A closer look at the efficiency calibration of LaBr3(Ce) and NaI(Tl) scintillation detectors using MCNPX for various types of nuclear investigations.

The nuclear spectroscopy method has long been used for advanced studies on nuclear physics. In order to decrease costs and increase the efficiency of nuclear radiation investigations, quick and efficient solutions are required. The purpose of this research was to calculate the whole energy peak efficiency values for a range of gamma-ray energies, from 30.973 keV to 1408 keV, at various source-detector distances using the MCNPX Monte Carlo code, which is extensively used in nuclear medicine, industry, and scientific research. As a result, the modeled detectors' full-energy peak efficiencies were calculated and compared to both experimental data and Monte Carlo simulations. Experiment results and prior studies using Monte Carlo simulations were found to be very consistent with these results. The counting efficiency against source-detector distance is then calculated using the modeled detectors. The data we have show that LaBr3(Ce) has outstanding detection properties. This study's findings might be used to improve the design of detectors for use in wide range of high-tech gamma spectroscopy and nuclear research applications.

Introduction

Various types and sizes of radiation detectors are used in radiographic examinations including nuclear medicine, as they play a significant role in quantifying the presence of high-energy particles, specifically as gamma cameras and Positron Emission Tomography (PET) Scanners. For many years, NaI(Tl) detectors with thallium doped sodium crystal have been employed in gamma spectroscopy systems exhibiting impressive results. Due to its excellent properties such as energy resolution, high gamma detection efficiency, and room temperature operation, Lanthanum Bromide LaBr3(Ce) detectors have been used as an alternative option to the NaI(Tl) detector system in recent years. One of the most important criteria determining the accuracy of radiation detection in these two scintillation detectors is calibration. On the other hand, sphere-shaped NaI(Tl) and LaBr3(Ce) detectors are also used in gamma-ray spectrometry, where nuclei produce gamma emissions with energy ranging from a few keV to more than 10 MeV. A typical detector should be designed to provide a satisfactory response to photons, which is dependent on the direction of travel of the photons concerning the center of the detector. However, depending on the detector’s geometry, certain distortions in response might arise due to the source’s non-uniform location relative to the detector surface. This is particularly the case for gamma rays with a high energy output. Therefore, an experimental approach and a numerical simulation method are used to calibrate a NaI(Tl) and LaBr3(Ce) detectors detector [1, 2]. Calibration of the detector efficiency is a critical component of successful gamma spectrometric analysis [3, 4]. Nowadays, mathematical efficiency calibration is gaining popularity, owing to its broad applications [5]. Some other major benefits over empirical efficiency calibration are the ability to optimize the surveying conditions, for example, by adjusting various geometrical parameters such as dimensions, locations of sample and detector, and other modifications in the composition and proportions of samples analyzed [6, 7]. However, physically altering these parameters each time, which may affect the detector’s performance, generates situations that may be costly and time-consuming for users [8, 9]. Fortunately, stochastic approaches such as Monte Carlo simulations are regarded as a powerful tool for the scientific community due to their adaptability and reproducibility in investigations of detection efficiency [10]. Various researchers have recently examined the use of Monte Carlo simulations for detector efficiency investigations, as shown by the literature review. In a previous study, Kuluozturk and Demir [11] examined the effect of distance on the photon pulse height distributions and full energy peak efficiency of 2 × 2 inch NaI(Tl) and LaBr3(Ce) detectors. The authors conducted a FLUKA Monte Carlo simulation investigation for detector-source distances of 2 cm and 5 cm at different radioisotope energies. Their findings indicated that the general distribution of pulse heights is consistent with experimental and theoretical estimates. Casanovas et al. [12], previously used the EGSnrc Monte Carlo algorithm to explore the energy, resolution, and efficiency calibrations of two scintillation detectors, NaI(Tl) and LaBr3. They examined calibration using a variety of fitting functions. Their findings revealed that experimental spectra may be used to verify simulation results. Calibration of LaBr3(Ce) and NaI(Tl) scintillation detectors using MCNP6 was performed by Bednar et al. [1], for different in-situ experiments related to nuclear power plants.

The purpose of this work was to model and analyze the efficiency of 2 × 2 inch NaI(Tl) and LaBr3 scintillation detectors across a broad range of gamma-ray energies that may be utilized for future research, particularly detector-related research. The study’s objective is also to achieve the least feasible discrepancy between estimated and measured efficiencies, not only for the complete energy peak, but also for the remainder of the measured spectrum. The work details the modeling and optimization of both detectors throughout the energy range 30.973–1408 keV. The detector models are appropriate for further investigation in the context of a particular practical application in nuclear studies.

Materials and methods

Monte Carlo simulations have been used efficiently in radiation protection [13, 14, 15, 16], materials research [17, 18, 19, 20], detector design [21, 22, 23], and medical applications [24]. In this work, two different 2 × 2 inch NaI(Tl) and LaBr3(Ce) scintillation detectors were modeled in terms of their full energy peak efficiency measurements and comparisons. We first modeled 2 × 2 inch NaI(Tl) and LaBr3(Ce) scintillation detectors in this three-phases investigation considering their physical properties and geometrical properties. Next, full energy peak efficiency measurements of NaI(Tl) and LaBr3 scintillation detectors were measured using MCNPX [25] general-purpose Monte-Carlo code (version 2.6.0). We compared results to those of other experimental and Monte Carlo investigations published in the literature to validate our findings. Finally, the gamma-ray spectra of simulated 2 × 2 inch NaI(Tl) and LaBr3(Ce) scintillation detectors were assessed in terms of counting efficiency using a 60Co radioisotope.

Modelling of 2 × 2 inch NaI(Tl) and LaBr3 scintillation detectors

Figure 1 shows the 2-D view of modeled detector setup obtained via MCNPX visual editor vised X22S in MCNPX (version 2.6.0) code. First, the INPUT file has been designed considering three main components as CELL card, SURFACE card, and DATA card. Each of these components contains essential information for constructing the desired simulation configuration.

Figure 1

2-D view of modeled detector setup in MCNPX (version 2.6.0) provided by MCNPX Visual Editor (visedX22S).

In the cell card, we designed all the cellular structures considering the compositions, densities as well as geometrical delimitation of the cellular structures on predefined surfaces. Additionally, for each cellular structure, the IMP variable of the cell card rows has been calculated, where the tracking of radiation types such as neutron, photon, and electron is determined using the n, p, and e Identifiers, respectively (i.e., IMP:p). The CELLs of the modeled detector geometry were presented in Figure 1 with the numbers within the colored areas. Next, the SURFACE card of the MCNPX INPUT file was designed considering the geometrical shapes as well as coordinates of the covering surfaces of the cellular structures [26]. Finally, the DATA card of the INPUT file was created considering the type of the source, distribution from the source, coordinates of the source, and material definitions. To calculate the full energy peak efficiencies of 2 × 2 inch NaI(Tl) and LaBr3 scintillation detectors, some specific gamma-ray energies such as 30.973 keV, 59.54 keV, 80.998 keV, 302.85 keV, 356.01 keV, 661.65 keV, 1173 keV, 1332 keV, and 1408 keV were used as gamma-ray source energies, respectively. It’s worth mentioning that the D00205ALLCP03 MCNPXDATA package has been utilized, which contains cross-section modules for DLC-200/MCNPDATA. ENDF/B-VI data are often increased in the energy range from 20 to 150 MeV using this library. The full energy peak efficiencies were calculated on a Lenovo® ThinkStation-P620/30E0008QUS Workstation-1x AMD-Ryzen, Threadripper PRO Hexadeca-core (16 Core) 3955 W × 3.90 GHz – 32GB DDR4-SDRAM RAM system. The error rates of entire simulation runs were reported as less than 0.1%. The photon and electron mode has been used to generate the data card part of the INPUT file (i.e., mode p e). Next, the source definition (sdef) has been set up with photons as the particle type (par = 2) and energy as the distribution (d1) utilizing the source information (si) and the source probability (sp) parameters. Afterwards, the data card was modified to include the source’s specified location (pos = 0 0 9.92).

Validation of calculated full-energy peak efficiencies

Following the modeling of scintillation detectors, the full energy peak efficiency (εp) (Eq. (1)) of the detectors was computed as the ratio of the number of photons emitted to the count in the full energy peak corresponding to the measured energy [27] to quantitatively assess the simulation model. For each energy (E), the term full energy peak efficiency should be calculated as the proportion of the count in the full energy peak [Np(E)] related to the incoming energy [εp(E)] to the number of photons with energy E emitted by the gamma-ray source [F(E)].

Accordingly, the modeled gamma-ray source has been located 2 cm away from the detector source. Using 108 number of particles, the simulation has been repeated for 30.973 keV, 59.54 keV, 80.998 keV, 302.85 keV, 356.01 keV, 661.65 keV, 1173 keV, 1332 keV and 1408 keV at 2 cm detector-source distance. Next, the source has been shifted from 2 to 5 cm and 8 cm, respectively. Figure 2 depicts the 3-D view of the detector and source (d1 = 2 cm) obtained from the MCNPX visual editor (visedX22S).

Figure 2

3-D view of modeled detector setup and distances of source in MCNPX (version 2.6.0) provided by MCNPX Visual Editor (visedX22S).

As it is seen, the gamma-ray source was located at the z-axis of the simulation setup. It’s worth mentioning that the source-detector distances have been increased from 2 to 5 cm (d2) and from 5 to 8 cm, respectively. The full energy peak efficiency calculations were repeated for the gamma-ray energies mentioned previously at 5 and 8 cm. Following that, the MCNPX OUTPUT file was used to export the number of counts at peak points, as well as the number of photons with energy E emitted by the gamma-ray source for each specific gamma-ray energy. Subsequently, the results of the full energy peak efficiency assessment were compared to those of several previous experimental [28] and Monte Carlo simulation results obtained from FLUKA [10], EGSnrc [11].

Assessment of 2 × 2 inch NaI(Tl) and LaBr3(Ce) scintillation detectors using 60Co radioisotope

There is no direct measurement method for the quantitative assessment of gamma-rays. Instead, to detect a gamma-ray, it should interact with material. In other respects, the main method of detecting a gamma ray is by ionization, in which it gives up part or all of its energy to an electron. Electrons that have been ionized interact with other atoms, releasing a large number of extra electrons. The emitted charge is either directly measured or indirectly measured to determine the gamma ray’s presence and energy. 60Co is a frequently used calibrating reference throughout many labs; it is synthesized artificially by neutron activation of Co59. Two large peaks appear in the gamma spectrum, one at 1173.2 keV and another at 1332.5 keV. On the other hand, it is broadly acknowledged that the 60Co radioisotope and its measurements are of tremendous interest to the scientific community due to its vast range of uses in fields ranging from medicine [29, 30] to industry [31]. To understand the responses of the modeled 2 × 2 inch NaI(Tl) and LaBr3(Ce) scintillation detectors against 60Co radioisotope, several comprehensive spectroscopy studies were performed at various distances such as 2 cm, 5 cm, and 8 cm, respectively. To assess the detection quantity of the modeled detectors, a vital specification of the data collection technique, which is specified as TALLY MESH has been inserted into the DATA card. In this study, the detector response function of modeled NaI(Tl) and LaBr3 scintillation detectors were recorded using the MCNPX’s pulse-height TALLY MESH [25] namely F8 tally. Meanwhile, F8 tally operates the energy distribution of radiation-induced pulses in a detector. The net response consists of a spectrum of pulses with heights proportional to the frequency of events occurring in discrete energy bins. In MCNPX simulation, Gaussian energy broadening (GEB) is a reliable method for counts to compute the widened response function of a detector in a manner that is consistent with experimentally measured spectra [32, 33]. As part of our research, we used the GEB function in conjunction with the F8 tally mesh. With and without the GEB card, we calculated two spectra of 60Co isotope. Our next step was to assess the results against experimental data [34]. The data acquired from the MCNPX (with/without GEB) and the experiment are shown in Figure 3. The findings acquired are shown to be highly compatible with one another. The GEB function has also modified the observed 60Co spectra in a manner compatible with experimental data. Finally, it is worth noting that the GEB function-based results were compared with a manually fitted Gaussian profile. The outcomes were observed with a high degree of consistency.

Figure 3

Gamma-ray spectrum of modeled NaI(Tl) scintillation detector for 60Co with GEB function.

Results and discussions

The purpose of this work was to utilize the MCNPX Monte Carlo code to evaluate the design of scintillation detectors, which are widely used in nuclear medicine, industry, and scientific research, and to compute the whole energy peak efficiency values for several gamma-ray energies such as 30.973 keV, 59.54 keV, 80.998 keV, 302.85 keV, 356.01 keV, 661.65 keV, 1173 keV, 1332 keV and 1408 keV at different source-detector distances. In addition, another aim of this study was to conduct a comprehensive study by modeling NaI(Tl) and LaBr3(Ce) detectors, which are widely used scintillation detectors, by providing the necessary modifications in the detector structure of the model. For this purpose, first full energy peak efficiency values were first calculated. Due to the fact that the detection efficiency of the NaI(Tl) detector varies with the distance to the detector face, the efficiencies were determined for two distinct detector distances. Figure 4 (a and b) shows the comparison of full peak efficiency values of the 2 × 2 NaI(Tl) scintillation detector at 2 cm and 5 cm source-detector distances, respectively.

Figure 4

Comparison of full peak efficiency values of 2 × 2 NaI(Tl) scintillation detector at (a) 2 cm and (b) 5 cm source-detector distance.

Full peak efficiency is increased by selecting a detector material with a high atomic number, which increases the possibility that the original photon’s entire energy will be absorbed because of the photoelectric effect. As seen in both images, peak efficiency values decreased as gamma-ray energy increased at 2 cm and 5 cm source-detector distance, except for the low gamma-ray energy range, where the photoelectric effect is the dominating process for photon-matter interaction. While complete energy absorption may occur in a single photoelectric contact, it is more probable that it occurs after the incoming photon has Compton-scattered one or more times in the detector. Similarly, if pair production is accompanied by simultaneous full absorption of both annihilation photons, full absorption is also seen. On the other hand, Figure 5 shows the full energy peak values of various methods in comparison with the finding of a recent investigation.

Figure 5

Comparison of full peak efficiency values of 2 × 2 LaBr3(Ce) scintillation detector at (a) 2 cm and (b) 5 cm source-detector distance.

Both experimental and Monte Carlo simulation findings are found to be in good agreement with the MCNPX results. On the other hand, some slight differences between the results are also reported (see Table 1). This might be related to the fact that experimental research and Monte Carlo simulations are fundamentally different. In other words, the simulation environment’s specification of any piece of equipment is faultless in terms of structural defects as well as material handicaps. However, providing these circumstances in experimental investigations is extremely challenging due to the nature of the materials and the possibility of small or large structural defects. Figure 5 (a and b) compares the full peak efficiency values of a 2 × 2 LaBr3(Ce) scintillation detector at a source-detector distance of 2 cm and 5 cm, respectively. Similar to the finding of the NaI(Tl) detector, peak efficiency values fell as gamma-ray energy rose, except in the low gamma-ray energy region at 2 cm and 5 cm source-detector distances, where the photoelectric effect dominates photon-matter interaction (see Table 2). However, our findings showed that the ability of the LaBr3(Ce) detector in terms of counting the photons in the peak level is higher than NaI(Tl) crystal (see Figures 4 and 5). This can be explained by the fact that full peak efficiency is increased by selecting a detector material with a high atomic number. Considering the average difference between the atomic numbers of NaI(Tl) and LaBr3(Ce), it can be said that LaBr3(Ce) may provide better full peak efficiency owing to its atomic number.

Table 1

Full energy peak efficiencies of 2 × 2 inch NaI(Tl) scintillation detector.

Energy (keV)	This Study		Kuluozturk and Demir		Casanovas et al.		Casanovas et al.		Mouthi et al.
	MCNPX MC simulation		FLUKA MC simulation		EGSnrc MC simulation		Experimental		Experimental
	Source-Detector distance		Source-Detector distance		Source-Detector distance		Source-Detector distance		Source-Detector distance
	2 cm	5 cm	2 cm	5 cm	2 cm	5 cm	2 cm	5 cm	2 cm	5 cm
30.973	5.19	2.47	5.15	2.4	-	-	-	-	-
59.54	13.1	3.31	12.8	3.25	-	2.57	-	2.5	13.04
80.998	6.94	3.05	6.89	3	-	3.141	-	3.2	-
302.85	4.71	1.94	4.55	1.9	-	-	-	-	-
356.01	3.94	1.72	3.86	1.69	-	1.506	-	1.5	-
661.65	3.11	1.12	3.03	1.05	-	1.045	-	1	3.05
1173.24	1.72	0.59	1.6	0.56	1.302	-	1.3	-	1.44
1332.5	1.48	0.53	1.4	0.49	1.141	-	1.2	-	1.27
1408.01	1.35	0.49	1.31	0.46	-	-	-	-	-

Table 2

Full energy peak efficiencies of 2 × 2 inch LaBr3(Ce) scintillation detector.

Energy (keV)	This Study		Kuluozturk and Demir		Casanovas et al.		Casanovas et al.		Mouthi et al.
	MCNPX MC simulation		FLUKA MC simulation		EGSnrc MC simulation		Experimental		Experimental
	Source-Detector distance		Source-Detector distance		Source-Detector distance		Source-Detector distance		Source-Detector distance
	2 cm	5 cm	2 cm	5 cm	2 cm	5 cm	2 cm	5 cm	2 cm	5 cm
30.973	7.07	3.14	7	3.07	-	-	-	-	-	-
59.54	10.36	3.81	10.28	3.76	-	3.09	-	3	-	3.95
80.998	8.01	3.41	7.94	3.38	-	3.576	-	3.8	-	-
302.85	5.24	2.30	5.19	2.24	-	2.01	-	2	-	2.34
356.01	4.58	1.98	4.53	1.96	-	2.01	-	1.8	-	2.05
661.65	4.14	1.44	4.08	1.39	-	1.31	-	1.3	-	1.45
1173.24	2.35	0.87	2.38	0.83	2.15	-	2.1	-	2.48	-
1332.5	2.18	0.76	2.13	0.74	1.90	-	2	-	2.2	-
1408.01	2.07	0.72	2.02	0.70	-	-	-	-	-	-

To summarize, efficiency decreased exponentially as the distance from the detector face increased; this result is comparable with those previously reported by Akkurt et al [35]. This fluctuation may be related to changes in the solid angle, as well as changes in a number of interactions conceivable as a result of photons having an oblique to nearly normal incidence. In the next phase, we used these modeled scintillation detectors to obtain a gamma-ray spectrum of 60Co radioactive isotope at different source-detector distances such as 2 cm, 5 cm, and 8 cm respectively. The vast majority of radioactive sources emit gamma-rays of varying energy and intensities. A gamma-ray energy spectrum may be generated by detecting and analyzing these emissions using a spectroscopic instrument.

A careful investigation of this spectrum is often used to establish the identification and number of gamma emitters present in a gamma source and is a critical tool in radiometric testing. Figure 6 shows the gamma-ray spectrum of modeled NaI(Tl) scintillation detector for 60Co radioisotope at 2, 5, and 8 cm source-detector distances. As the image indicates, there are two gamma-ray photopeaks. The detector exhibits sensitivity in the lower-energy zone owing to Compton scattering, as well as two smaller escape peaks at energies 0.511 MeV and 1.022 MeV underneath the photopeak for the formation of electron-positron couples when one or both annihilation photons escape, as well as a backscatter peak. When two or more photons contact the detector instantaneously, they appear as summation peaks with energy equal to the total of the two or more photopeaks combined. Due to the unstable structure of 60Co’s atomic nucleus, a neutron is converted to a proton, yielding 60Ni. This nuclear process, the conversion of a neutron to a proton, is followed by the discharge of a negatively charged particle namely the electron Although the resultant nickel nucleus currently displays a stable structure in terms of protons and neutrons, it has a large quantity of extra energy that will be released by the emission of electromagnetic radiation caused by gamma radiation. The excited nickel nucleus decays to the ground state in two phases. Gamma radiation of 1.17 MeV is emitted in the first stage, followed by radiation of 1.33 MeV in the second step. As a consequence, the gamma spectrum of 60Co will exhibit two distinct lines, dubbed gamma lines, as seen in Figure 5. A gamma spectrum of this kind provides conclusive proof for the presence of 60Co. The strength of the gamma lines, or the region under the peaks, is quantified. Moreover, Figure 6 depicts the gamma-ray spectrum of 60Co obtained from a 2 × 2 inch NaI(Tl) scintillation detector at different source-detector distances. It is seen that the maximum quantity is observed at 2 cm, which is the closest distance among the investigated source-detector configurations. There is a direct inverse relationship between the detector efficiency and source-detector distance. The correlation between full peak efficiency and length between source and detector is governed by the average travel length of gamma quanta in the detector’s active volume. The longer the gamma rays' travel length inside the detector, the more efficient it is. Our findings showed that the minimum distance is yielded for the maximum counting efficiency of the 2 × 2 inch NaI(Tl) scintillation detector. Our results indicate the minimum distance required to achieve the highest counting efficiency of a 2 × 2 inch NaI(Tl) scintillation detector. The Compton edge is a characteristic of the spectroscopy in spectrophotometry that emerges from Compton scattering in the scintillator or detector. Whenever a gamma-ray bounces off the scintillator but escapes, the detector registers just a portion of its energy. The amount of energy stored in the detector is proportional to the photon’s scattering angle, culminating in a spectrum of energies, each corresponding to a particular scattering angle. The maximum amount of energy that may be deposited, which corresponds to a complete backscatter, is referred to as the Compton edge [36]. The related information for determining the Compton edges has been extracted from the MCNPX OUTPUT file. In this study, Compton edges of 60Co radioisotope were also reported. Compton edges of photopeak 1 and 2 of the 60Co radioisotope were determined to be 963 keV and 1118 keV, respectively, in Figure 6. We found consistent results with the theoretical calculations [11].

Figure 6

Gamma-ray spectrum of modeled NaI(Tl) scintillation detector for 60Co radioisotope at 2, 5, and 8 cm source-detector distances.

Figure 7 depicts the gamma-ray spectrum of modeled LaBr3(Ce) scintillation detector for 60Co radioisotope at 2, 5, and 8 cm source-detector distances. Similar to NaI(Tl) findings, our findings showed that the characteristic behaviors, including the photopeak and Compton edges, were also obtained using LaBr3(Ce) scintillation crystal. However, the counting efficiency of modeled NaI(Tl) and LaBr3(Ce) detectors for the 60Co radioisotope at different distances was also one of the assessments investigated in the current study. Figures 8 and 9, and 10 depict the comparison of gamma-ray spectrums of modeled NaI(Tl) and LaBr3(Ce) scintillation detector for 60Co radioisotope at 2 cm, 5 cm, and 8 cm source-detector distances, respectively. Our findings indicate that the LaBr3(Ce) detector has a better counting efficiency than the NaI(Tl) detector, particularly at peak locations.

Figure 7

Gamma-ray spectrum of modelled LaBr3(Ce) scintillation detector for 60Co radioisotope at 2, 5, and 8 cm source-detector distances.

Figure 8

Comparison of gamma-ray spectrums of modeled NaI(Tl) and LaBr3(Ce) scintillation detector for 60Co radioisotope at 2 cm source-detector distance.

Figure 9

Comparison of gamma-ray spectrums of modelled NaI(Tl) and LaBr3(Ce) scintillation detector for 60Co radioisotope at 5 cm source-detector distance.

Figure 10

Comparison of gamma-ray spectrums of modeled NaI(Tl) and LaBr3(Ce) scintillation detector for 60Co radioisotope at 8 cm source-detector distance.

However, we also reported a decrement trend as a function of increasing source-detector distance from 2 to 8 cm. Additionally, when the distance between the source and detector grows, the total counting amount decreases. As the source-detector distance decreases, more features of the geometric model are reflected in the detector response during the MCNPX computation. This situation is also confirmed by Bednar et al. [1], where the researchers have performed the efficiency calibration of LaBr3(Ce) and NaI(Tl) scintillation detectors. The superiority of LaBr3(Ce) over NaI(Tl) has also been previously shown in the literature [37]. The given findings demonstrated that there is a reasonable agreement between previous research and computed gamma-ray spectra at the entire energy zone.

Conclusion

The purpose of this work was to evaluate the design of scintillation detectors using advanced Monte Carlo simulation methods. Scintillators are widely used in nuclear medicine, industry, and scientific research. For NaI(Tl) and LaBr3(Ce) detectors, full energy peak efficiency values were initially computed from 30.973 keV to 1408 keV at different source-detector distances. LaBr3(Ce) detector in terms of counting the photons in the peak level is higher than NaI(Tl) crystal. Full peak efficiency is increased by selecting a detector material with a high atomic number. In the next phase, we used these modeled scintillation detectors to obtain a gamma-ray spectrum of 60Co radioactive isotope. In 60Co, a neutron is converted to a proton, yielding 60Ni. This nuclear process is followed by the discharge of a negatively charged particle, namely the electron. The excited nickel nucleus decays to the ground state in two phases. Gamma radiation of 1.17 MeV is emitted in the first stage, 1.33 MeV in the second stage. Whenever a gamma-ray bounces off the scintillator but escapes, the detector registers just a portion of its energy. Compton edges of photopeak 1 and 2 of the 60Co radioisotope were determined to be 963 keV and 1118 keV. The theoretical computations yielded consistent findings. The results of this investigation may be beneficial for developing various types of detectors and optimizing them for advanced applications in gamma spectroscopy and nuclear investigations.

Declarations

Author contribution statement

Ghada ALMisned: Conceived and designed the experiments.

Hesham M.H. Zakaly; Shams A.M. Issa: Performed the experiments; Wrote the paper.

Fatema T. Ali: Analyzed and interpreted the data.

Antoaneta Ene; V. Ivanov: Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data.

Gokhan Kilic: Contributed reagents, materials, analysis tools or data.

H.O. Tekin: Conceived and designed the experiments; Wrote the paper.

Funding statement

The work of Antoaneta ENE and APC were supported by Dunarea de Jos University of Galati, Romania through the Grant No: RF 3621/2021.

Data availability statement

Data will be made available on request.

Declaration of interest’s statement

The authors declare no conflict of interest.

Additional information

No additional information is available for this paper.