**2. COMBO-FISH: principle and applications**

In contrast to standard FISH where probes are usually cut and amplified by molecular biology techniques, COMBinatorial Oligonucleotide FISH (COMBO-FISH) follows a completely different strategy for probe design [19, 20, 26, 27]. If a genome of a species is sequenced and the sequence is cataloged in a database, oligonucleotide probes can be searched and a set of probes can be designed in silico. Also, their specificity is controlled by computer analysis searching for all possible binding sites of each probe [20, 28, 29].

Any given genome target that should be specifically labeled, can be selected in a DNA sequence database and the numbers of the beginning and end nucleotides are determined exactly. This cannot only be done for human but also for any other species with completely known DNA sequences that can be read in established DNA database archives like NCBI.

The process works as follows [20]: firstly, the beginning and end numbers of a given target (for instance, a gene) are selected. Then oligonucleotide stretches of 15–30 distinct bases are determined in such a way that just the combination of those stretches is singularly co-localizing at the given target site. All binding sites of each probe are determined. Finally, several probe combinations may be excluded from consideration: (a) these are those that have accessory binding sites at other locations in a genome than at the given target site or (b) those that co-localize at several loci in the same genome (**Table 1**). In addition to these principles governing the basic probe selection, further characteristic features (experimental and theoretical) can be taken into account to ensure a stable homogenous hybridization protocol. Among these, the most important ones are oligonucleotide length, binding energy [28], homo-purine/ homo-pyrimidine sequences [30, 31], melting temperature [32], CG-content [32], etc. (see **Table 2** as an example). Typically, such oligonucleotide stretches included in a probe set have a length of 15–30 nucleotides each.

Several probe sets created according to this procedure (e.g., ABL, BCR, Her2neu, GRB7, AMACR, etc., see below) have been published or will be shown in the next chapter.

#### **Table 1.**

*Examples of COMBO-FISH target sites ("Gene"), their positions according to the banding annotation ("Positions on chromosome"), start and end position in the human data base ("Base position"), possible number of probes ("B") and the finally remaining specifically co-localizing probes ("R").*

Finally, the computer-optimized probe set can be synthesized as DNA probes, PNA probes, SMART probes, or TINA (Twisted Intercalating Nuclear Acid) probes with high purity [30, 33–35]. PNA probes have a peptide backbone instead of a sugar– phosphate backbone of DNA. SMART probes also called molecular beacons consist of a stem-loop conformation quenching fluorescence by the closed loop until loop

*Combinatorial Oligonucleotide FISH (COMBO-FISH): Computer Designed Probe Sets… DOI: http://dx.doi.org/10.5772/intechopen.108551*

*Note: This table was originally published under CC BY license in [32]. a Melting temperature (median value of the denaturation curve).*

#### **Table 2.**

*Example of a COMBO-FISH probe set for AMACR and some physical values considered in the selection of oligonucleotide stretches.*

opening when probe and target are binding. TINA probes are oligonucleotides with additional anchoring molecules incorporated. Custom made oligonucleotide probes usually carry one dye molecule at one end or both ends each.

Depending on the base composition of the oligonucleotide probes, they can be designed in their 30 –50 direction either for Watson–Crick binding (duplex forming probes result in complimentary probe-target double strands) or for Hoogsteen binding (triplex forming probes result in triple strands to homo-purine or homopyrimidine sequences as targets of the intact double strand) (**Figure 1**). COMBO-FISH probes targeting in Watson–Crick configuration are more flexible since they can be designed in a mixture of purine and pyrimidine bases. Hoogsteen binding probes use solely either purines or pyrimidines. It should mentioned that also exceptional (non-homo) Hoogsteen triples exist which can be incorporated into the probe design.

Due to the optical diffraction of a microscope lens, the point image of a fluorescence dye molecule spreads to an image of typically about 250 nm using a high numerical aperture lens. So the fluorescence of a probe combination within a target size less than typically about 250 kb merges into a homogeneous COMBO-FISH "spot". Typical examples are shown in **Figure 2**.

Detailed protocols for COMBO-FISH labeling of blood cells, fibroblasts, tumor culture cells, or tissue cells are described elsewhere [27, 29]. These protocols can be applied for duplex or triplex forming probe sets. COMBO-FISH for labeling of specific gene targets in cell nuclei has been described for several applications as for instance: The gene of the receptor tyrosine kinase 2 (HER2/NEU) [28, 34] (**Figure 2A** and **B**), the gene of the growth factor receptor-bound protein 7 (GRB7) [34], the breakpoint cluster region (BCR) on chromosome 22 [30], the ABL proto-oncogene 1 (ABL) on chromosome 9 [19, 30, 31], and T-box 1(TBX1) [33] (**Figure 2C** and **D**), the promotor region of the FMR1 gene [36] (**Figure 2E**) and the Alpha-Methylacyl-CoA Racemase coding gene (AMACR) on chromosome 5 [32] (**Table 2**; **Figure 2F**). Using the probe set for the ABL gene region and Spatially Modulated Illumination Microscopy [37], significant volume changes of the labeled regions were observed in cell nuclei of CML patients before and after medical treatment [35]. Beyond gene target labeling by probe sets of several

#### **Figure 2.**

*Example fluorescence microscopy images of COMBO-FISH labeling (arrows) of gene targets: (A) HER2/NEU gene labeling in a cell nucleus and (B) on metaphase (combination of 18 oligonucleotide probes); (C) TBX1 gene labeling in a cell nucleus and (D) in an early stage of mitosis (combination of 15 oligonucleotide probes); (E) FMR1 promotor region labeling on chromosome X of a male cell nucleus (combination of 20 oligonucleotide probes); AMACR gene labeling in a cell nucleus (combination of 29 oligonucleotide probes; see Table 2). Note: With the exception of the nucleus in (E), no counterstain was applied.*

*Combinatorial Oligonucleotide FISH (COMBO-FISH): Computer Designed Probe Sets… DOI: http://dx.doi.org/10.5772/intechopen.108551*

different probe-sequences co-localizing at a given target only, also unique probes were found that were specifically labeling either interspersed genome regions by one copy each or centromeres by multi-copies (see chapter 4). Such an 18mer oligonucleotide PNA probe repetitively binding on centromere 9 was micro-injected into lymphocyte cell nuclei under in vivo conditions. After further incubation of the labeled cells, the sample was fixed and subjected to microscopy. The results indicated that the probe material was binding to the target region in the nuclei before fixation [33].


*5.2. Suggestions*

From the perspective of planning the utilization of water resources nationwide, the research findings suggest reducing water pressures and virtual water-flow imbalances. The nationwide allocation of water resources and the rational use of precipitation in the territory can not only prevent floods and droughts, but also bring a sufficient water supply to China's industrial and agricultural development and residents' lives. It also has a certain value in transportation and power generation. There is little difference between the topography of North and South China, and water resources can be mobilized from north to south. The Beijing–Hangzhou canal and the middle route of the South-to-North Water Transfer Project are examples of the North–South distribution of water resources in China. In addition to these two major projects, China can also carry out the large-scale networking of rivers, lakes, and other water areas across the country to make them interconnected. In the case of a flood in a certain place, the excess water could be transferred to another area with fewer water resources. In the case of a drought in a certain place, the water resources of other areas could be mobilized to supplement it, so as to avoid the waste of water resources and maximize the utilization of water resources. According to the decision-making process and

the deployment of water-resource management in China, the multi-functional properties of water resources could be fully accomplished in the near future. Under the overall framework of the distribution scheme of the available water supply of the Yellow River, taking into account the ecological water demand, sediment transport volume, external water transfer volume, and water-use structure of provinces along the Yellow River Basin, a joint water-supply pattern of the Yangtze River and the Yellow River will be formed, a dynamic water-distribution scheme of the Yellow River Basin will be constructed, the water-right transfer and compensation system will be gradually improved, and the linkage mechanism between water-use indicators and land indicators will be explored. This will result in comprehensively coordinating the relationship between water, energy, and food; limiting water use for fossil energy development; improving the utilization efficiency of agricultural water resources; building a wind–water complementary power generation system, and implementing the transmission, storage, and utilization of hydrogen energy at normal temperatures and pressures. Ideally, water-scarce regions will import waterintensive products to meet the production and consumption of the region, rather than relying on local production, so as to protect the domestic water resources. In this case, the water resources required by the whole production chain actually come from export to import regions through interregional trade.

According to the research results mentioned above, this paper puts forward the following suggestions in order to achieve the rational allocation of water resources:

**Author Contributions:** Conceptualization, X.L. and R.X.; methodology, X.L.; software, R.X.; validation, R.X. and X.L.; formal analysis, R.X.; investigation, R.X., X.L., P.G. and L.N.; data curation, W.L.; writing—original draft preparation, R.X.; writing—review and editing, J.C. and Q.S.; visualization, Q.S.; supervision, X.L. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the National Natural Science Foundation of China (Grant No. 42001257 and Grant No. 71874119), the Philosophy and Social Sciences Research of Higher Learning Institutions of Shanxi (Grant No. 20210115), the Shanxi Postgraduate Education Innovation Project (Grant No. 2021Y571), and the Research Project of Social and Economic Statistics in Shanxi Province (Grant No. KY2021113).

**Institutional Review Board Statement:** Ethical review and approval were waived for this study, due to studies not involving humans or animals.

**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.

**Data Availability Statement:** The input-output table data are from *China's carbon accounting database*, and the water use and GDP data can be obtained from *China's statistical yearbook*.

**Acknowledgments:** We also thank our anonymous reviewers and the editor for their helpful suggestions.

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

### **References**


4.2.1. Load Combinations

The primary pipe loading on the bridge structure is the pipe dead load which includes the weight of the pipe, the water inside the pipeline, pipe accessories (*i.e*., expansion joints, couplings), and the pipe support system. Other loads that should be considered are wind, earthquake and impact. In the present study, only an internal load is evaluated: the water-hammer phenomenon.

In limit states, the loads are multiplied by safety factors and grouped into load combinations. For different codes (e.g., BSI 8010 UK; ASME B31.4 US; ISO 13623), different combinations of loads are established. The general requirement is that the defined load combination should be appropriate to the ultimate limit state. The Canadian code CSA-Z662 (1996) uses the following general equation for calculating the load combinations (*i = 1, 2, 3, ...*):

$$\text{Lci} = \alpha \left( \gamma\_G G + \gamma\_Q Q + \gamma\_E E + \gamma\_A A \right) \tag{1}$$

where, 9 = safety factor class; *γG*, *γQ*, *γE*, *γA* " loading factors (Table 2) for *G* = permanent loads (*i.e*., weight of the water inside the pipeline); *Q* = operational loads (overloading, *i.e.*, internal pressure during normal conditions); *E* = environmental loads (*i.e*., seismic load); *A* = live loads (*i.e*., transient events).

Note: \* For a faulted combination, the condition is associated to fault fluid transients resulting from an earthquake [27].

For each load combination, its effects are calculated as the level of stress and deformation. The resulting value of these effects is compared with the threshold value, to determine if the limit state is respected.

The safety factor class depends on the risk to which the structure is subjected. In the case of pipelines the safety factor is 2.0. According to the case study, two types of combinations were selected and applied as presented in Table 3.

**Table 3.** Selected combinations.

Note: \* The performed simulations do not take into account seismic events.


**Notes/thanks/other declarations**

We thank BioRender (https://biorender.com/) for providing the platform for plotting the **Figure 1c**.

*Gender Disparities in Bladder Cancer DOI: http://dx.doi.org/10.5772/intechopen.98225*


**4. Conclusions**

**Figure 11.** Time evolution of As(III) concentration during sorption and photocatalysis using PAC-TiO2

water [29]. The comparative results regarding As(III) removal in the presence of HA showed a

(c), and natural zeolite (d).

− concentration during photolysis (a), photocatalysis using Z-TiO2

Another common problem of groundwater source used for drinking water supply is given by the presence of ammonium, nitrite, and nitrate. Nitrate could not be removed from water on

good efficiency for both sorption and photocatalysis using *supported TiO2*

**Figure 12.** Time evolution of NO2

110 Photocatalysts - Applications and Attributes

concentration during photocatalysis using Z-TiO2

these tested materials, while ammonium and nitrate were removed from Z-TiO2

and GAC-TiO2

material (**Figure 11**).

(**Figure 12**).

(b), of NH4

+

.

In this chapter, two types of composites obtained by two different methods and applied in the drinking water treatment were studied:

The sorption and photocatalysis studies showed that for HA removal, the sorption capacity of tested materials increased in order, PAC-TiO2 > Z-TiO2 > GAC-TiO2 > CNT-TiO2 -Epoxy>CNF-TiO2 -Epoxy, and for the photocatalytic activity, GAC-TiO2 > Z-TiO2 > PAC-TiO2 > CNT-TiO2 - Epoxy> CNF-TiO2 -Epoxy.

The removal of pollutants/impurities dissolved in water is based, on the one hand, on the sorption capacity of activated carbon and/or zeolite, which exhibited selectivity for certain impurities dissolved under the lamp-off conditions. On the other hand, but taking into account the sorption phase, by starting the lamp, TiO2 on the active carbon surface and the zeolite acted by photocatalytic activity generating oxidation and reduction processes that cause the degradation and mineralization of the dissolved organic compounds and the transformation of the inorganic contaminants into the compounds that can be retained on the surface material or compounds that do not affect the quality of the water. Also, the oxidation and reduction processes under irradiation conditions allow the destruction of the contaminants and, implicitly, the cleaning of the sorbent surface. A mixture of zeolite and activated carbon as support for TiO2 could exhibit bifunctionality, depending on the water features. Thus, for low loading of water with contaminants that can be adsorbed on the filter material, the lamp is not switched on during system operation but only in the washing/ regeneration stage, and for a water loaded with contaminants that cannot be adsorbed on the filter material, the system works with the lamp on, ensuring water decontamination and self-cleaning during operation.


*2.2.6 Acid deposition*

*Advances in Forest Management under Global Change*

fertilization treatment (400 kg NH4NO3-N ha−1

2012 in Sichuan Province, China [47].

hydrolyze to Fe(OH)3 or Al(OH)3 and release 3H+

can replace the base cations such as Ca2+, Mg2+, K+

hence reducing the pH buffering capacity of top soil.

− + H2O + 2H+

transfers to NO3

When plant roots absorb a NH4

*2.2.3 Microbial-mediated nitrification*

+

development of *C. oleifera*.

factors.

K+

*2.2.1 Precipitation*

*2.2.2 Plant physiology*

to plant growth [48, 54, 55].

For example, NH4

+ 2O2 → NO3

absorbing a NO3

, and Na+

92.14 ± 47.01 mg m−2

**2.2 Soil acidification**

*2.1.3 Nitrous oxide emissions from* Camellia oleifera *plantation soils*

Our previous field study (1 year) found that soil N2O emissions were

Acid soil (pH < 5.5) as a main soil type covers about 30% free ice land [46]. However, soil acidification has been becoming more and more serious [47]. Soil acidification should be taken into consideration due to its constraint in the sustainable development of agricultural sector [48]. In China, soil pH (except alkaline soils at pH 7.10–8.80) from crop fields reduced by 0.13–0.76 during the year 1980–2000 [49]. For example, soil pH (surface layer) decreased by 0.30 units from 1981 to

With a long cultivation history, *C. oleifera* was widely cultivated in acid or strongly acid soil in subtropical China [7]. The optimum pH for the growth of *C. oleifera* is 5.5–6.5 [50]. However, acid deposition [51] and intensive N input [49] may stimulate soil acidification from *C. oleifera* plantations. Additionally, long-term N input may also increase the toxicity of aluminum (Al) [52], limiting the sustainable

Soil acidification from *C. oleifera* plantations is mainly related to the following

Long-term precipitation increased the loss of base cations such as Ca2+, Mg2+,

cipitation promoted the accumulation of Al3+ and Fe3+ in soil, which could further

from soil, an H+

In addition, anions of organic acids (e.g*.*, citric acid and malic acid) can chelate with Al3+ in the soil and inhibit the root system that absorbs Al3+, alleviating Al3+ toxicity

Plants such as *C. oleifera* [56] can uptake Al3+ by roots, promoting the accumulation of Al3+ in surface soil via litter decomposition [57]. The accumulation of Al3+

the process of nitrification [20]. Nitrification includes the pathway of ammonia oxidation to hydroxylamine, the pathway of hydroxylamine oxidation to nitrite, and the pathway of nitrite oxidation to nitrate (**Figure 1**) [58]. Ammonia can be oxidized by AOA or AOB to hydroxylamine via ammonia monooxygenase (*amo*).

+

Organic acid (R▬COOH) from root exudates can release an H+

− from soil will release an OH− into soil [53].

, reducing the soil pH buffering capacity. In addition, long-term pre-

.

, and Na+

) [53]. AOB, AOA, and fungi can participate in

− along with the 2H+

will release into soil; in turn,

release

after hydrolysis.

and accelerate leaching,

in control treatment and were 375.10 ± 60.30 mg m−2

in

) from *C. oleifera* plantations [1].

**6**

(NH4 +

Acid deposition (water-soluble acid gases such as CO2 and sulfur dioxide) and N deposition (especially NH4 + -N) increased soil acidification [51]. Precipitation with H+ can replace the soil base cations such as Ca2+, Mg2+, K+ , and Na+ , which directly reduce the soil pH buffering capacity [51].

## *2.2.7 Other factors*

For example, deforestation and other land uses can reduce litter accumulation in surface soil, hence declining the accumulation of base cations such as Ca2+, Mg2+, K+ , and Na+ that generate from litter decomposition [61].

#### **2.3 Effects of soil acidification on nitrous oxide emissions**

Acid soils have been facing an increased risk of acidification due to human activities, especially intensive N fertilization [47, 49, 62]. For example, after 6 years of application of 600 kg Urea-N ha−1 yr−1 , soil pH was significantly decreased (soil pH in control and fertilization treatment was 5.1 and 4.9, respectively) from a tea plantation in Yixing City, Jiangsu Province, China [63]. A meta-analysis of 1104 field data showed that a negative correlation between soil N2O emissions and pH (3.34–8.7) (N2O-N = −0.67x + 6.55, *R* = 0.22) is negatively related with N fertilization [9]. Moreover, deposition of sulfur dioxide increased soil acidification, stimulating soil N2O emissions [64].

The mechanism of soil acidification on the stimulation of soil N2O emissions is complex, which may include (but not limited to) the following points.

### *2.3.1 Chemical decomposition of nitrous acid*

Under acidic conditions, pH < 5.5, NO2 − (HNO2, pKa = 3.3) will naturally decompose into NO and/or NO2 (3HNO2 ⇌ 2NO + HNO3 + H2O or 2HNO2 ⇌ NO + NO2 + H2O) [65]. Soil NO can be further transformed to N2O with Fe2+ when it was not escaping soil [65].

#### *2.3.2 Shifts in microbial communities and abundance*

Generally, the abundance of AOB was lower in soil pH < 5.5 than that in neutral soil pH. Here, nitrification was weak and almost disappears at soil pH < 4 [66]. However, AOA could mediate the process of ammonia oxidation in extremely strong acidity soil (pH: 4.2–4.47) [43]. Another study reported that the abundance of AOB was positively correlated with pH (*R*2 = 0.2807), while the abundance of AOA was negatively correlated with pH (*R*2 = 0.2141) [67]. For example, AOA dominated in acid paddy soil (pH 5.6), while AOB dominated in alkaline soil (pH 8.2) [68]. Previous research indicated that fungi were the main microbial community that mediated N2O emissions in acid soil [69, 70]. Additionally, fungi-mediated denitrification accounted for 70% soil N2O emissions from a 100-year-old tea plantation (soil pH 3.8) [71].

In acid soils, the activity of N2O reductase was inhibited, leading to higher N2O emissions in lower soil pH [72]. Indeed, there was a positive correlation between the abundance of *nirS*, *nirK*, or *nosZ* and soil pH (4.0–8.0) and a negative correlation between N2O/(N2O + N2) and soil pH [73]. In agreement, N2O/(N2O + N2) was negatively correlated with soil pH (3.7–8.0) (*R*2 = 0.759, *P* < 0.001), and lime addition decreased N2O/(N2O + N2) [74]. The ratio of N2O/(N2O + N2) increased with decreasing pH (5.57–7.06) (*R*2 = 0.82) [75]. Consistently, soil pH was negatively correlated with N2O/N2 [76]. Intensive management consistently decreased soil pH and increased the ratio of N2O/(N2O + N2) [77]. Increasing dolomite dosage increased soil pH and hence increased the transcription of *nosZ* genes and reduced the potential of N2O production in acid soils [26].

#### *2.3.3 Microbes increased resistance to soil acidification*

Laboratory study showed that the potential of soil N2O emissions was increased with decreasing pH (soil pH ranging from 2.96 to 6.26) from tea plantations in Japanese [78]. In addition, higher soil N2O emissions and lower abundance of *nosZ* genes were observed in soil pH at 3.71 (control) than in pH at 5.11, 6.19, and 7.41 (lime amelioration) under NO3 −-N fertilization (50, 200, and 1000 mg kg−1 ) from a 100-year-old tea plantation [79]. Field study found a negative correlation between soil N2O emissions and pH (pH 3.6–5.9) (N2O-N = 636.6\* e−0.8028 \* pH, *R* = −0.93) from *Betula pendula* Roth forest [80]. Thus, denitrifying microorganisms may have been adapted extremely to acid soil environments, resulting in high N2O emissions when soil acidification happened.

#### **3. Sustainable forest management**

Soil amelioration (e.g*.*, application of lime, biochar nitrification inhibitors, and urease inhibitors) plays an important role in mitigation of soil acidification and N2O emissions.

#### **3.1 Lime**

Lime as an ameliorant was often used to amend acid soils in southern China due to increasing soil pH. It can relieve the toxic effect of soil Al3+ on plant growth by reducing soil exchangeable H+ [81]. Lime addition increased soil pH and salt saturation [82]. In addition, application of lime can reduce soil N2O emissions [40]. For example, under 60% WFPS or flooded conditions, dolomite addition at medium- or high-dose levels (1 or 2 g kg−1 soil) can reduce N2O emissions and increase the transcription of *nosZ* genes (N2O → N2) by increasing acid soil pH from a rice-rapeseed

**9**

H3SiO4

tion [107, 108].

*Nitrogen Cycling and Soil Amelioration in* Camellia oleifera *Plantations*

nologies for biochar application in soil amelioration.

*3.2.1 Effects of biochar on soil nitrous oxide emissions*

*3.2.2 Effects of biochar on soil pH buffer capacity*

of weakly acidic functional groups can associate with H+

− (present at a high pH) can combine with H+

rotation system [26]. However, lime addition reduced the content of soluble organic carbon in the soil layer 10–30 cm [83]. Consistently, long-term lime addition increased the soil pH but stimulated the decomposition of soil organic carbon [84].

Biochar was stable in the soil from Amazon basin of Brazil, and biochar input improved soil fertility [85]. This discovery accelerated the development of tech-

properties of biochar were mainly determined by pyrolysis temperature [87]. Presently, biochar was widely used as a soil ameliorant in agriculture and forestry field. For example, our previous studies reported that *C. oleifera* fruit shells are ideal feedstock for producing biochar as they are rich in C and N [1, 88]. Biochar

1.Carbon recalcitrance of biochar can increase soil C pool. The potential of biochar in mitigation of greenhouse gas emissions was 1.0–1.8 Pg CO2-Ceq yr−1

2.Biochar had excellent physicochemical characteristics in soil nutrient retention and utilization [90, 91] and water conservation [92]. Additionally, biochar can increase the plant resistance to Al3+ toxicity [81], the clone of arbuscular mycorrhizal fungi, and crop yield [93, 94]. It can decrease continuous cropping obstacles such as root-knot nematode [95] and *Ralstonia solanacearum* [96].

3.Biochar is rich in macro- and microelements [97], which can reduce the dosage

The physicochemical properties of biochar and soil can interactively influence soil N2O emissions [98]. However, the effects of biochar on soil N2O emissions varied, including positive effects [99], negative effects [100], and no effects [101]. Biochar addition increased soil N2O emissions with the release of N from biochar

[102]. By contrast, biochar reduced soil N2O emissions with (1) increased NO3

substances (pyrolysis by-products) on N-cycle microorganisms [106].

immobilization [103]; (2) increased copy numbers of *nos*Z gene [104, 105]; and (3) increased toxic effects of polycyclic aromatic hydrocarbons and other toxic

Biochar that increased soil pH buffer capacity may predominantly correlate with biochar riches in oxygen-containing functional groups in surface. The anions

pH. Meanwhile, exchangeable base cations can release into the solution, thus increasing soil pH buffer capacity [107, 108]. In addition, soluble silicon (Si) such as

[89].

−-N

, hence increasing soil

and generate H2SiO3 precipita-

Biochar is a carbon (C)-rich solid material by pyrolyzing of organic biomass such as crop straw, forestry by-products, urban waste, industrial by-products, animal manure, and urban sludge at low oxygen and high temperature (250–700°C) condition [86]. Biochar has been characterized by a high pH, specific surface area, degree of aromatization, and porosity. In addition, biochar is rich in C-containing functional groups (e.g., C–H, C–O, C=C and C=O) and relatively stable organic C. The physicochemical

*DOI: http://dx.doi.org/10.5772/intechopen.92415*

includes the following advantages:

of fertilizer.

**3.2 Biochar**

rotation system [26]. However, lime addition reduced the content of soluble organic carbon in the soil layer 10–30 cm [83]. Consistently, long-term lime addition increased the soil pH but stimulated the decomposition of soil organic carbon [84].


*Review*

#### **Methane Emissions and the Use of** *Desmanthus* **in Beef Cattle Production in Northern Australia**

#### **Bénédicte Suybeng 1, Edward Charmley 2, Christopher P. Gardiner 1, Bunmi S. Malau-Aduli 3 and Aduli E. O. Malau-Aduli 1,\***

Received: 10 May 2019; Accepted: 6 August 2019; Published: 9 August 2019

**Simple Summary:** An in-depth review of Australia's tropical beef cattle production system is presented with emphasis on the use of *Desmanthus*, a tropical legume, as a nutritional supplementation strategy for the abatement and mitigation of methane emissions. It also identifies current knowledge gaps in *in vivo* methane emissions research.

**Abstract:** The Australian beef industry is a major contributor to the economy with an estimated annual revenue generation of over seven billion dollars. The tropical state of Queensland accounted for 48% of Australian beef and veal production in 2018. As the third biggest beef exporter in the world, Australia supplies 3% of the world's beef exports and its agricultural sector accounts for an estimated 13.2% of its total greenhouse gas emissions. About 71% of total agricultural emissions are in the form of methane and nitrous oxide. In this review, an overview of the carbon footprint of the beef cattle production system in northern Australia is presented, with emphasis on the mitigation of greenhouse gases. The review also focuses on the tropical legume, *Desmanthus*, one of the more promising nutritional supplements for methane abatement and improvement of animal growth performance. Among the review's findings is the need to select environmentally well-adapted and vigorous tropical legumes containing tannins that can persistently survive under the harsh northern Australian conditions for driving animal performance, improving meat quality and reducing methane emissions. The paper argues that the use of appropriate legumes such as *Desmanthus*, is a natural and preferred alternative to the use of chemicals for the abatement of methane emanating from tropical beef cattle production systems. It also highlights current gaps in knowledge and new research opportunities for *in vivo* studies on the impact of *Desmanthus* on methane emissions of supplemented tropical beef cattle.

**Keywords:** methane emission; tropical beef cattle; *Desmanthus*; supplementation; growth performance; ruminant nutrition; legumes

## **1. Introduction**

Global climate change is principally caused by greenhouse gas (GHG) emissions that result in warming of the atmosphere [1]. According to the Australian National Greenhouse Accounts [2], 13.2% of GHG emissions emanate from agriculture, with methane and nitrous oxide accounting for 71% of total agricultural emissions [3]. In 2017, Australia produced an estimated 51,543.56 Gg CO2-e of CH4 from enteric fermentation [3].

The world population is predicted to increase from 7.6 to 9.8 billion by 2050 [4]. Consequently, the world has to match the increased demand for food from a larger and more a ffluent population to its supply in an environmentally sustainable manner [5]. Livestock products constitute an important source of food for global food security by providing 33% and 17% of world protein and kilocalorie consumption, respectively [6]. Climate change constitutes a risk to livestock production due to its impact on the feed quality of crops and forages, animal performance, milk production, water availability, animal reproduction, livestock diseases and biodiversity [6]. Therefore, the challenge is to find ways to increase livestock productivity without compromising household food security while sustainably improving the natural resource base [7].

Beef cattle productivity in north Queensland is beset with climatic and nutritional challenges, due to prolonged drought, high climate variability, inadequate feed resources, low-quality pastures and the poor body condition of cattle [8]. In this seasonally dry, low-elevation, heavy textured soils and inland tropical region of north Queensland, there is an overwhelming need for integrating more productive, nutritious and persistent summer-growing legumes into existing low quality, grass-dominant pastures [8].

Existing cultivars lack the capacity to adapt to seasonally waterlogged duplex soils, infertile light-textured soils, heavy cracking clays and low rainfall conditions [9]. Gardiner [10] evaluated the performance characteristics of *Desmanthus* in contrasting tropical environments and found that it thrived and spred on heavier vertisol soils. Hall and Walker [9] conducted a study over a 15-year period in six di fferent environments in the seasonally dry tropics of north Queensland and found that on cracking clay soils, *Desmanthus* species and *Clitoria ternatea* were the most persistent and productive legumes among 118 legume accessions.

Further evaluation and development of *Desmanthus* under commercial grazing management will be highly beneficial to northern Australian beef cattle graziers for improved productive and reproductive performances, better animal body condition and higher meat quality, particularly in the live cattle trade where northern Australia is the main gateway to this key business export market. The State of Queensland accounts for about 43% of the Australian cattle population [11] and has a CH4 emission from ruminants that has been estimated to account for 3% of Australia's GHG [3]. Australia has a target to reduce its emissions by 5% below the 2000 level by 2020 and 26–28% below 2005 emissions by 2030. The Government allocated \$2.55 billion to the Emissions Reduction Fund (ERF) to help livestock producers use modern farming methods to store carbon in vegetation and soils towards reducing GHG [12]. Research into contemporary, scientific and sustainable ways to produce high quality beef in tropical Northern Australia with low methane emissions are paramount to Australia being competitive in the international market. *Desmanthus* spp. are among the most promising sown legume species for the vastly undeveloped semi-arid clay soil regions across northern Australia.

This review focusses on the carbon footprint of the beef cattle industry in northern Australia, explores mechanisms and methods of enteric methane production and abatement with a focus on *Desmanthus* as a potential pasture legume for mitigating methane emissions. Finally, the current knowledge gaps that could underpin future research are also reviewed.

#### **2. Carbon Footprint from the Beef Industry in Queensland**

#### *2.1. The Australian Beef Cattle Market*

Australia is the third biggest beef exporter in the world, supplying 3% of the world's beef exports with 1,500,000 tons of carcass weight exported annually. The Australian beef cattle industry accounted for \$11.4 billion in 2017–2018 [11]. Furthermore, the beef cattle industry employed 191,800 people in 2016–2017. Therefore, the beef industry plays a central role in the Australian economy, especially in the state of Queensland, where its 11.1 million head of cattle accounted for 48.1% of the Australian beef and veal production in 2017–2018 [11].

#### *2.2. The Di*ff*erent Sectors Included in the Carbon Footprint of the Beef Industry in Queensland*

The total net emissions attributed to agriculture in Queensland was 18,672.5 Gg CO2-e in 2017 [3]. The beef industry in Queensland is the largest agricultural industry in the state [13]. Sources of GHG emissions from a typical beef enterprise comprise enteric fermentation in cattle (CH4 and N2O), burning of vegetation (intentional or accidental), energy use (electricity and fuel), land clearing, loss of pasture and decline in soil carbon [13,14]. A study conducted by Eady et al. [14] in two beef farms in Queensland showed that the carbon footprint of beef products at the farm gate ranged from 17.5–22.9 kg CO2-e/kg liveweight at Gympie and 11.6–15.5 kg CO2-e/kg liveweight in the Arcadia Valley. They also found that enteric fermentation represented about 80% (74% at Arcadia Valley and 85% at Gympie) of the overall 'cradle-to-farm gate' GHG emissions [14]. The last figures can be linked with the 70% (12,995.97 Gg CO2-e) of agriculture GHG emissions coming from enteric fermentation from grazing beef cattle in Queensland in 2017 [3].

#### *2.3. The Principal Causes Inducing Enteric Methane Emissions*

#### 2.3.1. Rumen Microbial Fermentation

The rumen is a dynamic and complex ecosystem composed essentially of anaerobic bacteria, protozoa, anaerobic fungi, methanogenic archaea and phages [15]. The microbes interact with each other and have a symbiotic relationship with the host. The breakdown of plant cell wall carbohydrates that are inedible by humans provides energy to the host [16]. Methane is produced exclusively by methanogenic archaea [15] via the hydrogenotrophic pathway using CO2 as the carbon source and H2 as the main electron donor, and less so through the utilization of methyl groups (methylotrophic pathway), or even less commonly from acetate (acetoclastic pathway) [15]. The methanogenesis reaction uses H2 to reduce CO2 to CH4: CO2 + 4H2 = CH4 + 2H2O [17].

The main products of rumen microbial fermentation, as depicted in Figure 1, are volatile fatty acids (VFA) (acetic, propionic and butyric acids), carbon dioxide and methane [18]. In the rumen, the VFA formed are absorbed and used as a source of energy. On the contrary, CO2 and CH4 are eliminated by eructation from the rumen. Over 80% of the methane is synthesised in the rumen and the lower digestive tract produces the rest [18]. Northern beef cattle in Australia can generate about 32.2 to 184 g of methane per day [19], which represents an important energy loss to the animal ranging from 2% to 12% of gross energy intake depending on the nature of the diet [20]. Under a high forage diet, these losses are on the average, 7.2% of gross energy intake; 6.3% for an intermediate forage and 3.84% for a low forage (feedlot) [21].

**Figure 1.** Principal end-products of carbohydrate fermentation in the rumen [18].

#### 2.3.2. Low Animal Performance Increases Methane Production

Less efficient cattle can take longer to reach market weight and might only breed two out of three seasons. The longer an animal takes to reach market weight, the longer that animal is producing methane, with very little beef being marketed in return [20,22]. Arthur et al. [23] estimated genetic and phenotypic parameters for feed intake in Angus bulls and heifers, and showed that the feed conversion ratio defined by the amount of feed consumed divided by live weight gain was correlated genetically (−0.62) and phenotypically (−0.74) with the average daily gain (ADG). For instance, Charmley et al. [22] showed that by maintaining a liveweight (LW) gain of 0.5 kg/day for steers in the northern spear grass region by adding supplements to the pasture diet would reduce the turn-off age of the Japanese Ox market from 4 years (526 kg LW) to 2.3 years (650 kg LW). Gross margin budget and cashflow analyses for a 100-cow herd showed a 61% internal rate of return over a 25-year investment period, despite the higher cost for purchasing efficient bulls. It represents an annual benefit per cow of A\$8.76 [24]. Low animal productivity is associated with high methane output per unit of product (methane intensity) and low pasture quality is associated with high methane output per unit of dry matter intake [12]. For that reason, northern Australian beef herds are estimated to produce more methane than the more intensive systems in southern Australia [20]. For instance, Eady [25] showed that the GHG emissions of beef produced from cattle supply chain from Northern Australia to the Indonesian market were higher (26 kg CO2 equivalent/kg liveweight) than beef produced in Southern Australian systems, where GHG emissions ranged from 5.4 to 14.5 kg CO2 equivalent/kg liveweight for finished steers. They attributed it to the higher reproduction rate, faster turn-off and lower methane emissions per unit of feed intake permitted by a high pasture quality in the southern systems [25].

#### 2.3.3. Northern Australian Forage Diet Influences Rumen Microbiome and Methane Production

In northern Australia, comprising the Kimberley and Pilbara districts of Western Australia, the Northern Territory and Queensland above the Tropic of Capricorn, the beef industry is dominated by large pastoral properties [10]. This part of Australia is characterised by a vast array of heavy clay or vertosol soils, where the range of available sown pasture legumes has long been regarded as being deficient [26]. There are also vast areas of light textured soils where the legume *Stylosanthes* has been successfully introduced. Pasture production is highly seasonal, with a wet season (November to April) characterised by growth, and a senescent period during the dry season. This induces a marked seasonal pattern of pasture availability and quality [27]. The prevailing pasture species are mainly C4 grasses, which have lower nutritional value than temperate grasses, and result in lower animal productivity than in temperate regions [28,29]. During the wet, hot summers, these pastures grow quickly and persist through the dry winter seasons as mature grasses [30–32]. The low livestock productivity in northern Australia is especially due to low protein content and low digestibility during the dry season [33]. The low digestibility (45% organic matter) and nitrogen content (less than 7g N/kg dry matter (DM)) of these grasses during the dry season results in poor forage intakes and low annual growth rate of young cattle [30–32]. Animals tend to put on weight in the wet season and lose weight in the dry season. In northern Australia, it is not uncommon for 4–6 years old steers to be marketed [34]. Consequently, depending on the time of the year, liveweight gains in Northern Australia are around 70–240 kg/year for native pastures [35] compared to 250–300 kg/year for temperate pastures [36]. Growth rate is directly related to metabolisable energy intake, and can be markedly increased by replacing the feed base or by giving supplements to the animals [36].

Archimede et al. [37] showed that ruminants fed C4 grass produced 17% more methane as L/kg organic matter intake than those fed C3 grass. Likewise, Perry et al. [29] found that steers fed a wet season pasture (crude protein (CP) = 90 g/kg DM) or a high quality hay (CP = 88 g/kg DM) produced 5–10 g CH4/kg, digested less dry matter intake (DMI) and had about 3% less digestible energy intake than steers fed low quality hay (CP = 25 g/kg DM). They observed shorter rumen retention times in high quality hay fed steers, which decreased methane production per kilogram of DMI compared with low quality hay and the dry season pasture. This phenomenon can be explained with an increased rumen outflow rate [38]. The rise in rumen outflow rates is associated with higher concentrations of dissolved H2 that increase the growth rate of methanogens. The greater cellulose and hemicellulose content in tropical C4 grasses rather than neutral detergent soluble carbohydrates in grain diets results in higher methane emissions and a shift in rumen fermentation pathways from propionate to acetate [12,29]. The production of methane in the rumen is associated with the production of VFA. The formation of both acetic and butyric acids is accompanied by the production of H2 and CO2, whereas propionic acid production requires a net uptake of H2, which can reduce methanogenesis [38]. The production of propionic acid instead of acetic acid can be realised by replacing structural carbohydrates (forage) with easily fermented carbohydrates [38].

#### **3. Mitigation Techniques against Methane Emission**

#### *3.1. The Use of Chemicals for Rumen Manipulation to Reduce Methane Production*

#### 3.1.1. The Use of Chemicals to Control Protozoa, the Main Hydrogen Producer

Some techniques such as defaunation and the utilisation of ionophores, have been used to control protozoa, the major producers of H2 from the rumen [39], so that less H2 is accessible for CH4 formation.


4. Efficiency of the light absorption channel via plasmon for various materials

Nanoparticles of gold and silver (sometimes also of copper) are mostly used in plasmon photovoltaics because their surface plasmon resonances are located within the visible light spectrum. These nanoparticles can be deposited on various semiconductor substrates with different material parameters. We list here the appropriate parameters usable for comparison with experiment for various configurations of the plasmon solar cell systems. In order to compare with the experiment, we can estimate the photocurrent in the case of a semiconductor photodiode with the metallically modified photoactive surface. This photocurrent is given by I 0 ¼ ∣e∣N q0 þ qm A, where N is the number of incident photons and q0 and qm are the probabilities of single photon absorption in the ordinary photo effect [8] and of single photon absorption mediated by the presence of metallic nanospheres, respectively, as derived in the previous paragraph; A ¼ τn f tn þ τ p f tp is the amplification factor (τ n pð Þ f is the annihilation time of both sign carriers, tn pð Þ is the drive time for carriers [the time of traversing the distance between electrodes]). From the above formulae, it follows that (here I ¼ I 0 qm ¼ 0 , i.e., the photocurrent without metallic modifications),

$$\frac{I'}{I} = 1 + \frac{q\_m}{q\_0} \,' \tag{19}$$

where the ratio qm=q0 is given by Eq. (18).

qm ¼ βC0

>>>>:

photo effect can be expressed as follows

qm q0 ¼

experiment in Ref. [14]), ns � 108

for various materials

where fð Þ¼ ω 1 ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ω2 1�ω2 ð Þ2

and D0 ¼ e2neE04πa3

q0

C0 ¼ Nm4=3πa3

124 Plasmonics

The ratio, qm

8 >>>><

> βC0 128 9

128 9 π2 a3

þ4ω2=τ02

4 ffiffiffi 2 p π2a3βC0

8 >>>>>><

>>>>>>:

This ratio turns out to be of order of 104 β40

8π2a2βC0 m∗

ffiffiffi 2 p π2

μ ffiffiffiffiffiffiffiffiffiffi μ∗ nμ∗ p

a2 μ3=2 m2

m2 ħω � Eg

� � e6n2

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ħω � Eg q e6n2

wave is next ruled out from Eq. (17) due to normalization per single photon as in Eq. (13);

p � �2

� �ε2 , for aξ ≫ 1:

ħω � Eg

semiconductor layer, which including the phenomenological factor β, and the thickness H (we have confirmed experimentally that the range of the near-field zone exceeds the Mie wavelength), is sufficient to explain the scale of the experimentally observed strong enhancement of absorption rate in semiconductors due to plasmons. The strong enhancement of this transition probability is linked with the allowance of momentum-non-conserved transitions, which is, however, reduced with the radius a growth. The strengthening of the near-field induced interband transitions, in the case of large nanospheres, is, however, still significant as the quenching of oblique interband transitions is partly compensated by � a3 growth of the amplitude of dipole plasmon oscillations. The trade-off between these two competing size-dependent factors is responsible for the observed experimental enhancement of light absorption and emission in diode systems mediated by surface plasmons in nanoparticle surface coverings [7, 14–18].

Nanoparticles of gold and silver (sometimes also of copper) are mostly used in plasmon photovoltaics because their surface plasmon resonances are located within the visible light spectrum. These nanoparticles can be deposited on various semiconductor substrates with different material parameters. We list here the appropriate parameters usable for comparison with experiment

ffiffiffiffiffiffiffiffiffiffiffiffi m∗ nm∗ p p m∗

3μ3=2m2 ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

e4n2 eω2f 2 ð Þ ω

p � �2

3μm2 ħω � Eg

4. Efficiency of the light absorption channel via plasmon

eω ħ4 ε3 f 2

eω ħ3 ε3 f 2

q corresponds to amplitude factor for driven damped oscillator

3m fð Þ ω (in Eq. (10)); the amplitude of the electric field, E0, in the incident e-m

V , V is the volume of the semiconductor, Nm is the number of metallic nanospheres.

, revealing the advantage of the plasmon-mediated photo effect over the ordinary

e4n2 eω2f 2 ð Þ ω

p ħε2 , for aξ ≪ 1,

ð Þ ω , for aξ ≪ 1,

(17)

(18)

ð Þ ω , for aξ ≫ 1,

H nm½ � for the surface density of nanoparticles (as in

=cm2; note that C0 ¼ ns4πa3=ð Þ 3H , H is a thickness of the

q

In Tables 1–3, we list parameters for several semiconductor substrates and for a metallic nanoparticle few materials, which allow for comparison of the ratio qm=q0 for various material configurations by formula (18).

Formula (18) is exemplified in Figure 1 for Au nanoparticles deposited on Si semiconductor (continuous line)–this reproduces well the experimental behavior (red dashed/dotted) [14]. Both channels of photon absorption resulting in photocurrent in the semiconductor sample are included, the direct ordinary photo effect absorption with probability of transitions given by q0 and the plasmon-mediated absorption with probability qm, respectively. Note also that

Table 1. Plasmon energies measured in metals.

Table 2. Mie frequency ω1 to formula (18).

Table 3. Substrate material parameters to formula (18) (m ¼ 9:1 � 10�31 kg, the mass of bare electron; lh–light holes, hh–heavy holes, L–longitudinal, T–transverse).

some additional effects like reflection of the incident photons or destructive interference on metallic net would contribute and it was phenomenologically accounted in the plasmonmediated channel by an experiment-fitted factor β. The collective interference type corrections are rather not strong for the considered low densities of metallic coverings of order of 108 =cm2, and nanosphere sizes well lower than the resonant wavelength, though for larger concentrations and larger nanosphere sizes, would play a stronger reducing role (reflecting photons) [6, 19]. The resonance threshold was accounted for the damped resonance envelope function in Eq. (19) including also semiconductor band-gap limit. The relatively high value of qm q0 � 104 β40 H nm½ � enables a significant growth of the efficiency of the photoenergy transfer to the semiconductor, mediated by surface plasmons in nanoparticles deposited on the active layer, by increasing β or reducing H (at constant ns). However, because of the fact that an enhancement of β easily induces the overdamped regime of plasmon oscillations, the more prospective would be lowering of H especially convenient in thin film solar cells. The overall behavior of I 0 =Ið Þ¼ ω 1 þ qm=q0 calculated according to the relation (19), and depicted in Figure 1, agrees quite well with the experimental observations [14], in the position, height and shape of the photocurrent curves for distinct samples (the strongest enhancement is achieved for a ¼ 40 nm, for Au and Si substrate).

blue shifted in comparison to Si and GaAs. Figure 2 reveals an increase in efficiency of the plasmon effect with growth of the value of forbidden gap Eg conserving other parameters not changed. Especially significantly influential material parameter occurs, however, a mass of

Figure 2. Comparison of the effectiveness of the plasmon channel for Si, GaAs and CIGS substrates with the same Au

.

Plasmonic Enhancement of Solar Cells Efficiency: Material Dependence in Semiconductor Metallic Surface Nano…

denominator in the formula (12) for the ordinary photo effect and next the numerator in

and Cu) is presented for two their sizes (a ¼ 50, 25 nm). The blue shift of spectral characteristics for Ag and Cu in comparison to Au is noticeable (cf. also Figure 5) and even more visible for lower radii of nanoparticles due to narrowing of spectral curves (cf. Figure 6). From the comparison in Figures 5 and 6, for Si and CIGS substrates with Au, Ag and Cu nanoparticles

nanoparticles utilize the visible spectrum in the better manner than Ag or Cu ones. The advantage of Au nanoparticles is greater in the case of Si substrate and is reduced for CIGS

Figure 3. Comparison of the effectiveness of the plasmon channel for varying Eg but the same effective masses of

substrates covered with the same Au nanoparticles with radius 50 nm and surface density 108

. In Figure 4, the material comparison of metal material of nanoparticles (Au, Ag

p the lower efficiency q0 of the ordinary photo effect is and higher

p 2

http://dx.doi.org/10.5772/intechopen.79113

=cm2), one can notice that Au

=cm2 . , enters the

127

holes, cf. Figure 3, which is also noticeable from Eq. (18). The mass of holes, m∗

of size a ¼ 50, 25 nm (at the nanoparticle concentration ns ¼ 108

Eq. (18). The higher mass m∗

nanoparticles with radius 50 nm and surface density 108=cm2

the ratio qm

q0

In Figure 2, we present the spectral dependence of the plasmonic efficiency enhancement with respect to substrate change (Si, CIGS and GaAs) for the same Au nanoparticles with radius a ¼ 50 nm and the same nanoparticle concentration ns ¼ 108 =cm2. One can note that for the CIGS substrate (copper-indium-gallium-diselenide) the spectral characteristics is narrower and

Figure 1. Spectral dependence of the normalized photocurrent I 0 I ð Þ λ according to formulae (19) and (18)—Comparison with the experimental data (red) from Ref. [14]: a ¼ 25 nm, ns ¼ 6:6 � 108 1=cm2, (center): a ¼ 40 nm, ns ¼ 1:6 � 108 1=cm2, (right): a ¼ 50 nm, ns ¼ 0:8 � 108 1=cm2 (H ¼ 3μm).

Plasmonic Enhancement of Solar Cells Efficiency: Material Dependence in Semiconductor Metallic Surface Nano… http://dx.doi.org/10.5772/intechopen.79113 127

some additional effects like reflection of the incident photons or destructive interference on metallic net would contribute and it was phenomenologically accounted in the plasmonmediated channel by an experiment-fitted factor β. The collective interference type corrections are rather not strong for the considered low densities of metallic coverings of order of 108

Table 3. Substrate material parameters to formula (18) (m ¼ 9:1 � 10�31 kg, the mass of bare electron; lh–light holes,

and nanosphere sizes well lower than the resonant wavelength, though for larger concentrations and larger nanosphere sizes, would play a stronger reducing role (reflecting photons) [6, 19]. The resonance threshold was accounted for the damped resonance envelope function in

enables a significant growth of the efficiency of the photoenergy transfer to the semiconductor, mediated by surface plasmons in nanoparticles deposited on the active layer, by increasing β or reducing H (at constant ns). However, because of the fact that an enhancement of β easily induces the overdamped regime of plasmon oscillations, the more prospective would be lowering of H especially convenient in thin film solar cells. The overall behavior of

=Ið Þ¼ ω 1 þ qm=q0 calculated according to the relation (19), and depicted in Figure 1, agrees quite well with the experimental observations [14], in the position, height and shape of the photocurrent curves for distinct samples (the strongest enhancement is achieved for a ¼ 40 nm,

In Figure 2, we present the spectral dependence of the plasmonic efficiency enhancement with respect to substrate change (Si, CIGS and GaAs) for the same Au nanoparticles with radius

CIGS substrate (copper-indium-gallium-diselenide) the spectral characteristics is narrower and

0

the experimental data (red) from Ref. [14]: a ¼ 25 nm, ns ¼ 6:6 � 108 1=cm2, (center): a ¼ 40 nm, ns ¼ 1:6 � 108 1=cm2,

a ¼ 50 nm and the same nanoparticle concentration ns ¼ 108

Figure 1. Spectral dependence of the normalized photocurrent I

(right): a ¼ 50 nm, ns ¼ 0:8 � 108 1=cm2 (H ¼ 3μm).

Eq. (19) including also semiconductor band-gap limit.

Semiconductor m∗

126 Plasmonics

hh–heavy holes, L–longitudinal, T–transverse).

=cm2,

� 104 β40 H nm½ �

q0

p Eg

=cm2. One can note that for the

I ð Þ λ according to formulae (19) and (18)—Comparison with

Figure 2. Comparison of the effectiveness of the plasmon channel for Si, GaAs and CIGS substrates with the same Au nanoparticles with radius 50 nm and surface density 108=cm2 .

blue shifted in comparison to Si and GaAs. Figure 2 reveals an increase in efficiency of the plasmon effect with growth of the value of forbidden gap Eg conserving other parameters not changed. Especially significantly influential material parameter occurs, however, a mass of holes, cf. Figure 3, which is also noticeable from Eq. (18). The mass of holes, m∗ p 2 , enters the denominator in the formula (12) for the ordinary photo effect and next the numerator in Eq. (18). The higher mass m∗ p the lower efficiency q0 of the ordinary photo effect is and higher the ratio qm q0 . In Figure 4, the material comparison of metal material of nanoparticles (Au, Ag and Cu) is presented for two their sizes (a ¼ 50, 25 nm). The blue shift of spectral characteristics for Ag and Cu in comparison to Au is noticeable (cf. also Figure 5) and even more visible for lower radii of nanoparticles due to narrowing of spectral curves (cf. Figure 6). From the comparison in Figures 5 and 6, for Si and CIGS substrates with Au, Ag and Cu nanoparticles of size a ¼ 50, 25 nm (at the nanoparticle concentration ns ¼ 108 =cm2), one can notice that Au nanoparticles utilize the visible spectrum in the better manner than Ag or Cu ones. The advantage of Au nanoparticles is greater in the case of Si substrate and is reduced for CIGS

Figure 3. Comparison of the effectiveness of the plasmon channel for varying Eg but the same effective masses of substrates covered with the same Au nanoparticles with radius 50 nm and surface density 108 =cm2 .

Figure 4. Comparison of the effectiveness of the plasmon channel for varying hole mass m∗ p but the same electron mass and Eg of substrates covered with the same Au nanoparticles with radius 50 nm and surface density 108=cm2 .

Figure 5. Comparison of the effectiveness of the plasmon channel for the same substrate Si with Au (red), Ag (blue) and Cu (brown) nanoparticles of the same radius 50 nm and surface density 108=cm2 .

concentration (from the 5% colloidal solution sputtering over the surface) has been applied to

Figure 6. Comparison of the effectiveness of the plasmon channel for the same substrate Si (upper) and CIGS (lower) with

Plasmonic Enhancement of Solar Cells Efficiency: Material Dependence in Semiconductor Metallic Surface Nano…

http://dx.doi.org/10.5772/intechopen.79113

129

, versus the

Au (red), Ag (blue) and Cu (green) nanoparticles of the same radius 50 nm and surface density 108=cm2

Worth noting is an agreement of experimentally observed difference in the increase of the efficiency due to the plasmon effect in both cases, of mc-Si and CIGS cells, if one compares the results of application of Au and Ag particles (at the same size of metallic nanoparticles and the same their surface concentration). This behavior agrees with the theoretical study of the material dependence of the plasmon effect, as shown above. From Figures 2–5, we see that for Si substrate Au nanoparticles with radii 50 nm better utilize the solar light spectrum than Ag or Cu particles (cf. Figure 5), and indeed in the experiment (cf. Figure 8) for Au nanoparticles the efficiency growth is ca. 10% larger than for Ag nanoparticles of the same size and concentration on the substrate m-Si solar cell. Interestingly, for the substrate CIGS cell, the effect is weaker and inverted, cf. Figure 9. This also is noticeable from the theoretical modeling—due to different Eg and effective masses of carriers for CIGS with respect to mc-Si. The maxima for efficiency enhancement for Au and Ag mutually shift in such a way that for CIGS Ag nanoparticles a bit better suit to solar light spectrum than Au nanoparticles. However, to analyze these effects in more detail, a measurement of spectral characteristics of all considered structures at varying but

monochromatic illumination uniformly calibrated should be performed.

different samples)—for more detail cf. Ref. [19].

sunlight spectrum on the earth surface.

substrate because the blue shift of Eg in CIGS with respect to Si. In the case of CIGS (especially for large nanoparticles, a ¼ 50 nm), the advantage of Au beyond Ag in overall utilization of sunlight spectrum disappears, whereas is pronounced in the case of Si substrate. Later, we describe an experimental confirmation of this behavior of Si and CIGS substrates, at laboratory sunlight-type illumination by Yamashita DensoYSS-50Aunder AM1.5 [19].

For nanoparticles of gold (Au) and silver (Ag) of size, a ¼ 50 nm, optimized due to formula (18), deposited on the multi-crystalline silicon (mc-Si) and on the copper-indium-galliumdiselenide (CIGS) solar cells, the measured [19] overall increase of cell efficiency attains the level of even 5%. The application of suitable concentration of Au and Ag nanoparticles onto mc-Si solar cells increases their efficiency by 5.6 and 4.8%, respectively [19]. Application of Au and Ag nanoparticles onto surface of CIGS solar cells improves their efficiency by 1.2 and 1.4%, respectively [19]. This is visualized in Figures 7 and 8, where it is compared an increase in solar cell overall efficiency (the ratio of the field beneath the I-V curve for the metallically improved solar cell and the clean solar cell; the same size (50 nm for radius) and the same

Plasmonic Enhancement of Solar Cells Efficiency: Material Dependence in Semiconductor Metallic Surface Nano… http://dx.doi.org/10.5772/intechopen.79113 129

Figure 6. Comparison of the effectiveness of the plasmon channel for the same substrate Si (upper) and CIGS (lower) with Au (red), Ag (blue) and Cu (green) nanoparticles of the same radius 50 nm and surface density 108=cm2 , versus the sunlight spectrum on the earth surface.

concentration (from the 5% colloidal solution sputtering over the surface) has been applied to different samples)—for more detail cf. Ref. [19].

substrate because the blue shift of Eg in CIGS with respect to Si. In the case of CIGS (especially for large nanoparticles, a ¼ 50 nm), the advantage of Au beyond Ag in overall utilization of sunlight spectrum disappears, whereas is pronounced in the case of Si substrate. Later, we describe an experimental confirmation of this behavior of Si and CIGS substrates, at laboratory

Figure 5. Comparison of the effectiveness of the plasmon channel for the same substrate Si with Au (red), Ag (blue) and

.

p but the same electron mass

.

For nanoparticles of gold (Au) and silver (Ag) of size, a ¼ 50 nm, optimized due to formula (18), deposited on the multi-crystalline silicon (mc-Si) and on the copper-indium-galliumdiselenide (CIGS) solar cells, the measured [19] overall increase of cell efficiency attains the level of even 5%. The application of suitable concentration of Au and Ag nanoparticles onto mc-Si solar cells increases their efficiency by 5.6 and 4.8%, respectively [19]. Application of Au and Ag nanoparticles onto surface of CIGS solar cells improves their efficiency by 1.2 and 1.4%, respectively [19]. This is visualized in Figures 7 and 8, where it is compared an increase in solar cell overall efficiency (the ratio of the field beneath the I-V curve for the metallically improved solar cell and the clean solar cell; the same size (50 nm for radius) and the same

sunlight-type illumination by Yamashita DensoYSS-50Aunder AM1.5 [19].

Cu (brown) nanoparticles of the same radius 50 nm and surface density 108=cm2

Figure 4. Comparison of the effectiveness of the plasmon channel for varying hole mass m∗

128 Plasmonics

and Eg of substrates covered with the same Au nanoparticles with radius 50 nm and surface density 108=cm2

Worth noting is an agreement of experimentally observed difference in the increase of the efficiency due to the plasmon effect in both cases, of mc-Si and CIGS cells, if one compares the results of application of Au and Ag particles (at the same size of metallic nanoparticles and the same their surface concentration). This behavior agrees with the theoretical study of the material dependence of the plasmon effect, as shown above. From Figures 2–5, we see that for Si substrate Au nanoparticles with radii 50 nm better utilize the solar light spectrum than Ag or Cu particles (cf. Figure 5), and indeed in the experiment (cf. Figure 8) for Au nanoparticles the efficiency growth is ca. 10% larger than for Ag nanoparticles of the same size and concentration on the substrate m-Si solar cell. Interestingly, for the substrate CIGS cell, the effect is weaker and inverted, cf. Figure 9. This also is noticeable from the theoretical modeling—due to different Eg and effective masses of carriers for CIGS with respect to mc-Si. The maxima for efficiency enhancement for Au and Ag mutually shift in such a way that for CIGS Ag nanoparticles a bit better suit to solar light spectrum than Au nanoparticles. However, to analyze these effects in more detail, a measurement of spectral characteristics of all considered structures at varying but monochromatic illumination uniformly calibrated should be performed.

Figure 7. Comparison of the effectiveness of the plasmon channel for the same substrate Si (upper) and CIGS (lower) with Au (red), Ag (blue) and Cu (green) nanoparticles of the same radius 25 nm and surface density 108=cm2 , versus the sunlight spectrum on the earth surface.

5. Conclusion

nanoparticles, (right) by Ag nanoparticles [19].

semiconductor parameters for Si and CIGS.

We have demonstrated by application of the Fermi Golden Rule scheme, that the efficiency of the energy transfer channel between the surface plasmon oscillations in a metallic nanoparticles and a substrate semiconductor depends on parameters of both deposited metallic particles (its radius and material) as well as on semiconductor parameters (energy gap, and effective masses of electron and holes). Found by us formula which generalizes the ordinary photo effect onto the plasmon-mediated one, agrees well with the experimental measurements in laboratory photodiode configuration. The measured ratio of photocurrent in the setup with and without metallic nano-components is compared with the theoretically predicted scenario. The quantitative consistence is obtained both in the shape of the spectral characteristics and in the particle size dependence (as illustrated for Si diode with deposited Au nanoparticles with radii 25, 40 and 50 nm). The qualitative agreement has been achieved also for complete solar cells where the plasmon effect is obscured by other elements of the long series of effects resulting in overall solar cell efficiency beyond only efficiency of the absorption of photons. We have compared the experimental data for multi-crystalline Si solar cell and CIGS (copper-indium-gallium-diselenide) solar cell covered or not with gold and silver nanoparticles with radii of order of 50 nm. The increase of the overall photovoltaic efficiency for metallically modified cells varies between 1.5 (CIGS) and 6% (Si), depending on nanoparticle concentration (for too dense concentration the efficiency drops down). A bit better increase (ca. 10% difference) causes Au nanoparticles for Si cell in comparison to Ag nanoparticles, whereas for CIGS cell, the difference between effect of Ag nanoparticles and Au ones is inverted and strongly reduced. This also agrees qualitatively with theory predictions taken into account differences in Mie frequency in Au and Ag and also different

Figure 9. Comparison of solar cell efficiency due to plasmon modification for the CIGS cell, (left) modified by Au

Plasmonic Enhancement of Solar Cells Efficiency: Material Dependence in Semiconductor Metallic Surface Nano…

http://dx.doi.org/10.5772/intechopen.79113

131

Figure 8. Comparison of solar cell efficiency due to plasmon modification for the multi-crystal Si solar cell, (left) modified by Au nanoparticles, (right) by Ag nanoparticles [19].

Plasmonic Enhancement of Solar Cells Efficiency: Material Dependence in Semiconductor Metallic Surface Nano… http://dx.doi.org/10.5772/intechopen.79113 131

Figure 9. Comparison of solar cell efficiency due to plasmon modification for the CIGS cell, (left) modified by Au nanoparticles, (right) by Ag nanoparticles [19].

### 5. Conclusion

Figure 7. Comparison of the effectiveness of the plasmon channel for the same substrate Si (upper) and CIGS (lower) with

Figure 8. Comparison of solar cell efficiency due to plasmon modification for the multi-crystal Si solar cell, (left) modified

, versus the

Au (red), Ag (blue) and Cu (green) nanoparticles of the same radius 25 nm and surface density 108=cm2

sunlight spectrum on the earth surface.

130 Plasmonics

by Au nanoparticles, (right) by Ag nanoparticles [19].

We have demonstrated by application of the Fermi Golden Rule scheme, that the efficiency of the energy transfer channel between the surface plasmon oscillations in a metallic nanoparticles and a substrate semiconductor depends on parameters of both deposited metallic particles (its radius and material) as well as on semiconductor parameters (energy gap, and effective masses of electron and holes). Found by us formula which generalizes the ordinary photo effect onto the plasmon-mediated one, agrees well with the experimental measurements in laboratory photodiode configuration. The measured ratio of photocurrent in the setup with and without metallic nano-components is compared with the theoretically predicted scenario. The quantitative consistence is obtained both in the shape of the spectral characteristics and in the particle size dependence (as illustrated for Si diode with deposited Au nanoparticles with radii 25, 40 and 50 nm). The qualitative agreement has been achieved also for complete solar cells where the plasmon effect is obscured by other elements of the long series of effects resulting in overall solar cell efficiency beyond only efficiency of the absorption of photons. We have compared the experimental data for multi-crystalline Si solar cell and CIGS (copper-indium-gallium-diselenide) solar cell covered or not with gold and silver nanoparticles with radii of order of 50 nm. The increase of the overall photovoltaic efficiency for metallically modified cells varies between 1.5 (CIGS) and 6% (Si), depending on nanoparticle concentration (for too dense concentration the efficiency drops down). A bit better increase (ca. 10% difference) causes Au nanoparticles for Si cell in comparison to Ag nanoparticles, whereas for CIGS cell, the difference between effect of Ag nanoparticles and Au ones is inverted and strongly reduced. This also agrees qualitatively with theory predictions taken into account differences in Mie frequency in Au and Ag and also different semiconductor parameters for Si and CIGS.