Analysis of fibres using microscopy

Donaldson, Lloyd

doi:10.1533/9781845696504.1.121

Cited by 4 publications

(3 citation statements)

References 158 publications

(171 reference statements)

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“…But the color of the S group is red−purple, which is darker than the yellow−brown color of the G group (Table 1). 18,19 The relationship between the UV-shielding performance and molecular weight of the lignin oligomer was studied by Lim et al 24 Forming oligomers at certain molecular weights (1790− 2230 Da) benefits the UV-shielding properties. Two methods were used to form oligomers: O1−O3 were obtained from polymerization of monomeric and dimeric lignin model compounds, and O4 was obtained from depolymerization of kraft lignin.…”

Section: ■ Intrinsic Factors Affecting Uv-shieldingmentioning

confidence: 99%

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Lignin: A Review on Structure, Properties, and Applications as a Light-Colored UV Absorber

Zhang

Naebe

2021

ACS Sustainable Chem. Eng.

239

120

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Lignin is a promising UV-shielding material to substitute the synthetic absorbers in a composite due to its excellent UV-shielding property. The chromophores group of lignin is responsible for the UV-shielding property of composites, but it brings an undesirable dark color. This perspective is the first to review the recent progress on fabricating light-colored UVshielding composites that contain lignin; we provide a clear picture of the concept of light-colored UV-absorbing lignin composite materials that can be used in food packaging, healthcare products, and solar panel protection. The UV-absorbing and photostability mechanisms are introduced by correlating UV absorption with intrinsic factors, like phenolic substructures and molecular weight of lignin. Extrinsic factors that affect the UV-shielding properties and color of lignin, such as the extraction process, chemical modification method, and obtained size of lignin, are also systematically discussed in this perspective. By summarizing recent studies on the synergetic effect between the lignin and the second constitute materials, this perspective discusses the benefits of lignin to the composite's overall properties, such as stability under UV radiation, mechanical property, dispersity, and water permeability.

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Section: ■ Intrinsic Factors Affecting Uv-shieldingmentioning

confidence: 99%

“…But the color of the S group is red–purple, which is darker than the yellow–brown color of the G group (Table ). , …”

Section: Intrinsic Factors Affecting Uv-shielding Ability and Photost...mentioning

confidence: 99%

Lignin: A Review on Structure, Properties, and Applications as a Light-Colored UV Absorber

Zhang

Naebe

2021

ACS Sustainable Chem. Eng.

239

120

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“…With an automated characterization method, more precise and efficient quality control can be obtained. Combining ML with image analysis enables the use of online and offline measurements for improved, automated categorization of pulp and paper [1,3,11]. The potential cost reduction by implementing ML based on existing and future technologies in the paper and forest product industry is estimated to be 9.5% [12].…”

Section: Introductionmentioning

confidence: 99%

Pulp Particle Classification Based on Optical Fiber Analysis and Machine Learning Techniques

Lindström,

Amjad,

Gåhlin

et al. 2023

Fibers

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In the pulp and paper industry, pulp testing is typically a labor-intensive process performed on hand-made laboratory sheets. Online quality control by automated image analysis and machine learning (ML) could provide a consistent, fast and cost-efficient alternative. In this study, four different supervised ML techniques—Lasso regression, support vector machine (SVM), feed-forward neural networks (FFNN), and recurrent neural networks (RNN)—were applied to fiber data obtained from fiber suspension micrographs analyzed by two separate image analysis software. With the built-in software of a commercial fiber analyzer optimized for speed, the maximum accuracy of 81% was achieved using the FFNN algorithm with Yeo–Johnson preprocessing. With an in-house algorithm adapted for ML by an extended set of particle attributes, a maximum accuracy of 96% was achieved with Lasso regression. A parameter capturing the average intensity of the particle in the micrograph, only available from the latter software, has a particularly strong predictive capability. The high accuracy and sensitivity of the ML results indicate that such a strategy could be very useful for quality control of fiber dispersions.

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Tribological Characteristics of P. stocksii Bamboo Fiber Composites with Permomparative Performance Assessment of Pineapple and Kevlar Fibers Based Friction Compositesanganization Enhancement

N.,

Sasidharan,

Kumar

2024

Mater Circ Econ

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Analysis of fibres using microscopy

Cited by 4 publications

References 158 publications

Lignin: A Review on Structure, Properties, and Applications as a Light-Colored UV Absorber

Lignin: A Review on Structure, Properties, and Applications as a Light-Colored UV Absorber

Pulp Particle Classification Based on Optical Fiber Analysis and Machine Learning Techniques

Tribological Characteristics of P. stocksii Bamboo Fiber Composites with Permomparative Performance Assessment of Pineapple and Kevlar Fibers Based Friction Compositesanganization Enhancement

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