**2. Experimental**

94 Radioisotopes – Applications in Physical Sciences

external magnetic field, the magnetic field produces a torque on the magnetic dipole. The torque is tending to align the dipole with the field, associated with this torque; there is a

*μl* is the orbital magnetic dipole moment of an electron. According to the quantum theory, all spectral lines arise from transitions of electrons between different allowed energy levels within the atom and the frequency of the spectral line is proportional to the energy difference between the initial and final levels. The slight difference in energy is associated with these different orientations in the magnetic field. In the presence of a magnetic field, the elementary magnetic dipoles, whether permanent or induced, will act to set up a field of

Today investigations of magnetic effects on X-ray spectra became actual both from theoretical and experimental points of view. The numbers of works on this subject deal with magnetic circular dichroism (MCD) in X-ray absorption spectroscopy (XAS), that gives information on empty electron states in a valence band and their spin configurations (Thole et al., 1992, Stöhr&Wu, 1994). Several experiments have been performed on the external magnetic field effect on the *K* shell X-ray emission lines. Demir et al., (2006a) determined how the radiative transitions and the structures of the atoms in a strong magnetic field are affected, K*α* and K*β* X-ray production cross sections, the *K*-shell fluorescence yields and *I*(*Kβ/Kα*) intensity ratios for ferromagnetic Nd, Gd, and Dy and paramagnetic Eu and Ho were investigated using the 59.5 keV incident photon energy in the external magnetic fields intensities ± 0.75 T. On the other hand, Demir et al., (2006b) measured *L*3 subshell fluorescence yields and level widths for Gd, Dy, Hg and Pb at 59.5 keV incident photon energy in the external magnetic field of intensities ± 0.75 T. Porikli et al. (2008a; 2008b; 2008c) conduct measurements using pure Ni, Co, Cu and Zn and their compounds. Characteristic quantities such as position of line maxima, full widths at half maximum (FWHM), indices of asymmetry and intensity ratio values were determined in the values of external magnetic field 0.6 T and 1.2 T. Several experiments have been performed on the external magnetic field effect on the *K* shell X-ray emission lines. Commonly, experimental *L* X-ray intensities are measured using radioisotopes as excitation sources (Han et al., 2010; Porikli, 2011b). They have the advantages of stable intensity and energy and of small sizes, which allow compact and efficient geometry, and

Our motivation in performing this experiment has been two fold. First, with the aim of a better understanding of the chemical effect and external magnetic field effect, we conduct measurements using pure yttrium (Y) and its compounds. Characteristic quantities such as position of line maxima, full widths at half maximum (FWHM), indices of asymmetry and *Kβ*1/*Kα*, *Kβ*2/*Kα*, *Kβ*2/*Kβ*1 and *Kβ*/*Kα* intensity ratio values are determined in the values of external magnetic field 0.6 T and 1.2 T. In the present work, the measurements were done using a filtered 22.69 keV from Cd-109 and 59.54 keV from Am-241 point source and Si(Li) detector. Particle size effects were circumvented. Peak areas were determined using Gaussian fitting procedures and the errors in various corrections such as self-absorption and detector efficiency were minimized. The measured values were compared due to the external magnetic field and chemical effect. The measured values for B=0 were compared with other experimental and theoretical results. To our knowledge, these intensity ratio values of Y in the external magnetic field have not been reported in the literature and appear

∆*E*=-*μlB (*1)

potential energy of orientation:

induction of their own that will modify the original field.

they operate without any external power.

#### **2.1 Experimental set up (EDXRF)**

Yttrium compounds can serve as host lattices for doping with different lanthanide cations and they used as a catalyst for ethylene polymerization. As a metal, it is used on the electrodes of some high-performance spark plugs. Yttrium is also used in the manufacturing of gas mantles for propane lanterns as a replacement for thorium, which is radioactive. Developing uses include yttrium-stabilized zirconia in particular as a solid electrolyte and as an oxygen sensor in automobile exhaust systems. Yttrium is used in the production of a large variety of synthetic garnets. Small amounts of yttrium (0.1 to 0.2%) have been used to reduce the grain sizes of chromium, molybdenum, titanium, and zirconium. It is also used to increase the strength of aluminium and magnesium alloys. The addition of yttrium to alloys generally improves workability, adds resistance to high-temperature recrystallization and significantly enhances resistance to high-temperature oxidation (see graphite nodule discussion below).

The studied elements were Y, YBr3, YCl3, YF3, Y(NO3)3.6H2O, Y2O3, YPO4, Y(SO4)3.8H2O and Y2S3. The purity of commercially obtained materials was better than 99%. For powdered samples, particle size effects have a strong influence on the quantitative analysis of infinitely thick specimens. Even for specimens of intermediate thickness, in which category the specimens analyzed in the present study fall, these effects can be significant. Therefore, to circumvent particle size effects all samples were grounded and sieved through a -400 mesh (<37 μm) sieve. The powder was palletized to a uniform thickness of 0.05-0.15 g cm-2 range by a hydraulic press using 10 ton in-2 pressure. The diameter of the pellet was 13 mm.

All of the lines were excited using a 100 mCi Am-241 annular radioactive source and Cd-109 point source of 10 mCi strength (providing 5.0x103 steradian-1 photon flux of Ag Xradiation). The fluorescent X-rays emitted from the targets were analyzed by a Si(Li) detector (effective area 12.5 mm2, thickness 3 mm, Be window thickness 0.025 mm).

For each sample three separate measurements have been made just to see the consistency of the results obtained from different measurements agreed with a deviation of less than 1%. The experimental setup consist of a Si(Li) detector and Cd-109 radioactive source as shown in Fig. 1. The mechanical arrangement to house the source-sample-detector combination in a definite geometry was shown in Fig. 1. An Al, Pb conical collimator was used between the sample and the detector for the excitation to obtain a large beam of emergent radiation and to avoid the interaction of the X-rays emitted by the component elements of the radioactive capsule and detector.An Al, Pb conical collimator was used between the sample and the detector for the excitation to obtain a large beam of emergent radiation and to avoid the interaction of the X-rays emitted by the component elements of the radioactive capsule and detector. This collimator has an external diameter of 13 mm and it was placed in the internal diameter of the radioactive source (8 mm). A graded filter of Pb, Fe and Al to obtain a thin beam of photons scattered from the sample and to absorb undesirable radiation shielded the

Determination of Chemical State and External Magnetic Field

X-ray intensity (normalized)

0

0

spectra were plotted after smoothing.

1

2

3

X-ray intensity (normalized)

4

5

The spectra were plotted after smoothing.

1

2

3

4

Effect on the Energy Shifts and X-Ray Intensity Ratios of Yttrium and Its Compounds 97

Kα

*K*α Energy (keV)

Fig. 2. A typical *K* X-ray spectrum of the Y target in B=0, B=0.6T and B=1.2 T magnetic field.

14.8 15.6 16.4 17.3

Kβ

*K*β

> Y2 (SO4 ) 3 .8H2 O

14.8 15.6 16.4 17.3

Energy (keV)

Fig. 3. Measured *Kα*, *Kβ*1,3 and *Kβ*2,4 spectra of Y, YBr3, YCl3, Y(SO4)3.8H2O and Y2S3. The

B=0.6 T

B=1.2 T

*K*β2,4

*K*β1,3

16.5 16.7 16.9 17.1 17.3

B=0 T

Y

YCl3

YBr3

Y2 S3

detector. The sample-detector and excitation source-sample distances were optimized to get maximum count rate in the fluorescent peaks. The sample was placed approximately at 45° to the source-plane as well as to the detector-plane so that the intensity of scattered radiation could be minimized (Giauque et al., 1973). The count rate kept below 1000 counts s-1 in order to avoid peak broadening, energy shift and non-linearity. The data were collected into 16384 channels of a digital spectrum analyzer DSA-1000. The energy per channel was adjusted as 4 eV to determine the peak centroits and to discriminate the overlapped peaks. The samples were mounted in a sample holder placed between the pole pieces of an electromagnet capable of producing the magnetic field of approximately 2.66T at 2 mm pole range. During the study, the magnetic field intensities of, 0.6 T and 1.2 T were applied to the samples. An ammeter monitored the continuity and stability of the currents feeding the electromagnet. A typical *K* X-ray spectrum of Y at the 0.0 T, 0.6 T and 1.2 T is shown in Fig. 2. A typical *Kα*, *Kβ*1,3 and *Kβ*2,4 spectrum of Y, YBr3, YCl3, Y(SO4)3.8H2O and Y2S3 are shown in Fig. 3.

Fig. 1. Experimental set-up.

detector. The sample-detector and excitation source-sample distances were optimized to get maximum count rate in the fluorescent peaks. The sample was placed approximately at 45° to the source-plane as well as to the detector-plane so that the intensity of scattered radiation could be minimized (Giauque et al., 1973). The count rate kept below 1000 counts s-1 in order to avoid peak broadening, energy shift and non-linearity. The data were collected into 16384 channels of a digital spectrum analyzer DSA-1000. The energy per channel was adjusted as 4 eV to determine the peak centroits and to discriminate the overlapped peaks. The samples were mounted in a sample holder placed between the pole pieces of an electromagnet capable of producing the magnetic field of approximately 2.66T at 2 mm pole range. During the study, the magnetic field intensities of, 0.6 T and 1.2 T were applied to the samples. An ammeter monitored the continuity and stability of the currents feeding the electromagnet. A typical *K* X-ray spectrum of Y at the 0.0 T, 0.6 T and 1.2 T is shown in Fig. 2. A typical *Kα*,

*Kβ*1,3 and *Kβ*2,4 spectrum of Y, YBr3, YCl3, Y(SO4)3.8H2O and Y2S3 are shown in Fig. 3.

Fig. 1. Experimental set-up.

Fig. 2. A typical *K* X-ray spectrum of the Y target in B=0, B=0.6T and B=1.2 T magnetic field. The spectra were plotted after smoothing.

Fig. 3. Measured *Kα*, *Kβ*1,3 and *Kβ*2,4 spectra of Y, YBr3, YCl3, Y(SO4)3.8H2O and Y2S3. The spectra were plotted after smoothing.

Determination of Chemical State and External Magnetic Field

the samples, a sample spinner facility was used in all cases.

measured for same conditions to determine these errors.

**2.4 Spectral profile analysis (WDXRF)**

**2.3 Experimental set up (WDXRF)**

Effect on the Energy Shifts and X-Ray Intensity Ratios of Yttrium and Its Compounds 99

A commercial WDXRF spectrometer (Rigaku ZSX 100e) was used for analysis of the different samples. This instrument is usually equipped with a 3 kW Rh-anode tube working at a voltage range of 20–50 kV and a current from 20 to 50 mA. It is possible to use primary beam filters (made of Zr, Al, Ti or Cu) between the primary radiation and the sample holder to reduce the background continuum and to improve the signal-to-noise ratio. Energy resolution and efficiency for each analytical line also depend on the collimator aperture and the analyzer crystal in use. Several different collimators can be used to reduce the step/scan resolution, as well as up to ten analyzer crystals, to better enhance spectral data for a specific element. Detection can be performed using a flow proportional counter (light elements) or a scintillation counter (heavy elements). In this work, analyses were made in vacuum atmosphere. Moreover, to avoid possible problems with inhomogeneity when measuring

To investigate the spectrometer sensitivity in measuring of intensity and energy shift, one sample at same conditions was measured for three times. Because of the use of instruments such as sieve weight and hydraulic press, errors are caused in the results of analysis. These errors were called manual and instrumental errors. Three samples were prepared and

The common method for evaluation of spectra in WDXRF is by the use of net peak line intensity. This is due to the high efficiency in the analytical results from the scintillation and/or the flow counter detectors. These detectors can receive up to 2×106 cps. In contrast, the common spectra evaluation in EDXRF is based on integration of the gross or net peak area due to a lower efficiency in the solid state detectors, usually limited to a maximum count of 5×104 cps. Taking into account these facts and to improve the sensitivity of the signal, the spectral data obtained by the WDXRF equipment were treated using the deconvolution software (Microcal Orgin 7.5), traditionally used in EDXRF spectrometry, to obtain the peak areas. The total number of counts increases considering the total peak area instead of only the analytical line. This leads also to an improvement of sensibility and detection limits. Once samples were analyzed, the identification of elements from the WDXRF spectra was done by using the qualitative scanning mode linked to the equipment, which includes automatic peak and element identification. The principle of WDXRF spectrometry is the use of different analyzer crystals to diffract and separate the different characteristic wavelengths of the elements present in the sample. For that reason, in WDXRF measurements, a multi-spectrum was obtained

resulting from the use of different analyzing crystals, excitation conditions, etc.

line represents the overall fit. The background is shown as a blue line.

ultra-light elements), varying atmospheres, impurities and different sample sizes.

Rigaku has improved their semi-quantitative software package further with the introduction of SQX. It is capable of automatically correcting for all matrix effects, including line overlaps. SQX can also correct for secondary excitation effect by photoelectrons (light and

The obtained multispectra were split into the different individual spectra and were converted to energies by inversion of the channels to be treated using the means of the SQX software to perform spectral deconvolution and fitting and to evaluate element net peak areas from the spectra. Peak fitting was done by iteration to better adjust the peak and the background to minimize the chi-square of the fitting on each spectra. Fig. 4 shows the spectrum of Y. Measured numbers of counts are shown as solid black circles, while the red

Spectrum evaluation is a crucial step in X-ray analysis, as much as sample preparation and quantification. As with any analytical procedure, the final performance of X-ray analysis is determined by the weakest step in the process. The processing of ED spectra by means of computers has always been more evident because of their inherent digital nature. Due to relatively low resolving power of the employed Si(Li) detector, the process of evaluating XRF spectra is prone to many errors and requires dedicated software. For this purpose a software package called ORGIN was used for peak resolving background subtraction and determination of the net peak areas of *K* X-rays which is based on the non-linear least squares fitting of a mathematical model of the XRF spectrum.

#### **2.2 Data analysis (EDXRF)**

The *Kβ*/*Kα* X-ray intensity ratio values have been calculated by using the relation

$$\frac{I(K\beta)}{I(K\alpha)} = \frac{N(K\beta)}{N(K\alpha)} \frac{\varepsilon(K\alpha)}{\varepsilon(K\beta)} \frac{\beta(K\alpha)}{\beta(K\beta)}\tag{2}$$

where *N*(*Kα*) and *N*(*Kβ*) are the net counts under the *Kα* and *Kβ* peaks, respectively. *β*(*Kα*) and *β*(*Kβ*) are the self-absorption correction factor of the target and ε(*Kα*) and ε(*Kβ*) are the detector efficiency for *Kα* and *Kβ* rays. The values of the factors, *I*0*Gε* which contain terms related to the incident photon flux, geometrical factor and the efficiency of the X-ray detector, were determined by collecting the *Kα* and *Kβ* X-ray spectra of Ti, As, Br, Sr, Y, Zr and Ru with the mass thickness 0.02-0.17 g/cm2 in the same geometry and calculated by using the following equation

$$I\_0 \mathbf{G} \boldsymbol{\varepsilon}\_{Ki} = \frac{N\_{Ki}}{\sigma\_{Ki} \beta\_{Ki} \mathbf{t}\_i} \tag{3}$$

where *NKi* and *βKi* (*i*=*α*, *β*) have the same meaning as in Eq. (2). *σKi* is X-ray fluorescence crosssection, *G* is a geometry factor and *t* is the mass of the sample in g/cm2.

The self absorption correction factor *β* is calculated for both *Kα* and *Kβ* separately by using the following expression

$$\beta\_{\rm Ki} = \frac{1 - \exp\{-\[\mu(E\_0)\sec\theta\_l + \mu\_{\rm Ki}(E)\sec\theta\_2]t\}}{[\mu(E\_0)\sec\theta\_l + \mu\_{\rm Ki}(E)\sec\theta\_2]t} \tag{4}$$

where *µ*(*E*0) and *µKi*(*E*) are the total mass absorption coefficients taken from WinXCOM programme which is the Windows version of XCOM. XCOM is the electronic version of Berger and Hubbell's Tables (Berger et al., 1987). The angles of incident photons and emitted X-rays with respect to the normal at the surface of the sample *θ*1 and *θ2* were equal to 45○ in the present setup.

The term *σKi* represents the *K* X-ray fluorescence cross-sections and is given by

$$
\sigma\_{Ki} = \sigma\_K^P w\_K f\_{Ki} \tag{5}
$$

*P* σ *K* is the *K* shell photo ionization cross-section (Scofield, 1973), *wK* is the fluorescence yield (Krause et al., 1979) and *fKi* is fractional X-ray emission rate (Scofield, 1974a).

#### **2.3 Experimental set up (WDXRF)**

98 Radioisotopes – Applications in Physical Sciences

Spectrum evaluation is a crucial step in X-ray analysis, as much as sample preparation and quantification. As with any analytical procedure, the final performance of X-ray analysis is determined by the weakest step in the process. The processing of ED spectra by means of computers has always been more evident because of their inherent digital nature. Due to relatively low resolving power of the employed Si(Li) detector, the process of evaluating XRF spectra is prone to many errors and requires dedicated software. For this purpose a software package called ORGIN was used for peak resolving background subtraction and determination of the net peak areas of *K* X-rays which is based on the non-linear least squares fitting of a mathematical model of the XRF

The *Kβ*/*Kα* X-ray intensity ratio values have been calculated by using the relation

β

α

0

section, *G* is a geometry factor and *t* is the mass of the sample in g/cm2.

μ

The term *σKi* represents the *K* X-ray fluorescence cross-sections and is given by

σ

(Krause et al., 1979) and *fKi* is fractional X-ray emission rate (Scofield, 1974a).

*Ki*

β *Ki*

ε

μ

*N I G*

σ β

where *NKi* and *βKi* (*i*=*α*, *β*) have the same meaning as in Eq. (2). *σKi* is X-ray fluorescence cross-

The self absorption correction factor *β* is calculated for both *Kα* and *Kβ* separately by using

 θμ

where *µ*(*E*0) and *µKi*(*E*) are the total mass absorption coefficients taken from WinXCOM programme which is the Windows version of XCOM. XCOM is the electronic version of Berger and Hubbell's Tables (Berger et al., 1987). The angles of incident photons and emitted X-rays with respect to the normal at the surface of the sample *θ*1 and *θ2* were equal to 45○ in

*P*

*K* is the *K* shell photo ionization cross-section (Scofield, 1973), *wK* is the fluorescence yield

 σ

( ) ( )( ) ( ) ( ) ( )( ) ( ) *IK NK K K IK NK K K*

where *N*(*Kα*) and *N*(*Kβ*) are the net counts under the *Kα* and *Kβ* peaks, respectively. *β*(*Kα*) and *β*(*Kβ*) are the self-absorption correction factor of the target and ε(*Kα*) and ε(*Kβ*) are the detector efficiency for *Kα* and *Kβ* rays. The values of the factors, *I*0*Gε* which contain terms related to the incident photon flux, geometrical factor and the efficiency of the X-ray detector, were determined by collecting the *Kα* and *Kβ* X-ray spectra of Ti, As, Br, Sr, Y, Zr and Ru with the mass thickness 0.02-0.17 g/cm2 in the same geometry and calculated by

ββ β

*Ki*

*Ki Ki i*

*t*

01 2

*E Et*

*Ki*

 θ  θ

*Ki K K Ki* = *w f* (5)

−− + = + (4)

01 2 1 exp{ [ ( )sec ( )sec ] } [ ( )sec ( )sec ]

 θμ

*E Et*

*Ki*

= (2)

= (3)

 β ε α β α

 αε

spectrum.

**2.2 Data analysis (EDXRF)**

using the following equation

the following expression

the present setup.

*P* σ

A commercial WDXRF spectrometer (Rigaku ZSX 100e) was used for analysis of the different samples. This instrument is usually equipped with a 3 kW Rh-anode tube working at a voltage range of 20–50 kV and a current from 20 to 50 mA. It is possible to use primary beam filters (made of Zr, Al, Ti or Cu) between the primary radiation and the sample holder to reduce the background continuum and to improve the signal-to-noise ratio. Energy resolution and efficiency for each analytical line also depend on the collimator aperture and the analyzer crystal in use. Several different collimators can be used to reduce the step/scan resolution, as well as up to ten analyzer crystals, to better enhance spectral data for a specific element. Detection can be performed using a flow proportional counter (light elements) or a scintillation counter (heavy elements). In this work, analyses were made in vacuum atmosphere. Moreover, to avoid possible problems with inhomogeneity when measuring the samples, a sample spinner facility was used in all cases.

To investigate the spectrometer sensitivity in measuring of intensity and energy shift, one sample at same conditions was measured for three times. Because of the use of instruments such as sieve weight and hydraulic press, errors are caused in the results of analysis. These errors were called manual and instrumental errors. Three samples were prepared and measured for same conditions to determine these errors.

#### **2.4 Spectral profile analysis (WDXRF)**

The common method for evaluation of spectra in WDXRF is by the use of net peak line intensity. This is due to the high efficiency in the analytical results from the scintillation and/or the flow counter detectors. These detectors can receive up to 2×106 cps. In contrast, the common spectra evaluation in EDXRF is based on integration of the gross or net peak area due to a lower efficiency in the solid state detectors, usually limited to a maximum count of 5×104 cps. Taking into account these facts and to improve the sensitivity of the signal, the spectral data obtained by the WDXRF equipment were treated using the deconvolution software (Microcal Orgin 7.5), traditionally used in EDXRF spectrometry, to obtain the peak areas. The total number of counts increases considering the total peak area instead of only the analytical line. This leads also to an improvement of sensibility and detection limits. Once samples were analyzed, the identification of elements from the WDXRF spectra was done by using the qualitative scanning mode linked to the equipment, which includes automatic peak and element identification. The principle of WDXRF spectrometry is the use of different analyzer crystals to diffract and separate the different characteristic wavelengths of the elements present in the sample. For that reason, in WDXRF measurements, a multi-spectrum was obtained resulting from the use of different analyzing crystals, excitation conditions, etc.

Rigaku has improved their semi-quantitative software package further with the introduction of SQX. It is capable of automatically correcting for all matrix effects, including line overlaps. SQX can also correct for secondary excitation effect by photoelectrons (light and ultra-light elements), varying atmospheres, impurities and different sample sizes.

The obtained multispectra were split into the different individual spectra and were converted to energies by inversion of the channels to be treated using the means of the SQX software to perform spectral deconvolution and fitting and to evaluate element net peak areas from the spectra. Peak fitting was done by iteration to better adjust the peak and the background to minimize the chi-square of the fitting on each spectra. Fig. 4 shows the spectrum of Y. Measured numbers of counts are shown as solid black circles, while the red line represents the overall fit. The background is shown as a blue line.

Determination of Chemical State and External Magnetic Field

*Kα*, *Kβ*1,3 and *Kβ*2,4 FWHM values.

Element External Magnetic

Effect on the Energy Shifts and X-Ray Intensity Ratios of Yttrium and Its Compounds 101

The FWHM values for all compounds investigated for Cd-109 and Am-241 radioactive sources are given in Tables 1 and 2. Among all the Y compounds, the Y(NO3)3.6H2O *Kα* emission line shows large widths for both lines. Both the *Kα*, *Kβ*1,3 and *Kβ*2,4 lines become narrow when the compounds crystal system is monoclinic. In all cubic compounds such as Y2O3 and Y2S3 the lines FWHM values come closer to pure Y FWHM value. As can be seen from Table 1 and 2, for Zn compounds, the variation in the values of FWHM is relatively small. But when we compare the Y compounds with pure forms, we realize changes in both

The nonmonotonic behavior of the *Kα* widths is probably due to the behavior of the *L* levels widths rather than the *K* level ones. The smaller overlap of the *M* and *K* wave functions, as compared to the *K* and *L* ones, may reduce the relative influence of possible similar sized nonmonotonic contributions originating in the final state level widths. Since experimental results FWHM and the index of asymmetry for B≠0 cannot be found in the literature, the comparison is not made with the other experimental values. As can be seen from Table 1 and 2, all FWHM values systematically decrease with increasing magnetic field intensity. Table 1 and 2 show that the experimental values of FWHM are unity within experimental

uncertainties, suggesting the absence of chemical and external magnetic field effects.

  Full width at half maximum (FWHM) and asymmetry index values of *Kα, Kβ*1,3 and

*Kβ*2,4 emission lines in Y compounds for Cd-109 radioactive source.

Field Asymmetry Index (eV) FWHM (eV)

Fig. 4. Solid circles: Measured spectrum of Y *K* X-rays. Lines: Overall fitting function (red) and its components (green).

#### **3. Results and discussion**

A frequently used and convenient (but not very accurate) quantification of the line shape is by its full width at half maximum (FWHM) and index of asymmetry. These characteristics of lines (line parameters) are sensitive to change: line position (chemical shift), line shape (full width at half maximum (FWHM) and index of asymmetry) and additionally mutual ratios of line intensities. The peak position was determined at the center point of the 9/10 intensity of the smoothed line shape as illustrated in Fig. 5. It was known from our experience that the standard deviation of the peak position was determined using the peak top. Parameters such as FWHM and asymmetry index, defined in Fig. 5, were evaluated using the smoothed data. The Savitzky-Golay smoothing method was iteratively processed one time. Spectral smoothing was important for reducing the standard deviation of these parameters.

Fig. 5. Definition of asymmetry index, FWHM and peak position determined from 9/10 intensity.

0,0 2,0x101 4,0x101

Kβ2,4

Energy (keV) 16.5 16.7 16.9 17.1 17.3

Kβ1,3

6,0x101 8,0x101 1,0x102

Intensity (kcps)

Fig. 4. Solid circles: Measured spectrum of Y *K* X-rays. Lines: Overall fitting function (red)

A frequently used and convenient (but not very accurate) quantification of the line shape is by its full width at half maximum (FWHM) and index of asymmetry. These characteristics of lines (line parameters) are sensitive to change: line position (chemical shift), line shape (full width at half maximum (FWHM) and index of asymmetry) and additionally mutual ratios of line intensities. The peak position was determined at the center point of the 9/10 intensity of the smoothed line shape as illustrated in Fig. 5. It was known from our experience that the standard deviation of the peak position was determined using the peak top. Parameters such as FWHM and asymmetry index, defined in Fig. 5, were evaluated using the smoothed data. The Savitzky-Golay smoothing method was iteratively processed one time. Spectral

smoothing was important for reducing the standard deviation of these parameters.

Fig. 5. Definition of asymmetry index, FWHM and peak position determined from 9/10

14,5 14,6 14,7 14,8 14,9 15,0 15,1 15,2 15,3

Energy (keV)

Kα

0 1x102 2x102 3x102 4x102 5x102 6x102

and its components (green).

**3. Results and discussion**

Intensity (kcps)

intensity.

The FWHM values for all compounds investigated for Cd-109 and Am-241 radioactive sources are given in Tables 1 and 2. Among all the Y compounds, the Y(NO3)3.6H2O *Kα* emission line shows large widths for both lines. Both the *Kα*, *Kβ*1,3 and *Kβ*2,4 lines become narrow when the compounds crystal system is monoclinic. In all cubic compounds such as Y2O3 and Y2S3 the lines FWHM values come closer to pure Y FWHM value. As can be seen from Table 1 and 2, for Zn compounds, the variation in the values of FWHM is relatively small. But when we compare the Y compounds with pure forms, we realize changes in both *Kα*, *Kβ*1,3 and *Kβ*2,4 FWHM values.

The nonmonotonic behavior of the *Kα* widths is probably due to the behavior of the *L* levels widths rather than the *K* level ones. The smaller overlap of the *M* and *K* wave functions, as compared to the *K* and *L* ones, may reduce the relative influence of possible similar sized nonmonotonic contributions originating in the final state level widths. Since experimental results FWHM and the index of asymmetry for B≠0 cannot be found in the literature, the comparison is not made with the other experimental values. As can be seen from Table 1 and 2, all FWHM values systematically decrease with increasing magnetic field intensity. Table 1 and 2 show that the experimental values of FWHM are unity within experimental uncertainties, suggesting the absence of chemical and external magnetic field effects.

Table 1. Full width at half maximum (FWHM) and asymmetry index values of *Kα, Kβ*1,3 and *Kβ*2,4 emission lines in Y compounds for Cd-109 radioactive source.

Determination of Chemical State and External Magnetic Field

the others.

**Element External**

in Y compounds for Cd-109 radioactive source.

Effect on the Energy Shifts and X-Ray Intensity Ratios of Yttrium and Its Compounds 103

When the crystal system of Y compounds is cubic, the *Kα* emission lines are almost symmetric for Y2O3and Y2S3. It is clear from results that, except for Y and Y(NO3)3.6H2O ,the asymmetry are generally larger for the *Kα* peak. So we can say that, the line shapes of *Kα* are not symmetric. It is also found from the Table 2, when the crystal system of Y compounds is trigonal (YBr3), the *Kα*, *Kβ*1,3 and *Kβ*2,4 emission lines are almost symmetric too. For Am-241, line shapes are more symmetric than the Cd-109. As seen from Table 1 and 2, in the presence of an external magnetic field, the asymmetry index of the Y compounds change. The *Kα*, *Kβ*1,3 and *Kβ*2,4 emission lines asymmetry indices values decrease with external magnetic field. However, a more asymmetric structure is encountered for the elements of which their crystal symmetry is cubic. Also, it is observed that monoclinic group is more symmetric than

**Magnetic Field Chemical shift (***ΔE***) (eV) Energy shift (***δE***) (eV)**

 *Kα Kβ*1,3 *Kβ*2,4 *Kα Kβ*1,3 *Kβ*2,4 **Y B=0 0 0 0 0 0 0**  B=0.6T 0.074 0.109 0.101 B=1.2T 0.161 0.260 0.237 **Y(NO3)3.6H2O B=0 -0.333 -0.441 -0.216 0 0 0**  B=0.6T 0.086 0.098 0.115 B=1.2T 0.141 0.137 0.227 **YCl3 B=0 -0.238 -0.309 -0.115 0 0 0**  B=0.6T 0.481 0.235 0.288 B=1.2T 0.612 0.368 0.339 **YPO4 B=0 -0.121 -0.235 -0.103 0 0 0**  B=0.6T 0.335 0.279 0.336 B=1.2T 0.455 0.355 0.399 **YBr3 B=0 -0.033 -0.065 -0.065 0 0 0**  B=0.6T 0.444 0.131 0.338 B=1.2T 0.657 0.4 0.551 **Y2O3 B=0 0.135 0.017 0.056 0 0 0**  B=0.6T 0.111 0.124 0.543 B=1.2T 0.185 0.303 0.441 **YF3 B=0 0.164 0.191 0.198 0 0 0**  B=0.6T 0.167 0.166 0.112 B=1.2T 0.533 0.529 0.144 **Y(SO4)3.8H2O B=0 0.609 0.387 0.33 0 0 0**  B=0.6T 0.089 0.12 0.051 B=1.2T 0.354 0.293 0.237 **Y2S3 B=0 0.899 0.872 0.808 0 0 0**  B=0.6T 0.173 0.204 0.206 B=1.2T 0.199 0.307 0.333 Table 3. Chemical shift (*ΔE*) and energy shift (*δE*) values of *Kα, Kβ*1,3 and *Kβ*2,4 emission lines

To obtain more definite conclusions on FWHM dependency of the external magnetic field, more experimental data are clearly needed. The experimental uncertainties are always <0.05 eV for the FWHM.

According to Allinson (1933), the index of asymmetry of an X-ray emission line is defined as the ratio of the part of the FWHM lying to the long-wavelength side of the maximum ordinate to that on the short-wavelength side. In Table 1 and 2, the index of asymmetry for *Kα*, *Kβ*1,3 and *Kβ*2,4 emission lines are presented. The experimental uncertainties in the values cited in the table were determined taking into account multiple measurements and multiple fits of each spectrum. The errors for the index of asymmetry are ≤0.1 eV for *Kα*, *Kβ*1,3 and *Kβ*2,4.

Table 2. Full width at half maximum (FWHM) and asymmetry index values of *Kα, Kβ*1,3 and *Kβ*2,4 emission lines in Y compounds for Am-241 radioactive source.

To obtain more definite conclusions on FWHM dependency of the external magnetic field, more experimental data are clearly needed. The experimental uncertainties are always <0.05

According to Allinson (1933), the index of asymmetry of an X-ray emission line is defined as the ratio of the part of the FWHM lying to the long-wavelength side of the maximum ordinate to that on the short-wavelength side. In Table 1 and 2, the index of asymmetry for *Kα*, *Kβ*1,3 and *Kβ*2,4 emission lines are presented. The experimental uncertainties in the values cited in the table were determined taking into account multiple measurements and multiple fits of each spectrum. The errors for the index of asymmetry are ≤0.1 eV for *Kα*, *Kβ*1,3 and

 *Kα Kβ*1,3 *Kβ*2,4 *Kα Kβ*1,3 **Y B=0 1.120 1.113 1.022 3.229 4.297** B=0.6T 1.089 1.100 1.016 3.212 4.203 B=1.2T 1.009 0. 989 0.984 3.050 4.100 **Y(NO3)3.6H2O B=0 1.113 1.110 1.020 3.114 4.116** B=0.6T 1.028 1.104 1.013 3.017 4.111 B=1.2T 0.996 0.993 1.001 3.091 4.075 **YCl3 B=0 1.109 1.105 1.017 3.077 4.122** B=0.6T 1.003 1.007 1.009 3.041 4.110 B=1.2T 0.978 0.987 0.987 3.000 4.004 **YPO4 B=0 1.111 1.075 0.993 3.201 4.177** B=0.6T 0.989 1.032 0.984 3.126 4.170 B=1.2T 0.969 1.008 0.953 3.013 4.005 **YBr3 B=0 1.101 1.031 0.988 3.421 4.136** B=0.6T 1.017 1.004 0.980 3.234 4.065 B=1.2T 0.994 0.971 0.965 3.048 3.993 **Y2O3 B=0 1.056 1.006 0.974 3.555 4.008** B=0.6T 1.031 0.999 0.972 3.229 3.989 B=1.2T 0.987 0.977 0.954 3.096 3.974 **YF3 B=0 1.077 1.001 0.970 3.301 4.123** B=0.6T 1.005 0.984 0.954 3.137 4.012 B=1.2T 0.993 0.975 0.950 3.009 3.940 **Y(SO4)3.8H2O B=0 1.064 0.991 0.964 3.202 4.109** B=0.6T 1.001 0.990 0.966 3.113 3.989 B=1.2T 0.978 0.946 0.956 3.074 3.866 **Y2S3 B=0 1.011 0.995 0.949 3.441 4.301**  B=0.6T 1.010 0.994 0.937 3.368 4.039 B=1.2T 0.974 0.961 0.921 3.222 3.974 Table 2. Full width at half maximum (FWHM) and asymmetry index values of *Kα, Kβ*1,3 and

*Kβ*2,4 emission lines in Y compounds for Am-241 radioactive source.

Magnetic Field Asymmetry Index (eV) FWHM (eV)

eV for the FWHM.

Element External

*Kβ*2,4.

When the crystal system of Y compounds is cubic, the *Kα* emission lines are almost symmetric for Y2O3and Y2S3. It is clear from results that, except for Y and Y(NO3)3.6H2O ,the asymmetry are generally larger for the *Kα* peak. So we can say that, the line shapes of *Kα* are not symmetric. It is also found from the Table 2, when the crystal system of Y compounds is trigonal (YBr3), the *Kα*, *Kβ*1,3 and *Kβ*2,4 emission lines are almost symmetric too. For Am-241, line shapes are more symmetric than the Cd-109. As seen from Table 1 and 2, in the presence of an external magnetic field, the asymmetry index of the Y compounds change. The *Kα*, *Kβ*1,3 and *Kβ*2,4 emission lines asymmetry indices values decrease with external magnetic field. However, a more asymmetric structure is encountered for the elements of which their crystal symmetry is cubic. Also, it is observed that monoclinic group is more symmetric than the others.

Table 3. Chemical shift (*ΔE*) and energy shift (*δE*) values of *Kα, Kβ*1,3 and *Kβ*2,4 emission lines in Y compounds for Cd-109 radioactive source.

Determination of Chemical State and External Magnetic Field

which crystal structure different are needed.

measurement from the average value was 0.1 eV.

**Element Differences between FWHM values**

emission lines in Y compounds obtained for WDXRF.

the *Kβ*1,3 and *Kβ*2,4 peak shifts are large, show prominent asymmetry.

discuss the values of these ratios as obtained in our measurements.

Effect on the Energy Shifts and X-Ray Intensity Ratios of Yttrium and Its Compounds 105

The chemical shift was the difference between the center point of the 9/10 peak intensity of a compound and that of pure Y measured before and after the measurement of the compound. When the environment of the emitting atom is changed, there are changes in the position of emission lines with respect to those in the pure metal. These changes are called chemical shifts. They are presented in Tables 3 and 4. Both the 4*d* electron configuration and crystal structure affect the chemical shift and energy shift with applied external magnetic field. There is a clear relationship with the external magnetic field values and the energy shift values of Y compounds, as is found in Table 3 and 4's last column. For higher values of external magnetic field, the values of energy shift increases systematically. But we do not find any relationship between external magnetic field and crystal structure of the compound. To obtain more definite conclusion, more experimental data for 4*d* compounds

The errors of the chemical and energy shifts, originate mainly from the limited precision of our measurements, determined by repetitive measurements of all pure targets, which were prepared and placed in the same experimental geometry. The precision in the position of *Kα*, *Kβ*1,3 and *Kβ*2,4 lines was determined as 0.05 eV, whereas the maximum deviation of a single

*Kα Kβ***1,3** *Kβ***2,4** *Kα Kβ***1,3** *Kβ***2,4 Y** 0 0 0 0 0 0 **Y(NO3)3.6H2O** 0.155 0.031 0.012 0.132 0.126 0.125 **YCl3** 0.091 0.058 0.033 0.268 0.238 0.284 **YPO4** -0.223 -0.087 -0.107 -0.454 -0,444 -0.415 **YBr3** 0.114 0.071 0.067 0.144 0.179 0.156 **Y2O3** -0.077 -0.068 -0.021 -0.096 -0.126 -0.085 **YF3** -0.095 -0.034 -0.048 -0.115 -0.127 -0.167 **Y(SO4)3.8H2O** 0.054 0.023 0.181 0.183 0.145 0.106 **Y2S3** -0.013 -0.064 -0.079 -0.301 -0.397 -0.385 Table 5. Chemical shift *(ΔE)* and differences between FWHM values of *Kα, Kβ*1,3 and *Kβ*2,4

It is also seen from Table 5 that the *Kα* line width of YPO4 compound is wider than that of the other compounds. Compare with the *Kα* peak of the pure Y, that of cubic crystal structure Yttrium compounds shifted to lower energy, and the peak shift ordering was YF3<Y2O3<Y2S3<YPO4. The line shapes of *Kα* are generally symmetric. Y2S3 and Y2O3, where

The accurate knowledge of the *Kβ*1/*Kα*, *Kβ*2/*Kα*, *Kβ*2/*Kβ*1 and *Kβ*/*Kα* intensity ratios is required for a number of practical applications of X-rays, e.g. molecular and radiation physics investigations, in non-destructive testing, elemental analysis, medical research etc. Therefore, these ratios depend sensitively on the atomic structure. Thus they have been widely used also for critical evaluation of atomic structure model calculations. We now

The relevant information in a spectrum is contained in its peaks whose position and area are linked respectively to the photon energy and the activity of the connected radionuclide. The peak areas can also be used to determine emission probabilities. In this work, peak areas

**[ΔFWHM=FWHMcom.-FWHMpure] (eV) Chemical shift (ΔE) (eV)**

The more unpaired 3d or 4d electrons the atom possesses, the more asymmetric will be the line observed. This kind observation led Tsutsumi (1959) to consider that the interaction between the hole created in the 2p3/2 or 2p1/2 shell (due to the transition of an electron from this shell to the 1s level) and the electrons in the incomplete 3d shell in the transition metal atoms is responsible for asymmetric nature of *Kα* lines. They proposed a theoretical model based on this idea to account for the asymmetry in the X-ray emission lines in the firs-row transition metal compounds. However, this is not the only consideration which can explain the origin of the asymmetry of the line; there are other considerations which are based on the relaxation effect of the inner state proposed by Parratt (1959) or on the interactions between 2p hole and electrons in the Fermi sea as proposed by Doniach and Sunjic (1970).

Table 4. Chemical shift (*ΔE*) and energy shift (*δE*) values of *Kα, Kβ*1,3 and *Kβ*2,4 emission lines in Y compounds for Am-241 radioactive source.

The more unpaired 3d or 4d electrons the atom possesses, the more asymmetric will be the line observed. This kind observation led Tsutsumi (1959) to consider that the interaction between the hole created in the 2p3/2 or 2p1/2 shell (due to the transition of an electron from this shell to the 1s level) and the electrons in the incomplete 3d shell in the transition metal atoms is responsible for asymmetric nature of *Kα* lines. They proposed a theoretical model based on this idea to account for the asymmetry in the X-ray emission lines in the firs-row transition metal compounds. However, this is not the only consideration which can explain the origin of the asymmetry of the line; there are other considerations which are based on the relaxation effect of the inner state proposed by Parratt (1959) or on the interactions between 2p hole and electrons in the Fermi sea as

**Magnetic Field Chemical shift (***ΔE***) (eV) Energy shift (***δE***) (eV)**

 *Kα Kβ*1,3 *Kβ*2,4 *Kα Kβ*1,3 *Kβ*2,4 **Y B=0 0 0 0 0 0 0**  B=0.6T 0.125 0.121 0.132 B=1.2T 0.179 0.219 0.269 **Y(NO3)3.6H2O B=0 -0.401 -0.347 -0.316 0 0 0**  B=0.6T 0.091 0.111 0.158 B=1.2T 0.104 0.169 0.201 **YCl3 B=0 -0.023 -0.109 -0.153 0 0 0**  B=0.6T 0.226 0.254 0.259 B=1.2T 0.559 0.472 0.318 **YPO4 B=0 0.231 0.304 0.221 0 0 0**  B=0.6T 0.553 0.602 0.436 B=1.2T 0.598 0.556 0.511 **YBr3 B=0 -0.133 -0.227 -0.194 0 0 0**  B=0.6T 0.342 0.313 0.387 B=1.2T 0.600 0.499 0.458 **Y2O3 B=0 0.440 0.316 0.241 0 0 0**  B=0.6T 0.447 0.423 0.505 B=1.2T 0.682 0.613 0.553 **YF3 B=0 0.206 0.391 0.190 0 0 0**  B=0.6T 0.367 0.276 0.211 B=1.2T 0.533 0.590 0.438 **Y(SO4)3.8H2O B=0 0.194 0.276 0.133 0 0 0**  B=0.6T 0.194 0.146 0.115 B=1.2T 0.402 0.281 0.296 **Y2S3 B=0 0.575 0.503 0.421 0 0 0**  B=0.6T 0.197 0.340 0.247 B=1.2T 0.331 0.349 0.366 Table 4. Chemical shift (*ΔE*) and energy shift (*δE*) values of *Kα, Kβ*1,3 and *Kβ*2,4 emission lines

proposed by Doniach and Sunjic (1970).

**External**

in Y compounds for Am-241 radioactive source.

**Element**

The chemical shift was the difference between the center point of the 9/10 peak intensity of a compound and that of pure Y measured before and after the measurement of the compound. When the environment of the emitting atom is changed, there are changes in the position of emission lines with respect to those in the pure metal. These changes are called chemical shifts. They are presented in Tables 3 and 4. Both the 4*d* electron configuration and crystal structure affect the chemical shift and energy shift with applied external magnetic field. There is a clear relationship with the external magnetic field values and the energy shift values of Y compounds, as is found in Table 3 and 4's last column. For higher values of external magnetic field, the values of energy shift increases systematically. But we do not find any relationship between external magnetic field and crystal structure of the compound. To obtain more definite conclusion, more experimental data for 4*d* compounds which crystal structure different are needed.

The errors of the chemical and energy shifts, originate mainly from the limited precision of our measurements, determined by repetitive measurements of all pure targets, which were prepared and placed in the same experimental geometry. The precision in the position of *Kα*, *Kβ*1,3 and *Kβ*2,4 lines was determined as 0.05 eV, whereas the maximum deviation of a single measurement from the average value was 0.1 eV.

Table 5. Chemical shift *(ΔE)* and differences between FWHM values of *Kα, Kβ*1,3 and *Kβ*2,4 emission lines in Y compounds obtained for WDXRF.

It is also seen from Table 5 that the *Kα* line width of YPO4 compound is wider than that of the other compounds. Compare with the *Kα* peak of the pure Y, that of cubic crystal structure Yttrium compounds shifted to lower energy, and the peak shift ordering was YF3<Y2O3<Y2S3<YPO4. The line shapes of *Kα* are generally symmetric. Y2S3 and Y2O3, where the *Kβ*1,3 and *Kβ*2,4 peak shifts are large, show prominent asymmetry.

The accurate knowledge of the *Kβ*1/*Kα*, *Kβ*2/*Kα*, *Kβ*2/*Kβ*1 and *Kβ*/*Kα* intensity ratios is required for a number of practical applications of X-rays, e.g. molecular and radiation physics investigations, in non-destructive testing, elemental analysis, medical research etc. Therefore, these ratios depend sensitively on the atomic structure. Thus they have been widely used also for critical evaluation of atomic structure model calculations. We now discuss the values of these ratios as obtained in our measurements.

The relevant information in a spectrum is contained in its peaks whose position and area are linked respectively to the photon energy and the activity of the connected radionuclide. The peak areas can also be used to determine emission probabilities. In this work, peak areas

Determination of Chemical State and External Magnetic Field

 B=1.2T *Kβ*1,3/*Kα* 0.2322±0.010 *Kβ*2,4/*Kα* 0.0324±0.008

 *Kβ***/***Kα* **0.1818±0.010**  YPO4 **B=0** *Kβ*1,3/*Kα* 0.2355±0.010

 *Kβ*2,4/*Kβ*1,3 0.1999±0.010 *Kβ***/***Kα* **0.1840±0.009**  B=0.6T *Kβ*1,3/*Kα* 0.2336±0.007 *Kβ*2,4/*Kα* 0.0320±0.009 *Kβ*2,4/*Kβ*1,3 0.1966±0.011

 B=1.2T *Kβ*1,3/*Kα* 0.2322±0.011 *Kβ*2,4/*Kα* 0.0304±0.009

 *Kβ***/***Kα* **0.1738±0.011**  YBr3 **B=0** *Kβ*1,3/*Kα* 0.2359±0.008

 *Kβ*2,4/*Kβ*1,3 0.2004±0.009 *Kβ***/***Kα* **0.1848±0.010**  B=0.6T *Kβ*1,3/*Kα* 0.2342±0.008 *Kβ*2,4/*Kα* 0.0321±0.009 *Kβ*2,4/*Kβ*1,3 0.1987±0.010

 B=1.2T *Kβ*1,3/*Kα* 0.2321±0.009 *Kβ*2,4/*Kα* 0.0303±0.011

 *Kβ***/***Kα* 0.1794±0.009 Y2O3 **B=0** *Kβ*1,3/*Kα* 0.2364±0.011

 *Kβ*2,4/*Kβ*1,3 0.2008±0.010 *Kβ***/***Kα* 0.1856±0.011 B=0.6T *Kβ*1,3/*Kα* 0.2351±0.008 *Kβ*2,4/*Kα* 0.0340±0.008 *Kβ*2,4/*Kβ*1,3 0.1998±0.010

 B=1.2T *Kβ*1,3/*Kα* 0.2302±0.010 *Kβ*2,4/*Kα* 0.0307±0.010

Intensity Ratio

Element

External Magnetic Field

Effect on the Energy Shifts and X-Ray Intensity Ratios of Yttrium and Its Compounds 107

Scofield (1974a)

Manson &Kennedy (1974)

Ertuğral et al. (2007)

This Work

*Kβ***/***Kα* **0.1821±0.011**

*Kβ*2,4/*Kβ*1,3 0.1972±0.009

*Kβ*2,4/*Kα* 0.0333±0.010

*Kβ***/***Kα* **0.1773±0.008**

*Kβ*2,4/*Kβ*1,3 0.1693±0.009

*Kβ*2,4/*Kα* 0.0341±0.007

*Kβ***/***Kα* 0.1818±0.010

*Kβ*2,4/*Kβ*1,3 0.1976±0.010

*Kβ*2,4/*Kα* 0.0349±0.010

*Kβ***/***Kα* 0.1831±0.010

*Kβ*2,4/*Kβ*1,3 0.1985±0.010

were determined after the *Kα*, *Kβ*1,3 and *Kβ*2,4 areas were separated by fitting the measured spectra with multi-Gaussian function plus polynomial backgrounds using Microcal Orgin 7.5 software program. Details of the experimental set up and data analysis have been reported earlier (Porikli et al., 2011b).

Table 6 lists the theoretical values which were calculated by Scofield (Scofield 1974a; Scofield, 1974b). Addition to this, the measured values of the *Kβ*1/*Kα*, *Kβ*2/*Kα*, *Kβ*2/*Kβ*1 and *Kβ*/*Kα* intensity ratios in Y, and previous experimental and the other theoretical values of these ratios for pure elements and their compounds are listed in Table 6.

were determined after the *Kα*, *Kβ*1,3 and *Kβ*2,4 areas were separated by fitting the measured spectra with multi-Gaussian function plus polynomial backgrounds using Microcal Orgin 7.5 software program. Details of the experimental set up and data analysis have been

Table 6 lists the theoretical values which were calculated by Scofield (Scofield 1974a; Scofield, 1974b). Addition to this, the measured values of the *Kβ*1/*Kα*, *Kβ*2/*Kα*, *Kβ*2/*Kβ*1 and *Kβ*/*Kα* intensity ratios in Y, and previous experimental and the other theoretical values of

> This Work

*Kβ*2,4/*Kα* 0.0317±0.008 0.02902 *Kβ*2,4/*Kβ*1,3 0.1981±0.011 0.19220

Scofield (1974a)

*Kβ***/***Kα* **0.1822±0.008 0.16960 0.1685 0.1856±0.009**

Manson &Kennedy (1974)

Ertuğral et al. (2007)

these ratios for pure elements and their compounds are listed in Table 6.

*Kβ*2,4/*Kα* 0.0311±0.008 *Kβ*2,4/*Kβ*1,3 0.1956±0.011 *Kβ***/***Kα* **0.1753±0.011**

*Kβ*2,4/*Kα* 0.0304±0.008 *Kβ*2,4/*Kβ*1,3 0.1941±0.011 *Kβ***/***Kα* **0.1712±0.011**

*Kβ*2,4/*Kα* 0.0325±0.010 *Kβ*2,4/*Kβ*1,3 0.1987±0.011 *Kβ***/***Kα* **0.1829±0.006**

*Kβ*2,4/*Kα* 0.0316±0.008 *Kβ*2,4/*Kβ*1,3 0.1977±0.010 *Kβ***/***Kα* **0.1796±0.011**

*Kβ*2,4/*Kα* 0.0310±0.011 *Kβ*2,4/*Kβ*1,3 0.1954±0.011 *Kβ***/***Kα* **0.1742±0.010**

*Kβ*2,4/*Kα* 0.0329±0.008 *Kβ*2,4/*Kβ*1,3 0.1992±0.009 *Kβ***/***Kα* **0.1836±0.010**

Intensity Ratio

Y **B=0** *Kβ*1,3/*Kα* 0.2307±0.010 0.22910

B=0.6T *Kβ*1,3/*Kα* 0.2289±0.007

B=1.2T *Kβ*1,3/*Kα* 0.2275±0.007

B=0.6T *Kβ*1,3/*Kα* 0.2320±0.008

B=1.2T *Kβ*1,3/*Kα* 0.2315±0.008

H2O **B=0** *Kβ*1,3/*Kα* 0.2339±0.008

YCl3 **B=0** *Kβ*1,3/*Kα* 0.2341±0.010

 B=0.6T *Kβ*1,3/*Kα* 0.2337±0.006 *Kβ*2,4/*Kα* 0.0305±0.009 *Kβ*2,4/*Kβ*1,3 0.1952±0.009

reported earlier (Porikli et al., 2011b).

External Magnetic Field

Element

Y(NO3)3.6

Determination of Chemical State and External Magnetic Field

(0.5-3.0%) and the other systematic errors (1.0-2.0%).

environment.

compounds.

**4. Conclusion**

characteristic X-rays.

Effect on the Energy Shifts and X-Ray Intensity Ratios of Yttrium and Its Compounds 109

When you look at the Table 6, a serious difference between the *Kβ*1,3/*Kα* experimental and theoretical values can be seen. This situation is mainly because of the limited resolution of the detector. The *Kα*1 and *Kα*2 X-ray components appear as one line. In the most general case, chemical speciation is preferably performed via the analysis of the *Kβ*1,3 or *Kβ*2,4 lines. These lines, emitted after transition of valance electrons are more sensitive to the chemical

As can be seen from Table 6, the *Kβ*/*Kα* ratios of Y in all Y compounds are in close agreement with the ratios of corresponding pure metals. The greatest increase of the *Kβ*/*Kα* ratio has been observed for Y2S3. We found a general increase of the *Kβ*/*Kα* intensity ratios for different compounds. This situation is more complex because the *Kβ*/*Kα* intensity ratio is affected by the chemical bonding type, (ionic, metallic, covalent), the individual characteristics of the structure of molecules, complexes and crystals (polarity, valency and

We found that the chemical effect on the *Kβ*/*Kα* ratios for 4d elements is small but the dependence of the *Kβ*2/*Kα* ratios on the chemical environments is appreciable. This can be understood by the fact that in 4d elements the valance state consists of the 4d, 5s and 5p electrons and the influence of the chemical state on the *Kβ*1,3 (3p→1s) X-ray emission is negligible. Yamoto et al. (1986) found similar results for compounds involving Tc isotopes and Mukoyoma et al. (2000) found similar results theoretically for Mo and Tc

The overall error in the present measurements is estimated to be 3-8%. This error is attributed to the uncertainties in different parameters used to determine the *Kβ*1/*Kα*, *Kβ*2/*Kα*, *Kβ*2/*Kβ*1 and *Kβ*/*Kα* values; such as, *I*0*Gε* product (1.0-2.5%), in the absorption correction factor (0.3-1.5%), the error in the area evaluation under the *Kα*, *Kβ*1, *Kβ*2 and *Kβ* X-ray peak

There has been increasing interest in chemical speciation of the elements in recent years which can be attributed to the great alterations in the chemical and biological properties of the elements depending on their oxidation state, the type of chemical bonds etc. Usually, the influence of the chemical environment results in energy shifts of the characteristic X-ray lines, formation of satellite lines and changes in the emission linewidths and relative X-ray intensities. High resolution X-ray spectroscopy, employing crystal spectrometers of a few eV resolutions, can be applied to probe these phenomena efficiently, exploiting them for chemical state analysis. Measurements of the shapes and wavelengths of certain X-ray lines have been made by previous investigators with both EDXRF and WDXRF. It has been shown that the WDXRF spectrometer is capable of measuring X-ray wavelengths with a precision equal to or greater than that attained with the EDXRF system. Both EDXRF and WDXRF technique has been used to study the effect of chemical state of an element on

We have presented and discussed the effect of chemical composition and external magnetic field on the *Kβ*1,3/*Kα*, *Kβ*2,4/*Kα*, *Kβ*2,4/*Kβ*1,3 and *Kβ*/*Kα* intensity ratios for some Yttrium compounds. The experimental measurements have been performed with a Si(Li) detector. The observed spectral features, namely the asymmetry indices, FWHM values, chemical shifts, energy separations between *Kα* and *Kβ* lines and *Kβ/Kα* intensity ratio values show an interesting correlation with crystal symmetries. Furthermore, these values change

electronegativity of atoms, co-ordination number, ionicities of covalent bond etc.).

Table 6. *Kβ*1,3/*Kα, Kβ*2,4/*Kα, Kβ*2,4/*Kβ*1,3 and *Kβ*/*Kα* X-ray intensity ratios of pure Y their compounds.

When you look at the Table 6, a serious difference between the *Kβ*1,3/*Kα* experimental and theoretical values can be seen. This situation is mainly because of the limited resolution of the detector. The *Kα*1 and *Kα*2 X-ray components appear as one line. In the most general case, chemical speciation is preferably performed via the analysis of the *Kβ*1,3 or *Kβ*2,4 lines. These lines, emitted after transition of valance electrons are more sensitive to the chemical environment.

As can be seen from Table 6, the *Kβ*/*Kα* ratios of Y in all Y compounds are in close agreement with the ratios of corresponding pure metals. The greatest increase of the *Kβ*/*Kα* ratio has been observed for Y2S3. We found a general increase of the *Kβ*/*Kα* intensity ratios for different compounds. This situation is more complex because the *Kβ*/*Kα* intensity ratio is affected by the chemical bonding type, (ionic, metallic, covalent), the individual characteristics of the structure of molecules, complexes and crystals (polarity, valency and electronegativity of atoms, co-ordination number, ionicities of covalent bond etc.).

We found that the chemical effect on the *Kβ*/*Kα* ratios for 4d elements is small but the dependence of the *Kβ*2/*Kα* ratios on the chemical environments is appreciable. This can be understood by the fact that in 4d elements the valance state consists of the 4d, 5s and 5p electrons and the influence of the chemical state on the *Kβ*1,3 (3p→1s) X-ray emission is negligible. Yamoto et al. (1986) found similar results for compounds involving Tc isotopes and Mukoyoma et al. (2000) found similar results theoretically for Mo and Tc compounds.

The overall error in the present measurements is estimated to be 3-8%. This error is attributed to the uncertainties in different parameters used to determine the *Kβ*1/*Kα*, *Kβ*2/*Kα*, *Kβ*2/*Kβ*1 and *Kβ*/*Kα* values; such as, *I*0*Gε* product (1.0-2.5%), in the absorption correction factor (0.3-1.5%), the error in the area evaluation under the *Kα*, *Kβ*1, *Kβ*2 and *Kβ* X-ray peak (0.5-3.0%) and the other systematic errors (1.0-2.0%).


**Author details**

Kacey B. Anderson and Samuel M. Poloyac

*University of Pittsburgh, School of Pharmacy, Department of Pharmaceutical Sciences, Pittsburgh, PA, USA*

Therapeutic Hypothermia: Implications on Drug Therapy 147

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24-27.


**2. Defining the collaborative virtual workspace landscape**

Over the past few decades, computing sciences has grappled with different approaches to presenting digitally generated content. In recent years the field of virtual reality (VR) has become one of the most intriguing technologies in the area of content presentation. Although most people tend to relate virtual reality to its use in more common entertainment arenas like gaming, the real impacts are in the broader areas of the, "arts, business, communication, design, education, engineering, medicine and many other fields" (Briggs, 1996). Over the last 15 years, John Brigg's prediction has proven to be correct--the biggest impacts of virtual reality technology use are now being felt especially in the business, communication and medical fields (Briggs, 1996). While the virtual world technology is still evolving and developing, it has finally matured to a level where we are routinely implementing it in whole or part in educational and business settings. The issue for businesses seems to be less one of will they implement VR technology in workspaces, but rather more one of how it will be done and in what sequence investment in the requisite technology will be made. This includes considering not just current VR technology but also requires understanding the likely evolution and trajectory of the technology development in the future. Additionally, savvy users should ask themselves now what other technologies are being or might be co-implemented to supplement the virtual environments along the way.

Appropriate planning and scaling of implementation of VR technologies based on knowing your industry goals, your company's historical and future growth patterns, your immediate real needs and your tolerance for risk are all crucial to planning implementation of VR platforms and workspaces. Poor planning for implementation of VR workspaces or any new technology may result in unnecessary risk exposure, cost overruns and simple ineffective use of costly infrastructure that is both unnecessary and/or inefficient. While there are many studies of the implementation of virtual, mixed and augmented reality spaces most or many of those have been focused on education and medical uses (Caudell & Mizell, 1992)(Steuer, 1992) (Barfield & Caudell, 2001) (van Krevelen & Poelman, 2010). Before considering the available studies and their place in the ROTATOR model though it's important to understand exactly what virtual reality is and how it is affected by other technologies like augmented reality. Additionally as cloud computing becomes more widely used in industry we will consider how that storage process along with some other most common storage processes may affect the implementation plan for VR workspaces in a

virtual world implementation is needed to achieve desired business outcomes; and (3) create a practical framework that represents the varying levels of both functionality and capability for establishing and maintaining virtual workspaces. In this chapter the authors propose the ROTATOR model to establish a series of incremental stages that form the foundation for a

As a foundation for this analysis, the concepts, history and use of the terms virtual collaboration and virtual workspaces are also discussed in this article to clarify their import and use in industry. These discussions include a description of the recent evolution of virtual collaborative environments with a focus on the most important online global workforce drivers. The impact of other key technologies with respect to the ROTATOR Model within the virtual workspace arena including cloud computing, semantic web, and

Over the past few decades, computing sciences has grappled with different approaches to presenting digitally generated content. In recent years the field of virtual reality (VR) has become one of the most intriguing technologies in the area of content presentation. Although most people tend to relate virtual reality to its use in more common entertainment arenas like gaming, the real impacts are in the broader areas of the, "arts, business, communication, design, education, engineering, medicine and many other fields" (Briggs, 1996). Over the last 15 years, John Brigg's prediction has proven to be correct--the biggest impacts of virtual reality technology use are now being felt especially in the business, communication and medical fields (Briggs, 1996). While the virtual world technology is still evolving and developing, it has finally matured to a level where we are routinely implementing it in whole or part in educational and business settings. The issue for businesses seems to be less one of will they implement VR technology in workspaces, but rather more one of how it will be done and in what sequence investment in the requisite technology will be made. This includes considering not just current VR technology but also requires understanding the likely evolution and trajectory of the technology development in the future. Additionally, savvy users should ask themselves now what other technologies are being or

**2. Defining the collaborative virtual workspace landscape**

might be co-implemented to supplement the virtual environments along the way.

Appropriate planning and scaling of implementation of VR technologies based on knowing your industry goals, your company's historical and future growth patterns, your immediate real needs and your tolerance for risk are all crucial to planning implementation of VR platforms and workspaces. Poor planning for implementation of VR workspaces or any new technology may result in unnecessary risk exposure, cost overruns and simple ineffective use of costly infrastructure that is both unnecessary and/or inefficient. While there are many studies of the implementation of virtual, mixed and augmented reality spaces most or many of those have been focused on education and medical uses (Caudell & Mizell, 1992)(Steuer, 1992) (Barfield & Caudell, 2001) (van Krevelen & Poelman, 2010). Before considering the available studies and their place in the ROTATOR model though it's important to understand exactly what virtual reality is and how it is affected by other technologies like augmented reality. Additionally as cloud computing becomes more widely used in industry we will consider how that storage process along with some other most common storage processes may affect the implementation plan for VR workspaces in a

virtual workspace framework.

web 3-D are also discussed.

business setting. Often people mix up virtual reality and augmented reality so let's begin first with clear definitions of those concepts as used in this chapter and for purposes the ROTATOR model of virtual reality and augmented reality implementation.

One expert defines virtual reality as a "three-dimensional, computer generated simulation in which one can navigate around, interact with and be immersed in another environment" (Briggs, 1996). While this definition has been expanded over time to include spaces that are less real in terms of mimicking the real world for business purposes, these business spaces almost always mirror closely in some way a real world setting. Virtual reality, (Milgram & Kishino, 1994) takes a computer-generated world and begins to immerse the user through varying levels of "real" content (Hampshire, Seichter, Grasset, & Billinghurst, 2006) (Haller, Billinghurst, & Thomas, 2007) (Ritsos, Ritsos, & Gougoulis, 2011).

As for augmented reality, various technologies have and continue to be developed that seek to enhance a user's current perception of real world reality in varying degrees. Where virtual reality attempts to replace the entire real world perception with a simulated one the concept of augmented reality takes the user's unmodified or actual reality and begins to infuse computer-generated elements into that real world reality (Alem & Huang, 2011). The computer-generated elements in this environment then in effect 'augment' what the user senses in their real world environment (Kroeker, 2010). So, for example someone working in a design environment and as a support the person might see computer-generated materials that are overlaid by computer projection into the client's landscape environment so the client can see what it would look like if there were palm trees in the west corner of the garden. The most common example of simple augmentation in fact is in TV sports casting where the viewer of an American football game might see lines and graphics depicting the ball placement or movement overlaid on the live TV feed from the game.

As computer graphics and generations become more sophisticated these augmentations are becoming more and more sophisticated as well and are drifting closer to merging with the virtual reality environment in a natural way. For example, as applications become mobile there are new and more challenging options for the use of augmented reality. Some recent examples of mobile applications that augment one's reality include Layar, a 'reality browser' that retrieves point-of-interest data on the basis of GPS, compass, and camera view (Alem & Huang, 2011). GraffittiGeo is another augmented reality application that lets users read and write virtual Twitter-style comments on the walls of restaurants, movies and cafes (Kroeker, 2010). There are additional advanced uses of augmented reality being employed in design and urban renewal work as well; allowing designers to literally sit together at a table and manipulate and overlay computer generated materials and design drawings on say a real world table in front of them using programs like ARUDesigner (Wang, 2009).

Additionally, the concepts of augmentation coincide with real-time presentation that is in semantic context with the real world. So if we had a sliding scale viewpoint of these concepts, we would see the real world reality on one end of the continuum—say to the left with full immersive virtual reality worlds (we have not yet reached the capability to use practically fully immersive virtual reality technologies) on the far other end of the scale say to the far right. Augmented reality would of course fall on that scale somewhere in between but close to real world reality and relatively far away from the virtual reality side of the scale at the other end. However, as augmentation develops it trends closer and closer to the VR side of the equation. One challenge for planning implementation of VR worlds

ROTATOR Model:

A Framework for Building Collaborative Virtual Workspaces 99

expression in communication thereby facilitating increasingly effective communication processes like training programs and client communications, users are finding that they are often costly to implement and fraught with more risk factors than the earlier technologies like Skype and instant messaging (IM). For example, there is a steeper learning curve for use in VR technologies and there are risks associated with storage of information that may be considered meeting or business communications required by law to be housed and accessible. Additionally there may be significant upfront costs for software development or implementation that businesses don't anticipate. This does not mean that use of VR technologies should be avoided by businesses as effective implementation can have far more

advantages then disadvantages when they are properly planned and implemented.

planned and appropriate implementation policies and procedures.

construct and share knowledge (Ghaoui, 2003).

these solutions providing for:

**2.2 Collaborative virtual workspaces: What they are and why we use them**

Collaboration itself is a recursive process where two or more co-workers, groups or organizations bring their knowledge and experience together by interacting toward a common goal in the best interests of their customers and to improve their organization's success (Martinez-Moyano, 2006) (Wagner, 2005). A virtual synchronous collaboration involves interaction within a virtual space wherein all colleagues, without respect to time or geographic separation, are able to negotiate, discuss, brainstorm, share knowledge, and generally work together to carry out a given work task. These environments aim to provide technology-based solutions where participants can cooperatively work as a group to

Virtual collaborative workspaces provide a common working environment where employees can not only collaborate systematically with corporate computing resources but also communicate with other co-workers and customers. Many of the virtual collaborative environments are 3D virtual environments that allow for multiple users. Recent evolution of virtual collaborative environments has focused heavily on online digital solutions, with

1. **Shifts in training and instruction for business and academia**. Business organizations have begun to shift their training practices using distributed teams in blended approaches (Alavi & Gallupe, 2003) (Simeon, 2003) with the use of online and webbased networking contributing to a boost in what is often referred to as e-Learning. The

We are now beginning to see more extensive adoption by Universities, hospitals and medical companies as well as industry of these VR environments and augmented reality technologies. However, there seems to be little consideration given to the actual choice and implementation of the technologies with regard to their place on the continuum, their longterm viability and evolution, and the requisite return on investment (ROI) of implementation and use. By placing technologies on the proposed ROTATOR continuum and evaluating the goals and needs of an organization, companies can better plan and more efficiently use limited resources for implementation and adoption of VR and augmented technologies in business applications. Additionally, use of the ROTATOR model should aid in illuminating any potential risks associated with their implementation depending on the company and application. Use of the ROTATOR model can also shed light on possible risks associated with use and maintenance of VR and should help minimize risks based on

and/or augmented reality use then is to better understand the characteristics between the two extremes of 3D virtual reality and real world reality with augmented reality spanning the spectrum in between.


*Edited by Ameenah Al-Ahmadi*

The book Quantum dots: A variety of a new applications provides some collections of practical applications of quantum dots. This book is divided into four sections. In section 1 a review of the thermo-optical characterization of CdSe/ZnS core-shell nanocrystal solutions was performed. The Thermal Lens (TL) technique was used, and the thermal self-phase Modulation (TSPM) technique was adopted as the simplest alternative method. Section 2 includes five chapters where novel optical and lasing application are discussed. In section 3 four examples of quantum dot system for different applications in electronics are given. Section 4 provides three examples of using quantum dot system for biological applications. This is a collaborative book sharing and providing fundamental research such as the one conducted in Physics, Chemistry, Biology, Material Science, Medicine with a base text that could serve as a reference in research by presenting up-to-date research work on the field of quantum dot systems."

ISBN 978-953-51-0483-4

Quantum Dots - A Variety of New Applications

Photo by StudioM1 / iStock

## Quantum Dots A Variety of New Applications

*Edited by Ameenah Al-Ahmadi*


**18. Early start in eyes with perimacular edema**

Recent or chronic edema close to the macula seldom affects the visual acuity, however it tends to progress centrally after major non-ocular surgeries, severe infections

and exacerbations of cardiovascular and renal complications (**Figures 3**, **4** and **7**). Early intravitreal treatment is usually effective and results in high visual acuity – in our cohort, 40% of the eyes with final BCVA 20/40 and better had significant perimacular edema at baseline. In eyes with more distant chronic lesions where persistent leakage and hypoperfusion are evident, intravitreal treatment can be followed by delicate focal laser once the edema has regressed. The classical perivascular technique of P. Hamilton performed with the 50 micrometer spot and minimal power settings applied in the temporal half of the posterior pole is suitable in severe chronic cases.


**4. Adaptive reuse of heritage for affordable housing: planningpolicy-partnership nexus**

Understanding the multiple perspectives of adaptive reuse of heritage for affordable housing requires comprehensive planning and coordination of design intervention. It makes the case for an integration of different theoretical approaches to adaptive reuse and heritage conservation—typological, technical and strategic [25]. These dimensions are presented in **Figure 1**.


**Author details**

Saravanan Priyadharshini, Muthusamy Velusamy Nivedha, Molabanti Vasupriya, Santhanakrishnan Jayasree, Subramaniam Mahalakshmi, Gunasekaran Sinduja and Usha Singaravelu\*

Integrated Bio-computing Laboratory, Department of Bioinformatics, Bharathiar University, Coimbatore, India

\*Address all correspondence to<EMAIL_ADDRESS>
© 2022 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.

*Trending Advancements in Technologies Pertinent to Therapeutical Pharmacodynamics DOI: http://dx.doi.org/10.5772/intechopen.107341*


**6. Conclusions**

The construction industry is yet to reap the true benefits of using big data aptly. Over the last two decades, the rapid growth of big data technologies has caused a spike in the number of models and platforms that have been developed for increasing digitalization across different fields. However, the same level of digitalization has not truly been harnessed or integrated by the construction industry. A critical overview of the existing literature points towards the bulk of existing resources and platforms that can easily be applied for construction management. However, the state of implantation of adoption in construction is below par. Therefore, the utilization and commercialization of big data to benefit the construction industry are crucial. An extensive literature review enabled us to identify the potential of big data in construction as the industry generates huge amounts of data daily and can greatly improve using the latest technologies. The development of online tools and software which enable infrastructure modeling and CAD is a crucial step in the right direction for futuristic constructions. Having explored the existing ML tools, we found that these tools, coupled with big data, can be applied in the construction industry. In this paper, we have discussed the existing tools used in big data, the use of statistics, big data storage, and BDE. Overlap between these variables further creates complications in that more data are present and the field of big data is ever-expanding.

The current study contributes to the body of knowledge by providing a state-of-theart review of relevant articles focused on big data applications in construction published between 2010 and 2021. It further provides various current applications and future opportunities of big data in the construction industry for practitioners and researchers to ponder upon and initiates the necessary debate around practical implementation and adoption of big data applications in construction.

There are currently various gaps and pitfalls that act as barriers to using big data to its full potential. Firstly, data generation is much faster than the tools available for processing it. Moreover, big data integration into the construction industry is quite an uphill task even with the existing data processing tools.

The current study is limited to the literature published in the last decade and may not include all the available papers due to specific selection criteria developed in this study. Similarly, the search terms may not be holistic and thus not exhaustive; a study conducted in the future with slightly different search strings may produce different results. In the future, the researchers can expand upon and explore the five clusters identified in Figure 4. The individual relations and adoption frameworks for big data in these clusters can be explored.

**Author Contributions:** Conceptualization, H.S.M. and F.U.; methodology, H.S.M., F.U. and S.Q.; software, H.S.M. and F.U.; validation, H.S.M., F.U., S.Q. and D.S.; formal analysis, H.S.M. and F.U.; investigation, H.S.M., F.U. and S.Q.; resources, H.S.M. and F.U.; data curation, H.S.M., F.U., S.Q. and D.S.; writing—original draft preparation, H.S.M. and F.U.; writing—review and editing, H.S.M., F.U., S.Q. and D.S.; visualization, H.S.M. and F.U.; supervision, F.U.; project administration, H.S.M. and F.U.; funding acquisition, H.S.M. and F.U. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

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

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Data are available with the first author and can be shared upon reasonable request.

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


*Article* **Intelligent Mapping of Urban Forests from High-Resolution Remotely Sensed Imagery Using Object-Based U-Net-DenseNet-Coupled Network**

**Shaobai He 1,2,3, Huaqiang Du 1,2,3,\*, Guomo Zhou 1,2,3, Xuejian Li 1,2,3, Fangjie Mao 1,2,3, Di'en Zhu 4, Yanxin Xu 1,2,3, Meng Zhang 1,2,3, Zihao Huang 1,2,3, Hua Liu 1,2,3 and Xin Luo 1,2,3**

Received: 26 October 2020; Accepted: 25 November 2020; Published: 30 November 2020

**Abstract:** The application of deep learning techniques, especially deep convolutional neural networks (DCNNs), in the intelligent mapping of very high spatial resolution (VHSR) remote sensing images has drawn much attention in the remote sensing community. However, the fragmented distribution of urban land use types and the complex structure of urban forests bring about a variety of challenges for urban land use mapping and the extraction of urban forests. Based on the DCNN algorithm, this study proposes a novel object-based U-net-DenseNet-coupled network (OUDN) method to realize urban land use mapping and the accurate extraction of urban forests. The proposed OUDN has three parts: the first part involves the coupling of the improved U-net and DenseNet architectures; then, the network is trained according to the labeled data sets, and the land use information in the study area is classified; the final part fuses the object boundary information obtained by object-based multiresolution segmentation into the classification layer, and a voting method is applied to optimize the classification results. The results show that (1) the classification results of the OUDN algorithm are better than those of U-net and DenseNet, and the average classification accuracy is 92.9%, an increase in approximately 3%; (2) for the U-net-DenseNet-coupled network (UDN) and OUDN, the urban forest extraction accuracies are higher than those of U-net and DenseNet, and the OUDN e ffectively alleviates the classification error caused by the fragmentation of urban distribution by combining object-based multiresolution segmentation features, making the overall accuracy (OA) of urban land use classification and the extraction accuracy of urban forests superior to those of the UDN algorithm; (3) based on the Spe-Texture (the spectral features combined with the texture features), the OA of the OUDN in the extraction of urban land use categories can reach 93.8%, thereby the algorithm achieved the accurate discrimination of di fferent land use types, especially urban forests (99.7%). Therefore, this study provides a reference for feature setting for the mapping of urban land use information from VHSR imagery.

**Keywords:** urban forests; OUDN algorithm; deep learning; object-based; high spatial resolution remote sensing