On Light and Diatoms: A Photonics and Photobiology Review

Ghobara, Mohamed; Mazumder, Nirmal; Vinayak, Vandana; Reissig, Louisa; Gebeshuber, Ille C.; Tiffany, Mary Ann; Gordon, Richard

doi:10.1002/9781119370741.ch7

Cited by 23 publications

(30 citation statements)

References 284 publications

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“…Diatoms are unicellular photosynthetic algae having shells (exoskeletons, frustules, carapaces) composed of amorphous, hydrated silica. These frustules consist of two overlapping shells called thecae (upper part-epitheca, lower-hypotheca) with a characteristic bilateral Petri dish-like structure [32][33][34][35][36].…”

Section: Introductionmentioning

confidence: 99%

Diatom Biosilica Doped with Palladium(II) Chloride Nanoparticles as New Efficient Photocatalysts for Methyl Orange Degradation

Sprynskyy

Szczyglewska

Wojtczak

et al. 2021

IJMS

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A new catalyst based on biosilica doped with palladium(II) chloride nanoparticles was prepared and tested for efficient degradation of methyl orange (MO) in water solution under UV light excitation. The obtained photocatalyst was characterized by X-ray diffraction, TEM and N2 adsorption/desorption isotherms. The photocatalytic degradation process was studied as a function of pH of the solution, temperature, UV irradiation time, and MO initial concentration. The possibilities of recycling and durability of the prepared photocatalysts were also tested. Products of photocatalytic degradation were identified by liquid chromatography–mass spectrometry analyses. The photocatalyst exhibited excellent photodegradation activity toward MO degradation under UV light irradiation. Rapid photocatalytic degradation was found to take place within one minute with an efficiency of 85% reaching over 98% after 75 min. The proposed mechanism of photodegradation is based on the assumption that both HO• and O2•− radicals, as strongly oxidizing species that can participate in the dye degradation reaction, are generated by the attacks of photons emitted from diatom biosilica (photonic scattering effect) under the influence of UV light excitation. The degradation efficiency significantly increases as the intensity of photons emitted from biosilica is enhanced by palladium(II) chloride nanoparticles immobilized on biosilica (synergetic photonic scattering effect).

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Section: Introductionmentioning

confidence: 99%

Diatom Biosilica Doped with Palladium(II) Chloride Nanoparticles as New Efficient Photocatalysts for Methyl Orange Degradation

Sprynskyy

Szczyglewska

Wojtczak

et al. 2021

IJMS

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“…The continual folding and unfolding of a colony of cells stacked in parallel results in cyclic gliding movements. Each cell is an intrinsic oscillator [31], so the partial synchronization may be due to entrainment of these oscillators by an unknown mechanism, perhaps light piping within each colony [32,33]. The mechanism of gliding movement is still being worked out [34].…”

Section: Organism Descriptionmentioning

confidence: 99%

Towards a digital diatom: image processing and deep learning analysis ofBacillaria paradoxadynamic morphology

Alicea¹,

Gordon

Harbich³

et al. 2019

Preprint

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Recent years have witnessed a convergence of data and methods that allow us to approximate the shape, size, and functional attributes of biological organisms. This is not only limited to traditional model species: given the ability to culture and visualize a specific organism, we can capture both its structural and functional attributes. We present a quantitative model for the colonial diatom Bacillaria paradoxa, an organism that presents a number of unique attributes in terms of form and function. To acquire a digital model of B. paradoxa, we extract a series of quantitative parameters from microscopy videos from both primary and secondary sources. These data are then analyzed using a variety of techniques, including two rival deep learning approaches. We provide an overview of neural networks for non-specialists as well as present a series of analysis on Bacillaria phenotype data. The application of deep learning networks allow for two analytical purposes. Application of the DeepLabv3 pre-trained model extracts phenotypic parameters describing the shape of cells constituting Bacillaria colonies. Application of a semantic model trained on nematode embryogenesis data (OpenDevoCell) provides a means to analyze masked images of potential intracellular features. We also advance the analysis of Bacillaria colony movement dynamics by using templating techniques and biomechanical analysis to better understand the movement of individual cells relative to an entire colony. The broader implications of these results are presented, with an eye towards future applications to both hypothesis-driven studies and theoretical advancements in understanding the dynamic morphology of Bacillaria.

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“…6 One of the beststudied examples of biomineralization is in diatoms, unicellular algae that use silica to construct micron-scale cell walls with nanoscale features, yielding a multiscalar structure ranging over eight orders of magnitude. 7 A model diatom, Thalassiosira pseudonana, like all diatoms, has a cylindrical cell wall-a frustule made from silica. The top and bottom of the cylinder, termed valves, are connected by a series of circular girdle bands.…”

Section: Introductionmentioning

confidence: 99%

Deep data analytics for genetic engineering of diatoms linking genotype to phenotype via machine learning

et al. 2019

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Genome engineering for materials synthesis is a promising avenue for manufacturing materials with unique properties under ambient conditions. Biomineralization in diatoms, unicellular algae that use silica to construct micron-scale cell walls with nanoscale features, is an attractive candidate for functional synthesis of materials for applications including photonics, sensing, filtration, and drug delivery. Therefore, controllably modifying diatom structure through targeted genetic modifications for these applications is a very promising field. In this work, we used gene knockdown in Thalassiosira pseudonana diatoms to create modified strains with changes to structural morphology and linked genotype to phenotype using supervised machine learning. An artificial neural network (NN) was developed to distinguish wild and modified diatoms based on the SEM images of frustules exhibiting phenotypic changes caused by a specific protein (Thaps3_21880), resulting in 94% detection accuracy. Class activation maps visualized physical changes that allowed the NNs to separate diatom strains, subsequently establishing a specific gene that controls pores. A further NN was created to batch process image data, automatically recognize pores, and extract pore-related parameters. Class interrelationship of the extracted paraments was visualized using a multivariate data visualization tool, called CrossVis, and allowed to directly link changes in morphological diatom phenotype of pore size and distribution with changes in the genotype.

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On Light and Diatoms: A Photonics and Photobiology Review

Cited by 23 publications

References 284 publications

Diatom Biosilica Doped with Palladium(II) Chloride Nanoparticles as New Efficient Photocatalysts for Methyl Orange Degradation

Diatom Biosilica Doped with Palladium(II) Chloride Nanoparticles as New Efficient Photocatalysts for Methyl Orange Degradation

Towards a digital diatom: image processing and deep learning analysis ofBacillaria paradoxadynamic morphology

Deep data analytics for genetic engineering of diatoms linking genotype to phenotype via machine learning

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