Beating of hemp bast fibres: an examination of a hydro-mechanical treatment on chemical, structural, and nanomechanical property evolutions

In this study, a gradually increased hydro-mechanical treatments duration were applied to native hemp bast fibres with a traditional pulp and paper beating device (laboratory Valley beater). There is often a trade-off between the treatment applied to the fibres and the effect on their integrity. The multimodal analysis provided an understanding of the beating impact on the fibres at multiple scales and the experimental design made it possible to distinguish the effects of hydro- and hydro-mechanical treatment. Porosity analyses showed that beating treatment doubled the macroporosity and possibly reduced nanoporosity between the cellulose microfibrils. The beating irregularly extracted the amorphous components known to be preferentially located in the middle lamellae and the primary cell walls rather than in the secondary walls, the overall increasing the crystallinity of cellulose from 49.3 % to 59.1 %, but a non-significant change in the indentation moduli of the cell wall was observed. In addition, beating treatments with two distinct mechanical severities showed a disorganization of the cellulose conformation, which significant dropped the indention moduli by 11.2 GPa and 8.4 GPa for 10 and 20 minutes of Valley beater hydro-mechanical treatment, respectively, compared to hydro-treated hemp fibres (16.6 GPa). Pearson’s correlation coefficients between physicochemical features and the final indentation moduli were calculated. Strong positive correlations were highlighted between the cellulose crystallinity and rhamnose, galactose and mannose as non-cellulosic polysaccharide components of the cell wall

Reconstruction of three-dimensional porous media using generative adversarial neural networks

To evaluate the variability of multi-phase flow properties of porous media at the pore scale, it is necessary to acquire a number of representative samples of the void-solid structure. While modern x-ray computer tomography has made it possible to extract three-dimensional images of the pore space, assessment of the variability in the inherent material properties is often experimentally not feasible. We present a novel method to reconstruct the solid-void structure of porous media by applying a generative neural network that allows an implicit description of the probability distribution represented by three-dimensional image datasets. We show, by using an adversarial learning approach for neural networks, that this method of unsupervised learning is able to generate representative samples of porous media that honor their statistics. We successfully compare measures of pore morphology, such as the Euler characteristic, two-point statistics and directional single-phase permeability of synthetic realizations with the calculated properties of a bead pack, Berea sandstone, and Ketton limestone. Results show that GANs can be used to reconstruct high-resolution three-dimensional images of porous media at different scales that are representative of the morphology of the images used to train the neural network. The fully convolutional nature of the trained neural network allows the generation of large samples while maintaining computational efficiency. Compared to classical stochastic methods of image reconstruction, the implicit representation of the learned data distribution can be stored and reused to generate multiple realizations of the pore structure very rapidly.Comment: 21 pages, 20 figure

Microstructure of single-droplet granules formed from ultra-fine powders

A quantitative analysis of variations in granule microstructure based upon changes in primary particle size and bed preparation is presented. The granule microstructures are obtained using X-Ray Computed Tomography (XRCT). An algorithm is developed to measure the number and size of macro-voids (pore space with volume equivalent size greater than or equal to 30 μm or 3 times the primary particle size). Four size fractions of alumina, ranging in primary particle size from 0.5 μm to 108 μm, are sieved using three different sieve sizes to create static powder beds from which single-droplet granules are produced. The analysis shows that large macro-voids exist in ultra-fine powders (0.1–10 μm). The macro-voids take up to 7% of the granule volume and the largest macro-voids are 200–700 μm in volume equivalent size. Changing the sieve preparation changes the size and total volume of macro-voids. In contrast, there are very few macro-voids in granules formed from coarser powders. This study shows that micron sized powders have the opportunity to form complex structures during granulation and that the handling history of the materials should receive greater scrutiny than it currently gets

A novel μCT analysis reveals different responses of bioerosion and secondary accretion to environmental variability

Corals build reefs through accretion of calcium carbonate (CaCO3) skeletons, but net reef growth also depends on bioerosion by grazers and borers and on secondary calcification by crustose coralline algae and other calcifying invertebrates. However, traditional field methods for quantifying secondary accretion and bioerosion confound both processes, do not measure them on the same time-scale, or are restricted to 2D methods. In a prior study, we compared multiple environmental drivers of net erosion using pre- and post-deployment micro-computed tomography scans (μCT; calculated as the % change in volume of experimental CaCO3 blocks) and found a shift from net accretion to net erosion with increasing ocean acidity. Here, we present a novel μCT method and detail a procedure that aligns and digitally subtracts pre- and post-deployment μCT scans and measures the simultaneous response of secondary accretion and bioerosion on blocks exposed to the same environmental variation over the same time-scale. We tested our method on a dataset from a prior study and show that it can be used to uncover information previously unattainable using traditional methods. We demonstrated that secondary accretion and bioerosion are driven by different environmental parameters, bioerosion is more sensitive to ocean acidity than secondary accretion, and net erosion is driven more by changes in bioerosion than secondary accretion

Detecting and quantifying stress granules in tissues of multicellular organisms with the Obj.MPP analysis tool

International audienceStress Granules (SGs) are macromolecular assemblies induced by stress and composed of proteins and mRNAs stalled in translation initiation. SGs play an important role in the response to stress and in the modulation of signaling pathways. Furthermore, these structures are related to the pathological ribonucleoprotein (RNP) aggregates found in neurodegenerative disease contexts, highlighting the need to understand how they are formed and recycled in normal and pathological contexts. Although genetically tractable multicellular organisms have been key in identifying modifiers of RNP aggregate toxicity, in vivo analysis of SG properties and regulation has lagged behind, largely due to the difficulty of detecting SG from images of intact tissues. Here, we describe the object detector software Obj.MPP and show how it overcomes the limits of classical object analyzers to extract the properties of SGs from wide-field and confocal images of respectively C. elegans and Drosophila tissues. We demonstrate that Obj.MPP enables the identification of genes modulating the assembly of endogenous and pathological SGs, and thus that it will be useful in the context of future genetic screens and in vivo studies. This article is protected by copyright. All rights reserved

Investigating the microstructure of plant leaves in 3D with lab-based X-ray Computed Tomography

Background Leaf cellular architecture plays an important role in setting limits for carbon assimilation and, thus, photosynthetic performance. However, the low density, fine structure, and sensitivity to desiccation of plant tissue has presented challenges to its quantification. Classical methods of tissue fixation and embedding prior to 2D microscopy of sections is both laborious and susceptible to artefacts that can skew the values obtained. Here we report an image analysis pipeline that provides quantitative descriptors of plant leaf intercellular airspace using lab-based X-ray Computed Tomography (microCT). We demonstrate successful visualisation and quantification of differences in leaf intercellular airspace in 3D for a range of species (including both dicots and monocots) and provide a comparison with a standard 2D analysis of leaf sections. Results We used the microCT image pipeline to obtain estimates of leaf porosity and mesophyll exposed surface area (Smes) for three dicot species (Arabidopsis, tomato and pea) and three monocot grasses (barley, oat and rice). The imaging pipeline consisted of (1) a masking operation to remove the background airspace surrounding the leaf, (2) segmentation by an automated threshold in ImageJ and then (3) quantification of the extracted pores using the ImageJ ‘Analyze Particles’ tool. Arabidopsis had the highest porosity and lowest Smes for the dicot species whereas barley had the highest porosity and the highest Smes for the grass species. Comparison of porosity and Smes estimates from 3D microCT analysis and 2D analysis of sections indicates that both methods provide a comparable estimate of porosity but the 2D method may underestimate Smes by almost 50%. A deeper study of porosity revealed similarities and differences in the asymmetric distribution of airspace between the species analysed. Conclusions Our results demonstrate the utility of high resolution imaging of leaf intercellular airspace networks by lab-based microCT and provide quantitative data on descriptors of leaf cellular architecture. They indicate there is a range of porosity and Smes values in different species and that there is not a simple relationship between these parameters, suggesting the importance of cell size, shape and packing in the determination of cellular parameters proposed to influence leaf photosynthetic performance