**2. Coastal geomorphology and ICZM**

The coast is easily defined as one of the most diverse and dynamic environments found anywhere on earth. Many factors (geologic, physical, biological and anthropomorphic) are responsible for shaping the coast and carrying on its dynamic characteristics. Geological events created the sediment that formed the foundation of the modern coastal zone. Over time, various physical processes have acted on this pre-existing geology, eroding, shaping and modifying the landscape. These mechanisms are influenced by tectonics, climate, ecology and human actions. At the same time, many of these drivers can be affected by the evolution of the Earth's surface. Geomorphology is the science that focuses on the quantitative analysis of these drivers and these physical processes that shape the Earth's surface (Sanders & Clark, 2010). The nature of these processes depends strongly on the landscape or landform under investigation and the time scales and length scales of interest. The primary driving forces that cause change in most landforms include wind, waves, chemical dissolution, groundwater movement, surface water flow, glacial action, tectonics and volcanism.

Coastal geomorphology deals with the shaping of coastal features (landforms), the processes at work on them and the changes taking place (Bird, 2008). Understanding changes at the coast can require an examination of processes well outside the coastal zone, focusing on the interactions of coastal-zone features and hydrologic, meteorological and fluvial forces by means of sediment transport. Fluid dynamics produce sediment transport, causing geomorphic change to a continuous extent of temporal and spatial scales. On the other hand, the change of geomorphic features alters the boundary conditions for fluid dynamics. In

tributaries or a river basin for a study of coastal erosion). Accordingly, local variability might be missed both in time and in space. The extent of the research area (whether it is a sand grain, a cliff, a coastal town or a region) is determined by a spatial scale using two axes: planform (also called 'long-shore') or profile ('cross-shore') (Woodroffe, 2002). A range of geomorphologic processes and human activities exist on both axes, influencing ICZM plans. The temporal scale is another multilayer factor that is important for the preparation of ICZM plans. Overall, geomorphologic scales exist across a wide range, from seconds to hundreds of years (Davies, 1993). On one side, human activities on coastal areas take a longer time to impact shorelines. First of all, most actions take a couple of years to carry out (for example, dams along river basins causing coastal erosion). Next, human use is expected to continue for 50 to 100 years (maybe more). This range of the temporal scale is also defined as the engineering timescale (French & Burningham, 2009). On the other hand, ICZM requires a long-term planning perspective, considering short-term benefits and solutions for urgent problems as well as integrating future risks, such as climate change. Although ICZM started as a form of shoreline management and flood risk planning, it evolved into a management concept, covering social, economic and ecological assets and including a diverse range of problems from natural disasters to man-made events - such as oil spillage - being generally accepted within coastal management literature (examples given in McFadden et al., 2007 such as Bower & Turner, 1997; Sorensen, 1997; European Commission, 1999; Kay & Alder, 1999; de Groot & Orford, 2000). Thus, different spatial and time scales exist within an ICZM plan (McFadden et al, 2007) and the geomorphology of coastal areas is one of the important

The coast is easily defined as one of the most diverse and dynamic environments found anywhere on earth. Many factors (geologic, physical, biological and anthropomorphic) are responsible for shaping the coast and carrying on its dynamic characteristics. Geological events created the sediment that formed the foundation of the modern coastal zone. Over time, various physical processes have acted on this pre-existing geology, eroding, shaping and modifying the landscape. These mechanisms are influenced by tectonics, climate, ecology and human actions. At the same time, many of these drivers can be affected by the evolution of the Earth's surface. Geomorphology is the science that focuses on the quantitative analysis of these drivers and these physical processes that shape the Earth's surface (Sanders & Clark, 2010). The nature of these processes depends strongly on the landscape or landform under investigation and the time scales and length scales of interest. The primary driving forces that cause change in most landforms include wind, waves, chemical dissolution, groundwater movement, surface water flow, glacial action, tectonics

Coastal geomorphology deals with the shaping of coastal features (landforms), the processes at work on them and the changes taking place (Bird, 2008). Understanding changes at the coast can require an examination of processes well outside the coastal zone, focusing on the interactions of coastal-zone features and hydrologic, meteorological and fluvial forces by means of sediment transport. Fluid dynamics produce sediment transport, causing geomorphic change to a continuous extent of temporal and spatial scales. On the other hand, the change of geomorphic features alters the boundary conditions for fluid dynamics. In

parameters that define these scales.

and volcanism.

**2. Coastal geomorphology and ICZM**

turn, it produces further variations in sediment-transport patterns which again cause changes in geomorphic features. These processes happen over a wide range of spatial and temporal scales (Fig. 1). For example, interaction with the near shore profile changes the properties of waves (generated by offshore storms) when entering the coastal zone (Woodroffe, 2002). The resulting wave and flow characteristics control the cross-shore and long-shore variations. The characteristics of the bottom slope and the variations of waves and tides dominate the dynamics of sediment fluxes, causing changes such as erosion and accretion. On the other hand, small-scale processes control the turbulent dissipation of breaking waves, the bottom boundary layer and the bed form mechanisms that shape the local sediment flux (US Army Corps of Engineers, 2003).

Fig. 1. Temporal and spatial scales of geomorphologic and coastal processes.

The rate of the response of geomorphic features to coastal processes depends on the scale, with larger features taking relatively longer to change. For example, under large waves significant changes in small-scale bed forms can occur within a single wave cycle, but changes in large-scale bed forms are established some time after the occurrence of the main driving force. Winds, waves, tides, storms and stream discharge are important driving forces in the coastal zone (Woodroffe, 2002).

Spatial and Time Balancing Act:

Fig. 2. Mechanisms affecting coastal geomorphology.

Coastal Geomorphology in View of Integrated Coastal Zone Management (ICZM) 145

Vulnerability assessments are one of the tools used by coastal researchers to highlight the problem areas\sectors\processes that need management, both at the present and in the future. There are different vulnerability assessment methods which use only numerical modelling or highly qualitative procedures answering a range of questions from different perspectives (IPCC, 2007). Despite the use of computers, geographical information systems, remote sensing technologies, data management and support systems and developed methodologies, there remain many unknowns, uncertainties and challenges to overcome, both for the research community and for practitioners (IPCC, 2007; Klein and Nicholls, 1999). Most coastal areas lack continuous data collection or monitoring systems, which hinders the implementation of many of the available numerical models. For these regions, relying on the local experience of practitioners as well as historical events making up the concept of expert opinion is what the decision-making process generally corresponds to. Expert opinion - although a valuable tool - is subjective in nature when decision-making is considered. Keeping in mind that the wrong decision-making process could cause

In addition to the two main axes (long-shore and cross-shore), the coastal zone can be classified as micro-, meso- or macro-cell so as to define the spatial scale (Schwartz, 2005). Micro-cells include smaller geomorphic features such as ripples and small beach-face features which change over the period of a day or even hours. Meso-cells include geomorphic features such as beach profiles which change over a year or else months. Macrocells extend for kilometres and include large coastal geomorphic features.

It is this macro-scale that presents one of the grand challenges of coastal geomorphology. Relating the prediction of morphodynamic behaviour at a meso-scale is of particular relevance for the understanding and management of coastal responses to environmental change. However, the study of coastal processes has traditionally been restricted to small and intermediate scales (Thornton et al. 2000 cited in French & Burningham, 2009), making it but one of several influences on the coastal zone (Fig. 2). For decadal-scale studies, coastal researchers use the 'one line' shoreline change model, which remains popular, especially for the prediction of the wider impact of engineering schemes. However, system linkages are rarely linear and numerical morphodynamic models are required to understand the quantitative response of the coastline to environmental forcing. Thus, meso-scale morphodynamic modelling is likely to be one of the most active research fronts in coastal geomorphology for the near future (French & Burningham, 2009). Unfortunately, at present the application of a single model cannot address most large-scale problems. Multiple models are typically required to achieve a reasonable qualitative and quantitative prediction of morphological changes (Hommes et al. 2007 cited in French & Burningham, 2009).

While many mechanisms such as human intervention, the climate and the sea level affect the coastal geomorphology in addition to coastal processes (Fig. 2), coastal geomorphology has a significant influence on those mechanisms as well. Depending on the characteristics of certain coastal landforms, most of the human activities that take place in coastal areas relate to tourism, agriculture and transportation (Woodroffe, 2002). Moreover, many structures are built on shorelines so as to ensure the sustainability of these activities. This cycle of affecting one another is what makes the design and implementation of ICZM complicated. Different uses of the same coastal resources might generate serious problems, especially under the threat of global forces such as climate change (IPCC, 2007). Also, the application of geomorphologic methods to predict the status of landforms usually covers small spatial scales and short term temporal changes as compared with scales of climate change and ICZM. ICZM practice requires longer time scales and larger spatial scales, and it relies on prediction of shoreline movements and landforms. There are various tools\models presented in the literature for different landforms and mechanisms, and it is the main challenge for ICZM practitioners to integrate and discuss the results of different models in order to come up with one plan for the coastal domain. That is why integrated assessment models have recently gained importance in coastal zone management research (McFadden et al, 2007). The threat of climate change and the impact related to sea level change has seen focus on longer term predictions as well. However, longer term predictions come with higher uncertainties. Thus, at the moment coastal policymakers need to select appropriate scales and tools so as to ensure the sustainable development of coastal areas, taking account of the different spatial and temporal scales of many mechanisms, each possessing different levels of uncertainties (IPCC, 2007). On the other hand, international agencies continue to call for integrated assessments of all disciplines – not just coastal processes - while much of the research focuses solely on one aspect of the question.

In addition to the two main axes (long-shore and cross-shore), the coastal zone can be classified as micro-, meso- or macro-cell so as to define the spatial scale (Schwartz, 2005). Micro-cells include smaller geomorphic features such as ripples and small beach-face features which change over the period of a day or even hours. Meso-cells include geomorphic features such as beach profiles which change over a year or else months. Macro-

It is this macro-scale that presents one of the grand challenges of coastal geomorphology. Relating the prediction of morphodynamic behaviour at a meso-scale is of particular relevance for the understanding and management of coastal responses to environmental change. However, the study of coastal processes has traditionally been restricted to small and intermediate scales (Thornton et al. 2000 cited in French & Burningham, 2009), making it but one of several influences on the coastal zone (Fig. 2). For decadal-scale studies, coastal researchers use the 'one line' shoreline change model, which remains popular, especially for the prediction of the wider impact of engineering schemes. However, system linkages are rarely linear and numerical morphodynamic models are required to understand the quantitative response of the coastline to environmental forcing. Thus, meso-scale morphodynamic modelling is likely to be one of the most active research fronts in coastal geomorphology for the near future (French & Burningham, 2009). Unfortunately, at present the application of a single model cannot address most large-scale problems. Multiple models are typically required to achieve a reasonable qualitative and quantitative prediction of

cells extend for kilometres and include large coastal geomorphic features.

morphological changes (Hommes et al. 2007 cited in French & Burningham, 2009).

the research focuses solely on one aspect of the question.

While many mechanisms such as human intervention, the climate and the sea level affect the coastal geomorphology in addition to coastal processes (Fig. 2), coastal geomorphology has a significant influence on those mechanisms as well. Depending on the characteristics of certain coastal landforms, most of the human activities that take place in coastal areas relate to tourism, agriculture and transportation (Woodroffe, 2002). Moreover, many structures are built on shorelines so as to ensure the sustainability of these activities. This cycle of affecting one another is what makes the design and implementation of ICZM complicated. Different uses of the same coastal resources might generate serious problems, especially under the threat of global forces such as climate change (IPCC, 2007). Also, the application of geomorphologic methods to predict the status of landforms usually covers small spatial scales and short term temporal changes as compared with scales of climate change and ICZM. ICZM practice requires longer time scales and larger spatial scales, and it relies on prediction of shoreline movements and landforms. There are various tools\models presented in the literature for different landforms and mechanisms, and it is the main challenge for ICZM practitioners to integrate and discuss the results of different models in order to come up with one plan for the coastal domain. That is why integrated assessment models have recently gained importance in coastal zone management research (McFadden et al, 2007). The threat of climate change and the impact related to sea level change has seen focus on longer term predictions as well. However, longer term predictions come with higher uncertainties. Thus, at the moment coastal policymakers need to select appropriate scales and tools so as to ensure the sustainable development of coastal areas, taking account of the different spatial and temporal scales of many mechanisms, each possessing different levels of uncertainties (IPCC, 2007). On the other hand, international agencies continue to call for integrated assessments of all disciplines – not just coastal processes - while much of

Fig. 2. Mechanisms affecting coastal geomorphology.

Vulnerability assessments are one of the tools used by coastal researchers to highlight the problem areas\sectors\processes that need management, both at the present and in the future. There are different vulnerability assessment methods which use only numerical modelling or highly qualitative procedures answering a range of questions from different perspectives (IPCC, 2007). Despite the use of computers, geographical information systems, remote sensing technologies, data management and support systems and developed methodologies, there remain many unknowns, uncertainties and challenges to overcome, both for the research community and for practitioners (IPCC, 2007; Klein and Nicholls, 1999). Most coastal areas lack continuous data collection or monitoring systems, which hinders the implementation of many of the available numerical models. For these regions, relying on the local experience of practitioners as well as historical events making up the concept of expert opinion is what the decision-making process generally corresponds to. Expert opinion - although a valuable tool - is subjective in nature when decision-making is considered. Keeping in mind that the wrong decision-making process could cause

Spatial and Time Balancing Act:

2010).

in representing the mechanism of coastal erosion (Table 1).

6. Tidal Range

mentioned are related to a long shore spatial scale.

Coastal Geomorphology in View of Integrated Coastal Zone Management (ICZM) 147

Of the impacts assessed by the FCVAM model, coastal erosion is the one that both influences and is influenced by geomorphologic processes the most. The FCVAM model uses 6 parameters for physical characteristics and 5 parameters for anthropogenic activities

Coastal Erosion 1. Rate of Sea Level Rise 1. Reduction of Sediment Supply

**Physical Parameters Human Influence Parameters**

4. Significant Wave Height 4. Natural Protection Degradation 5. Sediment Budget 5. Coastal Protection Structures

2. Geomorphology 2. River Flow Regulation 3. Coastal Slope 3. Engineered Frontage

Table 1. Parameters of FCVAM model representing the coastal erosion mechanism (Ozyurt,

The physical parameters for erosion include and integrate the impact of climate change through sea level rise, geomorphology through landforms, and coastal processes through waves, the sediment budget and tidal range. Waves and tides are used to classify the shoreline as a high\low energy shoreline so as to determine whether or not coastal geomorphologic processes are governed by natural drivers. The sediment budget parameter - originating from the historical evolution of the shoreline - shows whether geomorphologic processes are dominant along the coastal area. The type of landforms points out the overall susceptibility of the shoreline to geomorphologic processes. All of the parameters

The human influence parameters - also given in Table 1 - require assessments of their own which need to derive information from geomorphology studies as well. Due to activities outside the coastal zone, natural ecosystems (particularly within the catchments draining to the coast) have been fragmented and the downstream flow of water, sediment and nutrients has been disrupted (Nilsson et al. 2005). Land-use change - particularly deforestation - and hydrological modifications have had downstream impacts in addition to localised development on the coast. As stated by Jiongxhin (2004) erosion in the catchment has increased the river sediment load; for example, suspended loads in the Huanghe (Yellow) River have increased 2 to 10 times over the past 2000 years. In contrast, damming and channelisation have significantly reduced the supply of sediments to the coast on other rivers through retention of sediment in dams. Indeed, this latter effect will likely dominate the 21st century. On the other hand, land use change along the shoreline (long shore scale) also changed the amount of sediment supply available to coastal processes. Excavation of the coastal zone, sand mining and urbanisation contribute to the change in the sediment budget because of human use of the coast. Human activities controlling the flow rate of

rivers also have a significant impact on the supply of sediment to coastal areas.

Two parameters (the reduction of sediment supply and river flow regulation) are inserted into the FCVAM model in order to reflect these mechanical processes and the spatial scales of these mechanisms. The reduction of sediment supply is defined as the ratio of

irreversible consequences for coastal areas, tools that could integrate expert opinion in an objective way with the available data and generic models and tools would mark an important step for the management of coastal areas where the monitoring and assessment of the implementation of developed plans could be scientifically verified. One such vulnerability assessment model was developed by Ozyurt (2007) and then upgraded in 2010 as a tool using fuzzy expert systems to integrate physical characteristics with human activities on both cross-shore and long-shore spatial extents for coastal areas (Ozyurt, 2010).


**6. Features of microarrays and NGS and their relevance in drug discovery**

In general, there are a variety of features for each of microarrays and NGS that render these platforms highly valuable in the arena of drug discovery and therapeutics, and these are summarized in **Table 4**.

Microarrays offer various advantages including expression analysis of cells or tissues at different states of disease, pharmacogenomics, toxicogenomics, and as well as for analysis and identification of SNPs. The microarray technology is useful for obtaining a good amount of information from small volume samples, and it is quite valuable for use in incorporating low-cost high-throughput assays in the drug discovery process. However, this technology has a number of disadvantages. These include high costs and long timelines, particularly related to re-design of microarray chips to include newly discovered genetic targets.

In comparison to microarrays, NGS offers more flexibility and higher costefficacy. In particular, this technology allows for identification of targets, screening of large numbers of compounds for use in therapeutics or treatments, as well as for identifying of unique biomarkers useful for discovery of new drug targets.

**73**

*Microarrays and NGS for Drug Discovery DOI: http://dx.doi.org/10.5772/intechopen.96657*

Overall, although the word "limitation" still floats around, and with only 5% of novel molecular compounds are ultimately selected to enter the drug and therapeutic marketplace, new innovations in science and technology are critical in the arena of drug discovery and therapeutics. It is these ongoing research advances and technological innovations that will empower scientists to continue on in the pursuit of additional and more sophisticated, reliable, and efficient molecular tools, such as NGS and microarrays, that will be useful in the arena of drug discovery and therapeutics. These efforts, innovations, and technologies will undoubtedly continue to revolutionize the drug discovery industry that will aid in identifying better and more effective drugs, at much lower costs, and within shorter periods

The funding for this book chapter is ensured by the PCCDI National Project: Genomic mapping of the population in the areas radioactively contaminated with

heavy metals in order to increase national security- ARTEMIS.

The authors declare no conflict of interest.

**7. Conclusions**

of time.

**Funding**

**Conflict of interest**


**Author details**

Valentina Zharkova Department of Mathematics, Physics and Electrical Engineering, Faculty of Engineering and Environment, Northumbria University, Newcastle Upon Tyne, United Kingdom

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\*Address all correspondence to<EMAIL_ADDRESS>
© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*Millennial Oscillations of Solar Irradiance and Magnetic Field in 600–2600 DOI: http://dx.doi.org/10.5772/intechopen.96450*


*Article* **Chronic Treatment of Ascorbic Acid Leads to Age-Dependent Neuroprotection against Oxidative Injury in Hippocampal Slice Cultures**

**Kyung Hee Lee 1, Un Jeng Kim 2, Myeounghoon Cha 2 and Bae Hwan Lee 2,3,\***

**Abstract:** Increased oxidative damage in the brain, which increases with age, is the cause of abnormal brain function and various diseases. Ascorbic acid (AA) is known as an endogenous antioxidant that provides neuronal protection against oxidative damage. However, with aging, its extracellular concentrations and uptake decrease in the brain. Few studies have dealt with age-related functional changes in the brain to sustained ascorbate supplementation. This study aimed to investigate the susceptibility of hippocampal neurons to oxidative injury following acute and chronic AA administration. Oxidative stress was induced by kainic acid (KA, 5 μM) for 18 h in hippocampal slice cultures. After KA exposure, less neuronal cell death was observed in the 3 w cultured slice compared to the 9 w cultured slice. In the chronic AA treatment (6 w), the 9 w-daily group showed reduced neuronal cell death and increased superoxide dismutase (SOD) and Nrf2 expressions compared to the 9 w. In addition, the 9 w group showed delayed latencies and reduced signal activity compared to the 3 w, while the 9 w-daily group showed shorter latencies and increased signal activity than the 9 w. These results suggest that the maintenance of the antioxidant system by chronic AA treatment during aging could preserve redox capacity to protect hippocampal neurons from age-related oxidative stress.

**Keywords:** ascorbic acid; antioxidant; aging; organotypic hippocampal slice culture; neuroprotection


*3.5. Visual Characterization*

For the gravity separation, the macroscope visual observation of the products allowed us to conclude that the heavy fraction (Figure 3b) was rich in metallic elements (many filaments from electronic connectors) and had a low quantity of nonmetallic elements. However, metallic elements were also visible in the light fraction (Figure 3a), but in reduced amounts. In both cases, the separations were satisfactory.

**Figure 3.** Light fraction (**a**) and the heavy fraction (**b**) resulting from gravity separation for the class size of 0.3–0.6 mm.

Nonetheless, for the electrostatic separation, it can be seen in Figure 4b that the product obtained was composed mostly of metals in copper color and some in gray/silver color. On the other hand, the nonconductive fraction, as can be observed in Figure 4a, was mainly made up of a mix of resin and fiberglass, having a green dark color.

**Figure 4.** Nonconductive (**a**) and conductive (**b**) products resulting from electrostatic separation for the class size <0.3 mm.


**8. UWB Imaging**

## **8.1. Imaging in distributed multi-static sensor networks**

Although imaging in distributed multi-static sensor networks results in a rough image of the environment, its quality is usually better than the quality of the image obtained by a single bat-type sensor autonomously operating in an unknown environment. Sensor networks offer more diverse information for imaging algorithms. The resulting quality of the image greatly depends on the number and the positions of the illuminating and observing nodes. If there are enough illuminating and observing nodes available simultaneously, then the instantaneous information can be used even for imaging of time variant scenarios with moving objects. For imaging of static environments, one moving observer is enough. The moving observer collects data, and the image is built sequentially and improved gradually by an imaging algorithm. The time domain imaging algorithms are referred to as Kirchhoff migration. They rely on Born's approximation, which presumes undisturbed ray-optical propagation [9]. Their basic principle is depicted in Fig. 50 (a).

The illuminator transmits a signal at the fixed point [*xT*, *yT*]. At the variable position [*xRi*, *yRi*] the receiver collects the impulse responses. Assuming single bounce reflection and the propagation velocity *v*, the echo reflected from an object situated at the position [*xo*, *yo*] can be found at the time delay *τi* = (*rT* + *rRi*)/*v* in the measured impulse response *Ri*(*τ*). One observation determines an ellipse around the transmitter-receiver pair. In order to focus the image, observations from different positions must be fused. Conventional migrations propose to fuse the observations by a simple summation

$$\rho(\mathbf{x}\_0, y\_\vartheta) = \frac{1}{N} \sum\_{i=1}^N R\_i(\mathbf{r}\_i) \tag{17}$$

**Figure 50.** Principle of imaging algorithms.

46 Will-be-set-by-IN-TECH

Research investigations and algorithms had to be transferred onto a new mobile platform to demonstrate the essential results obtained in CoLOR. Many challenges in the area of robotics, localization and robot motion resulted. Additionally, an optimal alignment of the sensors and robust UWB radar sensing conditions had to be taken into account for the motion of the robot. The motion algorithm for the robot when moving around the objects are designed to be contour adaptive, exhibit collision avoidance features, and use the shortest tracks through

0 0.5 1 1.5

Although imaging in distributed multi-static sensor networks results in a rough image of the environment, its quality is usually better than the quality of the image obtained by a single bat-type sensor autonomously operating in an unknown environment. Sensor networks offer more diverse information for imaging algorithms. The resulting quality of the image greatly depends on the number and the positions of the illuminating and observing nodes. If there are enough illuminating and observing nodes available simultaneously, then the instantaneous information can be used even for imaging of time variant scenarios with moving objects. For imaging of static environments, one moving observer is enough. The moving observer collects data, and the image is built sequentially and improved gradually by an imaging algorithm. The time domain imaging algorithms are referred to as Kirchhoff migration. They rely on Born's approximation, which presumes undisturbed ray-optical propagation [9]. Their basic

The illuminator transmits a signal at the fixed point [*xT*, *yT*]. At the variable position [*xRi*, *yRi*] the receiver collects the impulse responses. Assuming single bounce reflection and the propagation velocity *v*, the echo reflected from an object situated at the position [*xo*, *yo*] can be found at the time delay *τi* = (*rT* + *rRi*)/*v* in the measured impulse response *Ri*(*τ*). One observation determines an ellipse around the transmitter-receiver pair. In order to focus the image, observations from different positions must be fused. Conventional migrations propose

*N*

*N* ∑ *i*=1

*o*(*xo*, *yo*) = 1

Distance [m]

*Ri*(*τi*) (17)

Intensity

−2

0

2

0.2 0.4 0.6 0.8 1

Distance [m]

0.4 0.6 0.8 1 1.2

Distance [m]

**Figure 49.** Example of the inspected object o1. The obtained radargram (left), the wavefront-based imaging of the object with robot track (middle) and the same object with a migrated image (right).

**7.8. Transfer onto the mobile robot**

Number of Measurement

20 40 60 80 0.5

**8. UWB Imaging**

principle is depicted in Fig. 50 (a).

to fuse the observations by a simple summation

Distance [m]

1 1.5 2 2.5 3

a room while maintaining optimal antenna alignments.

h

0

**8.1. Imaging in distributed multi-static sensor networks**

0.5

Distance [m]

1

1.5

−1 −0.5 0 0.5 1

norm [V]

where *N* is the number of observations available for the data fusion. A disadvantage of this fusion is indicated in Fig. 50 (a). The signal energy does not only cumulate at the desired pixel. Other local maxima arise at every position where ellipses are crossing. Moreover, the elliptical traces are added to the focused image as well. Both effects decrease the image quality. In [73] we have proposed a data fusion method which reduces these artifacts and enhances the image quality. The algorithm is based on the cross-correlation of multiple observations. The spatial scheme for the cross-correlation algorithm is indicated in Fig. 50 (b). The observation from the reference receiver Rx*ref* is cross-correlated with each observation of the moving receiver. The cross-correlation results in non-zero values only at positions where the ellipse related to the reference receiver crosses the ellipse related to the moving receiver. The non-zero values which result from the cross-correlation are summed by the data fusion algorithm according to

$$\rho(\mathbf{x}\_{0\prime}, y\_0) = \frac{1}{N} \sum\_{i=1}^{N} \int\_{-T/2}^{T/2} \mathbf{R}\_i(\tau\_i + \zeta) \mathbf{R}\_{ref}(\tau\_{ref} + \zeta) d\zeta \tag{18}$$

where *τref* = (*rT* + *rRre f*)/*v* is the time delay which is related to the echo measured by the reference receiver, and *T* is the pulse duration of the autocorrelation function of the stimulation signal. In order to improve image quality, the number of reference receivers can be increased. The performance of this imaging algorithm strongly depends on the spatial arrangement of the cross-correlated receivers. If the arrangement of the receivers is disadvantageously chosen, the performance of this algorithm tends toward the conventional migration described by (17). If the spatial arrangement is selected in an optimum way the performance of the cross-correlated imaging outperforms the conventional algorithm. In order to illustrate the differences between the conventional and the cross-correlated algorithm, data simulated by the ray tracing algorithm [60] were used for the data fusion. The simulated scenario consisted of a sensor node which moved through a rectangular room of size 8 m by 9 m. The inspected room contained seven objects, namely, one large distributed object (some meters) and six smaller objects (decimeters). The sensor node had one transmitting and two receiving antennas and operated in the frequency band from 4.5 GHz to 13.4 GHz. The antennas were directional, therefore, the moving sensor scanned the room by a full turn at selected positions. Almost 10 thousand impulse responses were simulated for this scenario. Figure 51 (a) shows the result obtained by the conventional migration algorithm. The focused image contains intersecting ellipses. However, as indicated before, the intersections are not exclusively at the target positions. The ellipses also intersect at positions where no target

48 Will-be-set-by-IN-TECH 226 Ultra-Wideband Radio Technologies for Communications, Localization and Sensor Applications Cooperative Localization and Object Recognition in Autonomous UWB Sensor Networks 49

is situated and even traces of these ellipses make the interpretation of the image difficult. Figure 51 (b) illustrates the image of the same scenario obtained by the cross-correlated algorithm. The color scaling of both images in Fig. 51 (a) and (b) is the same. The reduction of image artifacts by means of the cross-correlated algorithm is evident. Elliptical traces were significantly reduced. This helps to identify the inspected environment in this figure more clearly. A more detailed discussion on the cross-correlated imaging with measurement examples is given in [74] and [75].

**L***l*

**L***L*

*Rm*

*RM*

**L**1

wall

*R*1

segment with the foci *Rm* and *Tn*.

**Figure 53.** Flow chart of the algorithm .

*di f f*

in which the parameter *t*

operation is described as

position *Tn*, and *t*

operation,

*R*2

*T*1 *n TN T*

**Figure 52.** Measurement configuration. *T*1...*Tn*...*TN* are different transmission positions on the track **L***l*. "*R*1...*RM*" are sparsely spaced receivers with different reception angles. The dashed curve is an ellipse

> Signal Enhancement (time-shift & accumulation)

Gaussian clutter and noise

Stationary Target detection Detector design based on reflection characteristic deviations

designed based on Gaussian clutter and noise. Mathematically, the "*time-shift* & *accumulation*"

*sRm*,*Tn*(*t* + *t*

where *sRm*,*Tn*(*t*) is the signal received by the receiver *Rm* with respect to the transmission

are the position vectors of *Tn* and reference transmission position *T*1 , respectively. In the

• the responses of the objects (*Oi*) in the direction of interest (for example the direction of **L***l* in Fig. 52, are time-shifted, aligned and then accumulated. It is a coherent operation

• The responses of objects (*Cj*) out of the direction of interest, are non-uniformly time-shifted, disturbed and then accumulated. It is an incoherent operation. As a consequence, for a certain time-slot, the responses from objects located on different ellipses (or ellipsoids) are non-uniformly time-shifted and accumulated. Hence, these responses are attenuated

*di f f*

*Tn*,*T*1 = |**T***n* − **T**1| /*c* is the time-delay to be compensated for. **T***n* and **T**1

*Tn*,*T*1 is used for time-delay correction. Hence, responses are

*N* ∑ *n*=1

*di f f*

enhanced by *N* times compared to the case of single channel data.

compared to the output of the coherent operation above, [70].

Unknown environment

Down Converter

*Cj*

Cooperative Localization and Object Recognition in Autonomous UWB Sensor Networks 227

Target

*Oi*

*Tn*,*T*1) (19)

Clutter

**Figure 51.** Imaging in sensor networks.


*2.11. Degradation by Nucleases*

In order to assess the ability of the SEDDS to protect the encapsulated OND complexes against degradation by nucleases, formulations were incubated with S1 nuclease (100 U/µL), an enzyme that specifically degrades single-stranded nucleic acids. Stability studies were carried out in the following formulations: aqueous solution of naked OND, aqueous dispersion of complexed DDAB-OND in the SEDDS and DOTAP-OND in the SEDDS and dispersion of the nonloaded SEDDS in an aqueous solution of naked OND. The SEDDS were dispersed immediately before the experiment.

The S1 nuclease assay (Thermo Fisher Scientific, Waltham, MA, USA) was performed according to the manufacturer's instructions. Briefly, equivalents containing 1.2 µg of OND were incubated with 12 U of S1 nuclease in the presence of the reaction buffer pH 4.5 at 25 ◦C. As controls, the same formulations were incubated in an acetate buffer of pH 4.5. The reaction was terminated after 30 min by the addition of 0.5-M EDTA and incubation for 10 min at 70 ◦C. Nondegraded intact OND was precipitated via ethanol precipitation. From each formulation, 60 µL was taken, and 1.3 µL of 5%NH4OH, 30 µL of 7.5-M ammonium acetate and 400 µL of 96% ethanol were added into the samples, which were kept at −80 ◦C for at least one hour. Following this, the samples were centrifuged at 15,000× *g* for 30 min at 4 ◦C. Intact OND was pelleted and dissolved in 500 µL of 100-mM triethylammonium acetate (TEAA). The samples were subjected to HPLC analysis. Additionally, 100 µL of supernatant was diluted with 500 µL of 100-mM TEAA.

Thirty microliters of the samples were analyzed using the Waters Symmetry C18 (3.5 µm; 4.6 × 75 mm) HPLC column (Waters Corporation, Milford, MA, USA). The mobile phase consisted of 2 eluents, eluent A of 100-mM TEAA (pH 7.0) and eluent B of acetonitrile, with a linear gradient starting from 1 min at 10% to 30% B in 4 min. Thirty percent B eluent was maintained for 1 min, then decreased to 10% B in 1 min and maintained at this level for 4 min before analysis of the following sample. The flow rate was 1 mL/min. FAM-labeled OND was detected by fluorescent detection excitation/emission 495 nm/520 nm according to the procedure described previously [42]. Each formulation was tested in triplicate. The percentage of intact OND was calculated as a ratio of area under the curve (AUC) of protected OND found in the pellets after incubation with S1 nuclease and pelleted OND recovered from the respective formulations, as described by Equation (2).

intact OND = [AUC of pelleted OND (formualtation + S1 nuclease)/AUC of pelleted OND (formulation)] × 100% (2)


**Abstract:** As a promising candidate for expanding the capacity of drug loading in silica nanoplatforms, hollow mesoporous silica nanoparticles (HMSNs) are gaining increasing attention. In this study, porous nanosilica (PNS) and HMSNs were prepared by the sol-gel method and template assisted method, then further used for Rhodamine (RhB) loading. To characterize the as-synthesized nanocarriers, a number of techniques, including X-ray diffraction (XRD), transmission electron microscopy (TEM), nitrogen absorption-desorption isotherms, dynamic light scattering (DLS), thermogravimetric analysis (TGA), and Fourier transform infrared spectroscopy (FTIR) were employed. The size of HMSN nanoparticles in aqueous solution averaged 134.0 ± 0.3 nm, which could be adjusted by minor changes during synthesis, whereas that of PNS nanoparticles was 63.4 ± 0.6 nm. In addition, the encapsulation of RhB into HMSN nanoparticles to form RhB-loaded nanocarriers (RhB/HMSN) was successful, achieving high loading efficiency (51.67% ± 0.11%). This was significantly higher than that of RhB-loaded PNS (RhB/PNS) (12.24% ± 0.24%). Similarly, RhB/HMSN also possessed a higher RhB loading content (10.44% ± 0.02%) compared to RhB/PNS (2.90% ± 0.05%). From those results, it is suggested that prepared HMSN nanocarriers may act as high-capacity carriers in drug delivery applications.

**Keywords:** hollow mesoporous silica; porous silica; high drug loading capacity; nanoparticles; drug delivery system

#### **1. Introduction**

Various advantages of porous nanosilica (PNS), such as a large surface area, chemical and thermal stability, high biocompatibility and biodegradability, and readiness for surface functionalization, have made it an excellent nanocarrier system for drug delivery applications [1–4]. Due to the unique porous structure of the PNS nanocarriers, anticancer drugs could be effectively encapsulated, and protected from temperature variation and pH-induced enzymatic degradation of the surrounding

media [5–7]. However, concerns about toxicity of the carriers have been expressed [8,9]. As a result, the reduction in toxicity of the nanocarrier by improving the drug loading capacity of silica nanoplatforms is of great significance.

A sub class of PNS, hollow mesoporous silica nanoparticles (HMSNs), is a structure of a mesoporous silica shell possessing a large cavity at the core of the particle [10–13], offering a potential solution to the problem of low capacity. It can be said that the HMSN was developed bearing in mind the importance of PNS applications in drug and bio-sensing agents' delivery. The large compartment inside each HMSN vastly increases their drug loading capacity and pore volume compared to conventional PNS [14,15]. Accordingly, it can be theoretically concluded that a lower amount of HMSNs is needed to deliver the same load of drug, and thus to achieve the desired therapeutic effect by comparison with that of PNS, hence minimizing the potential accumulation of foreign materials in the host. Moreover, HMSNs retain the property of a readily conjugatable surface, making them an increasingly recognized platform for fabricating stealth mechanisms and stimuli-responsive functionalization [16–21].

In a recent report, a HMSNs-b- cyclodextrin (CD) nanocarrier was formed by grafting 3-(3,4-dihydroxypheneyl) propionic acid (DHPA)-functionalized beta-cyclodextrin (b-CD) onto the surface of HMSNs, and was further anchored with polyethylene glycol (PEG)-conjugated adamantane (Ada) via host–guest interaction for the delivery of doxorubicin (DOX). It was revealed from thermogravimetric analysis (TGA) results that the DOX encapsulation efficiency of the HMSNs-b-CD/Ada-PEG carrier reached around 10 wt% of DOX. This figure was greater than that of PNS of a similar size, at approximately 5 wt%, which could be explained by the hollow cavity of HMSNs [22]. In another study, positively charged CDPEI nanoparticles were grafted on the pore openings of HMSNs through disulfide bonds. The carrier was later then loaded with DOX and grafted with hyaluronic acid (HA) to yield a more complex structure—DOX/HMSN-SS-CDPEI@HA, resulting a drug loading efficiency of 30.5%. Compared to the DOX-loaded PNS, the HMSN-SS-CDPEI@HA nanoparticles had higher loading capacity and excellent biocompatibility. These advantages are generally due to mesoporous structure, high specific surface area, and particularly, the hollow cavity of the HMNS nanoparticle, suggesting its use as a high capacity nanocarrier for drug delivery [23].

Among the various methodologies to fabricate HMSN, the template assisted method is one of the most controllable methods, regardless of a wide range of template-free routes that have emerged recently [10,11,24]. With regard to template assisted fabrication, the hard template in the form of a solid SiO2 core (sSiO2) is first synthesized via a modified Stöber method. Afterwards, the sol-gel process of tetrathylorthorsilicates (TEOS) catalyzed by acid or base in the presence of hexadecyltrimethylammnomium bromide (CTAB) was performed to form a controllable PNS layer, which was then used to coat the sSiO2 spheres, prepared as mentioned. The silica cores were then separated through selective etching in a suitable solvent, Na2CO3, for etching at a high temperature [25].

In this study, a process for synthesizing PNS and HMSN nanocarriers was reported. The as-synthesized systems were compared for Rhodamine (RhB) loading capacity and efficiency. Both carriers were characterized by a number of techniques, including X-ray diffraction (XRD), transmission electron microscopy (TEM), nitrogen absorption-desorption isotherms, dynamic light scattering (DLS), thermogravimetric analysis (TGA), and Fourier transform infrared spectroscopy (FTIR), respectively to evaluate the crystallinity, morphology, BET surface area, size and size distribution, and composition of the materials.

#### **2. Materials and Methods**

#### *2.1. Materials*

Reagent grade chemicals, including Tetraethyl orthosilicate (TEOS, 98%), Cetyltrimethylammonium bromide (CTAB, 99%), and Rhodamine B were commercially obtained from Sigma-Aldrich (St. Louis, MO, USA), Merck (USA), and HiMedia (India), respectively. Ammonia solution (NH3 (aq), 28%) and ethanol were purchased from Scharlau (Barcelona, Spain). All chemicals were used as received.

#### *2.2. Preparation of PNS*

The sol gel technique was used to prepare PNS in which a TEOS, CTAB, ethanol, and water, act as the silicone source, structure-directing agent, solvent, and reactant, respectively. The TEOS was hydrolyzed and condensed with ammonia (NH3) (Figure 1a). Initially, 2.8% NH3 solution (0.07 M), deionized water (deH2O), ethanol (2.2 M), and CTAB (2.6 g) were mixed together at 60 ◦C with a stir-bar for 30 min. Afterwards, the solution was introduced drop-wise with TEOS (0.4 M) for 2 h under stirring at 60 ◦C, followed by dialysis using MWCO 12–14 kDa membrane (Spectrum Laboratories, Inc., Rancho Dominguez, CA 90220, USA) against an acetic acid (2 M)/ethanol (1:1, *v*/*v*) solution and then with deH2O at room temperature. A solid product was finally obtained by calcination at 500 ◦C for 24 h.

**Figure 1.** Schematic representation of the formation of porous nanosilica (PNS) (**a**) and hollow mesoporous silica nanoparticles (HMSNs) (**b**).

#### *2.3. Preparation of HMSNs*

HMSNs were prepared via a previously reported procedure with minor modification, including three main stages, as shown in Figure 1b [12,26]. The synthesis commenced with the preparation of hard templates of sSiO2 spheres via the Stöber method with modifications [27]. In detail, ethanol (13.5 M), deH2O (6.0 M), and ammonium hydroxide (0.38 M) were mixed together under constant stirring at 50 ◦C for 30 min. The resultant solution was then introduced with TEOS solution (0.29 M) at room temperature and subject to another 6h of stirring. Afterwards, dialysis with an MWCO 12–14 kDa membrane and freeze-drying took place to obtain the final solution. In the second step, a structure was formed by coating a thickness-controlled PNS layer on each sSiO2 using CTAB as the organic co-templates [28]. The core@shell structure was denoted as sSiO2/SiO2/CTAB. Briefly, CTAB (0.05 M) was dissolved in deH2O under magnetic stirring at room temperature for 15 min. sSiO2 solution (5 mL) was added into the CTAB solution using a magnetic stir bar for 30 min at 50 ◦C, followed by the addition of a mixture of ethanol/ammonia solution (1.43:0.05, M/M). TEOS solution (0.27) was added into the above mixture under magnetic stirring at 50 ◦C for 6 h and the obtained solution was dialyzed against deH2O (MWCO 12–14 kDa), which was changed 5 or 6 times per day, for 4 days. In the third step, the prepared sSiO2/SiO2/CTAB and free CTAB were mixed with an aqueous Na2CO3 solution (0.2 M) for 9 h under stirring at 50 ◦C. HMSN spheres, obtained by freeze-drying the above mixture (EyeLA FDU-1200, −38.3 ◦C, 29.5 Pa), were soaked in acetic acid (2 M)/ethanol (1:1, *v*/*v*) solution over 24 h and had the CTAB template removed by washing with deH2O several times.

#### *2.4. Characterization*

A Bruker D2 Phaser diffractometer (Germany) with Cu/Kα radiation at a scanning rate of 4◦/min (λ = 0.154056 nm, 40 kV, 40 mA) was employed to perform XRD measurements. PNS, sSiO2, sSiO2/ SiO2/CTAB, and HMSNs were morphologically characterized and measured for size via TEM using JEM-1400 (JEOL, Tokyo, Japan) operating at an accelerating voltage of 300 kV. To prepare the sample

for TEM analysis, one drop of each solution was added in deH2O (1 mg/mL) and placed onto a carbon-copper grid (300-mesh, Ted Pella, Inc., Redding, CA, USA), which was then subject to air-drying for 10 min.

A TRISTAR 3000 instrument (Micromeritics, Norcross, GA, USA) operating at 77 K under continuous adsorption conditions was employed to measure N2 adsorption–desorption isotherms. Before being analyzed, samples were subject to degassing at 103 Torr at 110 ◦C for 16 h. The size distribution of pores was derived via the Barrett–Joyner–Halenda method from the desorption branch of the nitrogen isotherm. The pore volume was taken at P/Po-0.97 single point.

To perform TGA, a TGAnalyzer (Perkin Elmer Pyris 1, Perkin Elmer, Hopkinton, MA, USA) operating at a heating rate of 20 ◦C/min in a nitrogen flow from 100 ◦C to 800 ◦C was employed. The measurement of the surface charge of PNS and HMSN was performed at 37 ◦C using a Zetasizer Nano ZS (ZEN 3600, Malvern Instruments, Malvern, UK), equipped with a 532 nm wavelength. UV-Vis spectroscopy (Shimadzu, Kyoto, Japan) with a wavelength of 544 nm was used to determine the loading content of HMNS with RhB.

#### *2.5. Preparation of RhB*/*PNS and RhB*/*HMSN*

The equilibrium dialysis method, which was described previously, was used to load RhB into PNS and HMSN [1]. Briefly, solutions with either PNS or HMSN were created by adding 16 mg of the carrier into RhB solution (10 μg/mL). These solutions were stirred for 12 h to allow the encapsulation of RhB into the carriers. Unloaded RhB in the solutions was then removed by dialysis. In the dialysis solution, the content of unloaded RhB (WU.RhB) was measured via the UV-Vis absorbance method, in which the standard curve was established by dissolving bare RhB in deH2O to create multiple solutions with pre-specified concentrations (0–5 μg/mL). The recording of absorbance of the standard and dialysis solutions were performed with a V-750 UV/Vis spectrophotometer (Jasco Co., Tokyo, Japan). Based on the standard curve, which explains the relationship between RhB content and absorbance, and the absorbance results, the contents of RhB in the samples were determined. RhB loading efficiency (LE) and loading capacity (LC) were calculated as follows:

$$\begin{array}{c} \text{LE} (\%) = \frac{(100 - \text{W}\_{\text{U.RhB}})}{100} \times 100\\ \text{LC} (\%) = \frac{(100 - \text{W}\_{\text{U.RhB}})}{\text{W}\_{\text{PNS or HMSN}} + 100 - \text{W}\_{\text{U.RhB}}} \times 100 \end{array}$$

In which the 100 (mg) was the initial amount of RhB for loading experiments. WU.RhB and WPNS or HMSN are the content of unloaded RhB in the dialysis solution and the dried weight of the nanocarrier respectively.

#### *2.6. Statistical Analysis*

Microsoft® Excel was used to process the data. The results were expressed as mean ± standard deviation.

#### **3. Result and Discussion**

Figure 1 shows the synthesis of PNS and HMSN following a previously described procedure [26]. While Figure 1a represented the procedure of synthesizing PNS, Figure 1b illustrated three main steps for preparation of HMSN, including the preparations of the sSiO2 core, mesoporous shell (sSiO2/SiO2/CTAB), and hollow core (HMSN). The successful preparation of PNS and HMSN was indicated by FTIR spectra shown in Figure 2. In Figure 2a, a band at 1640 cm−1 was attributed to the OH stretching vibration of water molecules present in PNS. Stretching vibrations of Si–O–Si, Si–OH, and Si–O could also be assigned to strong peaks at around 1089, 973, and 836 cm−1, which are typical peaks of silica nanoparticles. The broad band observed at around 3444 cm−1 was associated with OH stretching frequency for the silanol group. These aforementioned peaks were also present in the spectrum of HMSN.

**Figure 2.** The FTIR spectra of PNS (**a**) and HMSN (**b**).

The TGA curves of PNS (a) and HMSN (b) are shown in Figure 3, in which the temperature was elevated to 800 ◦C at a constant rate of 10 ◦C/min. As high temperature leads to decomposition and vaporization of an organic fraction, inorganic remainders are often found at the end of the analysis. Generally, a pattern of mass depreciation could be observed for both PNS and HMSN. First, in the temperature range of lower than 200 ◦C, the removal of physically adsorbed water produced by hydrolysis of the precursors and the solvent employed during the synthesis took place. In the second range of 200–600 ◦C, residual organic product (CTAB) from the decomposition of the precursors was lost and the dehydroxylation of silanol (SiOH) groups from the silica surface occurred. In this phase, the weight loss of PNS was approximately 30%, whereas that of HMSN was only 13%, indicating that a small amount of CTAB may have remained in the HMSN nanoparticles.

The prepared nanoparticles were further characterized by TEM for morphological examination and size measurement. Figure 4 revealed the average diameter of PNS to be 63.4 ± 0.6 nm, and sSiO2 to be 104.0 ± 0.7 nm (Figure 4a,b). As reported by Chen el al., the size of sSiO2 was essential in influencing the size of hollow core [11]. Different hollow structures can be obtained in the sizing process of sSiO2 by altering the reagent ratio in the initial stage. In the next step, by coating a mesoporous silica layer, the sSiO2/SiO2/CTAB and HMSN nanoparticles possessed spherical shapes, with significantly increased particle sizes, which were 167 ± 0.5 nm and 134 ± 0.3 nm for sSiO2/SiO2/CTAB and HMSN, respectively (Figure 4c). This could be explained by the binding of the outer PNS layer (Figure 1b), in which case the thickness and pore size of the mesoporous shell with the biodegradable property could be modified by using different organic solvents, adjusting concentration of TEOS, and reaction time [11]. In the last step aiming at selectively etching the solid silica core and protecting the integrity of silica shell, the absorption of positively charged CTAB (CTAB+) onto the surface of sSiO2/SiO2/CTAB took place via electronic attraction to keep the shell intact. After commencing, this mechanism was stimulated by the Na2CO3 existing in the solution, CTAB in the PNS shell, and free CTAB in the media. Following the etching process, the acid acetic acid (2 M)/ethanol (1:1, *v*/*v*) solution was used to remove CTAB from the mesopores.

**Figure 3.** Thermogravimetry analysis for PNS (**a**) and HMSN (**b**).

In the blood circulation, it was suggested that the extravasation and renal clearance of nanoparticles of less than 10 nm in diameter are more expedited than those of larger nanoparticles (<200 nm), which are likely to be retained for a longer period [29,30]. Accordingly, the achieved size of synthesized HMSN products have the potential to allow the particles to bypass early clearance through kidney and vascular leakage.

Figure 5 shows the XRD patterns of PNS (a) and HMSN (b). As shown, the broad XRD reflection peaks of PNS (2θ = 21.1◦) and HMSN (2θ = 23.0◦) both indicate the amorphous nature of silica nanoparticles [31]. In addition, apart from a small amorphous bump at 37◦ in the PNS sample that could be attributed to trace CTAB left after calcination, no considerable difference was found between the two XRD patterns of PNS and HMSN, indicating that these nanoparticles likely have similar structures.

In addition, the porosities of the prepared PNS and HMSNs were evaluated by the N2 adsorption-desorption technique. The results are presented in Figure 6b. It was noticed that the isotherms of both PNS and HMSN belong to the Langmuir type IV, characterized by a hysteresis loop, suggesting that both materials possess a mesoporous structure [32]. The hysteresis loops indicated the capillary condensation associated with mesopores, which occurred much more rapidly at higher pressure in PNS than in HMSN. Another difference between the materials is the type of the hysteresis loop, with PNS being H1, with the adsorption and desorption branches almost vertical and parallel at relative pressures of 0.9 to 1.0. This type of loop is associated with uniform particles and narrow distribution of pore size [33], while the H4 loop of HMSN—in which the adsorption and desorption branches remain nearly horizontal and parallel over a wide range of pressure—indicates irregular but parallel, open-ended, and slit-like pores, with connectivity between intragranular pores, and it indicates the existence of some micropores [33]. The N2 adsorption percentage of HMSN over 60 min was almost 96%, which was higher than that of PNS (84%). This phenomenon could be explained by the hollow cavity of HMSN. The specific area and pore volume of HMSNs were 983.7 m2/g and 2.33 cm3/g, respectively. These figures were starkly contrasted by values found in PNS, which were 155.2 m2/g and 8.75 cm3/g, respectively, indicating that the prepared HMSN could be a potential carrier for bioactive molecules, with a high capacity. Having said that, further investigations on strategies to modify the surface of HMSN are still required to improve the controllability of this system, due to its similar nature to silica nanoparticles, whose availability for functionalization is abundant [4,34].

**Figure 4.** TEM images and the particle size distribution of PNS (**a**,**a'**), sSiO2 (**b**,**b'**), sSiO2/SiO2/CTAB (**c**,**c'**), and HMSN (**d**,**d'**), respectively.

**Figure 6.** Nitrogen adsorption–desorption isotherms of HMSN (**a**) and PNS (**b**).

The surface charge of the prepared PNS, sSiO2, sSiO2/PNS(CTAB), and HMSN nanoparticles were characterized by zeta potential measurements. Results for the latter three are presented in Figure 7a. Not only does the zeta potential data expresses the stability of sSiO2 nanoparticles' dispersion in water, the technique has also been employed to elucidate the adsorption mechanisms of drugs and biological ligands on the surface of sSiO2 nanoparticles. It is stated that the absolute value of zeta potential is positively associated with the stability of colloidal systems. An absolute value of higher than 30 mV indicates a stable suspension [35]. The surface charges of the sSiO2 and PNS were found to be −43.3 ± 0.87 mV and −44.53 ± 0.23 mV, respectively, in this study. The highly negative charges of sSiO2 and PNS nanoparticles were attributed to the ionization of –OH on the surfaces of silica in the ethanol solutions. However, the zeta potential of sSiO2/PNS(CTAB) increased dramatically to 32.5 ± 0.66 mV, for the adsorption of CTA cations (CTA+) in CTAB on the sSiO2 surface, via electronic attraction. By contrast, there was then a steep decrease in the zeta potential of HMSN, reaching

−25.45 ± 0.07 mV, different to sSiO2/PNS(CTAB) due to the absence of CTA+ after the etching process. Moreover, an investigation into the stability of the particles in aqueous solution is also presented in Figure 7b, which demonstrated a stable surface charge after 48 h. These results suggest a potential application of PNS and HMSN nanoparticles for drug delivery systems with high stability.

**Figure 7.** (**a**) Zeta potential of sSiO2, sSiO2/SiO2/CTAB, and HMSN; (**b**) Zeta potential of PNS and HMSN in solutions over the course of 48 h.

Drug loading efficiency and capacity are considered to be essential in any drug delivery system design, since they play a direct role in the therapeutic activity of the system [36]. A high loading capacity is also desirable for our rationale of minimizing the dose-dependent toxicity of the delivery system. In our case, the LE, indirectly derived from the content of non-encapsulated RhB, of PNS and HMSN were 12.24% and 51.67%, respectively. The lower RhB LE in RhB/PNS nanocarriers was attributed to their non-hollow structure. In contrast, RhB/HMSN nanocarriers possessed a significantly higher LE due to the hollow nature of the HMSN's cavity, leading to a significantly improved drug loading capacity. Similarly, RhB/HMSN also showed a higher LC (10.44%) compared to RhB/PNS (2.9%). Therefore, it is suggested that the introduction of a hollow form may improve the LE and LC of HMSN nanoparticles, assisting the tumor-targeted delivery and therapeutic result of a drug that is loaded into HMSN nanocarriers.

#### **4. Conclusions**

The present study has reported a successful preparation attempt of PNS and HMSN nanoparticles via sol-gel method. The formed PNS and HMSN existed in spherical shapes with average particle sizes of 63.4 ± 0.6 nm and 134.0 ± 0.3 nm, and were proved to be potential carriers for anticancer drugs. The encapsulation of RhB into the HMSN nanoparticles achieved the LE of 51.67%. The high LE achieved could be a contributing factor to the reduction of dose-dependent toxicity in drug delivery with nanocarriers. However, further studies are required to further improve the HMSN system, especially regarding surface modification for stealth delivery, and to offer more sustainability in cargo release from this system.

**Author Contributions:** Conceptualization, investigation, and data analysis, N.-T.N.-T.; data curation, writing—original draft preparation, and visualization, L.P.P.T. and N.T.T.L.; writing—reviewing and editing, M.-T.C., T.-N.T., N.T.N., C.H.N.; supervision and project administration, D.-H.N., V.T.T., Q.T.L., and N.Q.T.

**Funding:** This research was funded by Tra Vinh University (grant number 177/HD.HDKH-DHTV).

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

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**Author details**

probably disappear during final straightening and polishing or even during irradiation. No special treatment is needed and this is not an issue. In the case of stainless steel claddings, there is no great mismatch between thermal coefficients, and no problems are presented during hot

Nonaluminum monolithic fuel plates need to be straightened after hot lamination. More power machinery than with aluminum dispersed fuel plates is needed. The oxide layer of the Zry‐4 cladding after hot colamination could only be removed by mechanical means. Since sand blasting must be done in an inert atmosphere, wet sand papers were used semimanually. In the case of stainless steel claddings the polishing can be assisted with chemical means. A more industrialized polishing can be performed using a scanning abrasive water blasting that can follow small surface deformations maintaining uniform cladding thickness. Also abrasive

Nonaluminum claddings in UMo monolithic fuels can be smaller than 150 μm [9]. MZ50 miniplate had a cladding thickness of 250 μm and a total plate thickness of 1 mm. This cladding reduction thickness can compensate lower thermal conductivity, compared with aluminum alloys, in heat extraction. The reduction of cladding and fundamentally plate thickness can help in new designs introducing a more satisfactory adjustment of neutron moderation ratios. The growth kinetics of oxide layer in Zry‐4 and stainless steel claddings during irradiation is much lower than in aluminum claddings. The lids and frame process of monolithic γ‐UMo with nonaluminum cladding using the lids and frame hot colamination fabrication process

The development of the HMD process was carried out with the production of more than 5 kg of γ‐UMo powder. Basic research is still needed to study thermodynamic properties of the just discovered hydride. Equilibrium stoichiometry, hydrogen allocation in traps and interstitial positions need more elaborated studies. Scalability of the HMD UMo powder production using enriched uranium is possible up to mass batches compatible with security standards, requiring

Many coverage techniques can be applied to UMo particles with different objectives and results. There is a great versatility of methods that have not all been tested under irradiation,

The traditional picture and frame technique for the fabrication of monolithic γ‐UMo plates can be used if aluminum cladding is replaced by Zircaloy‐4 or AISI 304L. This is a flexible and practical production scale technology that can be used for fuels with densities greater than 17

show, in PIE results, a gentle interaction zone without bubble nucleation. The monolithic UMo coupon can be fabricated from powder, with the possibility of blending burnable poisons, inert

powders, gas adsorption materials, and conforming special geometrical shapes.

. Monolithic γ‐U7Mo with Zry‐4 cladding miniplates irradiated up to 50% burn up

colamination with monolithic UMo.

108 Nuclear Material Performance

powders assisted by brushes can be used.

can be scaled up to full size plates.

low man‐power and equipment investment.

but it seems that more focused research is needed.

**5. Conclusions**

gU/cm3

Enrique E. Pasqualini\*

Address all correspondence to<EMAIL_ADDRESS>
Sabato Institute (UNSAM/CNEA). Nuclear Nanotechnology Laboratory, Constituyentes Atomic Center, Argentina