*2.4. Fluorescence-Activated Cell Sorting (FACS Sorting)*

To isolate CSCs from colorectal cancer cell lines, the cells were gently dissociated and incubated with anti-CD44-FITC antibody for 30 min on ice. CD44+ positive cells were sorted by a using a FACS Vantage SE flow cytometer (BD Biosciences) equipped with FlowJo software (Tree Star, Ashland, OR, USA).


**4. Conclusion**

**Figure 8.** The morphology of wear track in TiN film and wear scar in upper zirconia ball after friction tests under the

**Figure 7.** Morphology of wear track in TiN film and wear scar in upper silicon nitride ball after friction tests under the

starve lubrication regime.

starve lubrication regime.

108 Lubrication - Tribology, Lubricants and Additives

One thick TiN film was prepared by ion-plated technology on the grit-blasted steel substrate. The tribological performance was evaluated with four upper balls made up of different materials under the starved lubrication regime. Compared with their higher friction coefficients for grit-blasting steel against different balls, the friction coefficients for TiN films on the gritblasting steel were lower and steady. It was due to the hardness of TiN film and small contact area in the contact zone. During the sliding process, the strong load capacity of TiN films ensures the negligible wear rate, and the small contact area between of the friction pairs plays an important role in the steady friction coefficient.

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Effects of Different Materials on the Tribological Performance of PVD TiN Films under Starved…

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3614824551916

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s11666-015-0291-6


**4. Discussion**

For this study the compression molding of ROS-based CF-SMC into complex ribbed structures without defects, such as FMS, is accomplished although the chosen coarsely structured material with the scale and sti ffness of its 50 mm long and 8 mm wide UD strands is typically not suited to fill thin and high ribs like those existent in the part. This is achieved by increasing the material's flowability with a pre-heating step and an optimal charge pattern allowing an even part filling without untimely curing through long flow paths or inhomogeneous shear loads. The sizes and positions of each of the single smaller charge packages is devised by means of fill simulation studies using 3D TIMON CompositePRESS. Such filling simulations take less than half an hour on a standard tower PC so that several studies can be conducted in reasonable time frames. This simulation-aided charge pattern development process prevented long trial and error molding sessions to find a working charge pattern and thus shortened the experimental time. This molding success shows the possible capabilities of a good charge preparation technique and a proper process simulation tool.

The accuracy of the cavity filling prediction is qualitatively evaluated by a comparison with short shots. The consistency between the experimentally obtained and the predicted mold filling is conclusive so that the calculated flow field can be assumed to be a sound basis for the consecutive DFS of the part. The slightly faster numerical filling of the plate area could be traced back to the fact that only a one-way coupling is used in the flow simulation. The missing impact of the fibers on the fluid flow behavior may lead to an over prediction of the material flow velocity at longer flow path lengths. A promising approach to overcome these shortcomings could be the incorporation of a two-way coupling between fibers and fluid to achieve an anisotropic material flow. From the convincing fill simulation results in the hat profile section and the marginal over prediction of the plate area filling it can be further concluded that the developed HexMC material card with its determined Kamal parameters and isotropic viscosity properties is suited to be used. The applied simplifications for the flow prediction, such as the Hele-Shaw model for the compression molding process and the only one-layered tetra mesh, lead to accurate results and are thus valid. Apparently, the 20 calculation points per element over the part thickness are a reliable approach to use simple tetra meshes and yet deliver convincing results in comparable short calculation times.

In order to be able to validate the 3D TIMON CompositePRESS process simulation results, CT scans of three entire ribbed structures were recorded. The quality of the CT scans is remarkable for the part size and the carbon fiber reinforced material. Due to the low contrast between carbon fibers and polymer matrix systems, there is always a trade-o ff between the needed CT scan resolution and the maximal scannable size of carbon fiber composite samples. However, in this study an optimum interaction of the CT scan hardware and scan parameter settings enabled CT scans with su fficient scan resolution to determine local orientations in a complex ribbed CF-SMC part. Single scans were successfully merged to full-part 3D CT scan volumes. As far as is known, such high-quality CT scans of a full CF-SMC part of this size are not published elsewhere. The scans can be easily analyzed by using the local density gradients instead of discrete carbon fibers for the fiber orientation measurement with a commercial CT scan analysis software—in this study VGSTUDIO MAX 3.3. Here, the analysis benefits from scale of the strands, which is used to correlate inter- and intra-strand density gradients to local orientation states similar to findings by Denos et al. [20] and Favaloro et al. [21]. Although the scan resolution is not fine enough to discern individual fibers from each other or to di fferentiate between strands in thickness direction, the local orientation states can be accurately measured for the whole part volume. Due to the mesoscale character of ROS materials, the scan quality and the used analysis mesh is suited for mesostructure analyses and comparisons with process simulation results or as input data for subsequent integrative structural simulations. If more detailed information is really needed, a finer scan resolution must be applied and either more and smaller CT scan volumes have to be merged (higher expenses), or just local spots of interest can be scanned without gaining whole-part 3D fiber orientation information. Finer analysis meshes with several elements over the thickness might also be beneficial for coupled structural simulations. On the other hand, when 3D orientation information is needed for an even bigger part, the CT scan method comes to its limits if the costs may not be too extensive.

Subsequently, the measured orientations of the three scanned parts were averaged to receive a characteristic representation of the part's mesostructures in 21 analysis areas. The averaging reduces local di fferences between the samples and reveals low standard deviations between the measured orientations of the three CT scanned mesostructures. This consistency justifies using the averaged 3D fiber orientation information as basis for the DFS software validation. Although additional CT scans of further parts would obviously enhance the reliability of the CT scan measurement results, the detected low standard deviations are a good sign that the small number of just three scanned parts is su fficient to represent the general orientation state occurring in the ribbed structures. As the gained CT orientation information for the CF-SMC parts are the only expedient data basis to validate predicted orientations in 3D, the apparent full-part CT scan results are valuable data, without precedent.

As far as is known, there is no other commercially available software tool that enables one to simulate the compression molding of ROS-based SMCs by applying a DFS method other than 3D TIMON CompositePRESS. Therefore, 3D TIMON CompositePRESS and its novel strand setting feature is applied and evaluated in this study. In comparison to the DBS of Meyer et al., which uses one chain of truss elements per fiber bundle, 3D TIMON models each strand by several fibers representing the fiber bundles within one strand. This gives the opportunity to mimic the strand geometry and behavior, such as strand splitting, properly. Since there is so far no cohesive force between the fibers of one strand or any interaction between fibers or strands modeled, all fiber bundles are numerically handled separately and fiber spreading and strand deformation will be inevitably overestimated. Furthermore, the assumed higher sti ffness of strands compared with single fibers and especially its e ffect on the rib filling behavior is not taken into account. This could be addressed and studied in prospective simulations by numerically increasing the fibers' sti ffness in the material card. However, the numerical strands show realistic flow behavior when compared to the experiments and therefore the software's UD strand mimicking is considered as a valid and working modeling strategy. From these results it is concluded that cohesive forces and interactions may not be absolutely necessary to represent a realistic strand movement and that the additional CPU time, when implementing such feature in the software, can be saved without losing too much accuracy. As the DFS simulation takes more than four days, the number of mesh elements, fibers, and output steps should be adapted to the situation and the needed degree of detail. For very large structural CF-SMC parts, which are currently targeted by automotive engineers, the needed simulation power for DFSs is a limiting factor for the presented DFS method. Even with high performance clusters the calculation of billions of fibers will not be effective anymore at a certain part size. Possible improvements could be made by modeling the UD strands by deformable cuboids instead of a group of single fibers, ignoring the strands' splitting behavior. As commonly very high charge coverages are used for such big parts, split strands play a minor role and this modeling idea could decrease the calculation times e ffectively. Although it has a minor impact on the DFS results, the charge generation in tighter radii, where the initial strands are so far unrealistically cut, could be improved, so that the strands follow the charge curvature instead of being cut.

The used high-performance CF-SMC HexMC is a typical member of the ROS-SMC material class and is therefore utmost suited for the software validation. For the evaluation the averaged CT measurements of the three ribbed hat profile parts are used. With the process simulation tetra mesh imported into the CT analysis software, a one-to-one comparison with the DFS results in the defined 21 analysis areas using exactly the same elements was conducted. The average CT values for the FOTs, eigenvectors and eigenvalues in the regions of interest were juxtaposed with the predicted values. Besides the small CT database with just three scanned parts, the detected deviations between CT measurements and DFS results might also originate from the chosen analysis areas and their sizes. Appropriate sizes for the analysis areas are crucial in order to ensure that local di fferences between the three CT scans and also in the process simulation are not overrated by using too small analysis

areas. On the other hand, the analysis areas could also be too large so that clear orientation e ffects, for example, at cavity walls, are averaged out. Moreover, the underlying assumptions and boundary conditions in the DFS are a very simplified approach to simulate ROS materials and deviations from the real mesostructures in the ribbed structures are expectable. However, the calculated comparable low MAEs indicate overall reasonable agreement between measurement and simulation, especially when the initially random in-plane strand orientation and the comparably short flow paths (mold coverage of ~80%) are considered, which does not inherently lead to very pronounced flow-induced orientations. For this reason, it is concluded that 3D TIMON CompositePRESS delivers su fficiently accurate fill and orientation predictions that are valuable for ROS-based SMC part design processes and to avoid trial and error molding trials to find suitable processing conditions and a working charge pattern. The similarity between CT and DFS results was also visually shown in an orientation depiction method with 2D ellipses, allowing for an easy visual comparison. This method would be also suited to quickly compare simulation results using other settings, charge patterns or coming from di fferent software.


**1. Introduction**

While the resolution of images has been rapidly increasing in recent years with the development of high-performance cameras, advanced image compression, and display panels, the demands to generate high resolution images from pre-existing low-resolution images are also increasing for rendering on high resolution displays. In the field of computer vision, single image super-resolution (SISR) methods aim at recovering a highresolution image from a single low-resolution image. Since the low-resolution images cannot represent the high-frequency information properly, most super-resolution (SR) methods have focused on restoring high-frequency components. For this reason, SR methods are used to restore the high-frequency components from quantized images at the image and video post-processing stage [1–3].

Deep learning schemes such as convolutional neural network (CNN) and multi-layer perceptron (MLP) are a branch of machine learning which aims to learn the correlations between input and output data. In general, the output in the process of the convolution operations is one pixel, which is a weighted sum between an input image block and a filter, so an output image represents the spatial correlation of input image corresponding to the filters used. As CNN-based deep learning technologies have recently shown impressive results in the area of SISR, various CNN-based SR methods have been developed that

surpass the conventional SR methods, such as image statistical methods and patch- based methods [4,5]. In order to improve the quality of low-resolution images, CNN-based SR networks tend to deploy more complicated schemes, which have deeper and denser CNN structures and cause increases in the computational complexity like the required memory to store network parameters, the number of convolution operations, and the inference speed. We propose two SR-based lightweight neural networks (LNNs) with hybrid residual and dense networks, which are the "inter-layered SR-LNN" and "simplified SR-LNN", respectively, which we denote in this paper as "SR-ILLNN" and "SR-SLNN", respectively. The proposed methods were designed to produce similar image quality while reducing the number of networks parameters, compared to previous methods. Those SR technologies can be applied to the pre-processing stages of face and gesture recognition [6–8].

The remainder of this paper is organized as follows: In Section 2, we review previous studies related to CNN-based SISR methods. In Section 3, we describe the frameworks of the proposed two SR-LNNs for SISR. Finally, experimental results and conclusions are given in Sections 4 and 5, respectively.

#### **2. Related Works**

Deep learning-based SR methods have shown high potential in the field of image interpolation and restoration, compared to the conventional pixel-wise interpolation algorithms. Dong et al. proposed a three-layer CNN structure called super-resolution convolutional neural network (SR-CNN) [9], which learns an end-to-end mapping from a bi-cubic interpolated low-resolution image to a high-resolution image. Since the advent of SR-CNN, a variety of CNN networks with deeper and denser network structure [10–13] have been developed to improve the accuracy of SR.

In particular, He et al. proposed a ResNet [11] for image classification. Its key idea is to learn residuals through global or local skip connection. It notes that ResNet can provide a high-speed training process and prevent the gradient vanishing effects. In addition to ResNet, Huang et al. proposed densely connected convolutional networks (DenseNet) [12] to combine hierarchical feature maps available along the network depth for more flexible and richer feature representations. Dong et al. proposed an artifacts reduction CNN (AR-CNN) [14], which effectively reduces the various compression artifacts such as block artifacts and ringing artifacts on Joint Photographic Experts Group (JPEG) compression images.

Kim et al. proposed a super-resolution scheme with very deep convolutional networks (VDSR) [15], which is connected with 20 convolutional layers and a global skip connection. In particular, the importance of receptive field size and the residual learning was verified by VDSR. Leding et al. proposed a SR-ResNet [16], which was designed with multiple residual blocks and generative adversarial network (GAN) for improving visually subjective quality. Here, a residual block is composed of multiple convolution layers, a batch normalization, and a local skip connection. Lim et al. exploited enhanced deep super-resolution (EDSR) and multi-scale deep super-resolution (MDSR) [17]. In particular, as these networks have been modified in a way of removing the batch normalization, it can reduce graphics processing unit (GPU) memory demand by about 40% compared with SR-ResNet.

Tong et al. proposed an image super-resolution using dense skip connections (SR-DenseNet) [18] as shown in Figure 1. Because SR-DenseNet consists of eight dense blocks and each dense block contains eight dense layers, this network has a total of 67 convolution layers and two deconvolution layers. Because the feature maps of the previous convolutional layer are concatenated with those of the current convolutional layer within a dense block, total number of the feature map from the last dense block reaches up to 1040 and it requires more memory capacity to store the massive network parameters and intermediate feature maps.

On the other hand, the aforementioned deep learning-based SR methods are also applied to compress raw video data. For example, Joint Video Experts Team (JVET) formed the Adhoc Group (AhG) for deep neural networks based video coding (DNNVC) [19] in 2020, which aims at exploring the coding efficiency using the deep learning schemes. Several studies [20–22] have shown better coding performance than the-state-of-the-art video coding technologies.

**Figure 1.** The framework of SR-DenseNet [18].


*Article* **A Characterization Study of Reactive Sites in ALD-Synthesized WOx/ZrO2 Catalysts**

**Cong Wang 1 ID , Xinyu Mao 1 , Jennifer D. Lee 2 ID , Tzia Ming Onn 1 , Yu-Hao Yeh 1 , Christopher B. Murray 2,3 and Raymond J. Gorte 2,3,\* ID**

Received: 20 June 2018; Accepted: 17 July 2018; Published: 19 July 2018

**Abstract:** A series of ZrO2-supported WOx catalysts were prepared using atomic layer deposition (ALD) with W(CO)6, and were then compared to a WOx/ZrO2 catalyst prepared via conventional impregnation. The types of sites present in these samples were characterized using temperature-programmed desorption/thermogravimetric analysis (TPD-TGA) measurements with 2-propanol and 2-propanamine. Weight changes showed that the WOx catalysts grew at a rate of 8.8 × 1017 W atoms/m2 per cycle. Scanning transmission electron microscopy/energy-dispersive spectroscopy (STEM-EDS) indicated that WOx was deposited uniformly, as did the 2-propanol TPD-TGA results, which showed that ZrO2 was completely covered after five ALD cycles. Furthermore, 2-propanamine TPD-TGA demonstrated the presence of three types of catalytic sites, the concentrations of which changed with the number of ALD cycles: dehydrogenation sites associated with ZrO2, Brønsted-acid sites associated with monolayer WOx clusters, and oxidation sites associated with higher WOx coverages. The Brønsted sites were not formed via ALD of WOx on SiO2. The reaction rates for 2-propanol dehydration were correlated with the concentration of Brønsted sites. While TPD-TGA of 2-propanamine did not differentiate the strength of Brønsted-acid sites, H–D exchange between D2O and either toluene or chlorobenzene indicated that the Brønsted sites in tungstated zirconia were much weaker than those in H-ZSM-5 zeolites.

**Keywords:** solid acids; metal-oxide catalysts; tungstated zirconia; atomic layer deposition; Brønstedacid strength; temperature-programmed desorption/thermogravimetric analysis; H–D exchange

### **1. Introduction**

In the 1980s, it was reported that tungstated zirconia (WOx/ZrO2) could be used as a solid acid catalyst; however, there are still many questions and apparent contradictions regarding the nature and strength of the acid sites in this material, as demonstrated in the recent review by Zhou et al. [1]. A major difficulty with tungstated-zirconia catalysts is that their properties depend on how they are made. It is likely, for this reason, that some studies indicated that the structure of the underlying ZrO2, whether amorphous or crystalline (tetragonal or monoclinic), influences the activity of the sites [2], while other workers reported that the crystallographic structure of the zirconia is not important [3,4]. It was also suggested that the sites may be of varying strength, depending on the WOx cluster size, with one theoretical study reporting that the Brønsted sites not only depend on cluster size, but can also approach super-acid strength [1]. However, the observations that it is necessary to add Pt in order

for tungstated zirconia to exhibit alkane-isomerization activity [5,6] would suggest that the sites are less strong, in addition to tungstated zirconia being selective to ether formation in the dehydration of alcohols [7], without forming olefins, unlike with protonic zeolites.

Most researchers prepared their tungstated-zirconia catalysts via the aqueous impregnation of ammonium metatungstate ((NH4)10W12O41·5H2O), followed by calcination [1]. In addition to the fact that this precursor consists of a relatively large number of tungsten atoms, the drying process itself can result in multiple clusters coming together before decomposing upon calcination [8]. While the WOx species may spread over the support surface during calcination, the implications of there being so many tungsten atoms together in the initial state implies that there will likely be large clusters on the support in all cases. This is important because mono-tungstate species are not believed to be the active component [1]. Furthermore, the fact that the catalytic properties are a function of the tungsten-oxide coverage and pretreatment conditions [9,10] implies that discrepancies in the results from different studies are likely due to differences in the detailed nature of the clusters that are formed in the catalysts.

In the presented work, we set out to investigate the catalytic sites in WOx/ZrO2 catalysts prepared using atomic layer deposition (ALD). ALD is a self-limiting process in which WOx is deposited through repeated, cyclic exposures of W(CO)6 and oxidants [11–13]. Because the amount of WOx deposited per cycle in ALD is low and the deposition is uniform, ALD ensures the formation of uniform, atomic-scale layers, which maximize the interfacial contact between WOx and ZrO2. This allows the formation of isolated WOx species, as well as much more control over the tungsten cluster sizes by increasing tungsten coverage. A complicating factor in the characterization is that zirconia itself exhibits activity in the reaction of amines and alcohols, and contributes to the activity of catalysts for some reactions. Fortunately, as demonstrated in this work, the chemistries on bare zirconia are oftentimes distinguishable from those that occur on WOx/ZrO2 interfaces, and thus, provide additional information on coverage and dispersion.

We also set out to test claims that tungstated-zirconia could show super-acidic properties. The quantification of Brønsted-acid strength in solid acids is difficult, and some common measures, such as heats of adsorption for ammonia or pyridine, were shown to be uninformative [14]. A simple reaction that appears to depend only on the ability of the solid to protonate a weak base involves the H–D exchange between an aromatic molecule and deuterated acid sites [15]. In our case, we examined light-off curves for H–D exchange between D2O and either toluene (C7H8) or chlorobenzene (C6H5Cl) [15]. Essentially, all hydroxides on solids exchange readily with D2O [14,16], meaning that the presence of D2O ensures a high concentration of deuterated acid sites. However, the deuteration of toluene (proton affinity = 784 kJ/mol) or chlorobenzene (proton affinity = 753 kJ/mol) requires the formation of a carbenium ion, so that the temperature at which exchange becomes rapid should be a reasonable measure of the acid strength. In agreement with expectations based on proton affinities, higher temperatures are required for H–D exchange with chlorobenzene compared to that with toluene; however, the Brønsted sites in tungstated zirconia appear to be significantly weaker than those in H-ZSM-5.

#### **2. Results**

The ZrO2 substrate prepared for this study had an initial BET surface area of 65 m2/g. As shown by the X-ray diffraction (XRD) pattern in Figure 1a, its phase was primarily monoclinic with a small amount of the tetragonal phase. The WOx loadings were then determined as a function of the number of ALD cycles by measuring the sample weights. Table 1 shows these loadings, together with BET surface areas. In the first five ALD cycles, the sample weight increased almost linearly at 22 mg WOx/g ZrO2 per cycle. Assuming an O:W stoichiometry of three and uniform film growth over the entire ZrO2 surface, this corresponds to a growth rate of 0.048 nm/cycle, a value that is reasonable for the size of the W(CO)6 precursor, but that is somewhat larger than the value reported in the literature for the growth of WO3 films on flat surfaces (0.023 nm/cycle) for similar growth conditions [11,13]. It is

interesting to notice that the growth rate of WOx on the 140-m2/g SiO2 support, calculated from the amount deposited after five ALD cycles, was only 0.025 nm/cycle, implying that the substrate does influence the initial deposition rate. Finally, it is useful to consider that deposition of 22 mg of WO3 onto the 65-m2/g ZrO2 sample corresponds to 8.8 × 1017 W atoms/m2 . This is a fraction of an oxide monolayer, which implies that, in the absence of surface migration, WOx species are likely spatially isolated after a single ALD cycle. *Catalysts* **2018**, *8*, x FOR PEER REVIEW 3 of 15 amount deposited after five ALD cycles, was only 0.025 nm/cycle, implying that the substrate does influence the initial deposition rate. Finally, it is useful to consider that deposition of 22 mg of WO3 onto the 65-m2/g ZrO2 sample corresponds to 8.8 × 1017 W atoms/m2. This is a fraction of an oxide monolayer, which implies that, in the absence of surface migration, WOx species are likely spatially isolated after a single ALD cycle.

**Figure 1.** XRD patterns for (a) ZrO2, (b) 5W-Zr (5 ALD cycles), and (c) 40W-Zr (40 ALD cycles). Monoclinic phase (■); tetragonal phase (♦). **Figure 1.** XRD patterns for (a) ZrO2 , (b) 5W-Zr (5 ALD cycles), and (c) 40W-Zr (40 ALD cycles). Monoclinic phase (); tetragonal phase ().

**Table 1.** Weight gain, surface area, and calculated layer thickness for W-Zr and W-Si materials. The layer thickness was calculated using a WO3 density of 7.16 g/cm3. The nomenclature *x*W-Zr is used to refer to a sample exposed to *x* ALD cycles. **Table 1.** Weight gain, surface area, and calculated layer thickness for W-Zr and W-Si materials. The layer thickness was calculated using a WO3 density of 7.16 g/cm3 . The nomenclature *x*W-Zr is used to refer to a sample exposed to *x* ALD cycles.

5W-Si 120 135 0.12 After five ALD cycles, the BET surface area decreased to 46 m2/g. Some of the decrease was due to the increase in sample mass; however, the majority of the loss in surface area per mass of sample must be associated with the narrowing or blocking of pores. The WOx added with five ALD cycles did not cause any changes in the XRD pattern (Figure 1b). Because of the decreasing surface area of the sample, the rate at which the weight changed decreased somewhat with the number of ALD cycles. After 40 ALD cycles, the WOx loading was 42 wt%, or 0.72 g WOx/g ZrO2. Assuming that the film was uniform with a stoichiometry and density of bulk WO3, the film thickness after 40 cycles After five ALD cycles, the BET surface area decreased to 46 m2/g. Some of the decrease was due to the increase in sample mass; however, the majority of the loss in surface area per mass of sample must be associated with the narrowing or blocking of pores. The WOx added with five ALD cycles did not cause any changes in the XRD pattern (Figure 1b). Because of the decreasing surface area of the sample, the rate at which the weight changed decreased somewhat with the number of ALD cycles. After 40 ALD cycles, the WOx loading was 42 wt%, or 0.72 g WOx/g ZrO2. Assuming that the film was uniform with a stoichiometry and density of bulk WO3, the film thickness after 40 cycles was 1.5 nm, a thickness significantly greater than that of a monolayer. However, even with this relatively thick film,

was 1.5 nm, a thickness significantly greater than that of a monolayer. However, even with this

the XRD pattern (Figure 1c) showed no evidence of a well-defined crystalline WOx phase. The only change in the XRD pattern with the addition of this large amount of WOx was a slightly elevated baseline between 25 and 35 degrees 2θ, and again above 50 degrees 2θ. These broad features may be associated with a very small fraction of mixed WOx phases. However, if large, three-dimensional, crystalline clusters were being formed, it would have been apparent in the diffraction pattern. WOx phase. The only change in the XRD pattern with the addition of this large amount of WOx was a slightly elevated baseline between 25 and 35 degrees 2θ, and again above 50 degrees 2θ. These broad features may be associated with a very small fraction of mixed WOx phases. However, if large, three-dimensional, crystalline clusters were being formed, it would have been apparent in the diffraction pattern.

*Catalysts* **2018**, *8*, x FOR PEER REVIEW 4 of 15

Scanning transmission electron microscopy (STEM) imaging and energy-dispersive spectroscopy (EDS) elemental mapping on the 40W-Zr sample (Figure 2) demonstrate that the WOx ALD films deposited uniformly over the ZrO2 surface. While there were no obvious features in the image, despite the high WOx coverage, the EDS mapping of W and Zr indicates the co-existence of W and Zr elements in the sample. The overlap in the signals reveals their uniform distribution. Scanning transmission electron microscopy (STEM) imaging and energy-dispersive spectroscopy (EDS) elemental mapping on the 40W-Zr sample (Figure 2) demonstrate that the WOx ALD films deposited uniformly over the ZrO2 surface. While there were no obvious features in the image, despite the high WOx coverage, the EDS mapping of W and Zr indicates the co-existence of W and Zr elements in the sample. The overlap in the signals reveals their uniform distribution.

**Figure 2.** BF (Bright-Field) scanning transmission electron microscopy (STEM) image and energydispersive spectroscopy (EDS) elemental mapping of the 40W-Zr sample. The scale bars represent 20 nm. **Figure 2.** BF (Bright-Field) scanning transmission electron microscopy (STEM) image and energy-dispersive spectroscopy (EDS) elemental mapping of the 40W-Zr sample. The scale bars represent 20 nm.

To characterize the structure of the WOx ALD films, Raman spectra of the ZrO2 support and of the impW-Zr (10-wt% WOx) and 5W-Zr samples were measured, both of which had WOx loadings that were close to 10 wt%. The data for ZrO2 (Figure 3a) show vibrational bands at ~347, 380, 478, 616, and 636 cm−1, which are well known to be due to ZrO2 [17]. The spectrum for the impW-Zr sample (Figure 3b) exhibits the same bands, but with a new, broad feature centered at ~962 cm−1, which was previously assigned to the symmetric stretching mode of a terminal W=O bond [17]. This broad feature may also include a contribution from a bridging W–O–Zr bond at 915 cm−1. The spectrum of the 5W-Zr sample (Figure 3c) is similar, except that the vibrational features associated with WOx relative to those with ZrO2 are significantly more intense, despite having the same WOx loading. This is likely due to the WOx being spread more uniformly over the ZrO2 surface in the ALD-prepared sample. To characterize the structure of the WOx ALD films, Raman spectra of the ZrO2 support and of the impW-Zr (10-wt% WOx) and 5W-Zr samples were measured, both of which had WOx loadings that were close to 10 wt%. The data for ZrO2 (Figure 3a) show vibrational bands at ~347, 380, 478, 616, and 636 cm−1 , which are well known to be due to ZrO2 [17]. The spectrum for the impW-Zr sample (Figure 3b) exhibits the same bands, but with a new, broad feature centered at ~962 cm−1 , which was previously assigned to the symmetric stretching mode of a terminal W=O bond [17]. This broad feature may also include a contribution from a bridging W–O–Zr bond at 915 cm−1 . The spectrum of the 5W-Zr sample (Figure 3c) is similar, except that the vibrational features associated with WOx relative to those with ZrO2 are significantly more intense, despite having the same WOx loading. This is likely due to the WOx being spread more uniformly over the ZrO2 surface in the ALD-prepared sample.

A further indication that the WOx layer was more uniform on the ALD-prepared sample is demonstrated in Figure 4, which shows the temperature-programmed desorption/thermogravimetric analysis (TPD-TGA) results for 2-propanol on the ZrO2, 5W-Zr, and impW-Zr samples. The TGA data show that the initial coverages following room-temperature adsorption and 1-h evacuation were slightly higher on the ZrO2 and impW-Zr samples (between 250 and 300 µmol/g) compared to the A further indication that the WOx layer was more uniform on the ALD-prepared sample is demonstrated in Figure 4, which shows the temperature-programmed desorption/thermogravimetric analysis (TPD-TGA) results for 2-propanol on the ZrO2, 5W-Zr, and impW-Zr samples. The TGA data show that the initial coverages following room-temperature adsorption and 1-h evacuation were slightly higher on the ZrO2 and impW-Zr samples (between 250 and 300 µmol/g) compared to the

5W-Zr (200 µmol/g). However, this is almost certainly due to the lower surface area of the 5W-Zr sample. The initial specific coverages were roughly 2.7 × 1018 molecules/m2 for each of the samples. While some of the 2-propanol (*m*/*e* = 45) desorbed unreacted from all three samples, significant fractions of the 2-propanol desorbed from the samples as propene (*m*/*e* = 41) and H2O due to reactions on either Lewis- or Brønsted-acid sites [18]. Water is not shown because it tends to desorb over a broad temperature range. What is more interesting is that the dehydration reaction occurred at very different temperatures on ZrO2 and on WOx/ZrOx sites. On the ZrO2 and 5W-Zr samples, the dehydration reaction occurred over narrow temperature ranges in peaks centered at 560 K on ZrO2, and 405 K on 5W-Zr. The TPD result of impW-Zr shows similarly sized peaks at both temperatures, suggesting that impW-Zr has regions of bare ZrO2 and regions covered by WOx. Even though the WOx loadings were the same for 5W-Zr and impW-Zr, the distributions of WOx were clearly different, with tungsten distributed much more uniformly on the sample prepared using ALD. 5W-Zr (200 µmol/g). However, this is almost certainly due to the lower surface area of the 5W-Zr sample. The initial specific coverages were roughly 2.7 × 1018 molecules/m2 for each of the samples. While some of the 2-propanol (*m*/*e* = 45) desorbed unreacted from all three samples, significant fractions of the 2-propanol desorbed from the samples as propene (*m*/*e* = 41) and H2O due to reactions on either Lewis- or Brønsted-acid sites [18]. Water is not shown because it tends to desorb over a broad temperature range. What is more interesting is that the dehydration reaction occurred at very different temperatures on ZrO2 and on WOx/ZrOx sites. On the ZrO2 and 5W-Zr samples, the dehydration reaction occurred over narrow temperature ranges in peaks centered at 560 K on ZrO2, and 405 K on 5W-Zr. The TPD result of impW-Zr shows similarly sized peaks at both temperatures, suggesting that impW-Zr has regions of bare ZrO2 and regions covered by WOx. Even though the WOx loadings were the same for 5W-Zr and impW-Zr, the distributions of WOx were clearly different, with tungsten distributed much more uniformly on the sample prepared using ALD. 5W-Zr (200 µmol/g). However, this is almost certainly due to the lower surface area of the 5W-Zr sample. The initial specific coverages were roughly 2.7 × 1018 molecules/m2 for each of the samples. While some of the 2-propanol (*m*/*e* = 45) desorbed unreacted from all three samples, significant fractions of the 2-propanol desorbed from the samples as propene (*m*/*e* = 41) and H2O due to reactions on either Lewis- or Brønsted-acid sites [18]. Water is not shown because it tends to desorb over a broad temperature range. What is more interesting is that the dehydration reaction occurred at very different temperatures on ZrO2 and on WOx/ZrOx sites. On the ZrO2 and 5W-Zr samples, the dehydration reaction occurred over narrow temperature ranges in peaks centered at 560 K on ZrO2, and 405 K on 5W-Zr. The TPD result of impW-Zr shows similarly sized peaks at both temperatures, suggesting that impW-Zr has regions of bare ZrO2 and regions covered by WOx. Even though the WOx loadings were the same for 5W-Zr and impW-Zr, the distributions of WOx were clearly different, with tungsten distributed much more uniformly on the sample prepared using ALD.

*Catalysts* **2018**, *8*, x FOR PEER REVIEW 5 of 15

*Catalysts* **2018**, *8*, x FOR PEER REVIEW 5 of 15

**Figure 3.** Raman spectra of (a) ZrO2, (b) impW-Zr (10-wt% WOx), and (c) 5W-Zr samples under ambient conditions. **Figure 3.** Raman spectra of (a) ZrO2 , (b) impW-Zr (10-wt% WOx), and (c) 5W-Zr samples under ambient conditions. **Figure 3.** Raman spectra of (a) ZrO2, (b) impW-Zr (10-wt% WOx), and (c) 5W-Zr samples under ambient conditions.

**Figure 4.** TPD-TGA of 2-propanol over (**a**) ZrO2, (**b**) 5W-Zr, and (**c**) impW-Zr. The desorption features correspond to propene (*m*/*e* = 41) and unreacted 2-propanol *(m*/*e* = 45). The graphic symbols are a schematic of the WOx (purple) over the ZrO2 support (gray). **Figure 4.** TPD-TGA of 2-propanol over (**a**) ZrO2, (**b**) 5W-Zr, and (**c**) impW-Zr. The desorption features correspond to propene (*m*/*e* = 41) and unreacted 2-propanol *(m*/*e* = 45). The graphic symbols are a schematic of the WOx (purple) over the ZrO2 support (gray). **Figure 4.** TPD-TGA of 2-propanol over (**a**) ZrO2 , (**b**) 5W-Zr, and (**c**) impW-Zr. The desorption features correspond to propene (*m*/*e* = 41) and unreacted 2-propanol *(m*/*e* = 45). The graphic symbols are a schematic of the WOx (purple) over the ZrO2 support (gray).

To qualitatively assess the nature of the sites on the ZrO2 and 5W-Zr samples, FTIR measurements were performed following adsorption of pyridine, with results shown in Figure 5. The spectrum for ZrO2 (Figure 5a) shows only bands at 1440 and 1460 cm−1 , which are characteristic of adsorption at Lewis sites. In contrast to this, the spectrum of pyridine on the ALD-prepared 5W-Zr (Figure 5b) also exhibits a band near 1540 cm−1 , which can be assigned to adsorbed pyridinium ions, implying 5W-Zr contains a significant concentration of Brønsted sites. To qualitatively assess the nature of the sites on the ZrO2 and 5W-Zr samples, FTIR measurements were performed following adsorption of pyridine, with results shown in Figure 5. The spectrum for ZrO2 (Figure 5a) shows only bands at 1440 and 1460 cm−1, which are characteristic of adsorption at Lewis sites. In contrast to this, the spectrum of pyridine on the ALD-prepared 5W-Zr (Figure 5b) also exhibits a band near 1540 cm−1, which can be assigned to adsorbed pyridinium ions, implying 5W-Zr contains a significant concentration of Brønsted sites. To qualitatively assess the nature of the sites on the ZrO2 and 5W-Zr samples, FTIR measurements were performed following adsorption of pyridine, with results shown in Figure 5. The spectrum for ZrO2 (Figure 5a) shows only bands at 1440 and 1460 cm−1, which are characteristic of adsorption at Lewis sites. In contrast to this, the spectrum of pyridine on the ALD-prepared 5W-Zr (Figure 5b) also exhibits a band near 1540 cm−1, which can be assigned to adsorbed pyridinium ions, implying 5W-Zr contains a significant concentration of Brønsted sites.

**Figure 5.** FTIR spectra of pyridine adsorbed on (**a**) ZrO2 and (**b**) 5W-Zr. The samples were pretreated with helium at 373 K before pyridine was adsorbed onto the samples at room temperature. **Figure 5.** FTIR spectra of pyridine adsorbed on (**a**) ZrO2 and (**b**) 5W-Zr. The samples were pretreated with helium at 373 K before pyridine was adsorbed onto the samples at room temperature. **Figure 5.** FTIR spectra of pyridine adsorbed on (**a**) ZrO2 and (**b**) 5W-Zr. The samples were pretreated with helium at 373 K before pyridine was adsorbed onto the samples at room temperature.

To quantify the concentration of the sites, TPD-TGA measurements were performed using 2 propanamine as the probe molecule. On Brønsted sites, 2-propanamine forms 2-propylammonium which decompose during TPD to form ammonia and propylene between 573 and 650 K due to a Hoffman Elimination reaction [19,20], allowing site concentrations to be determined from this decomposition feature. Reaction of the amines can occur on catalytic sites other than Brønsted sites; however, the products that are formed and the temperature range in which the reactions occur are different and depend on the nature of the site [21]. The TPD-TGA results for the WOx/ZrO2 samples show evidence for the presence of three different types of reactive sites, with concentrations of those sites dependent on the WOx coverages. A summary of the three reaction pathways is illustrated in Scheme 1. To quantify the concentration of the sites, TPD-TGA measurements were performed using 2-propanamine as the probe molecule. On Brønsted sites, 2-propanamine forms 2-propylammonium which decompose during TPD to form ammonia and propylene between 573 and 650 K due to a Hoffman Elimination reaction [19,20], allowing site concentrations to be determined from this decomposition feature. Reaction of the amines can occur on catalytic sites other than Brønsted sites; however, the products that are formed and the temperature range in which the reactions occur are different and depend on the nature of the site [21]. The TPD-TGA results for the WOx/ZrO2 samples show evidence for the presence of three different types of reactive sites, with concentrations of those sites dependent on the WOx coverages. A summary of the three reaction pathways is illustrated in Scheme 1. To quantify the concentration of the sites, TPD-TGA measurements were performed using 2 propanamine as the probe molecule. On Brønsted sites, 2-propanamine forms 2-propylammonium which decompose during TPD to form ammonia and propylene between 573 and 650 K due to a Hoffman Elimination reaction [19,20], allowing site concentrations to be determined from this decomposition feature. Reaction of the amines can occur on catalytic sites other than Brønsted sites; however, the products that are formed and the temperature range in which the reactions occur are different and depend on the nature of the site [21]. The TPD-TGA results for the WOx/ZrO2 samples show evidence for the presence of three different types of reactive sites, with concentrations of those sites dependent on the WOx coverages. A summary of the three reaction pathways is illustrated in Scheme 1.

**Scheme 1.** A summary of reaction pathways for the conversion of 2-propanamine on the WOx/ZrO2 samples observed in the TPD-TGA measurements. **Scheme 1.** A summary of reaction pathways for the conversion of 2-propanamine on the WOx/ZrO2 samples observed in the TPD-TGA measurements. **Scheme 1.** A summary of reaction pathways for the conversion of 2-propanamine on the WOx/ZrO2 samples observed in the TPD-TGA measurements.

Figure 6a provides the TPD-TGA result for 2-propanamine with pure ZrO2. Approximately half of the 300 µmol/g that remained on the sample after evacuation desorbed as unreacted amine (*m*/*e* =

Figure 6a provides the TPD-TGA result for 2-propanamine with pure ZrO2. Approximately half of the 300 µmol/g that remained on the sample after evacuation desorbed as unreacted amine (*m*/*e* =

Figure 6a provides the TPD-TGA result for 2-propanamine with pure ZrO2. Approximately half of the 300 µmol/g that remained on the sample after evacuation desorbed as unreacted amine (*m*/*e* = 42, 43, 44) below 500 K. Above that temperature, most of the amine molecules reacted in a feature centered at approximately 565 K to form H2 (*m*/*e* = 2), a mixture of acetonitrile and butenes (*m*/*e* = 41), and ammonia (*m*/*e* = 17). The absence of a peak at *m*/*e* = 42 is particularly important for demonstrating that the high temperature peak at *m*/*e* = 41 is not propene, the product that would be formed on Brønsted sites. The identification of acetonitrile and propene as products was achieved by a more complete analysis of the mass spectra and was confirmed in steady-state flow-reactor measurements over ZrO2 at 673 K. The same products were observed in those measurements, with the addition of small amounts of dipropylamine. Apparently, ZrO2 catalyzes the dehydrogenation of 2-propanamine to form the imine, which is unstable and reacts to form the smaller nitrile and butenes. The reaction of adsorbed 1-propanamine on ZrO2, shown in Figure S1, was simpler, forming primarily propionitrile and H2. However, because the products formed by 1-propanamine on sites formed by WOx were more difficult to distinguish from those formed on ZrO2, most of our work focused on using 2-propanamine to characterize the samples. It is noteworthy that ZrO2 was previously demonstrated to exhibit dehydrogenation chemistry under some conditions, and it is this functionality that is apparently responsible for the amine reactions [22].

The TPD-TGA result for the 5W-Zr sample, shown in Figure 6b, differs significantly from that obtained on ZrO2. As in the case of 2-propanol, the initial coverage was slightly lower due to the lower specific surface area; similar to the case of ZrO2, unreacted amine desorbed from the sample below 500 K. However, on the 5W-Zr sample, there are two distinct reaction features at ~585 K and ~620 K, and the products formed in both peaks differ from that observed on ZrO2. The 620-K peak is characteristic of Brønsted-acid sites. The major products are propene (*m*/*e* = 41 and 42, in the correct ratio for propene) and ammonia (*m*/*e* = 17), and these were formed via the Hoffman elimination at exactly the same temperature reported for the reaction of adsorbed 2-propanamine on acidic zeolites [19]. The lower-temperature peak at ~585 K may have a small contribution from the bare ZrO2, since the temperature is similar to the reaction temperature on ZrO2; however, the major products formed in this case were very different. The major products determined from a complete analysis of the mass spectra in this temperature range were acetone (*m*/*e* = 43) and ammonia. Minimal amounts of acetonitrile and much less H2 were formed, suggesting that most of the ZrO2 was covered. The formation of acetone implies that partial oxidation took place. The total amount of 2-propanamine that reacted on the 5W-Z sample was ~100 µmol/g. By integrating the product peaks, we estimate that this sample had less than 10 µmol/g of dehydrogenation sites (from the amount of H2 and nitrile that formed), 80 µmol/g of Brønsted sites (from propene and ammonia), and 15 µmol/g of oxidation sites (acetone). These results are summarized in Table 2.

**Table 2.** Reaction site densities determined from the desorption features of 2-propanamine TPD, determined as described in the text. W/B = number of W atoms per Brønsted site.

42, 43, 44) below 500 K. Above that temperature, most of the amine molecules reacted in a feature centered at approximately 565 K to form H2 (*m*/*e* = 2), a mixture of acetonitrile and butenes (*m*/*e* = 41), and ammonia (*m*/*e* = 17). The absence of a peak at *m*/*e* = 42 is particularly important for demonstrating that the high temperature peak at *m*/*e* = 41 is not propene, the product that would be formed on Brønsted sites. The identification of acetonitrile and propene as products was achieved by a more complete analysis of the mass spectra and was confirmed in steady-state flow-reactor measurements over ZrO2 at 673 K. The same products were observed in those measurements, with the addition of small amounts of dipropylamine. Apparently, ZrO2 catalyzes the dehydrogenation of 2-propanamine to form the imine, which is unstable and reacts to form the smaller nitrile and butenes. The reaction of adsorbed 1-propanamine on ZrO2, shown in Figure S1, was simpler, forming primarily propionitrile and H2. However, because the products formed by 1-propanamine on sites formed by WOx were more difficult to distinguish from those formed on ZrO2, most of our work focused on using 2-propanamine to characterize the samples. It is noteworthy that ZrO2 was previously demonstrated to exhibit dehydrogenation chemistry under some conditions, and it is this

**Figure 6.** TPD-TGA of 2-propanamine over (**a**) ZrO2, (**b**) 5W-Zr, (**c**) impW-Zr, and (**d**) 20W-Zr. The desorption features correspond to hydrogen (*m*/*e* = 2), ammonia (*m*/*e* = 17), a mixture of butenes and acetonitrile (*m*/*e* = 41), acetone (*m*/*e* = 43), propene (*m*/*e* = 41 and 42), and unreacted 2-propanamine (*m*/*e* = 44). **Figure 6.** TPD-TGA of 2-propanamine over (**a**) ZrO2 , (**b**) 5W-Zr, (**c**) impW-Zr, and (**d**) 20W-Zr. The desorption features correspond to hydrogen (*m*/*e* = 2), ammonia (*m*/*e* = 17), a mixture of butenes and acetonitrile (*m*/*e* = 41), acetone (*m*/*e* = 43), propene (*m*/*e* = 41 and 42), and unreacted 2-propanamine (*m*/*e* = 44).

The TPD-TGA result for the 5W-Zr sample, shown in Figure 6b, differs significantly from that obtained on ZrO2. As in the case of 2-propanol, the initial coverage was slightly lower due to the lower specific surface area; similar to the case of ZrO2, unreacted amine desorbed from the sample below 500 K. However, on the 5W-Zr sample, there are two distinct reaction features at ~585 K and ~620 K, and the products formed in both peaks differ from that observed on ZrO2. The 620-K peak is TPD-TGA measurements were also performed for the impW-Zr sample (10-wt% WOx), with the result shown in Figure 6c. This sample exhibited all three types of reaction features, with a clearly defined H2 and acetonitrile/butene feature at 565 K, associated with ZrO2. An estimate of the site densities gave 60 µmol/g of dehydrogenation sites, 40 µmol/g of Brønsted sites, and 10 µmol/g of oxidation sites. Figure 6d is the TPD-TGA result obtained for 20W-Zr, the sample prepared using ALD

characteristic of Brønsted-acid sites. The major products are propene (*m*/*e* = 41 and 42, in the correct ratio for propene) and ammonia (*m*/*e* = 17), and these were formed via the Hoffman elimination at exactly the same temperature reported for the reaction of adsorbed 2-propanamine on acidic zeolites

spectra in this temperature range were acetone (*m*/*e* = 43) and ammonia. Minimal amounts of acetonitrile and much less H2 were formed, suggesting that most of the ZrO2 was covered. The formation of acetone implies that partial oxidation took place. The total amount of 2-propanamine that reacted on the 5W-Z sample was ~100 µmol/g. By integrating the product peaks, we estimate that this sample had less than 10 µmol/g of dehydrogenation sites (from the amount of H2 and nitrile that formed), 80 µmol/g of Brønsted sites (from propene and ammonia), and 15 µmol/g of oxidation

sites (acetone). These results are summarized in Table 2.

which had a 30-wt% WOx loading, corresponding to a uniform film of ~1 nm. In comparison to the near-monolayer 5W-Zr sample, the TPD trace shows more intense features associated with oxidation sites at ~585 K, and smaller features associated with Brønsted sites at 620 K.

To understand the effect of WOx coverage, TPD-TGA measurements of 2-propanamine were performed on samples exposed to a varying number of ALD cycles. All of the samples exhibited the same three features in the TPD-TGA but at different concentrations. A summary of the calculated site concentrations is reported in Table 2 and Figure 7. The concentration of accessible dehydrogenation sites decreased dramatically with the number of ALD cycles, and essentially disappeared after five ALD cycles. Using the deposition rate of 8.8 × 1017 W atoms/m2 per cycle reported earlier, five ALD cycles corresponds to 4.4 × 1018 W atoms/m2 , a reasonable value for an oxide monolayer. In agreement with previous reports that individual W atoms do not form Brønsted sites, the Brønsted-site concentration was negligible after one cycle, and increased non-linearly thereafter, converging to a maximum after about five cycles. At higher WOx coverages, the Brønsted-site concentration decreased, implying that contact with the ZrO2 substrate was essential for forming these sites. Confirmation of the importance of ZrO2 came from the fact that the silica-supported sample, 5W-Si, showed a low Brønsted-site acidity. Finally, the concentration of oxidation sites, those responsible for forming acetone, were negligible below five ALD rounds, the point at which the dehydrogenation sites disappeared. This implies that the oxidation sites are associated with bulk WOx that is not in contact with the ZrO2 surface. It is also confirmed by the fact that oxidation-site density reached a maxima for 20W-Zr when multilayer WOx was formed. However, the decrease in oxidation-site density for 40W-Zr was only due to a loss of surface area. *Catalysts* **2018**, *8*, x FOR PEER REVIEW 10 of 15

**Figure 7.** Reaction site densities of (▲) dehydrogenation sites, (●) Brønsted sites, and (■) oxidation sites as a function of the number of WOx ALD cycles. **Figure 7.** Reaction site densities of (N) dehydrogenation sites, ( ) Brønsted sites, and () oxidation sites as a function of the number of WOx ALD cycles.

To determine the relationship between catalytic activity and the presence of Brønsted sites, the steady-state conversion of 2-propanol was performed on a series of ALD-prepared WOx/ZrO2 catalysts at 403 K and a Weight Hourly Space Velocity (WHSV) of 0.40 h−1. Under these conditions, pure ZrO2 was not catalytically active. As shown in Figure 8, there was no significant conversion over catalysts prepared with one or two WOx ALD cycles. With higher WOx concentrations, the propanol reacted to a mixture of water with dipropyl ether (~60%) and propene (40%). Conversions were maximized after five ALD rounds, and there was a reasonable correspondence between the concentration of Brønsted sites on these samples and the conversions. The fact that the 40W-Zr sample showed a higher conversion than would be expected based on its Brønsted-site density suggests that the bulk WOx also has some activity for alcohol dehydration. To determine the relationship between catalytic activity and the presence of Brønsted sites, the steady-state conversion of 2-propanol was performed on a series of ALD-prepared WOx/ZrO2 catalysts at 403 K and a Weight Hourly Space Velocity (WHSV) of 0.40 h−1 . Under these conditions, pure ZrO2 was not catalytically active. As shown in Figure 8, there was no significant conversion over catalysts prepared with one or two WOx ALD cycles. With higher WOx concentrations, the propanol reacted to a mixture of water with dipropyl ether (~60%) and propene (40%). Conversions were maximized after five ALD rounds, and there was a reasonable correspondence between the concentration of Brønsted sites on these samples and the conversions. The fact that the 40W-Zr sample showed a higher conversion than would be expected based on its Brønsted-site density suggests that the bulk WOx also has some activity for alcohol dehydration.

As discussed in the introduction, the quantification of Brønsted-acid strength in solid acids is difficult. Here, we measured light-off curves for H–D exchange between D2O and either toluene (C7H8) or chlorobenzene (C6H5Cl) on the 5W-Zr sample, and compared the results to those obtained As discussed in the introduction, the quantification of Brønsted-acid strength in solid acids is difficult. Here, we measured light-off curves for H–D exchange between D2O and either toluene (C7H8)

H-ZSM-5 and 75 degrees higher on 5W-Zr. This is consistent with a lower barrier for the reaction of toluene due to its significantly higher proton affinity. Secondly, the temperatures at which H–D exchange occurred were significantly higher on the tungstated zirconia. Sorption effects and other factors can play a role in the observed rates on solid acids [23]; however, the fact that the reaction occurred at higher temperatures for both adsorbates suggests that tungstated zirconia has much

weaker acid sites than does H-ZSM-5.

on an H-ZSM-5 zeolite which had a similar Brønsted-site density of 80 µmol/g. Measurements began

20 mL/min He.

because of the uniformity that this approach provides.

because of the uniformity that this approach provides.

**3. Discussion**

**3. Discussion**

or chlorobenzene (C6H5Cl) on the 5W-Zr sample, and compared the results to those obtained on an H-ZSM-5 zeolite which had a similar Brønsted-site density of 80 µmol/g. Measurements began at room temperature, and were carried out in a steady-state reaction environment after each increment of 10 K. The results are shown in Figure 9. Firstly, on both H-ZSM-5 and 5W-Zr, the light-off temperatures for chlorobenzene occurred at higher temperatures, roughly 60 degrees higher on H-ZSM-5 and 75 degrees higher on 5W-Zr. This is consistent with a lower barrier for the reaction of toluene due to its significantly higher proton affinity. Secondly, the temperatures at which H–D exchange occurred were significantly higher on the tungstated zirconia. Sorption effects and other factors can play a role in the observed rates on solid acids [23]; however, the fact that the reaction occurred at higher temperatures for both adsorbates suggests that tungstated zirconia has much weaker acid sites than does H-ZSM-5. *Catalysts* **2018**, *8*, x FOR PEER REVIEW 11 of 15 *Catalysts* **2018**, *8*, x FOR PEER REVIEW 11 of 15

**Figure 8.** The 2-propanol conversions on *x*W-Zr (where *x* is the number of ALD cycles) samples for the steady-state dehydration reaction at 403 K. Reaction conditions: 2-propanol WHSV = 0.40 h−1. **Figure 8.** The 2-propanol conversions on *x*W-Zr (where *x* is the number of ALD cycles) samples for the steady-state dehydration reaction at 403 K. Reaction conditions: 2-propanol WHSV = 0.40 h−1 . **Figure 8.** The 2-propanol conversions on *x*W-Zr (where *x* is the number of ALD cycles) samples for the steady-state dehydration reaction at 403 K. Reaction conditions: 2-propanol WHSV = 0.40 h−1.

**Figure 9.** Conversions for H–D exchange of toluene (in red) and chlorobenzene (in blue) on (●) 5W-Zr and (■) H-ZSM-5 (280) as a function of temperature. The Brønsted-site densities for both materials were 80 µmol/g. Reaction conditions: 100 mg of catalyst, 1% toluene/chlorobenzene, and 1% D2O in **Figure 9.** Conversions for H–D exchange of toluene (in red) and chlorobenzene (in blue) on (●) 5W-Zr and (■) H-ZSM-5 (280) as a function of temperature. The Brønsted-site densities for both materials were 80 µmol/g. Reaction conditions: 100 mg of catalyst, 1% toluene/chlorobenzene, and 1% D2O in 20 mL/min He. **Figure 9.** Conversions for H–D exchange of toluene (in red) and chlorobenzene (in blue) on ( ) 5W-Zr and () H-ZSM-5 (280) as a function of temperature. The Brønsted-site densities for both materials were 80 µmol/g. Reaction conditions: 100 mg of catalyst, 1% toluene/chlorobenzene, and 1% D2O in 20 mL/min He.

differences reported by the various groups who have worked on tungstated zirconia. In the presented study, we focused on controlling the composition and structure by synthesizing catalysts using ALD

study, we focused on controlling the composition and structure by synthesizing catalysts using ALD

The TPD results also demonstrate the power of using reactive probe molecules, rather than simple bases like ammonia or pyridine, for characterizing the types of sites that are present on these materials. The application of TPD in this manner is certainly not new, and 2-propanol [18] and 2 propanamine [19] were previously used to characterize site concentrations for Lewis and Brønsted acids. However, the results demonstrate that additional information can also be obtained. For

The TPD results also demonstrate the power of using reactive probe molecules, rather than simple bases like ammonia or pyridine, for characterizing the types of sites that are present on these materials. The application of TPD in this manner is certainly not new, and 2-propanol [18] and 2 propanamine [19] were previously used to characterize site concentrations for Lewis and Brønsted acids. However, the results demonstrate that additional information can also be obtained. For

In agreement with previous reports [1,17], our data here indicate that tungstated zirconia is a complex material that can exhibit Brønsted acidity, Lewis acidity, and oxidation activity, along with

In agreement with previous reports [1,17], our data here indicate that tungstated zirconia is a complex material that can exhibit Brønsted acidity, Lewis acidity, and oxidation activity, along with dehydrogenation activity on ZrO2 [22]. The concentrations of the various types of sites depend on

#### **3. Discussion**

In agreement with previous reports [1,17], our data here indicate that tungstated zirconia is a complex material that can exhibit Brønsted acidity, Lewis acidity, and oxidation activity, along with dehydrogenation activity on ZrO2 [22]. The concentrations of the various types of sites depend on how the material is made, as shown by the differences we observed in materials synthesized via impregnation and using ALD. This complexity almost certainly accounts for at least some of the differences reported by the various groups who have worked on tungstated zirconia. In the presented study, we focused on controlling the composition and structure by synthesizing catalysts using ALD because of the uniformity that this approach provides.

The TPD results also demonstrate the power of using reactive probe molecules, rather than simple bases like ammonia or pyridine, for characterizing the types of sites that are present on these materials. The application of TPD in this manner is certainly not new, and 2-propanol [18] and 2-propanamine [19] were previously used to characterize site concentrations for Lewis and Brønsted acids. However, the results demonstrate that additional information can also be obtained. For example, the comparison of 2-propanol results for WOx/ZrO2 samples prepared via impregnation gave a strong indication that the WOx did not uniformly cover the ZrO2. The 2-propanamine results were also able to delineate concentrations of the various types of sites that were formed with increasing WOx coverage.

Based on our reactivity studies, the Brønsted-acid sites formed in tungstated zirconia are relatively weak, certainly compared to the Brønsted-acid sites formed in zeolites. While site strengths may well depend on the detailed structure and method of synthesis, it is worth noting that others reported that WOx/ZrO2 is selective for the etherification of alcohols [7], without forming olefins as with zeolites, which would again imply the sites are relatively weak. We suggest that the high activity sometimes observed at low temperatures in tungstated zirconia is due to the combination of oxidation sites and/or dehydrogenation sites, together with Brønsted sites, similar to that which was proposed for sulfated zirconia [24]. With sulfated zirconia, the oxidation sites were shown to transform alkanes to olefins, which were then more easily activated by the acid sites. A similar combination of sites likely explains at least some of the catalytic properties of tungstated zirconia. The combination of sites allows isomerization reactions to occur at low temperatures without the need for especially strong Brønsted sites.

It is interesting that there were essentially no Brønsted sites formed after the deposition of a single WOx ALD cycle on the ZrO2. One would expect to form isolated WOx species under these conditions, and the lack of Brønsted sites is interesting. The fact that more than one WOx species must be present in order to form a Brønsted site was stated by others [1], who argued that the W:Brønsted-site ratio is about four. The value of four is indeed the approximate minimum value observed in Table 2, where the Brønsted-site concentrations are listed as a function of the number of ALD cycles added to the ZrO2. While this ratio suggests that a (WOx)n cluster is required for forming Brønsted sites, it is worth noting that the ratio of four occurs after three cycles, at which point it is expected that most of the tungsten is deposited in the first monolayer, implying that the clusters would be two-dimensional and still in contact with ZrO2. The importance of zirconia is confirmed by the fact that we do not form significant quantities of Brønsted sites when WOx is deposited on silica.

Finally, our H–D exchange results imply that the Brønsted sites in WOx/ZrO2 are relatively weak compared to the sites in high-silica zeolites. The early work by Hino and Arata on the isomerization of small alkanes at low temperatures argued that these materials are very strong Brønsted acids [25]; similarly, the calculated deprotonation energies that were reported imply that tungstated zirconia should have much stronger sites than those present in zeolites [1]. However, the recent publication on dodecanol etherification showed that WOx/ZrO2 was more selective than acidic zeolites for forming ethers, and produced less olefin and oligomerization products [7], a result that would imply weaker Brønsted sites. We suggest that the high activity reported for alkane activation at low temperatures could be the result of a combination of oxidation sites and Brønsted sites. For example, the low-temperature activity of sulfated zirconia was associated in part with oxidation sites which

form olefins that then go on to react over the Brønsted sites [9]. Since the tungsten clusters on zirconia were shown to be reducible, both in this study and previously [26], the low-temperature, isomerization activity may be the result of a combination of acidic and oxidation sites. Similarly, the presence of Pt in low-temperature isomerization catalysts likely leads to the formation of alkenes, which are much more easily protonated [27,28].

Obviously, there is still much to learn about the nature and properties of the catalytic sites in tungstated zirconia. We believe that the combination of controlled synthesis using ALD with careful adsorption studies of the type performed here can help elucidate the nature of these sites.

#### **4. Experimental**

The ZrO2 support was prepared by titrating (0.2 mL/s) 30% aqueous NH4OH (Fischer) to a 5-wt% aqueous solution of ZrO(NO3)2•xH2O (99%, Sigma Aldrich, St. Louis, MO, USA) with vigorous stirring. The precipitate was then dried at 333 K for 12 h before being calcined in a Muffle furnace at 773 K for 5 h. ALD was performed in a custom-built, static system that could be evacuated by a mechanical pump, and was described in previous studies [12,29]. In a typical ALD cycle, the evacuated 300 mg of zirconia powder was first exposed to the vapor of W(CO)6 (99%, Strem, Newburyport, MA, USA) at 473 K for 3 min, followed by a 5-min evacuation. The samples were then exposed to air at the same temperature for 6 min, before once again being evacuated to complete the cycle. We used the nomenclature *x*W-Zr to refer to a sample exposed to *x* ALD cycles. For comparison purposes, a sample with 10-wt% WOx/ZrO2 (impW-Zr) was prepared via conventional aqueous incipient wetness of (NH4)10W12O41•5H2O (99.999%, Alfa Aesar, Haverhill, MA, USA), followed by a 4-h calcination at 773 K. To determine the effect of the support, a WOx/SiO2 sample (5W-Si) was prepared by depositing five ALD cycles onto a stabilized SiO2 (Degussa AG, Essen, Germany, Ultrasil VN 3 SP, 140 m2/g).

The temperature-programmed desorption/thermogravimetric analysis (TPD-TGA) measurements were performed on samples held in a system consisting of an evacuated CAHN 2000 microbalance, equipped with an SRI quadrupole mass spectrometer (RGA100), that is described elsewhere [30]. The 50-mg samples were first heated in a vacuum to 823 K, before being cooled to room temperature in a vacuum, and then exposed to the vapor of the probe adsorbate, either 2-propanol (99.9%, Fisher, Hampton, NH, USA), or 1-propanamine (99+%, Alfa Aesar), or 2-propanamine (99%, Alfa Aesar). After 1 h of evacuation, the TPD and TGA measurements were obtained while ramping the temperature at 10 K/min.

Scanning transmission electron microscopy (STEM) and elemental mapping via energy-dispersive X-ray spectroscopy (EDS) were performed with a JEOL 2010F field-emission scanning transmission electron microscope (JEOL, Tokyo, Japan), operated at an accelerating voltage of 200 kV with a 0.7 nm STEM probe. Infrared spectra of adsorbed pyridine were performed on a Mattson Galaxy FTIR (Madison Instruments Inc., Middleton, WI, USA) with a diffuse-reflectance attachment (Collector II™) in order to confirm the presence of Brønsted sites [31]. In the FTIR cell, the samples were initially heated to 373 K in flowing He at 60 mL/min to remove any adsorbed water for 10 min. After cooling the samples to room temperature, pyridine vapors were exposed to the sample for 10 min. The samples were then flushed with flowing He at 60 mL/min for 10 min. Raman spectra were obtained with an NTEGRA Spectra system (NT-MDT) with an excitation laser wavelength of 532 nm. The experiments were carried out with a laser power of 15 mW (10% of 150 W from the natural-density filter setting) at the samples and a collection time of 60 s. Powder X-ray Diffraction (XRD) patterns were collected from a Rigaku Smartlab diffractometer equipped with a Cu Kα source (The Woodlands, TX, USA).

The steady-state reaction rates for various reactions (2-propanol dehydration, H–D exchange between toluene (C7H8) and D2O, and H–D exchange between chlorobenzene (C6H5Cl) and D2O) were measured in a flow reactor that consisted of a 200-mm long, 4.6-mm ID stainless-steel tube, packed with 100 mg of catalyst that was held in place by two quartz-wool plugs and an inert tube from the back end. Products were monitored using an online GC-MS (QP-5000, Shimadzu, Kyoto, Japan) and the results were compared to reaction measurements over an H-ZSM-5 catalyst (Si/Al2 = 280; Zeolyst, CBV 28014, Conshohocken, PA, USA). For the dehydration of 2-propanol, the inlet flow to the reactor was 5% 2-propanol, achieved by feeding 0.9 µL/min liquid 2-propanol with a syringe pump (Harvard Apparatus, PHD 2000, Holliston, MA, USA) in a 5 mL/min He flow. In the H–D exchange measurements, an equal molar fraction of 1% toluene (or chlorobenzene) and D2O were co-fed with a syringe pump in a 20 mL/min He flow. Prior to the reaction, the catalysts were pretreated in flowing He at 523 K for 30 min before measuring steady-state reaction rates using an online GC-MS (QP-5000, Shimadzu). The conversions of H–D exchange were quantified via the deconvolution of mass fragmentations from *m*/*e* 91 to 97 for toluene, and from *m*/*e* 112 to 118 for chlorobenzene.


*1.2. Literature Review*

In this part, we review the literature from the perspectives of 'Existing methods and the limitations' and 'Advanced methods on human activity recognition' to demonstrate the previous work on the perception and identification of student classroom behaviors.

#### 1.2.1. Existing Methods and the Limitations

The previous research on students' classroom behavior in the traditional education field is often based on statistical survey methods, requiring teachers to observe the classroom behavior of the entire class or a smaller number of people as an observer over a period within the classroom and to record their behavior [16]. In these circumstances, the teacher plays the role of the evaluator to assess the student's behavior patterns. This manual, visionbased approach is usually surpassed in identifying inappropriate classroom activities, but the teacher's one-to-many nature at the time of the count results in the poor perception of the finer classroom behaviors of most students [17]. Furthermore, this visual-based artificial approach to behavior analysis is undoubtedly time-consuming, labor-intensive, and highly subjective. It is highly likely to violate students' privacy through external interventions and create learning ancients. It cannot give objective science-based judgments, thus preventing a comprehensive classroom behavior assessment. For school-age children with developmental disabilities, classroom behavior management and interventions are the primary methods for improving their classroom performance. There are three common primary types of classroom behavior interventions: one-on-one peer help or parent coaching [18], instructional task modification [19], and self-monitoring [20]. However, while this traditional intervention method has helped children with developmental disabilities improve their classroom task completion rate and classroom behavioral performance, this behavioral intervention undoubtedly consumes many resources in terms of monitoring children's classroom behavior. It requires significant human and material resources to assist children's classroom learning process.

#### 1.2.2. Advanced Methods on Human Activity Recognition

With the development and popularization of artificial intelligence technologies, research on scenario-based understanding and behavioral analysis has shone in practical application scenarios [21]. With the help of data-driven and algorithmic reasoning, machine learning theory provides the feasibility of achieving a one-to-one accurate understanding and assessment of students' classroom behaviors [22], especially for fine-grained behavioral analysis that is difficult to be taken into account by manual statistics. Some scholars have already implemented AI techniques with classroom scene understanding and achieved better results. For example, intelligent classroom systems that assist teachers in teaching and personalize students' learning by building front-end interactive learning environments for teachers and students and back-end intelligent learning management systems [23]. Adaptive education platforms that solve students' specific learning problems provide personalized teaching and improve students' learning experiences according to their needs and their abilities [24]. However, little research has been conducted on AI-based classroom behavioral understanding due to the immature combination of technologies and the niche nature of the educational scenario, making it almost impossible to find corresponding work for reference. Although classroom behavioral activities are too complex and refined, it still belongs to the domain of behavior recognition, so we can help build an intelligent classroom-acceptable behavior perception system by referring to the relevant theories of human activity recognition. A brief overview of the human activity recognition approach is presented in the following section.

The mainstream approaches for human activity recognition can be roughly classified into vision-based and sensor-based based on different data sources [25]. Vision-based behavioral analysis systems usually use single or multiple RGB or depth cameras to collect images or video data of participants' behavioral information, environment, and background information in a specific activity space [26]. Moreover, after feature extraction of the collected data through image processing techniques and computer vision methods, they can be used to identify participants' behavior through algorithm learning and inference. Research conducted by numerous scholars applying vision methods in the field of human activity recognition includes: identifying group behavior and classifying abnormal activities in crowded scenes for surveillance as well as public personal safety purposes [27–29],

including analysis of fall detection, patient monitoring and other behavioral recognition of individuals to improve the quality of human life through vision [30,31]. However, since the vision-based data acquisition equipment for human activity behavior recognition mainly relies on cameras, it is vulnerable to environmental conditions such as light and weather, the shooting range and angle, and a large amount of acquired data storage. The reference of participants' activity is easily affected by environmental occlusion and privacy issues. Due to the influence of these factors, vision-based behavior analysis systems have not yet been widely used. In contrast, sensors have the advantages of high sensitivity, small size, accessible storage, and wide applicability to various scenarios, which can avoid various problems in using vision devices, so they are now widely embedded in mobile phones, smartwatches and bracelets, eye-tracking devices, virtual/augmented reality headsets, and various intelligent IoT devices [32]. Meanwhile, along with the widespread popularity of mobile Internet and the increasing demand for daily public use of intelligent devices, the problems of inconvenience in carrying and limited endurance of traditional sensor-based devices have been effectively solved in various application scenarios, and they have now become one of the mainstream methods for human activity recognition [25]. Scholars have applied sensing devices for intelligent activity recognition in several daily domains: Alani et al. achieved 93.67% accuracy in 2020 using a deep learning approach to recognize twenty everyday human activities in intelligent homes [33]; Kavuncuo ˘glu et al. used only a waist sensor to achieve accurate monitoring of fall and daily motion data, achieving 99.96% accuracy in 2520 data [34].


**3. Thermal Coefficient of Linear Expansion (ТCLR)**

Thermal coefficient of linear expansion (TCLR) is a parameter which shows thermal expansion upon burning of ceramic products.

TCLR was measured on a DKV- 4 quartz dilatometer on specimens, 55◌ּ × 5 m in size, produced by pressing ground rocks to a grain size of 0.063 mm and subsequent burning in a silitic furnace at 1350°С to reach the maximum density of the specimens. TCLR was

estimated using the formula: 0 6 0 10 1 55,5 10 1 /deg., *t l l ltt* α− − =× + × − where t0 is initial

measurement temperature, 20°С , tl is final measurement temperature, 400, 700°С; l0 is the original length of the specimen, mm, lt is specimen lengthening, mm; 5.5◌ּ (10-6- 1/deg. of the TCLR of quartz glass.

The highest TCLR values (9.15 and 11.75◌ּ 10-6 1/deg., respectively) are characteristic of halleflinta (cake no. 8) at 400º and 700ºC, respectively. Halleflinta differs structurally from pegmatite in having finely dispersed quartz, which contributes to intense dissolution of

Figure 2 shows that high microcline and plagioclase concentrations in feldspathic rocks are responsible for a low angle tangent of dielectrical loss in cakes: (no. 8) potassic halleflinta - 0,050, (no. 10) syenite - 0,025 – 0.032, (no. 6) and pegmatite (Cjryla) – 0.027- and increases their electrical resistance (Fig.3) -(1,23, 2,0, 2,10◌ּ 1010 Ohm◌ּ cm), which is consistent with GOST parameters 2484-85 for electrical porcelain: dielectrical permeability not more than 7, the angle tangent of dielectrical loss – 0.030 and electrical resistance 1-2◌ּ 1012. Low dielectrical loss (0.025) and high electrical resistance (2.0◌ּ 1010 Ohm◌ּ cm) are characteristic of Elisenvaara syenites which have a high total percentage of microcline and plagioclase (97

Fig. 2. Dependence of dielectric properties on feldspathic rock composition.

for electrical porcelain production in terms of their electrical properties.

**3. Thermal Coefficient of Linear Expansion (ТCLR)**

estimated using the formula: 0 6

α

expansion upon burning of ceramic products.

the TCLR of quartz glass.

Elisenvaara syenites and Kostomuksha volcanics are most efficient feldspathic raw materials

Thermal coefficient of linear expansion (TCLR) is a parameter which shows thermal

TCLR was measured on a DKV- 4 quartz dilatometer on specimens, 55◌ּ × 5 m in size, produced by pressing ground rocks to a grain size of 0.063 mm and subsequent burning in a silitic furnace at 1350°С to reach the maximum density of the specimens. TCLR was

1 55,5 10 1 /deg., *t*

− − =× + × − where t0 is initial

0 10

*l l ltt*

measurement temperature, 20°С , tl is final measurement temperature, 400, 700°С; l0 is the original length of the specimen, mm, lt is specimen lengthening, mm; 5.5◌ּ (10-6- 1/deg. of

The highest TCLR values (9.15 and 11.75◌ּ 10-6 1/deg., respectively) are characteristic of halleflinta (cake no. 8) at 400º and 700ºC, respectively. Halleflinta differs structurally from pegmatite in having finely dispersed quartz, which contributes to intense dissolution of

mass.%).

quartz in a glass phase. Figure 3 shows that curves 1, 2 and 5 for the cakes of the rocks studied exhibit a non-uniform course of thermal expansion. A rapid increase in TCLR in the range 600 – 700о С is typical of compositions with a minimum quartz concentration and high microcline and plagioclase concentrations which form a less viscous liquid phase. Large quantities of quartz in rocks are responsible for the rectilinear course of the TCLR curves (3, 4, 6 and 7). A minimum TCLE value (7.10◌ּ 10-6 1/deg) in the range 600-700о С is observed in cake 7 with a minimum quartz concentration (44.0 mass.%), and a maximum TCLR value (8.9◌ּ 10-6 1/deg) in cake 1 with a quartz concentration of 2.5 mass. %.

Fig. 3. Temperature dependence of the thermal coefficient of linear expansion: (numbers as in Table 1).

According to the literature [5] the dependence of TCLR on quartz concentration is due to a difference in the density of minerals. It is known that as mineral density increases, TCLR decreases. As the density of quartz (2.65 g/cm2) is higher than that of albite (2.61 g/cm2) and that of microcline (2.55 g/cm2), low TCLR values in cakes with high quartz concentrations can well be expected. The dependence of TCLR on microcline-plagioclase ratio shows a more complex pattern. As quartz concentration rises with a decline in feldspar concentration [1], also observed in Roza-Lambi quartz porphyry, the porcelain burning expansion interval will expand to 100 о С without deformation of products upon burning.

Evaluation of Dielectric Properties from

feldspar from Finland (рН 8.3).

for electrical porcelain production.

1957. P.208-230.

[1] Avgustinnik, А.I. Ceramics. L.: Stroyizdat, 1975. 590 p.

Baltic Shield. Petrozavodsk, 1980. P.108-117.

**5. Conclusions**

**6. References**

the Cakes of Feldspathic Raw Material for Electrical Porcelain Production 585

Table 1 shows that suspensions of Khetolambino and Lupikko microcline pegmatites have the highest рН values: 9.67 and 9.82, respectively. Potassic halleflinta, a nonconventional type of feldspathic raw material, has a high рН (10.0). Comparison of the рН of quartzfeldspathic rock suspensions has shown that large quantities of quartz (рН of quartz is 6.4-

Figure 4 shows that rocks that contain a total microcline and plagioclase concentration of 75.8–78.0 mass. % and quartz concentration of 22.0–24.2 mass. % have the highest pH.

Thus, the рН of feldspar suspensions depends on total microcline and plagioclase concentrations and quartz concentration in the rock. The pH of Elisenvaara syenite is 8.25 is consistent with feldspathic raw material according to GOST 21119.3-91 (рН 6-9) and Kemio

The electrical properties ε , lg ρ, tg δ of the cakes of nonconventional types of feldspathic rocks and conventional types, such as pegmatite, are basically affected by alkaline oxides and quartz. Dielectrical permeability depends more on microcline and quartz concentrations in rocks. The angle tangent of dielectrical loss and electrical resistance depend on total microcline and plagioclase concentration. Elisenvaara syenites, which contain high microcline and plagioclase concentrations (97 mass.%), typically show lower tg δ values (0.025) and higher electrical resistance (2.0◌ּ 10-12 Ohm◌ּ cm) than other rocks. Their TCLR is consistent with that of microcline pegmatite. The рН of the suspensions of nonconventional feldspathic raw material depends on total microcline and plagioclase concentration and quartz concentration. Elisenvaara alkaline syenites are a promising feldspathic raw material

[2] Avgustinnik, А.I., Kholodok, N.I, Golod, M.I. et al. On the thermal-physical properties of

[3] Ilyina, V.P. Technological evaluation of feldspathic rocks from the Louhi district //

[5] Kholodok, N.I., Golod, М.I., Popova, I.А., Klabukov, B.N. On the dielectrical

[6] Karyukina, V.N. Determination of minerals from the рН of their suspension // Modern

characteristics of their host environment. L., "Наука",1975. P. 47-49.

and the Kola Peninsula. Petrozavodsk, 1977. P.160-164.

feldspathic minerals from pegmatite veins. // Geophysical study of the eastern

Geological and technological evaluation of minerals and rocks from the Republic of Karelia and some regions of the European continent. Petrozavodsk, 1997. P.57-59. [4] Golod, M.I., Grodnitsky, L.L., Klabukov, B.N. On the dielectrical permeability of

plagioclase from pegmatite veins of the Kola Peninsula// Indicator minerals of the

permeability of potassic feldspars from pegmatite veins // Pegmatites from Karelia

methods for mineralogical study of rocks, ores and minerals. М.: Gosgeotekhizdat,

6.9) decrease their рН considerably, as in Roza-Lambi volcanics (рН 7.1).

Quartz has thus been shown to decrease the TCLR of feldspathic rocks, as indicated by the absence of rapid changes in the curves (Fig.3) in the range 600 – 700 о С. High concentrations of feldspathic minerals (microcline, plagioclase - 95 – 97.5 mass. %), which form a less viscous glass phase, increase the TCLR of feldspathic rocks.


*1.3. Significance for Study of Energy in Wireless Sensor Networks*

To evaluate the network performance, one may consider parameters that evidence proper network operation directly influencing the energy consumption of each node. There are local and global parameters. Global parameters display the total energy costs for the network considering each type of energy for each specific activity. In contrast, local parameters provide total energy consumption rates for a single node. This energy depends on the location of the node within the topology regardless of how near or far they are located from the coordinator node and how much traffic is transmitted through it [31].

An energy-efficient routing protocol decreases the consumption of the nodes by routing data through paths that display the least amount of energy. There are some special mechanisms to achieve this goal such as optimization of jumps to the destination node, maintenance of optimal and valid routes, reduction of transmission delays, and reduction of packet retransmissions and attempts to listen to the channel [32,33]. In this paper, we assess the aforementioned aspects to compare against four sensor network protocols.

Concerning the communication channel, it is a factor that significantly influences the energy consumption because the protocol executes a series of listening attempts to determine whether the channel is already busy with other information packets. The carrier senses multiple accesses with the collision avoidance (CSMA/CA) protocol [34] and works as follows: first, a node begins listening to the wireless channel and if it is free, the node begins transmitting. If the wireless channel is not free, the node recalculates a random delay, waits, and listens again. We are using the MAC-level protocol used for all extensions of 802.15.4 (including the original version), which is the CSMA/CA that guarantees a high data rate. A network recognition is being carried out at all times to check the status of the channel (carrier detection). Only when free, data can be sent. In the 802.11 standard, the physical layer polls the energy level over the radio frequency to determine whether or not there is transmission. If the channel is busy, a random timer starts (with a maximum of five back off periods), the timer only discovers time with free channel, transmits when it expires, and finally, if it does not receive ACK, it increases the back off. This metric is known as CSMA/CA retries. If these CSMA/CA retries are frequent, the channel is busy most of the time. Consequently, there might be several collisions due to overload. In addition, when the wireless channel is permanently busy with information packets, there are many collisions and retransmissions of packets. This fact influences energy consumption because the nodes spend more time and capacity retransmitting over and over.

In a network layer, overloads are an important factor that influence energy consumption. The efficiency of the routing protocol may also be measured by the number of packets the protocol needs to route to its destination. A protocol with many control packets will contribute to packet collisions and overall performance reduction. In terms of route discovery, in all the protocols considered, the nodes exhibit capacity to know their neighbors. In AODV and DSR, the nodes update the routes as required. However, in ZTR and MPH routing, nodes periodically update routes. Known neighbors helps nodes to establish valid destination routes to forward messages and to reduce the number of retransmission packets required. The metric named 'valid routes' consists of paths that are not damaged and these routes are immediately available and ready to be used for sending information. If nodes are able to handle several valid routes, it is more likely that packets will be forwarded and will not be retransmitted continuously, thus saving energy resources.

Another important metric is found in the tables where the routes to different network destinations are located. These routes may either be valid or obsolete. Hence, their maintenance is essential to prevent loss of information within the network. Network energy consumption is directly related to the complexity in the administration of routing or neighbor tables. As sensors execute huge routing processes, energy consumption increases if these routes have not been properly updated. This is why it is also important to assess route delays; they are directly related to the number of jumps that a node takes to reach a destination.

This work seeks to further advance the study of performance metrics in the analysis of sensor networks. One of the most important contributions of this work is the relationship between metrics and their influence on energy consumption in a WSN, as well as the comparison of how these metrics perform in widely known routing protocols with respect to a protocol proposed by the authors. An important assessment of these parameters is that routing tables provide critical information about network reliability and the number of valid routes available to the nodes for sending information. These metrics will directly influence packet loss rates in the network as well as information delivery reliability. The aforementioned study is implemented in a sensor network for each routing protocol in which the proposed energy model is also implemented, as described in Table 1. This model is based

on the operation of the Texas Instruments CC2530 sensor [35] in active mode and stable conditions. Besides, for nodes under LoRa technology and WiFi, we use the model described in [36].

**Table 1.** Energy model base for each of the main tasks in a node [37].

Global parameters denote the energy rates that each node spends for all activities performed within the network. These types of energy include the energy consumed by the microcontroller, which is regulated by the time the sensor remains on; the CSMA energy used when the node is listening to the channel to determine whether packets may be transmitted or received and the node is executing the CSMA/CA algorithm; the switching energy consumed when switching between activities, i.e., changing from transmission to reception or vice versa; transmission and reception energy used to transmit or receive a packet, respectively; awakening energy used when the node turns on; and the shutdown energy consumed when the node turns off.

All of these energies will promptly report how nodes save or consume energy, as the case may be, together with the routing protocol. Each type of energy is provided as an overall network metric. In other words, each energy is incorporated to observe possible points of interest or zoning in the network.

Local energy balance depends on the proximity of a node to the destination node. In the scope of this work, the final destination is the coordinator or collector node. As a local aspect, the energy consumption at each node is calculated, which denotes consumption depending on the distance between the node and the coordinator. Thus, the nodes closest to the collector will send their packets and also retransmit packets coming from other nodes. For this reason, the relational position of a node within the network significantly matters.