6][7][8], and many studies on defect extraction from silicon wafer images using relevant deep learning algorithms have been conducted. Bartler et al [9] developed a deep learning model for detecting defective solar cells in a PV module based on the VGG-16 deep learning network model.…”

Section: Related Researchmentioning

confidence: 99%

Deep-Learning-Based Defect Evaluation of Mono-Like Cast Silicon Wafers

2021

Photonics

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Solar cells based on mono-like cast silicon (MLC-Si) have been attracting increasing attention in the photovoltaic (PV) market due to their high energy conversion efficiency and low cost. As in the production of monocrystalline silicon (MC-Si) and polycrystalline silicon (PC-Si) cells, various defects will inevitably occur during the production process of MLC-Si cells. Although computer vision technology has been employed for defect detection in the production processes, it is still difficult to achieve high accuracy in detecting defects in PV cells using traditional machine vision methods due to defect similarity and complex background. To address this challenge, a deep-learning-based quality assessment algorithm of MLC-Si wafers is proposed. Focusing on the dislocation defects, four different deep learning models are used to conduct migration learning and selected different optimizers (ADAM and SGDM) are used to optimize the network models, achieving good results in evaluating and comparing the quality of ML-Si wafers. On this basis, an improved network model MVGG-19 based on the VGG-19 is designed to improve the prediction accuracy further. The experimental results show that the prediction error of the improved network model is reduced by 63% (compared with VGG-19) and the reasoning speed reaches 10.22 FPS, indicating good detection performance.

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