*2.2. Image Acquisition*

All bright-field TEM (BF-TEM) and annular dark-field scanning TEM (ADF-STEM) images of CNCs were obtained using a JEOL 2200FS TEM/STEM at a 200 kV accelerating voltage with an in-column Omega energy filter. BF-TEM images with a pixel size of 2048 × 2048 were acquired on a Gatan slow-scan camera with pixel resolution at selected magnifications calibrated with a standard specimen (MAG\*I\*CAL, Electron Microscopy Science, USA). For imaging CNCs embedded in vitreous ice, the frozen specimen was transferred to a cryo-TEM holder (Gatan Inc., Pleasanton, CA, USA) while being kept below −170 ◦C. During TEM imaging, the specimen was kept at −180 ◦C, and the electron beam was blanked between image acquisitions to minimize beam damage on CNCs. The beam dose for each image was 2 e−/A2 . Ten to fifteen TEM images of CNCs from different regions on the TEM grid were acquired at each magnification.

In STEM mode, ADF-STEM images of CNCs were acquired using a 0.5 nm probe with a pixel dwelling time of 10 µs and an image size of 512 × 512 pixels. The pixel resolution was 0.5 nm, which was the same as the scanning step to avoid oversampling between pixels. Approximately 15 ADF-STEM images of CNCs taken from different areas were used for size measurement. tween pixels. Approximately 15 ADF-STEM images of CNCs taken from different areas were used for size measurement.

In STEM mode, ADF-STEM images of CNCs were acquired using a 0.5 nm probe with a pixel dwelling time of 10 µs and an image size of 512 × 512 pixels. The pixel resolution was 0.5 nm, which was the same as the scanning step to avoid oversampling be-

The TEMography® software package (Frontier Inc., Tokyo, Japan) was used for the acquisition of tilt-serial BF-TEM images, fine alignment of 2D images, reconstruction of 3D data, rendering and visualization of CNCs in 3D. The tilt-serial dataset was collected by tilting the holder along the *x*-axis from −70◦ to +70◦ with a 2◦ increment and a defocus value of 1 µm. Gold nanoparticles were used as fiducial markers and focusing objects during image acquisition and fine alignment. The TEMography® software package (Frontier Inc., Tokyo, Japan) was used for the acquisition of tilt-serial BF-TEM images, fine alignment of 2D images, reconstruction of 3D data, rendering and visualization of CNCs in 3D. The tilt-serial dataset was collected by tilting the holder along the *x*-axis from −70° to +70° with a 2° increment and a defocus value of 1 µm. Gold nanoparticles were used as fiducial markers and focusing objects during image acquisition and fine alignment.

#### *2.3. Size Measurement and Distribution Analysis 2.3. Size Measurement and Distribution Analysis*

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ImageJ (Version 1.53c), an open-source software, was used for image processing [20]. Single CNC particles or particles with clear separation outlines were manually selected for size measurement. The size of CNC particles was measured directly from TEM/STEM images. As shown in Figure 1, the length (L) is defined as the distance in a straight line between the two furthest points along a single CNC particle (major axis), and the width (W) (minor axis) is measured at the midpoint of its length unless a particle is clearly asymmetric. An ImageJ macro was used to open sequential images automatically after manual measurement of the length and width of selected CNCs on an active image. Measurement results were exported in a data format suitable for further statistical analysis in OriginPro 2017 or Excel. Approximately 200 particles were measured to give adequate statistical confidence. Histograms of length and width distributions for each dataset were plotted, and Gaussian fitting was applied for descriptive statistics. Box plots labeled with median, mean and one standard deviation (SD) above and below the mean were also used for comparing size distributions in datasets of stained CNCs and CNCs in vitreous ice. ImageJ (Version 1.53c), an open-source software, was used for image processing [20]. Single CNC particles or particles with clear separation outlines were manually selected for size measurement. The size of CNC particles was measured directly from TEM/STEM images. As shown in Figure 1, the length (L) is defined as the distance in a straight line between the two furthest points along a single CNC particle (major axis), and the width (W) (minor axis) is measured at the midpoint of its length unless a particle is clearly asymmetric. An ImageJ macro was used to open sequential images automatically after manual measurement of the length and width of selected CNCs on an active image. Measurement results were exported in a data format suitable for further statistical analysis in OriginPro 2017 or Excel. Approximately 200 particles were measured to give adequate statistical confidence. Histograms of length and width distributions for each dataset were plotted, and Gaussian fitting was applied for descriptive statistics. Box plots labeled with median, mean and one standard deviation (SD) above and below the mean were also used for comparing size distributions in datasets of stained CNCs and CNCs in vitreous ice.

**Figure 1.** The length (L) is defined as the straight line between the furthest two points (P1 & P2) along the individual CNC particle; the width (W) is defined as the distance between two edges of CNC particle, as indicated in BF-TEM image (**left**) and ADF-STEM image of CNCs (**right**). Scale bars are 20 nm. **Figure 1.** The length (L) is defined as the straight line between the furthest two points (P1 & P2) along the individual CNC particle; the width (W) is defined as the distance between two edges of CNC particle, as indicated in BF-TEM image (**left**) and ADF-STEM image of CNCs (**right**). Scale bars are 20 nm.

#### **3. Results and Discussions 3. Results and Discussion**

#### *3.1. Contrast of CNC Particles in a TEM Image 3.1. Contrast of CNC Particles in a TEM Image*

CNCs are low-density (1.6 g/cm3), high-aspect-ratio and electron-beam-sensitive biopolymers. The mass contrast of CNC nanoparticles in a normal BF-TEM image is very weak (Figure 2a). To enhance the contrast, conventional negative staining is widely used, and generally, 1–2% aqueous uranyl acetate is used as the staining reagent. However, due to the different staining methods/protocols, inconsistent quality and properties of the supporting films on TEM grids, as well as individual operator skills, staining results vary CNCs are low-density (1.6 g/cm3 ), high-aspect-ratio and electron-beam-sensitive biopolymers. The mass contrast of CNC nanoparticles in a normal BF-TEM image is very weak (Figure 2a). To enhance the contrast, conventional negative staining is widely used, and generally, 1–2% aqueous uranyl acetate is used as the staining reagent. However, due to the different staining methods/protocols, inconsistent quality and properties of the supporting films on TEM grids, as well as individual operator skills, staining results vary even for CNCs deposited on the same TEM grids [21]. As shown in Figure S2, a typical region of CNCs stained with 2% uranyl acetate has a gradient stain depth, resulting in CNCs with different contrasts. The very shallow stain cannot cover the overall contour of

CNCs, resulting in a weak contrast (Figure 2b). The CNCs in the deep stain appear bright with a fairly uniform dark background (Figure 2d), whereas those in the shallow stain are outlined by the stain (dark outlines), and the helical structure of the CNC particles is revealed clearly (Figure 2c). Despite this stain-depth variation, the rapid-flushing staining method described in the TEM specimen preparation was found to be the most suitable method among all those tested. To ensure that particles selected for image processing and size measurement were as homogenous as possible, regions of CNCs with similar stain depths were chosen as one dataset. CNCs, resulting in a weak contrast (Figure 2b). The CNCs in the deep stain appear bright with a fairly uniform dark background (Figure 2d), whereas those in the shallow stain are outlined by the stain (dark outlines), and the helical structure of the CNC particles is revealed clearly (Figure 2c). Despite this stain-depth variation, the rapid-flushing staining method described in the TEM specimen preparation was found to be the most suitable method among all those tested. To ensure that particles selected for image processing and size measurement were as homogenous as possible, regions of CNCs with similar stain depths were chosen as one dataset.

even for CNCs deposited on the same TEM grids [21]. As shown in Figure S2, a typical region of CNCs stained with 2% uranyl acetate has a gradient stain depth, resulting in CNCs with different contrasts. The very shallow stain cannot cover the overall contour of

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**Figure 2.** Variation in stain-depth of CNCs shown in BF-TEM images. (**a**) unstained; (**b**) very shallow; (**c**) shallow and (**d**) deep stained CNCs, respectively. Scale bars: 100 nm. **Figure 2.** Variation in stain-depth of CNCs shown in BF-TEM images. (**a**) unstained; (**b**) very shallow; (**c**) shallow and (**d**) deep stained CNCs, respectively. Scale bars: 100 nm.

In addition to heavy-metal staining such as UA, the contrast of CNCs can also be enhanced with other approaches such as electron energy filtering, objective lens defocusing and phase-plate insertion in the BF-TEM mode. As shown in Figure 3a, a zero-loss BF-TEM image of unstained CNCs was taken with a 10 eV energy filter and a defocus of −3 µm, in which the helical-like structure of CNCs was revealed, which indicates that the helical structure in stained CNCs is not an artifact. In the last decade, phase-plate imaging has also been explored in contrast enhancement of soft materials. However, considering the various phase-plate types, phase-shift interpretation and access to instruments with a phase plate, it might be challenging to standardize the size distribution with this approach. In addition to the above approaches of contrast enhancement, STEM imaging is an important method of contrast tuning for polymers and biomolecules [22,23]. The contrast of an ADF-STEM image is related to mass thickness and atomic numbers in the specimen. The pristine CNCs in the ADF-STEM images give high contrast, the individual CNC particles are distinguishable and some CNC particles (pointed by arrows) with nonuniform width along their long axis can be revealed (Figure 3b). On the other hand, electronbeam-induced contaminations can easily build up during scanning and blur the image at high resolution. For CNCs stained with uranyl acetate, edges of CNCs exhibit sharp contrast from the stain with a shallow depth in the ADF-STEM image, as shown in Figure 3d. It is easier to identify the individual CNCs than those in the BF-TEM images (Figure 3c) taken from an adjacent area, even though the helical or twist structure of CNCs is not revealed in the ADF-STEM images, which may not be important for size measurement. In addition to heavy-metal staining such as UA, the contrast of CNCs can also be enhanced with other approaches such as electron energy filtering, objective lens defocusing and phase-plate insertion in the BF-TEM mode. As shown in Figure 3a, a zero-loss BF-TEM image of unstained CNCs was taken with a 10 eV energy filter and a defocus of −3 µm, in which the helical-like structure of CNCs was revealed, which indicates that the helical structure in stained CNCs is not an artifact. In the last decade, phase-plate imaging has also been explored in contrast enhancement of soft materials. However, considering the various phase-plate types, phase-shift interpretation and access to instruments with a phase plate, it might be challenging to standardize the size distribution with this approach. In addition to the above approaches of contrast enhancement, STEM imaging is an important method of contrast tuning for polymers and biomolecules [22,23]. The contrast of an ADF-STEM image is related to mass thickness and atomic numbers in the specimen. The pristine CNCs in the ADF-STEM images give high contrast, the individual CNC particles are distinguishable and some CNC particles (pointed by arrows) with nonuniform width along their long axis can be revealed (Figure 3b). On the other hand, electron-beam-induced contaminations can easily build up during scanning and blur the image at high resolution. For CNCs stained with uranyl acetate, edges of CNCs exhibit sharp contrast from the stain with a shallow depth in the ADF-STEM image, as shown in Figure 3d. It is easier to identify the individual CNCs than those in the BF-TEM images (Figure 3c) taken from an adjacent area, even though the helical or twist structure of CNCs is not revealed in the ADF-STEM images, which may not be important for size measurement.

**Figure 3.** (**a**) under-focused (−3 µm) BF-TEM and (**b**) ADF-STEM images of unstained CNCs; (**c**) BF-TEM and (**d**) ADF-STEM image of stained CNCs. Scale bars: 100 nm. **Figure 3.** (**a**) under-focused (−3 µm) BF-TEM and (**b**) ADF-STEM images of unstained CNCs; (**c**) BF-TEM and (**d**) ADF-STEM image of stained CNCs. Scale bars: 100 nm.

With the contrast enhancement methods discussed above, sets of BF-TEM and ADF-STEM images of stained and unstained CNCs deposited on carbon TEM grids (Sp1 and Sp2 specimens) were acquired. The length and width of selected CNCs imaged at different conditions were measured, and the descriptive statistics of their distributions are summarized in Table S1. For curiosity, a set of BF-TEM images of CNCs was taken for each of the areas with a gradient stain depth, indicated as Zones A1, A2 and A3 in Figure S2, and the image resolution was 0.41 nm/pixel, 0.41 nm/pixel and 0.29 nm/pixel, respectively. The mean length of CNCs in Zones A1, A2 and A3 is 100.5 nm, 88.8 nm and 79.6 nm, and the mean width is 5.8 nm, 6.4 nm and 6.7 nm, respectively. Moving toward inward areas with a shallow stain (a similar stain depth as in Zone A3), an image set of ADF-STEM for Zone A4 was taken with an image resolution of 0.5 nm/pixel. The mean length and width of CNCs in Zone A4 are 76.3 nm and 7.0 nm, respectively. The histograms of length and width distributions are all included in Figure S3. Box plots of length, width and aspect ratio (length to width) for Zones A1 to A4 are shown in Figure 4. From Zones A1 to A4 with decreasing stain depths, the CNC width increases, while the CNC length and aspect ratio (AR) decrease, which suggests a correlation between size distribution and stain depth or imaging zone. Thus, the question to be answered is: does the stain depth affect the size of CNCs, or are CNC particles distributed differentially or fractionally on the substrate by size during droplet drying? With the contrast enhancement methods discussed above, sets of BF-TEM and ADF-STEM images of stained and unstained CNCs deposited on carbon TEM grids (Sp1 and Sp2 specimens) were acquired. The length and width of selected CNCs imaged at different conditions were measured, and the descriptive statistics of their distributions are summarized in Table S1. For curiosity, a set of BF-TEM images of CNCs was taken for each of the areas with a gradient stain depth, indicated as Zones A1, A2 and A3 in Figure S2, and the image resolution was 0.41 nm/pixel, 0.41 nm/pixel and 0.29 nm/pixel, respectively. The mean length of CNCs in Zones A1, A2 and A3 is 100.5 nm, 88.8 nm and 79.6 nm, and the mean width is 5.8 nm, 6.4 nm and 6.7 nm, respectively. Moving toward inward areas with a shallow stain (a similar stain depth as in Zone A3), an image set of ADF-STEM for Zone A4 was taken with an image resolution of 0.5 nm/pixel. The mean length and width of CNCs in Zone A4 are 76.3 nm and 7.0 nm, respectively. The histograms of length and width distributions are all included in Figure S3. Box plots of length, width and aspect ratio (length to width) for Zones A1 to A4 are shown in Figure 4. From Zones A1 to A4 with decreasing stain depths, the CNC width increases, while the CNC length and aspect ratio (AR) decrease, which suggests a correlation between size distribution and stain depth or imaging zone. Thus, the question to be answered is: does the stain depth affect the size of CNCs, or are CNC particles distributed differentially or fractionally on the substrate by size during droplet drying?

**Figure 4.** Box plots of length, width and aspect ratio (L/W) distribution of stained.CNCs at zones A1 to A4 with deceasing stain-depth. The line and dot in the box are the median and mean, respectively. The values of the median and one standard deviation (SD) above and below the mean are labelled to the left of boxes. **Figure 4.** Box plots of length, width and aspect ratio (L/W) distribution of stained.CNCs at zones A1 to A4 with deceasing stain-depth. The line and dot in the box are the median and mean, respectively. The values of the median and one standard deviation (SD) above and below the mean are labelled to the left of boxes.

#### *3.2. Dispersion of CNCs Adsorbed on Carbon-Film-Supported TEM Grids 3.2. Dispersion of CNCs Adsorbed on Carbon-Film-Supported TEM Grids*

As known, when a sessile droplet containing dispersed solid dries on a solid surface, a characteristic "coffee ring" is formed due to capillary flow toward the droplet edge [24]. In general, during the evaporation of a sessile droplet, the distribution of solids can be affected by capillary flow direction [24], Marangoni flows [25], particle−substrate interactions, particle–particle interaction and particle shape [26]. During TEM specimen preparation, the coffee-ring effect starts once the initial droplet is placed on TEM grids, and the degree of this effect depends on CNC concentration, incubation time, droplet volume and film surface properties. In the initial droplet, rod-shaped particles can be fractionated by particle size along the capillary flow direction when the contact lines of droplets recede on the substrate [27,28]. Particles with a high AR can be orderly arranged near the ring (droplet) edge with their major axis parallel to the contact lines [29]. After a certain incubation time, the initial capillary flow direction is interrupted by shear force from blotting, and the initial droplet is divided into many smaller droplets scattered on the carbon film surface, which might cause redistribution of the CNC particles on the substrate. In subsequent staining, the staining reagent may change the CNC distribution and orientation, but the lateral movement of CNCs should be confined within small areas. After drying, coffee rings might be formed, resulting in gradient thickness of stain. In Specimen Sp1 of stained CNCs, 3 µL of CNC solution and a 10 s incubation time for the initial droplet were used As known, when a sessile droplet containing dispersed solid dries on a solid surface, a characteristic "coffee ring" is formed due to capillary flow toward the droplet edge [24]. In general, during the evaporation of a sessile droplet, the distribution of solids can be affected by capillary flow direction [24], Marangoni flows [25], particle−substrate interactions, particle–particle interaction and particle shape [26]. During TEM specimen preparation, the coffee-ring effect starts once the initial droplet is placed on TEM grids, and the degree of this effect depends on CNC concentration, incubation time, droplet volume and film surface properties. In the initial droplet, rod-shaped particles can be fractionated by particle size along the capillary flow direction when the contact lines of droplets recede on the substrate [27,28]. Particles with a high AR can be orderly arranged near the ring (droplet) edge with their major axis parallel to the contact lines [29]. After a certain incubation time, the initial capillary flow direction is interrupted by shear force from blotting, and the initial droplet is divided into many smaller droplets scattered on the carbon film surface, which might cause redistribution of the CNC particles on the substrate. In subsequent staining, the staining reagent may change the CNC distribution and orientation, but the lateral movement of CNCs should be confined within small areas. After drying, coffee rings might be formed, resulting in gradient thickness of stain. In Specimen Sp1 of stained CNCs, 3 µL of CNC solution and a 10 s incubation time for the initial droplet were used to minimize the coffee-ring effect so that CNCs of all sizes are included for quantitative

analysis. However, it is difficult to void the coffee-ring effect completely, as seen in the gradient depth of stain in Figure S2. The results from Zone A1 to Zone A4 with decreasing stain depth, showing that the mean width increases while the mean length and the mean AR decrease, are due to the coffee-ring effect fractionating CNC dispersion on the substrate, instead of different stain thicknesses. analysis. However, it is difficult to void the coffee-ring effect completely, as seen in the gradient depth of stain in Figure S2. The results from Zone A1 to Zone A4 with decreasing stain depth, showing that the mean width increases while the mean length and the mean AR decrease, are due to the coffee-ring effect fractionating CNC dispersion on the substrate, instead of different stain thicknesses.

to minimize the coffee-ring effect so that CNCs of all sizes are included for quantitative

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To avoid the influence of staining reagent on CNC distribution, unstained CNCs dried on TEM grids (Specimen Sp2) were prepared with the same volume of the initial droplet (3 µL) as Sp1 but with a longer incubation time (60 s). As shown in Figure 5a, well-dispersed CNCs are surrounded by more concentrated CNCs lying along their major axis in a coffee-ring pattern (only part of the ring is shown). The major axis of CNCs was redirected to follow the blotting direction rather than being parallel to the pinned line. The size distribution of CNCs in the center area is shown as Dataset A5 in Table S1. The mean length and width are 85.6 nm and 8.9 nm, respectively. The mean length is comparable to those in Datasets A1 to A4, while the mean width is much larger. Such a large mean width was also reported by Johnston's team with a consensus distribution of 7.7 ± 2.2 nm [15]. In their case, a 10 µL volume and a 4 min incubation time were used for the initial droplet of CNCs, and an extra washing step with water was added before the subsequent staining of CNCs. The larger droplet volume and longer incubation time made the fractionation and ordered arrangement of CNCs on the substrate driven by the coffeering effect more predominant. The washing step may have removed or redistributed some loose CNCs, but the subsequent staining should not affect the dispersion of CNCs already adsorbed on the carbon film surface. Due to the polydispersity of CNCD-1 materials, the fractionated dispersion of CNCs caused by the coffee-ring effect may introduce variability of size distribution when CNCs are imaged and selected from different regions on TEM grids, especially in the radial direction (TEM grid center to edge). The measured mean width of CNCs adsorbed on the center of the TEM grids is larger than that near the edge. Unfortunately, the location of images taken for each laboratory was not specified. In general, areas near the center of the TEM grids would be used for imaging as the default location, likely resulting in biased size distributions. To avoid the influence of staining reagent on CNC distribution, unstained CNCs dried on TEM grids (Specimen Sp2) were prepared with the same volume of the initial droplet (3 µL) as Sp1 but with a longer incubation time (60 s). As shown in Figure 5a, welldispersed CNCs are surrounded by more concentrated CNCs lying along their major axis in a coffee-ring pattern (only part of the ring is shown). The major axis of CNCs was redirected to follow the blotting direction rather than being parallel to the pinned line. The size distribution of CNCs in the center area is shown as Dataset A5 in Table S1. The mean length and width are 85.6 nm and 8.9 nm, respectively. The mean length is comparable to those in Datasets A1 to A4, while the mean width is much larger. Such a large mean width was also reported by Johnston's team with a consensus distribution of 7.7 ± 2.2 nm [15]. In their case, a 10 µL volume and a 4 min incubation time were used for the initial droplet of CNCs, and an extra washing step with water was added before the subsequent staining of CNCs. The larger droplet volume and longer incubation time made the fractionation and ordered arrangement of CNCs on the substrate driven by the coffee-ring effect more predominant. The washing step may have removed or redistributed some loose CNCs, but the subsequent staining should not affect the dispersion of CNCs already adsorbed on the carbon film surface. Due to the polydispersity of CNCD-1 materials, the fractionated dispersion of CNCs caused by the coffee-ring effect may introduce variability of size distribution when CNCs are imaged and selected from different regions on TEM grids, especially in the radial direction (TEM grid center to edge). The measured mean width of CNCs adsorbed on the center of the TEM grids is larger than that near the edge. Unfortunately, the location of images taken for each laboratory was not specified. In general, areas near the center of the TEM grids would be used for imaging as the default location, likely resulting in biased size distributions.

**Figure 5.** (**a**) coffee-ring formed while unstained CNCs dried on carbon film and more CNCs are concentrated on the edge (white arrow) with well-dispersed CNCs in the center; (**b**) cryo-TEM images of CNCs embedded in vitreous ice taken from the areas near the rim of TEM grids; CNCs lined up parallel to the hole edge and filled across the hole; and (**c**) less ordered CNCs embedded in the ice near the hole edge taken from near the center of TEM grids; possible end-to-end or slightly sideby-side arranged CNCs are pointed by arrows. **Figure 5.** (**a**) coffee-ring formed while unstained CNCs dried on carbon film and more CNCs are concentrated on the edge (white arrow) with well-dispersed CNCs in the center; (**b**) cryo-TEM images of CNCs embedded in vitreous ice taken from the areas near the rim of TEM grids; CNCs lined up parallel to the hole edge and filled across the hole; and (**c**) less ordered CNCs embedded in the ice near the hole edge taken from near the center of TEM grids; possible end-to-end or slightly side-by-side arranged CNCs are pointed by arrows.

To further prove this coffee-ring effect or the dispersion of CNCs within a droplet, Specimen Sp4 was prepared using the plunge-freezing method to vitrify the CNC aqueous solution without a further drying process. Compared with Specimens Sp1 and Sp2, the incubation time of Sp4 was longer so that most CNCs could settle down within the droplet. Despite blotting from the backside of the droplet, the majority of CNCs near the edge of the droplet were arranged with their major axis parallel to each other (Figure 5b) and dispersed To further prove this coffee-ring effect or the dispersion of CNCs within a droplet, Specimen Sp4 was prepared using the plunge-freezing method to vitrify the CNC aqueous solution without a further drying process. Compared with Specimens Sp1 and Sp2, the incubation time of Sp4 was longer so that most CNCs could settle down within the droplet. Despite blotting from the backside of the droplet, the majority of CNCs near the edge of the droplet were arranged with their major axis parallel to each other (Figure 5b) and dispersed across the entire carbon film hole. CNCs near the center of the TEM grids were

less ordered and dispersed than those near the edge of the carbon film hole (Figure 5c). Cryo-TEM images were acquired from regions near the center of the TEM grids as Datasets A6 and near the edge as Dataset A7, respectively. The size distribution in each dataset is summarized in Table S1. The histograms of length and width distributions are shown in Figure S4. The mean width and length with standard deviation for A6 and A7 are 7.0 ± 1.1 nm and 112.4 ± 40.6 nm, 6.2 ± 1.2 nm and 96.4 ± 30.0 nm, respectively. The population of CNCs near the center (A6) has a larger length and width than that near the edge (A7), as shown in the box plots in Figure 6. The ARs in both regions are similar, in contradiction to the dried CNC specimen, Sp1. The blotting direction and TEM grid surface used for the cryo-TEM specimen and the dried CNC specimen were different, which may interfere with the CNC distribution differently during blotting. As illustrated in Figure S5, shear force is induced once the filter paper touches the edge of the continuous carbon-film-coated TEM grid, causing CNCs near the air–water interface to drain along the shear force direction. However, when blotting from the backside of perforated film, the shear force induced flow is through the holes so that CNCs near the air–water interface are drawn within thinned aqueous film rather than being removed [30,31]. CNCs within frozen aqueous film can be dispersed at different heights along the beam direction, which is different from CNCs drying on continuous film after blotting. As shown in Figure 5c, the larger particles marked by arrows might be composed of multiple end-to-end or slightly side-by-side CNC particles or overlapped projections from two particles embedded in different vertical positions, contributing to size inflation for both length and width. and dispersed than those near the edge of the carbon film hole (Figure 5c). Cryo-TEM images were acquired from regions near the center of the TEM grids as Datasets A6 and near the edge as Dataset A7, respectively. The size distribution in each dataset is summarized in Table S1. The histograms of length and width distributions are shown in Figure S4. The mean width and length with standard deviation for A6 and A7 are 7.0 ± 1.1 nm and 112.4 ± 40.6 nm, 6.2 ± 1.2 nm and 96.4 ± 30.0 nm, respectively. The population of CNCs near the center (A6) has a larger length and width than that near the edge (A7), as shown in the box plots in Figure 6. The ARs in both regions are similar, in contradiction to the dried CNC specimen, Sp1. The blotting direction and TEM grid surface used for the cryo-TEM specimen and the dried CNC specimen were different, which may interfere with the CNC distribution differently during blotting. As illustrated in Figure S5, shear force is induced once the filter paper touches the edge of the continuous carbon-film-coated TEM grid, causing CNCs near the air–water interface to drain along the shear force direction. However, when blotting from the backside of perforated film, the shear force induced flow is through the holes so that CNCs near the air–water interface are drawn within thinned aqueous film rather than being removed [30,31]. CNCs within frozen aqueous film can be dispersed at different heights along the beam direction, which is different from CNCs drying on continuous film after blotting. As shown in Figure 5c, the larger particles marked by arrows might be composed of multiple end-to-end or slightly side-by-side CNC particles or overlapped projections from two particles embedded in different vertical positions, contributing to size inflation for both length and width.

across the entire carbon film hole. CNCs near the center of the TEM grids were less ordered

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**Figure 6.** Box plots of length, width and aspect ratio (L/W) distribution of CNCs embedded in vitreous ice near the center and edge of TEM grids. The line and dot in the box is the median and mean, respectively. The values of the median and one standard deviation (SD) above and below the mean are labelled to the left of boxes. **Figure 6.** Box plots of length, width and aspect ratio (L/W) distribution of CNCs embedded in vitreous ice near the center and edge of TEM grids. The line and dot in the box is the median and mean, respectively. The values of the median and one standard deviation (SD) above and below the mean are labelled to the left of boxes.

#### *3.3. Orientation of CNCs on Carbon-Film-Supported TEM Grids 3.3. Orientation of CNCs on Carbon-Film-Supported TEM Grids*

As discussed above, CNCs on TEM grids could be fractionated during TEM specimen preparation, resulting in variability of size distribution when CNCs images are taken from different regions in the radial direction. In addition to the fractionated dispersion, the orientation of CNCs on the substrate also has a significant influence on size measurement from TEM images, as illustrated in Figure S1. In the CNCD-1 materials, about 28% of CNCs are particles with a square or a symmetric cross-section, while the remainder are asymmetric with one axis 2–3 times longer than the other, as analyzed in AFM [17]. When these asymmetric particles adsorb on the substrate surface with random orientations, the width distribution measured from TEM images may have large heterogeneity, as the TEM images are mixed projections of width and height from the minor-axial side of CNCs. Due to the strong hydrogen bond, CNCs tend to be laterally or twistedly jointed, and the widest side is normally the preferred orientation on the substrate. Figure 7 shows a typical example of aggregated CNCs in one region, which are either in a lateral (NP1, NP2), vertical and lateral (NP3) or twisted arrangement (NP4). NP3 and NP4 may be composed of more than two CNCs, and the width of NP4 along the long axis is not uniform. All these arrangements and orientations of CNCs on the substrate cause the variability of size measurement both in TEM and AFM. With proper staining of CNCs, the separation line between laterally jointed CNCs can be resolved in TEM. As shown in the line profile of NP1 in Figure 7, two particles with widths of 5.34 nm and 5.64 nm are well revealed and resolved. However, for unstained CNCs dried or embedded in vitreous ice or overstained CNCs, the separation between two CNC particles may not be revealed, resulting in inflated width measurement in TEM. In AFM, due to its limited resolving power in the lateral direction, it is difficult to resolve or separate laterally jointed CNCs. Therefore, the mean width measured with AFM may be overestimated when the orientation of CNCs is on the wider side. When CNC particles are arranged end-to-end or slightly side-by-side as illustrated in Figure S1, the length measurement will be affected if the separation cannot be resolved in either TEM or AFM. As discussed above, CNCs on TEM grids could be fractionated during TEM specimen preparation, resulting in variability of size distribution when CNCs images are taken from different regions in the radial direction. In addition to the fractionated dispersion, the orientation of CNCs on the substrate also has a significant influence on size measurement from TEM images, as illustrated in Figure S1. In the CNCD-1 materials, about 28% of CNCs are particles with a square or a symmetric cross-section, while the remainder are asymmetric with one axis 2–3 times longer than the other, as analyzed in AFM [17]. When these asymmetric particles adsorb on the substrate surface with random orientations, the width distribution measured from TEM images may have large heterogeneity, as the TEM images are mixed projections of width and height from the minor-axial side of CNCs. Due to the strong hydrogen bond, CNCs tend to be laterally or twistedly jointed, and the widest side is normally the preferred orientation on the substrate. Figure 7 shows a typical example of aggregated CNCs in one region, which are either in a lateral (NP1, NP2), vertical and lateral (NP3) or twisted arrangement (NP4). NP3 and NP4 may be composed of more than two CNCs, and the width of NP4 along the long axis is not uniform. All these arrangements and orientations of CNCs on the substrate cause the variability of size measurement both in TEM and AFM. With proper staining of CNCs, the separation line between laterally jointed CNCs can be resolved in TEM. As shown in the line profile of NP1 in Figure 7, two particles with widths of 5.34 nm and 5.64 nm are well revealed and resolved. However, for unstained CNCs dried or embedded in vitreous ice or overstained CNCs, the separation between two CNC particles may not be revealed, resulting in inflated width measurement in TEM. In AFM, due to its limited resolving power in the lateral direction, it is difficult to resolve or separate laterally jointed CNCs. Therefore, the mean width measured with AFM may be overestimated when the orientation of CNCs is on the wider side. When CNC particles are arranged end-to-end or slightly side-by-side as illustrated in Figure S1, the length measurement will be affected if the separation cannot be resolved in either TEM or AFM.

**Figure 7. Right**: Aggregated CNCs with different orientations marked by arrows (NP1, NP2, NP3 & NP4) on carbon film supported TEM grids. **Left**: The line profile of NP1 composed of two laterally jointed CNCs with width of 5.34 nm and 5.64 nm, respectively. **Figure 7. Right**: Aggregated CNCs with different orientations marked by arrows (NP1, NP2, NP3 & NP4) on carbon film supported TEM grids. **Left**: The line profile of NP1 composed of two laterally jointed CNCs with width of 5.34 nm and 5.64 nm, respectively.

To reveal particle orientation and arrangement on the substrate, typical regions of CNC particles on Specimen Sp3 were used for ET. Figure 8a shows a reconstructed 3D micrograph of two geometrically twisted CNCs, which are identified as one individual CNC particle (marked with an orange arrow) in the BF-TEM image (Figure 8b). These two CNCs are screw-like or helical along the long axis, as revealed in the reconstructed volume views. The length and width of these two CNCs measured from the reconstructed 3D data are 57 nm and 3.5 nm and 60 nm and 3.8 nm, respectively. Moreover, in the 2D TEM image (Figure 8b), the width measured for this twisted particle is 5.7 nm, which is used for width To reveal particle orientation and arrangement on the substrate, typical regions of CNC particles on Specimen Sp3 were used for ET. Figure 8a shows a reconstructed 3D micrograph of two geometrically twisted CNCs, which are identified as one individual CNC particle (marked with an orange arrow) in the BF-TEM image (Figure 8b). These two CNCs are screw-like or helical along the long axis, as revealed in the reconstructed volume views. The length and width of these two CNCs measured from the reconstructed 3D data are 57 nm and 3.5 nm and 60 nm and 3.8 nm, respectively. Moreover, in the 2D TEM image (Figure 8b), the width measured for this twisted particle is 5.7 nm, which is used for width distribution analysis. In the same field of view, there is another particle marked with a

white arrow. Tilting the particle along the long axis by about 20 degrees showed the particle is actually composed of three end-to-end-connected single CNC particles (Figure 9). Thus, the length measurement for size distribution will give a larger value. in Figure 8c) and 5.7 nm (right in Figure 8c), respectively. Thus, this asymmetric or rectangular cross-section suggests that CNC particles with a large width may be composed of more than one single crystallite. long axis. The length, height (thickness) and width of this particle are 60 nm, 3.6 nm (left in Figure 8c) and 5.7 nm (right in Figure 8c), respectively. Thus, this asymmetric or rectangular cross-section suggests that CNC particles with a large width may be composed of more than one single crystallite.

distribution analysis. In the same field of view, there is another particle marked with a white arrow. Tilting the particle along the long axis by about 20 degrees showed the particle is actually composed of three end-to-end-connected single CNC particles (Figure 9).

distribution analysis. In the same field of view, there is another particle marked with a white arrow. Tilting the particle along the long axis by about 20 degrees showed the particle is actually composed of three end-to-end-connected single CNC particles (Figure 9).

In addition to the particle–particle interactions and arrangement on TEM specimens, the intrinsic shape of CNC particles also contributes to size measurement variability. Ribbon-like CNCs marked with an orange arrow in Figure 8d coexist in the same materials, as shown in Figure 8c of a typical reconstructed 3D isosurface view. The reconstructed 3D data reveal that the CNC particle is helical-like, and its width is not uniform along the long axis. The length, height (thickness) and width of this particle are 60 nm, 3.6 nm (left

In addition to the particle–particle interactions and arrangement on TEM specimens, the intrinsic shape of CNC particles also contributes to size measurement variability. Ribbon-like CNCs marked with an orange arrow in Figure 8d coexist in the same materials, as shown in Figure 8c of a typical reconstructed 3D isosurface view. The reconstructed 3D data reveal that the CNC particle is helical-like, and its width is not uniform along the

Thus, the length measurement for size distribution will give a larger value.

Thus, the length measurement for size distribution will give a larger value.

*Polymers* **2021**, *13*, x FOR PEER REVIEW 11 of 14

*Polymers* **2021**, *13*, x FOR PEER REVIEW 11 of 14

**Figure 8.** Geometrically twisted CNCs bundles and ribbon-liked CNCs coexists with rod-like CNCs (Sp3 specimen). Scale bars 10 nm. (**a**) Reconstructed 3D volume view of two helical CNCs twisted with an angle (left) and overlapped view (right); (**b**) 2D BF-TEM image of the twisted CNCs (marked as orange arrow) used for 3D reconstruction; (**c**) Reconstructed 3D isosurface of a ribbon-like CNC viewed at an angle with the narrowest width (left), widest width (middle) and the slice view (right); (**d**) BF-TEM image of the CNC used for 3D reconstruction (marked as orange arrow). **Figure 8.** Geometrically twisted CNCs bundles and ribbon-liked CNCs coexists with rod-like CNCs (Sp3 specimen). Scale bars 10 nm. (**a**) Reconstructed 3D volume view of two helical CNCs twisted with an angle (left) and overlapped view (right); (**b**) 2D BF-TEM image of the twisted CNCs (marked as orange arrow) used for 3D reconstruction; (**c**) Reconstructed 3D isosurface of a ribbon-like CNC viewed at an angle with the narrowest width (left), widest width (middle) and the slice view (right); (**d**) BF-TEM image of the CNC used for 3D reconstruction (marked as orange arrow). **Figure 8.** Geometrically twisted CNCs bundles and ribbon-liked CNCs coexists with rod-like CNCs (Sp3 specimen). Scale bars 10 nm. (**a**) Reconstructed 3D volume view of two helical CNCs twisted with an angle (left) and overlapped view (right); (**b**) 2D BF-TEM image of the twisted CNCs (marked as orange arrow) used for 3D reconstruction; (**c**) Reconstructed 3D isosurface of a ribbon-like CNC viewed at an angle with the narrowest width (left), widest width (middle) and the slice view (right); (**d**) BF-TEM image of the CNC used for 3D reconstruction (marked as orange arrow).

**Figure 9.** The end-to-end arranged CNC particles is shown as one particle in BF-TEM (**Left**); as two particles (**Middle**) tilted at 10° along long-axis; and as three particles tilted at 20° (**Right**). **Figure 9.** The end-to-end arranged CNC particles is shown as one particle in BF-TEM (**Left**); as two particles (**Middle**) tilted at 10° along long-axis; and as three particles tilted at 20° (**Right**). **Figure 9.** The end-to-end arranged CNC particles is shown as one particle in BF-TEM (**Left**); as two particles (**Middle**) tilted at 10◦ along long-axis; and as three particles tilted at 20◦ (**Right**).

In addition to the particle–particle interactions and arrangement on TEM specimens, the intrinsic shape of CNC particles also contributes to size measurement variability. Ribbon-like CNCs marked with an orange arrow in Figure 8d coexist in the same materials, as shown in Figure 8c of a typical reconstructed 3D isosurface view. The reconstructed 3D data reveal that the CNC particle is helical-like, and its width is not uniform along the long axis. The length, height (thickness) and width of this particle are 60 nm, 3.6 nm (left in Figure 8c) and 5.7 nm (right in Figure 8c), respectively. Thus, this asymmetric or rectangular cross-section suggests that CNC particles with a large width may be composed of more than one single crystallite.

Therefore, the shape, geometrical variation and orientation of CNCs on the substrate all contribute to the size measurement. The size measured from 2D images may not be the true parameter of CNC particles; rather, it is the average size of the projected images of all possible shapes and geometrical arrangements of CNCs deposited on the substrate, resulting in large variability of size distribution.


**2.1 Experimental setup**

(PID) and lead–lag compensators still are regularly employed in the industrial applications due to their simple structure, straightforward design procedure and easy to implement. In the past, a lead compensator [2] and cascaded lead compensation scheme [3] were designed to stabilize the maglev system. However, the classical controllers can only perform well in limited operating range and failed to demonstrate a satisfactory robustness performance. Thus, many advanced controllers such as feedback linearization [4], sliding mode control [5], *H*∞ control technique [6], disturbance observer control approach [7], adaptive robust backstepping method [8] and model predictive control [9–11] as well as intelligent controllers which include fuzzy logic control [12] and neural network [13] have been dedicated to procure the high positioning and robustness performances in the maglev system. Despite the good positioning and robustness performances of the advanced controllers, sufficient knowledge of control theory is strictly needed in the design procedure. Furthermore, the intelligent controllers consist of complex architecture require a high computational effort. Often, the intelligent controllers do not have any systematic design

*Control Based on PID Framework - The Mutual Promotion of Control and Identification…*

procedure. These drawbacks depict barrier to their practical use.

mance was deteriorated as the levitation height was increased.

controllers.

**88**

Due to the above-mentioned reason, researchers kept devoting their effort in enhancing control performance of classic control in maglev system. The problem associated with 1-DOF PID control was overcome with the proposed of modified PID control and/or 2-DOF PID control. In 2007, Leva and Bascetta in [14] have realized a model-based feedforward control to the PID controller to improve the tracking performance of a maglev system. Unfortunately, it still demonstrated huge spike occurrence when the ball started moving at the initial position. After few years, Ghosh et al. has proposed a 2-DOF PID controller to improve the system transient response with zero percentage of overshoot. However, the proposed controller suffered from long settling time which was around 2 s. Besides, its positioning accuracy was recorded poor due to the derivative action on the reference signal [15]. In order to solve the positioning accuracy, Allan et al. has introduced the 2- DOF Lead-plus-PI controller [16]. The experimental evidence reported the improvement in the positioning accuracy, yet to point-to-point motion perfor-

Thus, in this research, a proportional integral-proportional derivative control with feedforward and disturbance compensations (FF PI-PD + *K*z) control approach is proposed to stabilize the maglev system and enhance the positioning performance as well as its robustness. The proposed controller consists of a PI-PD controller, a model-based feedforward control and a disturbance compensator. The PI-PD con-

positioning time is greatly reduced. Lastly, a disturbance compensator is integrated as an additional feedback element for robustness enhancement via lowering the sensitivity function magnitude. The effectiveness of the FF PI-PD + *K*z controller is validated experimentally through two types of motion control that are point-topoint and tracking motions. In this present paper, the robustness of the FF PI-PD + *K*z controller is examined via applying an impulse disturbance and varying the mass. The positioning and robustness performances of the FF PI-PD + *K*z controller are compared with the FF PI-PD and the full state feedback (FSF)

troller is designed by using the pole-placement method; the model-based feedforward control is constructed based on the system driving characteristic in open loop; the disturbance compensator is developed via the system current dynamics in closed loop. The derivative action of the PI-PD control amplified the measurement noise that affected the positioning accuracy. Hence, a low pass filter is featured with the PI-PD control to suppress the bad influence of the derivative action. Besides, a model-based feedforward control is incorporated with the PI-PD controller to further improve the following characteristic of the mechanism in attaining a better overshoot reduction characteristic. At the same time, the

The single-axis maglev mechanism (Googoltech GML 2001), as shown in **Figure 1** is used as a testbed to clarify the usefulness of the proposed controller. The maglev system is only able to control object to move up and down. The control purpose is to keep the magnetic levitation ball stable in a given position or to make the ball track a desired trajectory.

The maglev mechanism consists of an electromagnet (number of windings, *Nw* = 1000 turns) to exert a tractive force across the air gap to levitate a steel ball (mass, *M* = 94 g). Besides, it is a voltage-controlled (control signal, *u* = 0–10 V) maglev mechanism, which is comprised of a power amplifier to actuate the electromagnet. The maximum electrical power consumption is around 16 W. The maximum levitation height of the maglev system is 15 mm. In the experiments, the initial position is set at 10.5 mm and the operating range is within 2.5 mm. A laser position sensor (Panasonic laser distance sensor HG-C1050) with resolution of 1.83 μm is used to measure the levitation displacement. As experimentally examined, the resolution of the laser sensor output in open loop is recorded at 15 μm. To measure the controlled current of the mechanism, a hall effect current sensor

**Figure 1.** *Maglev system.*

(Magnelab hall effect current sensor HCT-0010-005) with resolution of 0.38 mA is used. The controller is implemented at a sampling rate of 1 kHz.


**2.1 Lymphatic drainage of the uterus**

Unlike superficial tumours, such as melanoma, the physiology of lymphatic drainage in deeper/visceral tumours is not well established [8]. Uterine drainage, as well as being bilateral, is therefore complex [9]. Anatomically, three segments of uterine lymphatic drainage have been established:

Physiologically, there are two main lymphatic pathways and a third accessory for uterine drainage [5]:

cranially over the course of the mesoureter. In these cases, SLN is usually observed in the internal iliac, common iliac or presacral region.

3.A third pathway has been described, the *infundibular pelvic*, but it is very rare except with fundal injections. This pathway has mainly fundal drainage towards the broad ligament and paraaortic chain [11].


**4. Conclusions**

The protection of Critical Infrastructures against extreme natural hazards by evaluating and improving their resilience is one of the main goals for many countries or groups of countries (like the EU). For this reason, methodologies able to quantify the possible criticalities of these infrastructures are needed to better plan and implement actions, countermeasures and investments allowing to limit or avoid the negative energy, social and economic consequences deriving from natural hazards impacts.

With respect to other studies available in the scientific literature, the approach proposed in this paper focuses on energy corridors and aims at defining a criticality index, which is a function of the spatial position along the analysed corridor, and so it is useful to quantify the criticality level for each section of the considered infrastructure. This index is able to take into account a large variety of parameters (related to the natural event, to the corridor, to the availability of alternative sources and to the involved users) and their interdependencies. The developed methodology can be an effective supporting tool for decision makers and public administrations, for companies that have to manage crucial infrastructures for energy commodities transport and for the civil protection, as it allows – through a simple mathematical formulation – to identify the sections of an energy corridor that are critical with respect to a specific natural hazard or that are close to a criticality status, thus defining priority areas of intervention, preventive investments, mitigation actions and *ad hoc* countermeasures.

The introduced criticality index assesses in a numerical way the socio-economic damage (measured in monetary units) due to the effects of an extreme natural event on the selected infrastructure and can be used to evaluate the maximum acceptable frequency and the corresponding intensity of the event itself, allowing a comparison with the design condition of the corridor.

Furthermore, the possibility to evaluate the criticality index also for negative damage values (i.e. for not critical configurations) permits to measure the distance from the criticality, allowing to pay preventive attention to those sections that are closer to critical situations.

In general, the described approach gives the opportunity of ranking the single branches of a corridor according to their criticality and for all the different natural hazards, and, as a consequence, it gives the authorities in charge of protecting critical infrastructures the opportunity of prioritising the interventions.

The implementation of this methodology on real cases requires specialists from different fields and complex information. This can be deduced also from the application to a simplified case study (considering one corridor and two extreme events). However, the case study has underlined the advantages of the procedure, especially if a reassessment of risk acceptability limit is introduced, because it puts into evidence the safety margin with respect to the design conditions or the need for performing structural tests, quantifying the infrastructure resilience.

Additional aspects should be deeply analysed in the case of an extensive application of the proposed methodology, including – in particular – the availability of complete and homogenous technological and environmental databases and the proper definition of the system boundaries that could be not trivial in the case of meshed networks like the natural gas distribution ones.

*Resilience of Critical Infrastructures: A Risk Assessment Methodology for Energy Corridors DOI: http://dx.doi.org/10.5772/intechopen.94755*

Further studies could also be devoted to the analysis of multi-risk scenarios, i.e. to the concurrent occurrence of two or more extreme natural events, defining suitable strategies to allocate the acceptable risk (for instance by taking into account the safety margins of the infrastructure, if they are present), in order to test the infrastructure resilience in the worst (and low-frequency) conceivable conditions.


**Contemporary methods** [21]

1.Electrical conductance measurement (Caries meter L):

The tooth surface is inserted with conducting gel and is gently air-dried. Every tooth surface is dampened between the measurements to establish proper contact between the electrode and tooth surface. The device has colour codes to indicate the extent of caries as given in **Table 4**.

#### **Table 2.**

*Different systems for evaluation on clinical examination.*

**Table 3.** *Gorelick index scoring.* 2.Quantitative light fluorescence (QLF):

According to Rodrigues et al. in 2011, there are two devices used in QLF. They are DIAGNOdent device and DIAGNOdent pen. The device consists of a laser diode, photo diode and a long pass filter [27]. A tip is placed on the tooth surface at a certain angle and fluorescence values are calculated as in **Table 5**.

3.Light-emitting diode fluorescence:

LED fluorescence is based on the principle of difference in optical property. There are two available systems: Midwest caries (MID) and Vista Proof (VP). MID probe is a small battery-operated device with a portable handpiece and a probe [27]. When the probe touches the demineralized tooth surface there is an audible signal with a colour change from green to red as given in **Table 6**.

4.Frequency-domain infrared photothermal radiometry and modulated luminescence (PTR/LUM) [21]:

A recent technology called the Canary system has been introduced in the year 2011. This system consists of a laser tip along with an intra-oral camera. The laser tip is placed on the tooth surface that has to be examined and the WSL is recorded based on the scoring from 0 to 100 on the digital display. The scores and inferences are given in **Table 7**.

#### **Table 4.**

*Colour codes and inference of caries meter L.*

#### **Table 5.**

*Scoring of DIAGNOdent device.*

#### **Table 6.**

*Scoring of Midwest caries device (LED fluorescence).*

*White Spot Lesions and Remineralization DOI: http://dx.doi.org/10.5772/intechopen.101372*

**Table 7.**

*Scores and inference of PTR/LUM.*


3.1.3. Observations

It should be noted that in all single-shot simulations, the predicted in-plane residual stresses are equi-biaxial, as would be expected due to symmetry. The *S*22 stresses in the plane normal to the *y*-axis are equivalent to the *S*11 stresses in the plane normal to the *x*-axis.

The results of these single-shot simulations suggest that a favorable state of residual stress in thin sections can be realized by selecting a peening pressure high enough to achieve the desired compression without the detrimental subsurface tensile stresses, along with su fficient peen layers to suppress the near-surface tension. As discussed in the following sections, however, a third parameter, namely, the peen patterning, can also strongly influence the resulting stresses in thin sections, and, as will be discussed, observations for single shots do not necessarily hold in multi-shot scenarios.

### *3.2. Simulation of a Peened Line*

Although the single shot simulations provide insight into the e ffects of applied pressure and layering, they cannot capture the interactions between adjacent and overlapping spots, or the e ffects of shot order and patterning. To investigate these e ffects, the FEA model used for the single shot simulations—a 2 mm thick plate of Al2024-T351—was extended lengthwise to accommodate the simulation of peened line under various peening conditions.

### 3.2.1. E ffects of Adjacent Spots

Figure 7 shows the simulation results for a scenario similar to that considered in [10], namely, a single layer of peen spots applied edge-to-edge along a line with minimal overlap. The applied pressure used in the simulations was 2.5 times the HEL. As is shown, the center of each peen spot in the line exhibits a surface tensile stress normal to the peen line with a magnitude of about 110 MPa and extending about 50 μm subsurface. The corresponding stress for a single peen spot is shown for comparison. Although the magnitude of the surface tensile stress is similar for the single spot and the peen line, the magnitude of the maximum compression resulting from the line configuration is about 20% lower than that of a single spot. The overall depth of compression is smaller in the line configuration as well, by about 0.25 mm.

Although the near-surface tensile stresses are contained only in the central regions near the spot center, significant tension (with a peak value of about 100 MPa) builds up subsurface in the regions between adjacent spots. For many fatigue applications these large subsurface tensile stresses can be undesirable, as they can lead to subsurface crack initiation that is undetectable by visual inspection of the component surface.

Figure 7 also compares the through-thickness profiles for the in-plane stresses taken parallel to (*S*11) and transverse to (*S*22) the peen line; as is shown, the stress states in the two directions are noticeably di fferent, with as much as a 50 MPa variation in some locations. In general, the stresses transverse to the peened line are less compressive (or more tensile) than those in the longitudinal direction. This could pose a concern for certain applications in which the peen line was designed to overlay a surface scratch. Because the stresses are of lower magnitude (or even more tensile) in the critical direction (i.e., the direction that would tend to encourage crack formation and opening), the true potential of the LP treatment would not be realized, and could even exacerbate the situation. It should also be noted that through the spot centers, the stresses transverse to the peen line are generally more tensile than those predicted by single shot simulations.

**Figure 7.** Comparison of stress profiles between a single peen spot and a peen line.

### 3.2.2. Effects of Peen Layers

As was the case for single shots, augmenting the peen line with additional layers can help alleviate the surface tensile stresses at the spot centers. In Figure 8, second and third layers of peen spots are applied along the peen line at a 50% offset between one layer and the next, as shown in the schematic. The line plots illustrate the in-plane stress through the center of a spot on the topmost layer (denoted Location L1). Similar to the case of a single peen spot, the surface tensile zones in the spot centers are reduced as additional layers are applied; however, the effects of adjacent spots are very apparent. A second layer flips the surface stress to compression (from 108 MPa to −243 MPa) while a third layer flips it again (from −243 MPa to 46 MPa). The end result is that although the stress at this location is reduced with additional peen layers, shallow tensile regions—about 20 μm deep and 20 μm in width—persist in the center of each spot that comprises the topmost layer of the plate.

It should be noted that while Figure 8 suggests that limiting the peening to two layers would alleviate the surface tensile stress at Location L1, detrimental stresses form elsewhere. Figure 9 illustrates this by extracting stress plots at location L2 after one and two layers of peening. As is shown, the surface stress at this location decreases more than 50% with the addition of the second layer (from 108 MPa to 48 MPa), but the tension persists.

**Figure 8.** Effect of peen layering on predicted in-plane residual stress in thin sections. Stresses were extracted from the FEA model at Location L1 after one, two, and three layers of peening.

**Figure 9.** Effect of peen layering on predicted in-plane residual stress in thin sections. Stresses were extracted from the FEA model at Location L2 after one and two layers of peening.

### 3.2.3. Effects of Peen Patterning

In Figure 10, we consider the effects of altering the peen patterning while still maintaining a coverage of 300% along the central portion of the peen line. Two additional scenarios were evaluated: Pattern 2, which is similar in application to the scenario considered in Section 3.2.2 (denoted in this section as Pattern 1) but reduces the offset between layers from one-half the spot width to one-third (Figure 10a); and Pattern 3, which uses a running overlap (Figure 10b) pattern. As is shown, both of these patterns achieve exactly three layers of peening along the central portion of the peen line.

**Figure 10.** Schematics for achieving 300% peen coverage: (**a**) Pattern 2; (**b**) Pattern 3. Figure 8 shows Pattern 1.

The corresponding contour and line plots for peen Patterns 2 and 3 are shown in Figure 11. As is illustrated, decreasing the offset from one-half to one-third the spot width does not alleviate the tensile stresses at the surface. As was observed with Pattern 1, small regions of tension persist in the spot centers of the topmost layer for Pattern 2. However, the build-up of subsurface tensile stresses is significantly reduced by reducing the offset.

The running peen pattern (Pattern 3), on the other hand, does result in a complete suppression of surface and near-surface tensile stress (Figure 11b), to a depth of almost 1 mm. For the peening parameters used in the simulations, the surface stress in the spot centers decreases from about 60 MPa to about −135 MPa, while the subsurface tension at mid-section decreases from a maximum of 100 MPa for Pattern 1 to about 0 MPa for Pattern 3. Note that the more compressive surface stresses observed from the running overlap pattern result from the beneficial interactions of subsequent peen spots applied over previously induced fields; for other combinations of peening parameters and specimen geometry other surface conditions may arise.

**Figure 11.** Effects of peen layering on predicted in-plane residual stress: (**a**) Pattern 2; (**b**) Pattern 3. The patterns are shown in Figure 10.


Preface

Using biomass to cogenerate energy and produce biological and biochemical products as well as bioproducts with economic importance is a great alternative to using fossil fuels, which have adverse effects on the environment. Biomass, as a sustainable and renewable energy source, has considerable importance in the production of several molecules with vast application in industries such as agriculture and pharmaceuticals. Additionally, alternative biomass sources and the development of thermochemical processes for efficient conversion of several biomass substrates contribute to the development of biofuel and bioenergy production.

The present book, *Biotechnological Applications of Biomass*, provides a comprehensive overview of the current state of the art of biomass utilization in agriculture and pharmaceuticals. It contains thirty-two chapters over eight sections: "Biomass (Pre)Treatments or Conversion," "Biomass for (Bio)Energy," "Alternative Biomass Sources," "Biochemical Products," "Microalgae Biomass," "Biotechnological Products," "Thermochemical Processes," and "Kinetic Models and Tools for Biomass Measurement".

This book will be interesting for researchers around the world involved with biomass utilization. It also contributes to the diverse research areas of biomass utilization and production of bioproducts.

**IV**

**Chapter 24 451**

Biotechnological Products **487**

**Chapter 25 489**

**Chapter 26 511**

**Chapter 27 525**

Thermochemical Processes **545**

**Chapter 28 547**

**Chapter 29 561**

Kinetic Models and Tools for Biomass Measurement **577**

**Chapter 30 579**

**Chapter 31 595**

**Chapter 32 607**

Economics, Sustainability, and Reaction Kinetics of Biomass Torrefaction

Soft Sensors for Biomass Monitoring during Low Cost Cellulase Production

Fungal Biomass Load and *Aspergillus flavus* in a Controlled Environment

*by Nusrat Iqbal, Amrish Agrawal, Saurabh Dubey and Jitender Kumar*

Numerical and Experimental Analysis of Thermochemical Treatment

Investigation of Nonisothermal Combustion Kinetics of Isolated Lignocellulosic Biomass: A Case Study of Cellulose from Date Palm

*by Emmanuel Galiwango and Ali H. Al-Marzouqi*

for the Liquefaction of Lemon Bagasse in a Jacketed Vessel *by Brenno S. Leite, Daniel J.O. Ferreira, Sibele A.F. Leite*

Use of Olive Mill Wastewaters as Bio-Insecticides for the Control

*by Abdelilah Meddich, Abderrahim Boutasknit, Mohamed Anli, Meriame Ait Ahmed, Abdelilah El Abbassi, Hanane Boutaj,*

of *Potosia Opaca* in Date Palm (*Phoenix dactylifera L.*)

Role of Decomposers in Agricultural Waste Management

*Mohamed Ait-El-Mokhtar and Ali Boumezzough*

*by Alfred Mitema and Naser Aliye Feto*

*by Thanh Phong Mai and Dinh Quan Nguyen*

Recent Advances in Algal Biomass Production *by Meghna Rajvanshi and Richard Sayre*

**Section 6**

**Section 7**

Gasification of Biomass

*and Vanessa F.C. Lins*

**Section 8**

Biomass Waste

*by Chitra Murugan*

*by Temitope Olumide Olugbade*

**Thalita Peixoto Basso,** Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba, Brazil

**Thiago Olitta Basso and Luiz Carlos Basso,** University of Sao Paulo, Piracicaba, Brazil

**1**

Section 1

Biomass (Pre)Treatments

or Conversion