**5. Silver nanoparticles incorporated cotton fabrics**

The current interest is to development of efficient, non-toxic, durable and cost effective antibacterial finishing textiles with increased application in medical, healthcare, hygienic products as well as protective textiles materials. However the ability of cotton fibres to absorb large amount of moisture makes them more prone to microbial attack under certain conditions of humidity and temperature. Cotton may act as a nutrient, becoming suitable medium for bacterial and fungal growth (Rosemary, 2006). Therefore, cotton fibres are treated with numerous chemicals to get better antibacterial cotton textiles. Among the various antibacterial agents, silver nanoparticles have shown strong inhibitory and antibacterial activity, has no negative effect on the human body (Gao, 2008). These particles can be incorporated in several kinds of materials such as clothes. These clothes with silver nanoparticles are sterile and can be used to prevent or to minimize infection with pathogenic bacteria. Nowadays, metal based topical dressings have been widely used as a treatment for infections in burns, open wounds, and chronic ulcers (Panyala, 2008). Incorporation of silver naoparticles was carried out by physical means, before being used; cotton fabrics were washed, sterilized and dried. These were submerged in an Erlenmeyer flask containing silver nanoparticles and agitated at 600 rpm for 24 hrs and dried at 70º C followed by curing at 150 º C. The schematic representation of the formation of silver nanoparticles on cotton fabrics is presented in Scheme 1.

Scheme 1. Incorporation of silver nanoparticles on cotton fabrics (G.Thirumurugan, 2011).

bilayer. The phenomenon causes deenergization of the membrane and consequently cell death (Dibrov, 2002). Shahverdi et al., 2007 studied the combined effect of silver nanoparticles with different antibiotics against S.aureus and E.coli using the disk diffusion method. The antibacterial activities of penicillin G, amoxicillin, erythromycin, clindamycin,

The current interest is to development of efficient, non-toxic, durable and cost effective antibacterial finishing textiles with increased application in medical, healthcare, hygienic products as well as protective textiles materials. However the ability of cotton fibres to absorb large amount of moisture makes them more prone to microbial attack under certain conditions of humidity and temperature. Cotton may act as a nutrient, becoming suitable medium for bacterial and fungal growth (Rosemary, 2006). Therefore, cotton fibres are treated with numerous chemicals to get better antibacterial cotton textiles. Among the various antibacterial agents, silver nanoparticles have shown strong inhibitory and antibacterial activity, has no negative effect on the human body (Gao, 2008). These particles can be incorporated in several kinds of materials such as clothes. These clothes with silver nanoparticles are sterile and can be used to prevent or to minimize infection with pathogenic bacteria. Nowadays, metal based topical dressings have been widely used as a treatment for infections in burns, open wounds, and chronic ulcers (Panyala, 2008). Incorporation of silver naoparticles was carried out by physical means, before being used; cotton fabrics were washed, sterilized and dried. These were submerged in an Erlenmeyer flask containing silver nanoparticles and agitated at 600 rpm for 24 hrs and dried at 70º C followed by curing at 150 º C. The schematic representation of the formation of silver

Scheme 1. Incorporation of silver nanoparticles on cotton fabrics (G.Thirumurugan, 2011).

and vancomycin increased in the presence of Ag-NPs against both test strains.

**5. Silver nanoparticles incorporated cotton fabrics**

nanoparticles on cotton fabrics is presented in Scheme 1.

The antibacterial properties and the toxicity of metals to micro-organisms is well known, thus, now a days, silver is used in different kinds of formulations like surface coating agents, wound dressing, etc., (Shahverdi, 2007). The silver dressings make use of delivery systems that release silver in different concentrations. But different factors like the distribution of silver in the dressing, its chemical and physical form, affinity of dressing to moisture also inuence the killing of micro organisms (Lansdown, 2002). In this direction, metal nanocomposite bres were prepared containing silver nanoparticles incorporated inside the fabric but from the scanning electron microscopic study it was concluded that the silver nanoparticles incorporated in the sheath part of fabrics possessed signicant antibacterial property compared to the fabrics incorporated with silver nanoparticles in the core part (Chopra, 2007). Similar results were obtained by using silver nanoparticles on polyester nonwovens. It is also reported that silver nanoparticles coated textile fabrics possess antibacterialactivity against S.aureus (Shahverdi, 2007).

#### **6. Silver nanoparticles in antibacterial drug delivery**

Drug delivery system provide useful adjuncts for therapeutics including drugs, nucleic acids and proteins, with variety or roles like improving poor solubility, enhancing invivo stability, optimizing the biodistribution and pharmacokinetics of drugs. In recent years, interest has been stimulated by capability of the metal nanoparticles like AgNPs to bind a wide range of organic molecules, their low toxicity, and their strong and tunable optical absorption. This has resulted in a broad array of studies in which silver nanoparticles have played a role as drug and vaccine carriers into target cells or specic tissues. Furthermore, the unique chemical, physical, and photo-physical properties of silver nanoparticles can be exploited in innovative ways to control the transport and controlled release of pharmaceutical compounds (Skirtach, 2006). Generally, this has been achieved by modifying the surface of the silver nanoparticles so that they can bind to the specic targeting drugs or other biomolecules. But direct conjucation of metal nanoparticle with drugs also possible, it has been shown that conjugates of metal nanoparticles with antibiotics provide promising results in the treatment of intracellular infections (Skirtach, 2006). The conjugation of silver nanoparticles in antibiotic can increase the effectiveness of drug delivery to target some cases. Generally, exact dose is required to kill the pathogens but the amount of antibiotic used in therapy is much higher than the actual dose required. The excess amount of antibiotic can cause adverse effects. Therefore, this conjugation of antibiotic with silver nanopaticles would be helpful to improve antibiotic efficacy. Silver nanoparticles can be directly conjugated with antibiotics or other drug molecules via ionic or covalent bonding, or by physical absorption. For example, Drug has been conjugated to silver nanoparticles [Figure 4]. The cytotoxic effect of free drug is about seven times lower than that of drug conjugated silver nanoparticles. Saha et al., 2007, conjugated directly different type of antibiotic to nonfunctionalized spherical metal nanoparticles, conjugated form showed greater degree of antibacterial activity with stability than free antibiotics. However, the conjugated form showed some aggregation after conjugation, a situation that other workers consider very deleterious. Therefore, it is likely that modication of the surface of the metal nanoparticles to prevent aggregation would improve the efficacy of such drug delivery systems further.

Surface chemistry of nanomaterial plays an important role, to improve the stability of metal nanoparticles and prevent their aggregation during the conjugation process between biomolecules and nanoparticles. Compared with other drug release materials, the unique

Silver Nanoparticles: Real Antibacterial Bullets 417

In addition to the surface chemistry of SNPs, their physical properties could be exploited for delivery applications (Asadishad, 2010). The release of a drug from silver nanoparticles could proceed via internal stimuli (pH or glutathione mediated) or also via external stimuli with the application of light. Silver nanoparticles of various shapes can undergo a strong plasmon resonance with light; therefore light induced plasmonic heating may be exploited to release a chemical payload which had been attached to the silver nanoparticles. This may be provided an interesting approach to deliver pharmaceutical compound directly into the cytoplasm or nucleus of target cells. Due to optical properties silver nanoparticles, light sensitive molecule can be attached onto silver nanoparticles in which SNPs serves as a substrate. In this case, light sensitive and fully reversible conformation-changing molecule had been attached onto the silver naoparticles (Jain, 2007). The conformation has been changed from a closed form to an open form when it is irradiated by UV light and the process can be reversed by the application of visible light or heat. This light mediated metal nanoparticle conjugate system could be very effective in the controlled release to treat selected conditions (Jain, 2007). Cheng, 2008, prepared the microcapsules via layer-by-layer technique to encapsulate fluoresceinlabeled dextran. The capsule-shells were doped with metal nanoparticles, which response against near infra red (NIR) light. FITC-dextran released upon laser (1064) treatment due

to rupture of the shell [Figure 6].

**7. Conclusion**

Fig. 6. Light- mediated drug release (G.Thirumurugan, 2011).

Increasing awareness towards green chemistry and biological processes has led to a desire to develop an environment-friendly approach for the synthesis of non toxic nanoparticles. Unlike other processes in physical and chemical methods, which involve hazardous chemicals, microbial biosynthesis of nanoparticles is cost-effective and eco friendly approach. Therefore, microbes regarded as potential bio factories for nanoparticles synthesis and serves as a new generation anti bacterial agent with their unique chemical and physical properties. The silver nanoparticle have also found diverse applications in the form of wound dressings, coating medical devices, silver nanoparticles impregnated textile fabrics,

surface plasmonic properties of the silver particles make it possible to observe the drug release process in living cells by surface enhanced Raman scattering (SERS) method. Jing Yang et al., 2009, found that silver nanoparticles can be used to control the release of drug in living cells. The reason may be that silver nanoparticles can hold the surrounding drug molecules to its surface until a monolayer is formed. The way drug absorbing on the silver surfaces plays an important role in their drug delivery effect in living cells. Figure 5, depicts the SERS spectra of drug in (a) solution and (b) living cells and viability of cells treated with different concentrations of drug and silver nanoparticles. (a) Pure drug; (b) Drug and silver nanoparticle complex; (c) pure silver nanoparticles.

Fig. 4. Cytotoxic effect of drug conjugated silver nanoparticles (G.Thirumurugan, 2011).

Fig. 5. Left side shows SERS spectra of drug in (a) solution and (b) living cells and right side shows viability of cells treated with different concentrations of drug and silver nanoparticles. (a) Pure drug; (b) Drug and silver nanoparticle complex; (c) pure silver nanoparticles.

surface plasmonic properties of the silver particles make it possible to observe the drug release process in living cells by surface enhanced Raman scattering (SERS) method. Jing Yang et al., 2009, found that silver nanoparticles can be used to control the release of drug in living cells. The reason may be that silver nanoparticles can hold the surrounding drug molecules to its surface until a monolayer is formed. The way drug absorbing on the silver surfaces plays an important role in their drug delivery effect in living cells. Figure 5, depicts the SERS spectra of drug in (a) solution and (b) living cells and viability of cells treated with different concentrations of drug and silver nanoparticles. (a) Pure drug; (b) Drug and silver

Fig. 4. Cytotoxic effect of drug conjugated silver nanoparticles (G.Thirumurugan, 2011).

Fig. 5. Left side shows SERS spectra of drug in (a) solution and (b) living cells and right side

shows viability of cells treated with different concentrations of drug and silver nanoparticles. (a) Pure drug; (b) Drug and silver nanoparticle complex; (c) pure silver

nanoparticles.

nanoparticle complex; (c) pure silver nanoparticles.

In addition to the surface chemistry of SNPs, their physical properties could be exploited for delivery applications (Asadishad, 2010). The release of a drug from silver nanoparticles could proceed via internal stimuli (pH or glutathione mediated) or also via external stimuli with the application of light. Silver nanoparticles of various shapes can undergo a strong plasmon resonance with light; therefore light induced plasmonic heating may be exploited to release a chemical payload which had been attached to the silver nanoparticles. This may be provided an interesting approach to deliver pharmaceutical compound directly into the cytoplasm or nucleus of target cells. Due to optical properties silver nanoparticles, light sensitive molecule can be attached onto silver nanoparticles in which SNPs serves as a substrate. In this case, light sensitive and fully reversible conformation-changing molecule had been attached onto the silver naoparticles (Jain, 2007). The conformation has been changed from a closed form to an open form when it is irradiated by UV light and the process can be reversed by the application of visible light or heat. This light mediated metal nanoparticle conjugate system could be very effective in the controlled release to treat selected conditions (Jain, 2007). Cheng, 2008, prepared the microcapsules via layer-by-layer technique to encapsulate fluoresceinlabeled dextran. The capsule-shells were doped with metal nanoparticles, which response against near infra red (NIR) light. FITC-dextran released upon laser (1064) treatment due to rupture of the shell [Figure 6].

Fig. 6. Light- mediated drug release (G.Thirumurugan, 2011).


**2. Materials and Methods**

In this study, 3C-SiC growth was performed in a horizontal hot-wall chemical vapor deposition (CVD) reactor (ACIS M10 supplied by LPE, Catania, Italy) using (100)-oriented Si substrates. The reaction system used was tri-chloro-silane (TCS), ethylene (C2H4) and hydrogen (H2) as the silicon precursor, carbon precursor and gas carrier, respectively. After the growth of a 75 µm thick layer, at a temperature of 1400 ◦C and pressure of 100 mbar, the temperature was increased to 1650 ◦C and the silicon substrate was completely melted inside the CVD chamber, resulting in free-standing samples. For more details, see

the references [20,21]. In particular, two different sets of samples were grown: 3C-SiC on Si (100) 4◦ off-axis along the [110], adding constant nitrogen flux, with a value of 0 (intrinsic), 300, 800 and 1600 sccm; 3C-SiC on Si (100) 4◦ off-axis along the [100], adding constant trimethylaluminum (TMA) flux, with a value of 0, 1, 2 and 4 sccm. The round brackets "()" indicate a crystallographic plane; square brackets "[]" indicate a direction. Atomic concentration values, for both types of doping, were obtained from calibration curves acquired through secondary-ion mass spectrometry (SIMS) analysis, whose values were determined to be: 1.2 × 1019, 2.9 × 1019 and 5.8 × 1019 at/cm3 , for nitrogen doping; 1.7 × 1018 at/cm3 , 3.4 × 1018 at/cm3 and 6.8 × 1018 at/cm3 , for aluminum. In this work, two different doping scales were used, 1018 at/cm3 and 1019 at/cm3 for aluminum and nitrogen, respectively.

Micro-Raman and micro-photoluminescence maps were acquired at room temperature using an HR800 spectrometer integrated system Horiba Jobin Yvon (Horiba, Lille, France) in a backscattering configuration. For both the analyses, the excitation wavelength was supplied by a 325 nm He-Cd continuous-wave laser that was focalized on the sample by a ×40 objective, with numerical aperture (NA) of 0.5. The scattered light was dispersed by an 1800 grooves/mm kinematic grating, for Raman analysis, and by a 300 grooves/mm kinematic grating, for PL analysis.

The etching in potassium hydroxide (KOH) was adopted for the evaluation of SFs. KOH etching was performed at 500 ◦C for 3 min. The densities were calculated based on the observation of optical and scanning electron microscopy (SEM) images, (FE-SEM ZeissSupra35, Carl Zeiss NTS GmbH, Oberkochen, Germany) operated at an acceleration voltage of 5 kV. An accurate count and identification of SFs were achieved using powerful image analysis software.

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

The Raman technique is sensitive to the average quality of the material. In particular, taking into account the wavelength of the laser used (325 nm), the technique is more sensitive to the surface (approximately 2–3 microns). The intensity of the transversal optical (TO) Raman mode and its full width at half maximum (FWHM) are the two parameters most closely related to the defectiveness of the material. Figure 1 shows the FWHM of the TO peak vs. doping concentration for nitrogen (black squares) and aluminum (red points). For the P-type samples, the FWHM of the TO shows a constant trend independent of the doping concentration. The FWHM value is approximately 11 cm−1 . For the N-type samples, the FWHM of the TO decreases with increasing nitrogen concentration, reaching values lower than those of the intrinsic sample (FWHM = 10 cm−1 ) for a nitrogen concentration of 5.8 × 1019 at/cm3 (FWHM = 7 cm−1 ). However, the N-type doping is a factor of 10 greater than that of the P-type.

Figure 2 shows the room-temperature PL spectra, centered at 540 nm, acquired on the surfaces of the N-type (left panel) and P-type (right panel) samples.

After increasing the nitrogen doping, we can observe that in the presence of N-type samples, the intensity of the band-edge peak (centered at 540 nm) grows. In N-doped 3C-SiC, there are more electrons near the Fermi level that increase the band-to-band transitions [22] and lead to the enhancement of the detected signals. The shoulder at approximately 560 nm is present in both N-type and P-type samples and it is due to the presence of levels near the conduction band introduced by nitrogen, which cannot be completely eliminated from the chamber during the growth process.

Instead, increasing the aluminum doping, in P-type samples, the intensity of the band-edge peak (centered at 540 nm) decreases. At the same time, a new band is visible in the red spectral region (approximately 600 nm), which rises in intensity when increasing the aluminum doping. In agreement with the literature [22], this band is related to the presence of intra-gap defects, which are responsible for reducing the emission intensity of the band-edge.

16

 N-type P-type

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**Figure 1.** FWHM of the TO peak vs. doping concentration for nitrogen (black squares) and aluminum (red points). N-type and P-type samples were grown on Si (100) off-axis [110] and off-axis [100], respectively.

**Figure 2.** PL spectra, centered at 540 nm, acquired on the surfaces of the N-type (**left panel**) and P-type (**right panel**) samples.

The presence of dopant elements not only influences the crystalline and optical properties of the material but could also influence the presence and development of SFs within 3C-SiC. Due to their high electrical activity, the concentration of SFs in the material is a fundamental parameter. Even today, the concentration of stacking faults is not compatible with the development of VLSI technology.

Therefore, all samples were treated by molten KOH etching to study the effect of doping concentration on the density and mean length of SFs. As an example, Figure 3 shows the optical and SEM images of the intrinsic (Figure 3a,c) and N-type doped (5.8 × 1019 at/cm3 ) samples (Figure 3b,d) subjected to selective KOH etching. Here, wet etching highlights the presence of SFs following the variation of the etching rate, which, according to the variation of binding energy in the presence of such defects, promotes groove formation along the extension of the extra planes [23].

In all images, the etched SFs are oriented along a preferential direction. Off-axis growth promotes propagation up to the surface of the SFs lying along the plane (111), while the SFs (−1−11) opposite to the growth step are forbidden. In the absence of the annihilation mechanism, the propagation of the Si-face SFs from the 3C-SiC/Si interface towards the surface is favored. In addition, the etching rate of the C-face SFs, lying on the (−111) and (1−11) crystallographic planes, is uniform to the (001) plane, while the etching rate of the Si-phase SFs is lower, displaying their distribution [24].

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**Figure 3.** Optical and SEM images of the intrinsic (**a**,**c**) and N-type doped (**b**,**d**) samples subjected to selective KOH etching.

− − For the observation of the samples attached in KOH, the optical images have proved useful for the detection of large SFs (>1 µm). For SFs smaller than 1 µm, SEM images allowed us to obtain a detection limit of 100–150 nm.

− − μ μ In Figures 4 and 5, we report the distribution of the lengths of the SFs for different N-type and P-type doping, respectively. For N-type samples (Figure 4a–d), we observe that as the nitrogen concentration increases (from intrinsic sample to 5.8 × 1019 at/cm3 ), the length distribution of the SFs widens and flattens. Consequently, the maximum shifts to greater lengths. For P-type samples (Figure 5a–d), we observe that as the aluminum concentration increases (from intrinsic sample to 6.8 × 1018 at/cm3 ), the maximum of the length distribution is quite constant.

The distributions of the lengths of the SFs were fitted with log-normal type curves, which are better suited to fit the experimental values and to reduce the fit error. From the fits, the mean values of length of the SFs were obtained for all the samples. The values are reported in Table 1 and they are plotted in Figure 6.

We observe that by increasing the dopant concentration for P-type samples (red points, Figure 6, left panel), the mean density values of SFs are closer to the density of the intrinsic counterpart. Indeed, the Al-doped samples exhibit an SF concentration ranging from 5.74 × 103 cm−1 for 1.7 × 1018 at/cm3 to 4.08 × 103 cm−1 for the sample with 6.8 × 1018 at/cm3 doping. For the N-type samples (black squares, Figure 6, left panel), on the other hand, the trend of the density of the SFs tends to move away from the intrinsic value. In fact, if for the intrinsic sample, the average SF density is 2.05 × 103 cm−1 , by increasing doping to 2.9 × 1019 at/cm3 and 5.8 × 1019 at/cm3 , such a value is notably reduced by almost an order of magnitude to 4.7 × 102 cm−1 and 2.4 × 102 cm−1 . Ab initio DFT calculation shows that when nitrogen is very close to the SFs, their formation energy increases, resulting in a reduction in the density of the SFs inside the material [25]. Comparing the density of the SFs between the two intrinsic samples grown on Si (100) 4 ◦ off-axis along the [110] (dashed green line), and on Si (100) 4◦ off-axis along the [100]

(dashed magenta line), two different densities were obtained: 2050 cm−1 and 4920 cm−1 , respectively. The dependence of density of the SFs on the step evolution and surface morphology on the shear angle was observed for 3*C*-SiC samples grown on (111) Si substrates with off-cut axes along [110] and [112] [26]. As mentioned above, off-axis growth promotes propagation up to the surfaces of the SFs lying along the plane (111), while the SFs (−1−11) opposite to the growth step are forbidden. However, while, for the N-type samples, the off-axis angle is along the [110] direction and therefore favors the suppression of the SFs along the plane (−1−11), for the P-type samples, the off-axis angle is along the direction [100]. In this way, not all the SFs along the plane (−1−11) are suppressed. At the same time, we observed that this cutting direction does not lead to a complete suppression of the APBs (SEM images not reported here), which act as a source and annihilation point for the SFs [27]. This leads to an increase in the density of the SFs that reach the surface.

Increasing the dopant concentration for the N-type samples (black squares, Figure 6, right panel), the average length of the SFs increases, starting from a value of approximately 2 µm for the intrinsic sample to 5 µm for the sample with a nitrogen concentration of 5.8 × 1019 at/cm3 . Instead, by increasing the dopant concentration for the P-type samples (red points, Figure 6, right panel), the length of the SFs remains constant, less than 1 µm. In the presence of N-type doping, the average values are greater than the value of the intrinsic sample grown off-axis [110] (dashed green line). The reduced density of SFs, increasing the nitrogen concentration (Figure 6 left panel), is susceptible to a reduced mutual annihilation mechanism. This contributes to the increase in the average size of SFs reaching the surface. **2021**, , x FOR PEER REVIEW 6 of 11

**Figure 4.** Length distribution of the SFs for different N-type doping. (**a**) Intrinsic sample, (**b**) 1.2 × 1019 at/cm3 , (**c**) 2.9 × 1019 at/cm3 and (**d**) 5.8 × 1019 at/cm3 . For all samples, there are a few SFs whose lengths are greater than the range reported in the X-axis range. However, all the lengths of the SFs were considered in the calculation of the mean lengths and densities.

**− μ − − μ −**

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**Figure 5.** Length distribution of the SFs for different P-type doping. (**a**) Intrinsic sample, (**b**) 1.7 × 1018 at/cm3 , (**c**) 3.4 × 1018 at/cm3 and (**d**) 6.8 × 1018 at/cm3 . For all samples, there are a few SFs whose lengths are greater than the range reported in the X-axis range. However, all the lengths of the SFs were considered in the calculation of the mean lengths and densities.

**Table 1.** Values of average lengths and densities for different doping concentrations.

In the presence of P-type doping, instead, the average values are smaller than both values of the intrinsic samples grown off-axis (green and magenta line). As mentioned above, for the P-type samples, the off-axis angle is along the direction [100]. In this way, not all the SFs along the plane (−1−11) are suppressed. This leads to an increase in the mutual annihilation mechanism, responsible for reducing the average lengths of the SFs on the surface.

Both values of density and mean length of the SFs depend on the direction of the cutting angle and, in particular, on the doping concentration. It is interesting to note that the trend of the density of the SFs, when increasing the doping concentration, is in agreement with the trend of the crystalline quality (FWHM of the TO) reported in Figure 1. For Ptype samples, as the dopant concentration increases, a constant SF density and crystalline quality were observed. Instead, for N-type samples, as the dopant concentration increases,

a clear reduction in the density of SFs and a constant increase in crystalline quality were obtained. In Figure 7, the FWHM of the TO peak vs. the 3C-SiC thickness (in cross-section) is reported, for the intrinsic (black line) and 5.8 × 1019at/cm3 (red line) samples. The zero value on the X-axis corresponds to the removed interface with the silicon.

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**Figure 6.** Distribution of SF density (**left panel**) and SF mean length (**right panel**) vs. doping concentration. The graphs also report the average density and average length of the intrinsic samples grown on Si (100) off-axis [110] (dashed green line) and off-axis [100] (dashed magenta line). **2021**, , x FOR PEER REVIEW 9 of 11

**Figure 7.** FWHM of the TO peak vs. 3C-SiC thickness in cross-section for intrinsic and 5.8 × 1019 at/cm3 (red line) samples. The zero value on the X-axis corresponds to the removed interface with the silicon.

μ μ μ In the first 10 µm of the films, a difference in crystalline quality was observed. In particular, lower crystalline quality was observed for the doped sample (red curve). The red curve shows a higher average value in this area. For thicknesses between 10 and 30 µm, the crystalline qualities are constant and show the same value for both samples. For thicknesses greater than 30 µm, a divergence was observed between the two curves. In particular, the doped sample continues to maintain the same crystalline quality, while for the intrinsic sample, the quality tends to worsen, reaching the value reported in the in-plan measurements (Figure 1).

The difference in thermal expansion coefficient and lattice parameters between Si and SiC leads to the formation of dislocations at the hetero-interface and the presence of a local strain field within the epilayer [15]. The presence of residual stress (depending on doping concentration [21]) can drive the formation of perfect and partial dislocations

−

μ μ

(PDs) in the epilayer. Every SF is bordered by two PDs, which limit the wrong sequence plane from the perfect 3C crystal. More investigations should be done, particularly in cross-sections, to determine the role of doping concentration in the density, shape and stability of PDs, because the expansion or shrinkage of SFs is driven by the energetics and kinetics of the PDs.

The association of current doping systems alongside compliant substrate adoption, such as inverted silicon pyramids [28] or a Si1−xGex buffer layer [29], could lead to the further implementation of growth strategies capable of reducing the concentration of SFs and leading to the development of efficient 3C-SiC power devices.

#### **4. Conclusions**

The 3C-SiC hetero-epitaxial layers, doped with nitrogen or aluminum, were grown in a horizontal hot-wall chemical vapor deposition (CVD) reactor on Si (100) substrate with different 4◦ off-axis. Stacking fault evaluation through molten KOH etching was performed on different doped samples: 1.2 × 1019, 2.9 × 1019 and 5.8 × 1019 at/cm3, for nitrogen doping; 1.7 × 1018, 3.4 × 1018 and 6.8 × 1018 at/cm3, for aluminum. Samples were grown on Si (100) 4◦ off-axis along the [110] and on Si (100) 4◦ off-axis along the [100], for nitrogen and aluminum, respectively.

In particular, it was observed that as the dopant concentration increases, the average length of the SFs increases, from a value of 2 µm to 5 µm, and the density decreases, for N-type doping. Instead, for P-type doping, when increasing the doping concentration, the mean length and density of the SFs remain constant.

Both values (density and mean length) depend on the direction of the cutting angle. In fact, the off-axis angle along the [110] favors the suppression of the SFs along the plane (−1−11), decreasing the density of the SFs on the surface and increasing their mean length. Instead, for an off-axis along the [100], not all the SFs along the plane (−1−11) were suppressed. This leads to an increase in the mutual annihilation mechanism, responsible for reducing the average lengths of the SFs on the surface.

**Author Contributions:** Conceptualization, V.S., C.C., F.L.V.; methodology, V.S., C.C.; synthesis, R.A., M.M., D.C.; investigation, V.S., C.C., A.C., S.B.; data curation, C.C., V.S.; writing—original draft preparation, V.S.; writing—review and editing, V.S., C.C., F.L.V.; supervision of the whole project, F.L.V.; funding acquisition, F.L.V. All authors read and approved the final manuscript.

**Funding:** This research was funded by the European Union within the framework of the project CHALLENGE, grant number 720827.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to industrial non-disclosure agreement.

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

#### **References**


*Edited by Elmer P. Dadios*

The capability of Fuzzy Logic in the development of emerging technologies is introduced in this book. The book consists of sixteen chapters showing various applications in the field of Bioinformatics, Health, Security, Communications, Transportations, Financial Management, Energy and Environment Systems. This book is a major reference source for all those concerned with applied intelligent systems. The intended readers are researchers, engineers, medical practitioners, and graduate students interested in fuzzy logic systems.

ISBN 978-953-51-0337-0

Fuzzy Logic - Emerging Technologies and Applications

Photo by Max Krasnov / Depositphotos


**1.**

## **Table**

*Correlation analysis of environmental variables with each other and with response ratio of SCP [*RR*(SCP)] and SNP [*RR*(SNP)] of surface soil (<15 cm).*

#### *Effects of Grazing Intensity on Belowground Carbon and Nitrogen Cycling DOI: http://dx.doi.org/10.5772/intechopen.90416*

*Grasses and Grassland Aspects*

exhibited positive effects on SNP at the depth of >15 cm, while both moderate and heavy grazing had the opposite effects on it at the same depth (**Figure 6**). These differences induced by livestock type, climate type and soil depth may results from the complex interaction between grazing intensity with water, temperature and nutrients, but the potential mechanisms was still unknown and need further

*Relationships of grazing duration (a, b), mean annual temperature (MAT, c, d), and mean annual precipitation (MAP, e, f) with response ratios (RR) of soil carbon pools (SCP, a, c, e) and soil nitrogen pools*

Overgrazing is a primary contributor to grassland degradation and desertification, which may significantly affect ecosystems functions and then lead to positive or negative climate-biosphere feedbacks [8, 25]. The regional and global studies showed that grazing intensity is a very important role in regulating belowground C and N pools and fluxes, which may offer some suggestions for future grassland management and model development. First, the effects of grazing intensity on C and N cycles may be regulated by environmental conditions (e.g., nitrogen and water availability; [8]). However, how the interactions of grazing with global change factors (e.g., warming, nitrogen addition, elevated CO2, increased precipitation and drought) is influenced by grazing intensity remain unknown [44, 45]. These knowledge gaps may impede us to fully understand how grazing affects C

Second, current global synthesized studies showed that most of current grazing studies were distributed in temperate climates, such as eastern Asia and North America, and only few studies were conducted in cold and tropical regions [5, 6]. Thus, more studies from other regions (e.g., Africa and Australia) should be conducted in order to develop a more comprehensive understanding of how grazing intensity influence C and N cycling of global grasslands. Another problem is the experimental duration. Most of current grazing experiments were less than 10 years, due to the high costs and long time scale. The grazing effects on C and N cycle may vary with time [5]. Hence, there is a need to conduct studies over one decade to bet-

ter understand the effects of grazing on belowground C and N cycling.

**102**

investigations.

**Figure 7.**

*(SNP, b, d, f).*

**4. Implication for grassland management**

and N cycles of grasslands at global scale.

Third, grazing intensity (light, moderate, and heavy grazing) significantly affects belowground C and N cycling in grassland ecosystems. Meanwhile, different combinations of grazing and global change factors (e.g., warming, nitrogen addition) also have disparate effects on C and N cycle of grasslands [8]. However, current land-surface models did not usually differentiate the effects of grazing intensities as well as their combinations with global change factors, which may trigger great challenges for us to predict the C-climate feedbacks in the Anthropocene. Therefore, future land-surface models may need thus to fully consider these processes in order to develop more precise process-based mechanism for forecasting the feedback of grassland ecosystems to climate change.

Fourth, environmental factors (both MAP and MAT) may be crucial in evaluating the response of belowground C and N cycling to different driving factors, as the effects of grazing, global change factors, and their combinations on belowground C and N cycling may change with MAT and MAP transects [6, 14]. The global study also demonstrated that response ratios of soil carbon content and soil nitrogen content to grazing in warmer biomes was clearly higher than those in the low range (**Figure 7**). These results demonstrated the importance of decreasing grazing frequency and intensity in warmer regions than colder ones, which may help to increase soil C sequestration in ecological fragile areas.

#### **Acknowledgements**

This research was financially supported by the "Thousand Young Talents" Program in China, National Natural Science Foundation of China (Grant No. 31600352, 31370489) and "Outstanding doctoral dissertation cultivation plan of action of East China Normal University (Grant No. YB2016023). We would like to acknowledge the permission from Global Change Biology to allow us to transfer our previous part work into monograph chapter to the publics.

**105**

**Author details**

, Lingyan Zhou1

East China Normal University, Shanghai, China

provided the original work is properly cited.

\*Address all correspondence to<EMAIL_ADDRESS>
and Xuhui Zhou1,2\*

1 Center for Global Change and Ecological Forecasting, Tiantong National Field Station for Forest Ecosystem Research, Shanghai Key Lab for Urban Ecological Processes and Eco-Restoration, School of Ecological and Environmental Sciences,

2 Shanghai Institute of Pollution Control and Ecological Security, Shanghai, China

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

Guiyao Zhou1

*Effects of Grazing Intensity on Belowground Carbon and Nitrogen Cycling*

*DOI: http://dx.doi.org/10.5772/intechopen.90416*

#### **Conflict of interest**

All authors declare no conflict of Interests.

*Effects of Grazing Intensity on Belowground Carbon and Nitrogen Cycling DOI: http://dx.doi.org/10.5772/intechopen.90416*

#### **Author details**

*Grasses and Grassland Aspects*

**Acknowledgements**

**Conflict of interest**

Third, grazing intensity (light, moderate, and heavy grazing) significantly affects belowground C and N cycling in grassland ecosystems. Meanwhile, different combinations of grazing and global change factors (e.g., warming, nitrogen addition) also have disparate effects on C and N cycle of grasslands [8]. However, current land-surface models did not usually differentiate the effects of grazing intensities as well as their combinations with global change factors, which may trigger great challenges for us to predict the C-climate feedbacks in the Anthropocene. Therefore, future land-surface models may need thus to fully consider these

processes in order to develop more precise process-based mechanism for forecasting

Fourth, environmental factors (both MAP and MAT) may be crucial in evaluating the response of belowground C and N cycling to different driving factors, as the effects of grazing, global change factors, and their combinations on belowground C and N cycling may change with MAT and MAP transects [6, 14]. The global study also demonstrated that response ratios of soil carbon content and soil nitrogen content to grazing in warmer biomes was clearly higher than those in the low range (**Figure 7**). These results demonstrated the importance of decreasing grazing frequency and intensity in warmer regions than colder ones, which may help to

This research was financially supported by the "Thousand Young Talents" Program in China, National Natural Science Foundation of China (Grant No. 31600352, 31370489) and "Outstanding doctoral dissertation cultivation plan of action of East China Normal University (Grant No. YB2016023). We would like to acknowledge the permission from Global Change Biology to allow us to transfer our

the feedback of grassland ecosystems to climate change.

increase soil C sequestration in ecological fragile areas.

previous part work into monograph chapter to the publics.

All authors declare no conflict of Interests.

**104**

Guiyao Zhou1 , Lingyan Zhou1 and Xuhui Zhou1,2\*

1 Center for Global Change and Ecological Forecasting, Tiantong National Field Station for Forest Ecosystem Research, Shanghai Key Lab for Urban Ecological Processes and Eco-Restoration, School of Ecological and Environmental Sciences, East China Normal University, Shanghai, China

2 Shanghai Institute of Pollution Control and Ecological Security, Shanghai, China

\*Address all correspondence to<EMAIL_ADDRESS>
© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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[14] Mcsherry ME, Ritchie ME. Effects of grazing on grassland soil carbon: A global review. Global Change Biology. 2013;**19**:1347-1357

[15] McNaughton S, Ruess R, Seagle S. Large mammals and process dynamics in African ecosystems. Bioscience. 1988;**38**:794-800

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[21] Zhang T, Zhang YJ, Xu MJ, Zhu JT, Wimberly MC, Yu GR, et al. Lightintensity grazing improves alpine meadow productivity and adaption to climate change on the Tibetan plateau. Scientific Reports. 2015;**5**:15949

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[23] Baker JM, Ochsner TE, Venterea RT, Griffis TJ. Tillage and soil carbon sequestration—What do we really know? Agriculture, Ecosystems and

[24] Reicosky DC. Tillage-induced CO2 emission from soil. Nutrient Cycling in Agroecosystems. 1997;**49**:273-285

[25] He NP, Zhang YH, Yu Q, Chen SP, Pan QM, Zhang GM, et al. Grazing intensity impacts soil carbon and nitrogen storage of continental steppe.

1995;**50**:294-298

1999;**9**:65-71

*Effects of Grazing Intensity on Belowground Carbon and Nitrogen Cycling DOI: http://dx.doi.org/10.5772/intechopen.90416*

management on the carbon and nitrogen balance of a mixed-grass rangeland. Ecological Applications. 1999;**9**:65-71

[18] Liu N, Zhang YJ, Chang SJ, Kan HM, Lin LJ. Impact of grazing on soil carbon and microbial biomass in typical steppe and desert steppe of Inner Mongolia. PLoS One. 2013;**7**:e36434

[19] Manley J, Schuman G, Reeder JD, Hart RH. Rangeland soil carbon and nitrogen responses to grazing. Journal of Soil and Conservation. 1995;**50**:294-298

[20] Stavi I, Ungar ED, Lavee H, Lavee H, Sarah P. Grazing-induced spatial variability of soil bulk density and content of moisture, organic carbon and calcium carbonate in a semi-arid rangeland. Catena. 2008;**75**:288-296

[21] Zhang T, Zhang YJ, Xu MJ, Zhu JT, Wimberly MC, Yu GR, et al. Lightintensity grazing improves alpine meadow productivity and adaption to climate change on the Tibetan plateau. Scientific Reports. 2015;**5**:15949

[22] Derner J, Briske D, Boutton T. Does grazing mediate soil carbon and nitrogen accumulation beneath C4, perennial grasses along an environmental gradient? Plant and Soil. 1997;**191**:147-156

[23] Baker JM, Ochsner TE, Venterea RT, Griffis TJ. Tillage and soil carbon sequestration—What do we really know? Agriculture, Ecosystems and Environment. 2007;**118**:1-5

[24] Reicosky DC. Tillage-induced CO2 emission from soil. Nutrient Cycling in Agroecosystems. 1997;**49**:273-285

[25] He NP, Zhang YH, Yu Q, Chen SP, Pan QM, Zhang GM, et al. Grazing intensity impacts soil carbon and nitrogen storage of continental steppe. Ecosphere. 2011;**2**:304-316

[26] Parton WJ, Schimel DS, Cole CV, Ojima DS. Analysis of factors controlling soil organic matter levels in Great Plains grasslands. Soil Science Society of America Journal. 1987;**51**:1173-1179

[27] Wu HH, Wiesmeier M, Yu Q, Steffens M, Han XG, Kögel-Knabner I. Labile organic C and N mineralization of soil aggregate size classes in semiarid grasslands as affected by grazing management. Biology and Fertility of Soils. 2012;**48**:305-313

[28] Detling J, Dyer M, Winn D. Net photosynthesis, root respiration, and regrowth of *Bouteloua gracilis* following simulated grazing. Oecologia. 1979;**41**:127-134

[29] Knops JMH, Bradley KL, Wedin DA. Mechanisms of plant species impacts on ecosystem nitrogen cycling. Ecology Letters. 2002;**5**:454-466

[30] Savadogo P, Sawadogo L, Tiveau D. Effects of grazing intensity and prescribed fire on soil physical and hydrological properties and pasture yield in the savanna woodlands of Burkina Faso. Agriculture Ecosystems and Environment. 2007;**118**:80-92

[31] Thomey ML, Collins SL, Vargas R, Johnson JE, Brown RF, Natvig DO, et al. Effect of precipitation variability on net primary production and soil respiration in a Chihuahuan Desert grassland. Global Change Biology. 2011;**17**:1505-1515

[32] Chapin IIFS, Matson PA, Mooney HA. Principles of Terrestrial Ecosystem Ecology. New York, NY, USA: Springer; 2002

[33] Su YZ, Li YL, Cui HY, Zhao WZ. Influences of continuous grazing and livestock exclusion on soil properties in a degraded sandy grassland, Inner Mongolia, northern China. Catena. 2005;**59**:267-278

**106**

*Grasses and Grassland Aspects*

[1] Hufkens K, Keenan TF, Flanagan LB, Scott RL, Bernacchi CJ, Joo E, et al. Productivity of North American grasslands is increased under future climate scenarios despite rising aridity. Nature Climate Change. 2016;**6**:710-716

change. Global Change Biology.

[10] Gong JR, Wang YH, Liu M, Huang YM, Yan X, Zhang ZY, et al. Effects of land use on soil respiration in the temperate steppe of Inner Mongolia,

China. Soil and Tillage Research.

for 61 years after agricultural

[12] Liu N, Kan HM, Yang GW, Zhang YJ. Changes in plant, soil, and microbes in a typical steppe from simulated grazing: Explaining potential change in soil C. Ecological Monographs. 2015;**85**:269-286

[11] Knops JMH, Tilman D. Dynamics of soil nitrogen and carbon accumulation

abandonment. Ecology. 2000;**81**:88-98

[13] Bagchi S, Ritchie ME. Introduced grazers can restrict potential soil carbon sequestration through impacts on plant community composition. Ecology

[14] Mcsherry ME, Ritchie ME. Effects of grazing on grassland soil carbon: A global review. Global Change Biology.

Seagle S. Large mammals and process dynamics in African ecosystems. Bioscience. 1988;**38**:794-800

Mongolian steppe. Biology and Fertility

[17] Schuman GE, Reeder JD, Manley JT, Hart RH, Manley WA. Impact of grazing

[16] Zhou XQ, Wang JZ, Hao YB, Wang YF. Intermediate grazing intensities by sheep increase soil bacterial diversities in an inner

of Soils. 2010;**46**:817-824

Letters. 2010;**13**:959-968

[15] McNaughton S, Ruess R,

2013;**19**:1347-1357

[9] Connell JH. Diversity in tropical rain forests and coral reefs. Science.

2019a;**25**:1119-1132

1978;**199**:1302-1310

2014;**144**:20-31

[2] Follett RF, Reed DA. Soil carbon sequestration in grazing lands: Societal benefits and policy implications. Rangeland Ecology and Management.

[3] Salvati L, Carlucci M. Towards sustainability in agro-forest systems? Grazing intensity, soil degradation and the socioeconomic profile of rural communities in Italy. Ecological

[4] Bai YF, Wu JG, Clark CM, Pan QM, Zhang LX, Chen SP, et al. Grazing alters ecosystem functioning and C:N:P stoichiometry of grasslands along a regional precipitation gradient. Journal of Applied Ecology. 2012;**49**:1204-1215

[5] Zhou GY, Zhou XH, He YH, Shao JJ, Hu ZH, Liu RQ, et al. Grazing intensity significantly affects belowground carbon and nitrogen cycling in grassland ecosystems: A meta-analysis. Global Change Biology. 2017;**23**:1167-1179

[6] Zhou GY, Luo Q, Chen YJ, Hu JQ, He M, Gao J, et al. Interactive effects of grazing and global change factors on soil and ecosystem respiration in grassland ecosystems: A global synthesis. Journal of Applied Ecology. 2019;**56**:2007-2019

[7] Yan L, Zhou GS, Zhang F. Effects of different grazing intensities on grassland production in China: A metaanalysis. PLoS One. 2013;**8**:e81466

[8] Zhou GY, Luo Q, Chen Y, He M, Zhou LY, Frank D, et al. Effects of livestock grazing on grassland carbon storage and release override impacts associated with global climate

Economics. 2015;**112**:1-13

2010;**63**:4-15

**References**

[34] Osem Y, Perevolotsky A, Kigel J. Site productivity and plant size explain the response of annual species to grazing exclusion in a Mediterranean semiarid rangeland. Journal of Ecology. 2004;**92**:297-309

[35] Shi XM, Li XG, Li CT, Zhao Y, Shang ZH, Ma QF. Grazing exclusion decreases soil organic C storage at an alpine grassland of the Qinghai–Tibetan plateau. Ecological Engineering. 2013;**57**:183-187

[36] Xia JY, Niu SL, Wan SQ. Response of ecosystem carbon exchange to warming and nitrogen addition during two hydrologically contrasting growing seasons in a temperate steppe. Global Change Biology. 2009;**15**:1544-1556

[37] Zhou XH, Talley M, Luo YQ. Biomass, litter, and soil respiration along a precipitation gradient in southern Great Plains, USA. Ecosystems. 2009;**12**:1369-1380

[38] Hu ZM, Li SG, Guo Q, Niu SL, He NP, Li LH, et al. A synthesis of the effect of grazing exclusion on carbon dynamics in grasslands in China. Global Change Biology. 2016;**22**:1385-1393

[39] Luyssaert S, Inglima I, Jung M. CO2 balance of boreal, temperate, and tropical forests derived from a global database. Global Change Biology. 2007;**13**:2509-2537

[40] Williams M, Eugster W, Rastetter EB, Mcfadden JP, Chapin FS III. The controls on net ecosystem productivity along an Arctic transect: A model comparison with flux measurements. Global Change Biology. 2000;**6**:116-126

[41] Wang CK, Yang JY, Zhang QZ. Soil respiration in six temperate forests in China. Global Change Biology. 2006;**12**:2103-2114

[42] Holland EA, Detling JK. Plant response to herbivory and belowground nitrogen cycling. Ecology. 1990;**71**:1040-1049

[43] Li CL, Hao XY, Zhao ML, Han GD, Willms WD. Influence of historic sheep grazing on vegetation and soil properties of a desert steppe in Inner Mongolia. Agriculture Ecosystems and Environment. 2008;**128**:109-116

[44] Lu M, Zhou XH, Luo YQ, Yang YH, Fang CM, Chen JK, et al. Minor stimulation of soil carbon storage by nitrogen addition: A metaanalysis. Agriculture Ecosystems and Environment. 2011;**140**:234-244

[45] Zhou LY, Zhou XH, Zhang BC, Lu M, Luo YQ, Liu LL, et al. Different responses of soil respiration and its components to nitrogen addition among biomes: A meta-analysis. Global Change Biology. 2014;**20**:2332-2343

**109**

**Chapter 6**

**Abstract**

*and Xue-Feng Ma*

United States, wheat

**1. Introduction**

Small Grains as Winter Pasture

in the Southern Great Plains

*Tadele T. Kumssa, Joshua D. Anderson, Twain J. Butler*

**Keywords:** forage, oat, rye, small grains, Southern Great Plains, triticale,

Small grains, such as wheat (*Triticum aestivum* L.), rye (*Secale cereale* L.), triticale (*X Triticosecale* Wittmack), and oat (*Avena sativa* L.), are an integral part of the forage-livestock system in the Southern Great Plains (SGP) of the United States, as they can be grazed during cool-season months when other forage species are not productive. On average in the last 3 years (2016–2018), 7 million hectares of land was planted annually by wheat alone for forage and grain production in the SGP, including Kansas, Oklahoma, and Texas [1], which is the largest area of low-rainfall winter wheat cropland worldwide. The SGP (32–40°N; 96–104°W) is generally classified as grassland, cropland, and forest land [2]. Although many crop species grow in the area, winter wheat covers the largest amount of cropland in the region. Small grains are well adapted to the SGP's environment, for both forage and grain (i.e., dual

Small-grain cereals are widely adapted and used as annual cool-season pastures in the Southern Great Plains (SGP) of the United States, where livestock and forage production are the largest contributors to agricultural income. The advantage of growing small grains in the region is evident due to the widespread adoption and flexibility of production for grain only, forage only, or both grain and forage (i.e., dual purpose). Farmers in the SGP often prefer the use of small grains for dual purpose mainly because of alternative income options from livestock and/or grain, ensuring stable income especially when product prices fluctuate with market demands. Small-grain forage is exceptionally important during autumn, winter, and early spring when forage availability from other sources is low. By providing nutritionally high-quality forage, small grains minimize the need for protein and energy supplements. Besides being used for winter pasture, small grains also serve as cool-season cover crops. While small grains offer different advantages in the integrated crop-livestock system in the region, farming management practices can play an important role to maximize the benefit. The objectives of this chapter are to summarize the significance of small grains as winter pasture and highlight the production status of each small-grain species in the SGP of the United States.

of the United States

#### **Chapter 6**

*Grasses and Grassland Aspects*

2004;**92**:297-309

2013;**57**:183-187

[34] Osem Y, Perevolotsky A, Kigel J. Site productivity and plant size explain the response of annual species to grazing exclusion in a Mediterranean semiarid rangeland. Journal of Ecology.

nitrogen cycling. Ecology. 1990;**71**:1040-1049

[44] Lu M, Zhou XH, Luo YQ, Yang YH, Fang CM, Chen JK, et al. Minor stimulation of soil carbon storage by nitrogen addition: A metaanalysis. Agriculture Ecosystems and Environment. 2011;**140**:234-244

[45] Zhou LY, Zhou XH, Zhang BC, Lu M, Luo YQ, Liu LL, et al. Different responses of soil respiration and its components to nitrogen addition among biomes: A meta-analysis. Global Change

Biology. 2014;**20**:2332-2343

[43] Li CL, Hao XY, Zhao ML, Han GD, Willms WD. Influence of historic sheep grazing on vegetation and soil properties of a desert steppe in Inner Mongolia. Agriculture Ecosystems and Environment. 2008;**128**:109-116

[35] Shi XM, Li XG, Li CT, Zhao Y, Shang ZH, Ma QF. Grazing exclusion decreases soil organic C storage at an alpine grassland of the Qinghai–Tibetan

plateau. Ecological Engineering.

[37] Zhou XH, Talley M, Luo YQ. Biomass, litter, and soil respiration along a precipitation gradient in southern Great Plains,

USA. Ecosystems. 2009;**12**:1369-1380

[39] Luyssaert S, Inglima I, Jung M. CO2 balance of boreal, temperate, and tropical forests derived from a global database. Global Change Biology.

measurements. Global Change Biology.

[41] Wang CK, Yang JY, Zhang QZ. Soil respiration in six temperate forests in China. Global Change Biology.

[42] Holland EA, Detling JK. Plant response to herbivory and belowground

2007;**13**:2509-2537

2000;**6**:116-126

2006;**12**:2103-2114

[40] Williams M, Eugster W, Rastetter EB, Mcfadden JP, Chapin FS III. The controls on net ecosystem productivity along an Arctic transect: A model comparison with flux

[38] Hu ZM, Li SG, Guo Q, Niu SL, He NP, Li LH, et al. A synthesis of the effect of grazing exclusion on carbon dynamics in grasslands in China. Global Change Biology. 2016;**22**:1385-1393

[36] Xia JY, Niu SL, Wan SQ. Response of ecosystem carbon exchange to warming and nitrogen addition during two hydrologically contrasting growing seasons in a temperate steppe. Global Change Biology. 2009;**15**:1544-1556

**108**


**Acknowledgements**

I want to thank the people working at EVEX hospitals-Krystyna Kiel Oncology Center for their encouragement and advice that led to the completion of this paper. I also thank my friends for their encouragement and words of advice. Special thanks to my mentor—Krystyna Kiel. Finally, if not for my family, who supports me all the time, I would never be able to work on this extraordinary project.