Development of a Solar-Powered, Automated Water Ionizer Using Graphite-Based Electrodes for Alkaline and Acidic Water Production

ISSN 1391-0256 Journal of the Sri Lanka Association for the Advancement of Science Volume 7 Issue 1, 2025 Founded in 1944 and incorporated by the Act of Parliament No 11 of 1966. JSLAAS 1 Journal of the Sri Lanka Association for the Advancement of Science is a biannual publication. Selected research work from annual research sessions (based on scientific merit) as well as other research articles are invited to submit research manuscripts as per the guidelines provided by SLAAS. SLAAS members may also separately submit their papers for publication. The Journal can be accessed on-line to view and download the full text of the articles published respective to the volumes free of charge Submission Manuscript Only online submission, Web: https://journal.slaas.lk, e-ISSN: 2682-6992 Members of the Editorial Board Editor in Chief Prof. K P S Chandana Jayaratne Department of Physics University of Colombo, Sri Lanka. email: chandanajayarat[email protected] CoEditor Dr. R Chinthaka L De Silva Materials Technology Section Industrial Technology Institute Sri Lanka. 363, Bauddhaloka Mw, Colombo, Sri Lanka. email: [email protected] General President SLAAS 2025 Prof. Udeni P. Nawagamuwa Department of Civil Engineering University of Moratuwa Katubedda 10400,Moratuwa email: [email protected] Exchanges: Please address all requests to the Secretary, Sri Lanka Association for the Advancement of Science, ”Vidya Mandiraya” 120/10, Wijerama Mawatha Colombo 07, Sri Lanka Managing Editor Dr. Lochandaka Ranathunga Department of Information Technology Faculty of Information Technology University of Moratuwa, Sri Lanka email: [email protected] Sectional Representatives Dr. Jeevani Dahanayake Dr. Rochana Weerasinghe Dr Ruminda Wimalasiri Mr Prabhath Dharmasena Dr. Monika Madhavi Prof. Hemamala Karunadasa Dr. Thilina Thanthiriwatte Dr Lakmini Gamage Editorial Board Prof. Mahesh Jayaweera Prof. B C Liyanage Athapattu Prof. Chandana Abeysinghe Prof. G M K B Gunaherath Prof. S Vasanthapriyan Prof. Prasanthi Gunawardene Dr. K M G P Yahampath Dr. Thanuja Paragoda Dr. Jasotha Prabagar International Editorial Advisory Board Prof. Don Nalin Nilusha Wijayawardene Dr. Udara Abeysekara Prof. Hemamala Karunadasa Dr. M Wasim Siddiqui ISSN 1391-0256 Copyright © 2023 by the Sri Lanka Association for the Advancement of Science, Sri Lanka. All rights reserve. e-ISSN: 2682-6992 2 Table of Contents Page 1 Molecular docking of potential antifungal compounds from Ulva fasciatamethanolic extract gainst Pseudopestalotiopsistheae A. H. D. Alahakoon, B.K. D. M. Rodrigo, B.M. Chathuranga, M. Balasooriya, H. M. Herath, R. P. Wanigatunge 03 2 Dosimetric Impact on IMRT Plans of Altering Per Control Point Statistical Uncertainty in Monaco TPS K. L. I. Gunawardhana, J. Jeyasugiththan, P. De Silva and D. Satharasinghe 16 3 Development of a Solar - Powered, Automated Water Ionizer Using Graphite-Based Electrodes for Alkaline and Acidic Water Production A.M.K.L Abeykoon, M.D.Y Milani, H.M. B. I. Gunathilaka ,R. C. W. Arachchige, D.M.K Muthumala 40 17 INTRODUCTION Radiotherapy is a medical treatment that uses high doses of radiation (gamma rays, highenergy X-rays, and Electrons) to kill or damage benign and malignant tumors (Hall & Giaccia, 2019) . It is a crucial component of cancer treatment and is employed either as radiotherapy itself or in combination with surgery, chemotherapy, or immunotherapy (Delaney, Jacob, Featherstone, & Barton, 2005). Radiotherapy can be given inside or outside of our bodies. The most common kind is External Beam Radiation Therapy (EBRT). It uses a large machine called a Linear Accelerator (Linac) to treat cancer patients using high-energy X-rays and electrons. At present, there are other advanced types of machines used for radiotherapy treatment too. Such as Tomotherapy machine, Cyberknife machine, MR-Lianc, PET-Linac, ProBeam machine, GammaKnife machine, and ZAP-X machine (Palta & Mackie, 2011). There are ordinary and advanced treatment techniques currently used in the oncology field using those advanced machines. Such as three-dimensional conformal Radiotherapy (3DCRT), IntensityModulated Radiation Therapy (IMRT), Image-Guided Radiation Therapy (IGRT), Volumetric Modulated Arc Therapy (VMAT), Stereotactic Body Radiation Therapy (SBRT), Proton Therapy, and Adaptive Radiation Therapy (ART) (Khan & Gibbons, 2014). Apart from those machines, the cobalt 60 machine is used to treat cancer patients using Gamma-rays, and it is an older version of a raditherapy treatment machine (Podgorsak, 2005) . Intensity-Modulated Radiation Therapy, also called IMRT, is an advanced type of radiation therapy technique, and it is an inverse planning technique (Bortfeld, 2006) . Inverse planning is a technique that uses a computer program to automatically achieve a treatment plan that has an optimal merit. Here, it is less dependent on the geometric parameters but more on the specification of volumes of tumor targets and organs at risk, as well as their dose constraints. IMRT uses computer-controlled linear accelerators to deliver precise radiation doses to a benign or malignant tumor or specific areas within the tumor. IMRT allows for the radiation dose to conform more precisely to the three-dimensional (3-D) shape of the tumor by modulating or controlling the intensity of the radiation beam in multiple small segments. Also, IMRT allows higher radiation doses to be focused on the tumor while minimizing the dose to surrounding normal critical structures. Because the ratio of normal tissue dose to tumor dose is reduced to a minimum with the IMRT approach, higher and more effective radiation doses can safely be delivered to tumors with fewer side effects compared with conventional radiotherapy techniques. IMRT also has the potential to reduce treatment toxicity, even when doses are not increased. Due to its complexity, IMRT does require slightly longer daily treatment times, additional planning, and safety checks before starting the patient treatment (IMRT Patient Specific Quality Assurance) when compared with conventional radiotherapy (Gupta, Agarwal, Ghosh-Laskar, & Shrivastava, 2009) . IMRT was first conceptualised in the 1960s (Intensity Modulated Radiation Therapy Collaborative Working Group, 2001) . Although the concept of IMRT and early algorithms for planning were developed in Sweden, clinical application did not begin until a fully integrated IMRT planning and delivery system, namely, the NOMOS Peacock system, was invented and commissioned in 1993 by the collaborated effort between NOMOS and Baylor College of Medicine/the Methodist Hospital (Houston, TX, USA) (Mohan, 2005). After obtaining investigational device exemptions and protocol approval by Baylor’s Investigational Review Board, the first patient with brain metastases was to have three brain tumors treated simultaneously using IMRT in September 1993 (Lawrence & Cox, 1995). In 1994, the NOMOS Peacock system was introduced as the first commercial IMRT delivery unit. The Peacock system required the use of a beam modulation device known as a dynamic multivane intensityK. L. I. Gunawardhana et al ., JSLAAS,Vol. 7, Issue 1 (2025 ) 1 6 - 39 18 modulating collimator (MIMiC). This particular form of IMRT, called serial tomotherapy, could be treated by a continuously rotating gantry (Carol, 1995). Step and shoot IMRT represents another commonly used technique whereby multiple static beams are subdivided into ‘segments’ (LoSasso, Chui, & Ling, 1998). In the sliding window technique (dynamic Multileaf CollimatordMLC), a window defined by the MLC leaves sweeps across the treatment field at variable speed, while the monitor units are delivered continuously (Zhang et al., 2007). Dose calculation accuracy in IMRT is an important and crucial factor to prevent mistreatment of radiation treatment delivery using linear accelerator machines (Papiez & Langer, 2006). Among the commercially available dose calculation algorithms, Monte Carlo (MC) is considered to be potentially more accurate and complex than others. Although MC dose calculation algorithms are recognized as the most accurate dose computation algorithms for treatment planning, their inherent Statistical Uncertainty (SU) determines the accuracy of the dose calculation and the time span of the dose calculation (Taleei & Tabrizi, 2019) . The SU decreases inversely with the square root of the time span of the dose calculation. By decreasing the SU, one can increase the dose calculation accuracy. But the SU decreases, resulting in a significant increase in the time span of the dose calculation. Therefore, it should be a compromise between the SU and the dose calculation accuracy, with a suitable time span for the dose calculation in IMRT planning. Therefore, by studying this, it is possible to get an idea about how to optimize the accuracy of the dose calculation and the SU with a suitable time span of the dose calculation in IMRT planning (Chetty et al., 2007) . There are many Treatment Planning Systems (TPSs) that can be used to develop IMRT plans, utilizing their own dose calculation algorithm (Vassiliev et al., 2010) . The Elekta’s Monaco TPS is one of the most powerful tool that bring increased automation, intelligent workflows, and high-quality treatment planning to a wide range of radiotherapy treatment delivery systems. The Monaco TPS combines t h e MC dose calculation algorithm with robust optimization tools to provide high-quality radiotherapy treatment plans for IMRT, VMAT, and SBRT (Goodall & Ebert, 2020) . The Monaco 5.11.02 TPS used two kinds of SUs. Such as per control point SU and per calculation SU, and the planner can manually select one of them. In this research, we used a per control point SU to generate IMRT plans. Also, the Monaco 5.11.02 TPS has an option to choose different percentage values between 0.1 % - 10 % (Kry et al., 2019). In the per control point SU, the percentage uncertainty is based on the per voxel on a per segment. Also, the uncertainty was not the same in all voxels. The low-dose voxels in the peripheral regions of the patient had a higher uncertainty of dose than the voxels in the region of the maximum dose (PTV) (Keall, Siebers, Jeraj, & Mohan, 2000) . The dose uncertainty in the target volume (PTV) for the final plan was calculated and appeared in the TPS console window after the second stage dose calculation. There are a few other studies that have previously evaluated the overall effect of SU on dose calculation. But not about altering the per control point SU in IMRT plans. In 2000, Keall, Siebers, Jeraj, and Mohan (2000) found that the dose in Monte Carlo (MC) calculation does not significantly affect isodose lines and Dose Volume Histogram (DVH) for SU of 2% or lesser values. In 2004, Cheong, Suh, and Cho (2004) investigated the effect of SU on photon dose calculation using BEAMnrc and DOXXYZnrc MC simulation systems and evaluated SU based on DVH, isodose comparison, and root mean-square. In 2005, Ma et al. (2005) studied the issues related to the statistical analysis of MC dose calculations for realistic clinical beams using various variance reduction or time-saving techniques. Also, they discussed the effect of statistical uncertainties on dose prescription and monitor unit calculation for conventional treatment and IMRT based on MC simulations. K. L. I. Gunawardhana et al., JSLAAS,Vol. 7, Issue 1 (2025) 16-39 19 In 2016, Sarkar et al. (2016) investigated the interplay between Monte Carlo Variance (MCV) and Fluence Smoothing Factor (FSF) in VMAT for carcinoma of esophagus patients using a CMSMonaco TM Treatment Planning System (TPS). They reported that variation in FSF causes a difference in dosimetric and physical parameters for the treatment plan. In 2019, Palanisamy et al. (2019) explored the dosimetric impact of varying SU when calculating the dose of VMAT plans, and in 2021, Rembish et al. (2021) determined the severity of the effects on VMAT dose calculations caused by varying per control point SU in an MC-based TPS. Also, they assessed the impact of the uncertainty during DVH evaluation. The goal of IMRT planning is to shape the radiation dose to avoid or reduce exposure of healthy tissue and limit the side effects of treatment while delivering a therapeutic dose to the cancer. According to the best of our knowledge, no precise data are available for the optimal acceptance level of SU% % per control point for different diagnoses in IMRT. Also, no one studied the Dosimetric Impact on IMRT Plans of altering the per control point SU in Monaco TPS. Therefore, the purpose of this study is to evaluate the dosimetric impact on IMRT plans of altering the per control point SU (1% - 6%) using Elekta’s Monaco TPS for three different diagnoses (Larynx, Esophagus, and Prostate). METHODOLOGY CT Simulation and Radiotherapy Treatment Machine In this research work, three different diagnoses, which have high diversity, such as the Larynx, Oesophagus, and Prostate, were planned using the IMRT technique. A total of nine patients, three from each diagnosis, were selected for this study. The necessary CT image sets of all nine patients were obtained using a CT simulator (Siemens Healthineers, SOMATOM Confidence). The CT slice thickness of 5 mm was obtained for each clinical case for treatment planning. All generated IMRT plans were delivered using a 6 MV photon beam of Elekta Synergy Platform linear accelerator and its having a 1 cm multi-leaf collimator (MLC) at the iso-center. Contouring and Dose Prescription The tumor volume (Planning Target Volume - PTV) and Organs At Risk (OARs) volumes were contoured, and the doses prescribed to the Larynx, Oesophagus, and Prostate were 66 Gy/30 fractions, 50.4 Gy/28 fractions, and 78 Gy/39 fractions, respectively. Treatment Planning System (TPS) In this research work, the Monaco 5.11.02 TPS (IMPAC Medical System, Inc., Maryland Heights, MU, USA) was used to generate IMRT plans. It has a two-stage process of optimizing dose distribution. At the first stage, the ideal fluence distribution of a beam is optimized to meet a user-defined prescription for a single set of beams. At the second stage, the ideal distribution is transmitted into a set of segments where the shapes and weights are optimized based on the same prescription. For this research work, the Monte Carlo (MC) algorithm was used for dose calculation to generate an IMRT plan. K. L. I. Gunawardhana et al., JSLAAS,Vol. 7, Issue 1 (2025) 16-39 20 The Dosimetric Parameters used in Monaco TPS All the 54 IMRT plans were generated using the dosimetric parameters, which are given in Table 1 below. By keeping these parameters constant, IMRT plans were generated using MC dose calculation algorithm only by varying per control point SU 1%, 2%, 3%, 4%, 5%, and 6%. To analyze IMRT plans, different dosimetry indices were used as mentioned below. Table 1. The Dosimetric Parameters used in Monaco TPS Parameter Value Delivery Mode dMLC CT Slice Thickness 0.5 cm Grid Size 0.3 cm Beamlet Width 0.3 cm Control Points 40 Segment Width 0.5 cm Auto Flash Margin 0.2 cm Surface Margin 0.3 cm Target Margin 0.8 cm Fluence Smoothing Medium Dosimetric Indices used for IMRT Plan Evaluation In this study, we used several dosimetric indices for IMRT Plan evaluation. Such as, Conformity Index (CI), Heterogeneity Index (HI), Target Dose and Critical Organ Doses, Dose Calculation Time (DCT), Treatment Delivery Results (IMRT Patient Specific QA and Gamma Indices), and Dose Volume Histogram (DVH). • Conformity Index (CI) The Conformity Index (CI) describes the degree to which the prescribed isodose volume conforms to the shape and size of the target volume. This value is only reported for Monaco plans (Elekta, 2017) . The CI formula is given in equation 1:   2 * Rx RI V CI TV V  (1) where, TV = Structure Volume, V Rx = is the structure volume covered by the Dose of Interest and V RI is the total volume of the Dose of Interest. K. L. I. Gunawardhana et al., JSLAAS,Vol. 7, Issue 1 (2025) 16-39 21 • Heterogeneity Index (HI) The Heterogeneity Index (HI) describes the uniformity of dose within a target volume and is directly calculated from the statistics of the DVH. This value is only reported for Monaco plans (Elekta, 2017). The HI formula is is given in equation 2: 5% 95% D HI D  (2) where, D5% is the dose delivered to the hottest 5% of the tissue volume. D95% is the minimum dose received by 95% of the tissue volume. • Target Dose and Organ At Risk (OAR) Doses The target dose (Dose to PTV) was analyzed as D95% (the dose received by 95% of the volume of PTV and V95% (the volume received 95% of the prescribed dose). Moreover, the maximum dose (Dmax) and mean dose (Dmean) for PTV and Organ at Risk (OAR) were analyzed for all three clinical cases. The OAR dose constraints (Table 2) were taken from the Practical Radiotherapy Planning book (5th Edition) (Morris, Roques, Ahmad, & Loo, 2023). K. L. I. Gunawardhana et al ., JSLAAS,Vol. 7, Issue 1 (2025 ) 1 6 - 39 22 Table 2: Organ at Risk (OAR) Dose Constraints Organ Constraint Optimal Mandatory Brainstem Dmax whole organ 54 Gy (60 Gy if PRV used) Spinal Cord Dmax to PRV (cord +5 mm or spinal 50 Gy (48 Gy if concomitant chemothercanal) apy) Parotid Gland Mean Dose 24 Gy Lens Dmax 10 Gy Heart Mean Dose 25 Gy 30 Gy Heart V30 Gy 45 % Heart V40 30 % Lungs V20 Gy 35 % (25 % if risk factors) Lungs Mean Dose 18 Gy Bladder V50 Gy 50 % Bladder V60 Gy 25 % 50 % Femoral Heads V50 Gy 5 % 50 % Kidney (each) V20 Gy 25 % 30 % Kidney (both) V20 Gy 30 % 35 % Rectum V30 Gy 70 % 80 % Rectum V40 Gy 51 % 65 % Rectum V50 Gy 38 % 50 % Rectum V60 Gy 27 % 35 % Rectum V70 Gy 15% 20 % • Dose Calculation Time (DCT) The dose calculation time was measured from the Monaco TPS optimization console window, which could give the dose calculation start and end time for all IMRT plans with different per control point SU values ranging from 1% to 6%. The total calculation time was defined as the time difference between the start and finish time of the MC dose calculation. For this research work, HP Z840 workstations, 128 GB RAM, Intel(R) Xeon(R) CPU E5-2697 v3 @ 2.60GHz (2 processors), and the 64-bit Operating system were used. K. L. I. Gunawardhana et al ., JSLAAS,Vol. 7, Issue 1 (2025 ) 1 6 - 39 29 Figure 7: Average Final Dose Uncertainty (%) Vs. Per Control Point Statistical Uncertainty (SU) (%) K. L. I. Gunawardhana et al ., JSLAAS,Vol. 7, Issue 1 (2025 ) 1 6 - 39 30 Table 3: Average Comparison Results of Dosimetric Indices for Different Per Control Point Statistical Uncertainty (SU) Levels for Larynx Target and OARs Larynx SU 1% SU 2% SU 3% SU 4% SU 5% SU 6% PTV 66 D95% (cGy) 6208.60 ± 11.60 PTV 66 Dmax (cGy) 6908.70 ± 12.90 PTV 66 Dmean (cGy) 6487.07 ± 12.12 6238.73 ± 23.11 6924.47 ± 25.63 6511.67 ± 24.11 6209.03 ± 33.96 6925.10 ± 37.86 6490.13 ± 35.48 6219.47 ± 45.65 6932.83 ± 50.85 6501.53 ± 47.69 6213.13 ± 56.78 6961.60 ± 63.59 6496.07 ± 59.35 6224.47 ± 68.31 6982.30 ± 76.58 6512.03 ± 71.43 PTV 66 V95% (%) 93.81 ± 0.19 94.38 ± 0.37 93.73 ± 0.55 93.78 ± 0.73 93.78 ± 0.91 94.16 ± 1.10 PTV 66 Heterogeneity Index 1.08 ± 0.002 1.08 ± 0.004 1.08 ± 0.006 1.08 ± 0.008 1.08 ± 0.009 1.08 ± 0.012 PTV 66 Conformity Index 0.80 ± 0.001 0.79 ± 0.003 0.80 ± 0.004 0.79 ± 0.006 0.80 ± 0.007 0.79 ± 0.009 Brain Stem Dmax (cGy) 1072.60 ± 2.68 1202.47 ± 6.08 1152.77 ± 8.75 1136.80 ± 11.36 1102.43 ± 13.59 1164.63 ± 17.52 Spinal Cord Dmax (cGy) 3680.90 ± 6.89 3679.20 ± 13.66 3678.57 ± 20.14 3687.30 ± 27.08 3712.33 ± 33.93 3676.87 ± 40.34 Lt Parotid Dmean (cGy) 1841.53 ± 3.66 1794.90 ± 7.13 1785.67 ± 10.54 Rt Parotid Dmean (cGy) 1775.77 ± 3.50 1836.20 ± 7.27 1824.23 ± 10.63 1819.60 ± 14.29 1810.53 ± 14.14 1797.97 ± 17.57 1790.10 ± 17.29 1792.87 ± 20.91 1784.20 ± 20.72 Lt Lens Dmax (cGy) 60.77 ± 0.12 62.33 ± 0.23 61.77 ± 0.35 63.83 ± 0.48 61.17 ± 0.57 64.07 ± 0.72 Rt Lens Dmax (cGy) 61.53 ± 0.12 62.30 ± 0.24 61.63 ± 0.35 63.17 ± 0.47 61.87 ± 0.58 64.10 ± 0.72 Global Max of the Plan (%) 104.68 ± 0.19 104.92 ± 0.37 104.92 ± 0.55 105.11 ± 0.73 105.48 ± 0.91 105.96 ± 1.10 Dose Calculation Time (mins) 77.32 ± 3.91 20.14 ± 1.02 10.54 ± 0.53 6.55 ± 0.33 4.90 ± 0.25 4.14 ± 0.22 Gamma Pass Rate 3%, 3 mm 98.1 ± 2.94 98.5 ± 2.95 98.3 ± 2.95 98.6 ± 2.96 98.4 ± 2.95 98.8 ± 2.96 K. L. I. Gunawardhana et al ., JSLAAS,Vol. 7, Issue 1 (2025 ) 1 6 - 39 31 Table 4. Average Comparison Results of Dosimetric Indices for Different Per Control Point Statistical Uncertainty (SU) Levels for Oesophagus Target and OARs Larynx SU 1% SU 2% SU 3% SU 4% SU 5% SU 6% PTV 50.4 D95% (cGy) 4950.47 ± 8.43 4963.87 ± 16.73 PTV 50.4 Dmax (cGy) 5189.47 ± 8.83 5209.37 ± 17.56 PTV 50.4 Dmean (cGy) 4997.70 ± 8.50 5013.07 ± 16.90 4952.93 ± 25.12 5210.03 ± 26.44 5002.13 ± 25.37 4955.83 ± 33.41 5212.10 ± 35.14 5010.37 ± 33.79 4939.50 ± 38.88 5226.63 ± 44.16 4999.80 ± 42.23 4949.03 ± 49.71 5261.10 ± 52.85 5011.73 ± 50.34 PTV 50.4 V95% 98.67 ± 0.17 98.71 ± 0.34 98.68 ± 0.51 98.73 ± 0.67 98.70 ± 0.84 98.71 ± 1.00 PTV 50.4 Heterogeneity Index 1.03 ± 0.002 1.03 ± 0.004 1.03 ± 0.005 1.03 ± 0.007 1.03 ± 0.009 1.03 ± 0.010 PTV 50.4 Conformity Index 0.68 ± 0.001 0.68 ± 0.002 0.68 ± 0.003 0.68 ± 0.005 0.70 ± 0.006 0.68 ± 0.007 Heart Dmean (cGy) 2303.07 ± 3.93 2278.23 ± 7.70 2285.87 ± 11.64 2276.40 ± 15.38 2273.73 ± 19.26 2290.83 ± 23.09 Spinal Cord Dmax (cGy) 3560.20 ± 6.23 3584.77 ± 12.43 3576.33 ± 18.70 3558.87 ± 24.73 3572.70 ± 31.15 3547.00 ± 36.66 Rt Lung Dmean (cGy) 1523.03 ± 2.61 1516.70 ± 5.11 1522.23 ± 7.74 1522.50 ± 10.28 1511.73 ± 12.79 1518.63 ± 15.28 Lt Lung Dmean (cGy) 1458.57 ± 2.49 1456.60 ± 4.92 1455.77 ± 740 1455.80 ± 9.83 1448.57 ± 12.26 1456.53 ± 14.65 Lt Kidney Dmean (cGy) 168.80 ± 0.38 167.77 ± 0.78 164.23 ± 1.12 162.93 ± 1.50 165.97 ± 1.91 162.77 ± 2.22 Rt Kidney Dmean (cGy) 40.70 ± 0.07 40.73 ± 0.15 40.80 ± 0.22 40.67 ± 0.29 40.57 ± 0.36 40.60 ± 0.43 Global Max of the Plan (%) 102.97 ± 0.17 103.36 ± 0.34 103.38 ± 0.51 103.42 ± 0.67 103.70 ± 0.84 104.39 ± 1.00 Dose Calculation Time (mins) 90.8 ± 4.66 26.06 ± 1.32 12.18 ± 0.62 8.42 ± 0.43 5.85 ± 0.30 4.89 ± 0.25 Gamma Pass Rate 3%, 3 mm 97.5 ± 2.93 98.3 ± 2.95 97.9 ± 2.94 98.1 ± 2.94 97.7 ± 2.93 98.4 ± 2.95 K. L. I. Gunawardhana et al ., JSLAAS,Vol. 7, Issue 1 (2025 ) 1 6 - 39 32 Table 5. Average Comparison Results of Dosimetric Indices for Different Per Control Point Statistical Uncertainty (SU) Levels for Prostate Target and OARs Larynx SU 1% SU 2% SU 3% SU 4% SU 5% SU 6% PTV 78 D95% (cGy) 7657.57 7672.97 7681.73 7662.20 7633.23 7615.20 ± 18.89 ± 38.12 ± 56.61 ± 75.37 ± 91.90 ± 109.41 PTV 78 Dmax (cGy) 8046.73 8075.70 8118.27 8139.67 8132.50 8202.93 ± 19.85 ± 40.12 ± 59.83 ± 80.06 ± 97.93 ± 117.88 PTV 78 Dmean (cGy) 7793.60 7811.20 7822.13 7810.90 7813.40 7827.07 ± 19.23 ± 38.80 ± 57.65 ± 76.83 ± 94.07 ± 112.47 PTV 78 V95% (cm3) 99.43 ± 0.2 99.54 ± 0.50 99.53 ± 0.74 99.39 ± 0.98 99 ± 1.20 98.77 ± 1.44 PTV 78 Heterogeneity Index 1.03 ± 0.003 1.03 ± 0.005 1.03 ± 0.008 1.03 ± 0.010 1.04 ± 0.012 1.05 ± 0.014 PTV 78 Conformity Index 0.72 ± 0.002 0.70 ± 0.004 0.70 ± 0.005 0.71 ± 0.007 0.72 ± 0.009 0.71 ± 0.011 Rectum Dmean (cGy) 4109.13 4164.23 4144.10 4132.20 4098.40 4083.50 ± 9.89 ± 20.69 ± 30.53 ± 40.63 ± 49.35 ± 58.68 Bladder Dmean (cGy) 3778.87 3811.00 3801.77 3768.13 3741.43 3741.30 ± 9.40 ± 19.09 ± 28.27 ± 37.42 ± 45.53 ± 54.08 Lt Pelvic Bone Dmean (cGy) 2168.93 2180.77 2170.37 2159.70 2181.27 2193.07 ± 5.39 ± 10.92 ± 16.11 ± 21.40 ± 26.47 ± 31.65 Rt Pelvic Bone Dmean (cGy) 2189.97 2189.70 2199.67 2187.13 2200.87 2223.80 ± 5.46 ± 11.02 ± 16.41 ± 21.80 ± 26.84 ± 32.26 Global Max of the Plan (%) 103.21 103.73 104.08 104.45 104.37 105.16 ± 0.25 ± 0.50 ± 0.74 ± 0.98 ± 1.20 ± 1.44 Dose Calculation Time (mins) 81.5 ± 4.11 24.74 ± 1.24 14.03 ± 0.71 9.12 ± 0.47 7.06 ± 0.36 5.92 ± 0.30 Gamma Pass Rate 3%, 3 mm 97.1 ± 2.91 97.7 ± 2.93 97.5 ± 2.93 97.6 ± 2.93 97.2 ± 2.92 97.6 ± 2.93 K. L. I. Gunawardhana et al ., JSLAAS,Vol. 7, Issue 1 (2025 ) 1 6 - 39 33 DISCUSSION The Monte Carlo (MC) methods are mainly used in three distinct problem classes: optimization, numerical integration, and generating draws from a probability distribution. In Monaco 5.11.02 TPS, MC was used for optimization, and the MC method has been identified as the gold standard for dose calculation (Clements, Schupp, Tattersall, Brown, & Larson, 2018) . At present, MC simulation calculates the dose very closely to reality, taking into account the contribution of secondary photons and electrons scattering and dose absorption, especially in homogeneous environments (Tugrul, 2021). The per control point SU is the percentage (%) SU per voxel, on a per-segment basis, that is willing to accept for the final dose calculation. So, the mean, per voxel, uncertainty in a central region of the dose of a segment is equal to the user-specified SU at the end of the dose calculation. A voxel is a measurement of volume in a structure that is to be imaged. Each voxel represents a defined volume and can be localized by coordinates on a three-dimensional (3D) grid. Here, the very important fact is that the smaller the per control point SU, the longer the dose calculation time. Also, when we used per control point SU values between 0.1% - 10%, the results should be a final dose uncertainty of approximately 1% for the plan in the central region of the target volume. The main difference between Per Calculation SU and Per Control Point SU is based on the number of histories (Uyar & Günekbay, 2023) and the voxel. In other words, Per Calculation SU is fast because it estimates the number of histories for the entire plan (recommended value 1%). Per control Point SU gives better resolution because it uses percentage uncertainty per voxel on a per-segment basis. So it should vary for the number of control points. In this work, we used 40 control points to generate each IMRT plan. The MC dose calculation without any SU is the most worthwhile in an IMRT plan from the accuracy point of view. However, it would take infinite time to calculate. So the planner should accept a certain range for this calculation uncertainty. The SU of MC is inversely proportional to the volume of the dose voxel (Mohan, Antolak, & Hendee, 2001) . For example, when decreasing the voxel size from 5 mm to 3 mm, it caused to increase in the Monte Carlo calculation time of approximately fivefold. Also, when reducing the SU by a factor of two, it caused the MC calculation time to be fourfold (Figure 6). So it was very crucial in decreasing/ increasing voxel size or SU in both ways. Overall analysis of this study suggests that there were no diagnosis-specific dosimetric variations. As reported by Jiang et al. (Jiang, Pawlicki, & Ma, 2000) , large Statistical Uncertainties (SUs) are expected to blur the Dose Volume Histogram (DVH) curves and may become unreliable. The statistical noise should have practically no effect on inverse treatment planning (as IMRT) because the intensity along a ray is affected by the average of dose values over a large number of voxels lying along the ray and not by the dose in any one voxel (Mohan, Antolak, & Hendee, 2001) . It was suggested that large SU can be used for large tumors and OARs such as parallel organs (Palanisamy, David, Durai, Bhalla, & Puri, 2019) . The effect of the per control point SU in this study showed no significant dose differences on the mean dose to the target and OAR volumes. So, it is suggested that SU can be used up to 5% for parallel organs. For the structures with small volumes (such as small tumors, lens), Monaco does not recommend using per control point SU higher than 5%. If we use a higher value (higher than 5%), it causes the system to underestimate the cost function value assigned to that structure (Elekta, 2017). Significant variation was observed in average dose calculation time and the per control point SU. There is an exponential relationship observed between average dose K. L. I. Gunawardhana et al., JSLAAS,Vol. 7, Issue 1 (2025) 16-39 34 calculation time and the per control point SU (Figure 6). The dose calculation time may not be too long. Also, the gamma index showed a good pass rate for all three different diagnoses (such as Larynx, Oesophagus, and Prostate), and there are no significant variations were observed in the gamma pass rate for all three diagnoses. The final dose uncertainty should be approximately 1% for the entire plan (Elekta, 2017), and it shouldn’t be greater than 1% for the entire plan (Figure 7). Finally, based on all the measured average results, as well as considering other important factors and constraints, we recommend maintaining a control point SU value of 3% without compromising the quality or delivery of the plan. Additionally, we analyzed the percentage variations concerning the 3% per control point SU for the Larynx, Oesophagus, and Prostate (refer to Tables 6-8). Table 6. Percentage Variation values with respect to 3% for Larynx Target and OARs Larynx (wrt.3%) SU 1% SU 2% SU 4% SU 5% SU 6% PTV 66 D95% (cGy) 0.01 0.48 0.17 0.07 0.25 PTV 66 Dmax (cGy) 0.24 0.01 0.11 0.53 0.83 PTV 66 Dmean (cGy) 0.05 0.33 0.18 0.09 0.34 PTV 66 V95% (%) 0.09 0.69 0.05 0.05 0.46 PTV 66 Heterogeneity Index 0 0 0 0 0 PTV 66 Conformity Index 0 1.25 1.25 0 1.25 Brain Stem Dmax (cGy) 6.95 4.31 1.39 4.37 1.03 Spinal Cord Dmax (cGy) 0.06 0.02 0.24 0.92 0.05 Lt Parotid Dmean (cGy) 3.13 0.52 1.9 0.69 0.4 Rt Parotid Dmean (cGy) 2.66 0.66 0.75 1.87 2.19 Lt Lens Dmax (cGy) 1.62 0.91 3.33 0.97 3.72 Rt Lens Dmax (cGy) 0.16 1.09 2.5 0.39 4.01 Global Max of the Plan (%) 0.23 0 0.18 0.53 0.99 Gamma Pass Rate 3%, 3 mm 0.20 0.20 0.31 0.10 0.51 K. L. I. Gunawardhana et al., JSLAAS,Vol. 7, Issue 1 (2025) 16-39 35 Table 7: Percentage Variation values with respect to 3% for Oesophagus Target and OARs Oesophagus (wrt.3%) SU 1% SU 2% SU 4% SU 5% SU 6% PTV 50.4 D95% (cGy) 0.05 0.22 0.06 0.27 0.08 PTV 50.4 Dmax (cGy) 0.39 0.01 0.04 0.32 0.98 PTV 50.4 Dmean (cGy) 0.09 0.22 0.16 0.05 0.19 PTV 50.4 V95% 0.01 0.03 0.05 0.02 0.03 PTV 50.4 Heterogeneity Index 0.00 0.00 0.00 0.00 0.00 PTV 50.4 Conformity Index 0.00 0.00 0.00 2.94 0.00 Heart Dmean (cGy) 0.75 0.33 0.41 0.53 0.22 Spinal Cord Dmax (cGy) 0.45 0.24 0.49 0.10 0.82 Rt Lung Dmean (cGy) 0.05 0.36 0.02 0.69 0.24 Lt Lung Dmean (cGy) 0.19 0.06 0.00 0.49 0.05 Lt Kidney Dmean (cGy) 2.78 2.16 0.79 1.06 0.89 Rt Kidney Dmean (cGy) 0.25 0.17 0.32 0.56 0.49 Global Max of the Plan (%) 0.40 0.02 0.04 0.31 0.98 Gamma Pass Rate 3%, 3 mm 0.41 0.41 0.20 0.20 0.51 Table 8. Percentage Variation values with respect to 3% for Prostate Target and OARs Prostate (wrt.3%) SU 1% SU 2% SU 4% SU 5% SU 6% PTV 78 D95% (cGy) 0.31 0.11 0.25 0.63 0.87 PTV 78 Dmax (cGy) 0.88 0.52 0.26 0.18 1.04 PTV 78 Dmean (cGy) 0.36 0.14 0.14 0.11 0.06 PTV 78 V95% (cm3) 0.1 0.01 0.14 0.53 0.76 PTV 78 Heterogeneity Index 0 0 0 0.97 1.94 PTV 78 Conformity Index 2.86 0 1.43 2.86 1.43 Rectum Dmean (cGy) 0.84 0.49 0.29 1.1 1.46 Bladder Dmean (cGy) 0.6 0.24 0.88 1.59 1.59 Lt Pelvic Bone Dmean (cGy) 0.07 0.48 0.49 0.5 1.05 Rt Pelvic Bone Dmean (cGy) 0.44 0.45 0.57 0.05 1.1 Global Max of the Plan (%) 0.84 0.34 0.36 0.28 1.04 Gamma Pass Rate 3%, 3 mm 0.41 0.21 0.10 0.31 0.10 According to the calculated percentage variation values (Table 6-8) with respect to a 3% per control point SU, showed no any significant variation between the dosimetric indices and the per control point SU (%) values for all three diagnoses (Larynx, Oesophagus, and Prostate). K. L. I. Gunawardhana et al ., JSLAAS,Vol. 7, Issue 1 (2025 ) 1 6 - 39 36 CONCLUTION This study proposed an optimal acceptable range for the per Control Point Statistical Uncertainty (SU) in Monte Carlo Dose calculations during IMRT planning in Monaco 5.11.02 Treatment Planning System (TPS). Based on the measured average results, a 3% per control point SU is acceptable for all three diagnoses (Larynx, Oesophagus, and Prostate) in IMRT planning, allowing for reduced calculation time without compromising target coverage, Organ at Risk (OAR) doses, or plan delivery. Conflict of Interest None declared. Financial Disclosure The authors did not receive any kind of grant or financial support from any organization for the present study. REFERENCES [1] Bortfeld, T. (2006). IMRT: A review and preview. Physics in Medicine and Biology, 51(13), R363– R379. https://doi.org/10.1088/0031-9155/51/13/R21 [2] Carol, M. P. (1995). Peacock: A system for planning and rotational delivery of intensitymodulated fields. International Journal of Imaging Systems and Technology, 6(1), 56–61. Webb, S., 2001. Intensity-modulated radiation therapy (IMRT): a clinical reality or a technical exercise? British Journal of Radiology, 74(881), pp.593–594. [3] Cheong, K. H., Suh, T. S., & Cho, B. C. (2004). The effects of the statistical uncertainties in Monte Carlo photon dose calculation for radiation therapy. Journal of Radiation Protection and Research, 29(2), 105–115. [4] Chetty, I. J., Curran, B., Cygler, J. E., DeMarco, J. J., Ezzell, G., Faddegon, B. A., … Seuntjens, J. (2007). Report of the AAPM Task Group No. 105: Issues associated with clinical implementation of Monte Carlo–based photon and electron external beam treatment planning. Medical Physics, 34(12), 4818–4853. https://doi.org/10.1118/1.2804935 [5] Clements, M., Schupp, N., Tattersall, M., Brown, A., & Larson, R. (2018). Monaco treatment planning system tools and optimization processes. Medical Dosimetry, 43(2), 106–117. https://doi.org/10.1016/j.meddos.2017.12.002 [6] Delaney, G., Jacob, S., Featherstone, C., & Barton, M. (2005). The role of radiotherapy in cancer treatment: Estimating optimal utilization from a review of evidence-based clinical guidelines. Cancer, 104(6), 1129–1137. https://doi.org/10.1002/cncr.21324 [7] Elekta AB. (2017). Monaco 5.11 Reference Guide – Physics Module. Stockholm, Sweden: Elekta AB. [8] Feuvret, L., Noël, G., Mazeron, J. J., & Bey, P. (2006). Conformity index: A review. International Journal of Radiation Oncology, Biology, Physics, 64(2), 333–342. https://doi.org/10.1016/j.ijrobp.2005.09.028 K. L. I. Gunawardhana et al., JSLAAS,Vol. 7, Issue 1 (2025) 16-39 37 [9] Goodall, S. K., & Ebert, M. A. (2020). Recommended dose voxel size and statistical uncertainty parameters for precision of Monte Carlo dose calculation in stereotactic radiotherapy: Investigation using Monaco 5.11.02. Journal of Applied Clinical Medical Physics, 21(12), 120– 130. https://doi.org/10.1002/acm2.13077 [10] Gupta, T., Agarwal, J. P., Ghosh-Laskar, S., & Shrivastava, S. K. (2009). A prospective comparative study of time requirements and resource burden for intensity modulated radiotherapy versus 3-dimensional conformal radiotherapy in head and neck cancer. Journal of Cancer Research and Therapeutics, 5(2), 126–131. [11] Hall, E. J., & Giaccia, A. J. (2019). Radiobiology for the radiologist (8th ed.). Philadelphia, PA: Wolters Kluwer. [12] Intensity Modulated Radiation Therapy Collaborative Working Group. (2001). Intensitymodulated radiation therapy: Emerging cancer treatment technology. British Journal of Cancer, 92(10), 1819–1824. https://doi.org/10.1038/sj.bjc.6602589 [13] International Commission on Radiation Units and Measurements (ICRU). (2010). Prescribing, Recording, and Reporting Photon-Beam Intensity-Modulated Radiation Therapy (IMRT). ICRU Report 83. Journal of the ICRU, 10(1), 1–106. https://doi.org/10.1093/jicru/ndq00 [14] Jiang, S. B., Pawlicki, T., & Ma, C.-M. (2000). Removing the effect of statistical uncertainty on dose-volume histograms from Monte Carlo dose calculations. Physics in Medicine & Biology, 45(8), 2151–2161. https://doi.org/10.1088/0031-9155/45/8/312 [15] Keall, P., Siebers, J., Jeraj, R., & Mohan, R. (2000). The effect of dose calculation uncertainty on the evaluation of radiotherapy plans. Medical Physics, 27(3), 478–484. https://doi.org/10.1118/1.598909 [16] Khan, F. M., & Gibbons, J. P. (2014). The physics of radiation therapy (5th ed.). Lippincott Williams & Wilkins [17] Kry, S. F., et al. (2019). Dosimetric impact of statistical uncertainty on Monte Carlo dose calculation in VMAT plans. Medical Physics, 46(3), 1225–1233. [18] Lawrence, T. S., & Cox, J. D. (1995). Intensity-modulated radiation therapy: Clinical applications and early experience. International Journal of Radiation Oncology, Biology, Physics, 31(4), 955– 962. [19] LoSasso, T., Chui, C. S., & Ling, C. C. (1998). Physical and dosimetric aspects of a multileaf collimation system used in the dynamic mode for implementing intensity modulated radiotherapy. Medical Physics, 25(10), 1919–1927. [20] Low, D. A., Moran, J. M., Dempsey, J. F., Dong, L., & Oldham, M. (2011). Dosimetry tools and techniques for IMRT. Medical Physics, 38(3), 1313–1338. [21] Ma, C., Li, J., Jiang, S., Pawlicki, T., Xiong, W., Qin, L., et al. (2005). Effect of statistical uncertainties on Monte Carlo treatment planning. Physics in Medicine & Biology, 50(5), 891– 907. https://doi.org/10.1088/0031-9155/50/5/001 K. L. I. Gunawardhana et al., JSLAAS,Vol. 7, Issue 1 (2025) 16-39 38 [22] Miften, M., Olch, A., Mihailidis, D., Moran, J., Pawlicki, T., Molineu, A., et al. (2018). Tolerance limits and methodologies for IMRT measurement-based verification QA: Recommendations of AAPM Task Group No. 218. Medical Physics, 45(4), e53–e83. [23] Mohan, R., Antolak, J., & Hendee, W. R. (2001). Monte Carlo techniques should replace analytical methods for estimating dose distributions in radiotherapy treatment planning. Medical Physics, 28(2), 123–126. https://doi.org/10.1118/1.1339879 [24] Mohan, R. (2005). Intensity-modulated radiation therapy: What is it, why it is done, and how it is done. Cancer Journal, 11(5), 317–324. [25] Morris, S., Roques, T., Ahmad, S., & Loo, S. (2023). Practical radiotherapy planning. CRC Press. [26] Palta, J. R., & Mackie, T. R. (2011). Teletherapy: Present and future. Medical Physics Publishing. [27] Palanisamy, M., David, K., Durai, M., Bhalla, N., & Puri, A. (2019). Dosimetric impact of statistical uncertainty on Monte Carlo dose calculation algorithm in volumetric modulated arc therapy using Monaco TPS for three different clinical cases. Reports of Practical Oncology and Radiotherapy, 24(2), 188–199. https://doi.org/10.1016/j.rpor.2018.10.006 [28] Papiez, L., & Langer, M. (2006). Monte Carlo dose calculations in radiation therapy: A review. Reports of Practical Oncology and Radiotherapy, 11(5), 245–253. https://doi.org/10.1016/S1507-1367(06)70955-5 [29] Podgorsak, E. B. (2005). Radiation oncology physics: A handbook for teachers and students. International Atomic Energy Agency. [30] Rembish, J., Myers, P., Saenz, D., Kirby, N., Papanikolaou, N., & Stathakis, S. (2021). Effects of varying statistical uncertainty using a Monte Carlo based treatment planning system for VMAT. Journal of BUON, 26(4), 1663–1668. [31] Sarkar, B., Manikandan, A., Nandy, M., Munshi, A., Sayan, P., & Sujatha, N. (2016). Influence of Monte Carlo variance with fluence smoothing in VMAT treatment planning with Monaco TPS. Indian Journal of Cancer, 53(1), 158–161. [32] Shipley, W. U., Tepper, J. E., Prout, G. R., Verhey, L. J., Mendiondo, O. A., Goitein, M., et al. (1979). Proton radiation as boost therapy for localized prostatic carcinoma. JAMA, 241(18),1912–19 [33] Taleei, R., & Tabrizi, P. R. (2019). Optimization of statistical uncertainty and calculation time in Monte Carlo dose calculation for radiotherapy treatment planning. Journal of Applied Clinical Medical Physics, 20(11), 153–160. https://doi.org/10.1002/acm2.12730 [34] Tugrul, T. (2021). Comparison of Monaco treatment planning system algorithms and Monte Carlo simulation for small fields in anthropomorphic RANDO phantom: The esophagus case. Journal of Cancer Research and Therapeutics, 17(6), 1370–1375. https://doi.org/10.4103/jcrt.JCRT_1562_20 [35] Uyar, E., & Günekbay, Z. A. (2023). Comparison of the number of history in Monte Carlo simulation programs. arXiv. https://arxiv.org/abs/2301.05916 K. L. I. Gunawardhana et al., JSLAAS,Vol. 7, Issue 1 (2025) 16-39 45 Figure 5. Flowchart depicting each sequential step in the water ionization process, from prefiltration to final pH-regulated output. It includes sequential configuration in water ionization, highlighting the importance of each component in achieving efficient and effective ionization. Figure 06 shows the change in pH levels over time during a typical trial using the automated water ionizer. Figure 6: Graph displaying the change in pH levels over time during a typical trial with the automated water ionizer, indicating system stability and efficiency in achieving desired pH levels. The pH of the water increases from an initial value of approximately 7.0 to around 10.0 within 12 minutes. This trend indicates the efficiency of the ionization process in raising the pH to a desired alkaline level. The steady rise in pH during the initial 10 minutes suggests that the electrolysis system and the graphite-based electrodes are effectively facilitating ion separation and production of hydroxide ions. After 12 minutes, the pH stabilizes near 10.0, indicating that the ionizer has reached a steady-state condition where the output matches the desired pH range for alkaline water production. This stability demonstrates the capability of the device’s control mechanisms, including the pH sensors and PLC system, to maintain the target pH with minimal deviation. The gradual pH increase and eventual stabilization A.M.K.L Abeykoon et al., JSLAAS,Vol. 7, Issue 1 (2025) 40-48 46 highlight both the reliability and precision of the system in generating alkaline water for potential applications. The performance of the high-efficiency water ionizer was evaluated by comparing the properties of inlet and outlet water samples, as summarized in Table 1. Table 1. Properties of inlet and outlet water samples ND – Not Detected The pH of the outlet water was measured at 8.4, an increase from the inlet pH of 7.4, indicating effective alkalization. This increase in pH suggests that the ionizer successfully modifies the water's composition to produce alkaline water, a characteristic associated with potential health benefits. Additionally, a slight rise in electrical conductivity was observed, from 71 μS/cm in the inlet to 73 μS/cm in the outlet, along with an increase in total dissolved solids (TDS) from 41 mg/L to 45 mg/L. These minor changes may be due to the increase in ion concentration in the outlet water as a results of the ionization process, which contributes to its enhanced alkalinity and mineral content. The concentrations of essential minerals, including calcium, magnesium, sodium, and potassium, were found to remain relatively stable. For instance, sodium increased only marginally from 3.5 mg/L to 3.6 mg/L, while the levels of calcium and magnesium showed no measurable change. This stability demonstrates that the ionizer preserves the water's naturally occurring beneficial minerals, ensuring nutrient content remains suitable for consumption. Importantly, all tested Unit Inlet Result Outlet Result pH at 25° 7.4 8.4 Electrical Conductivity at 25°C μS/cm 71 73 Total Dissolved Solids at 180°C mg/L 41 45 Calcium (as Ca) mg/L 5.1 5.1 Magnesium (as Mg) mg/L 0.9 0.9 Sodium (as Na) mg/L 3.5 3.6 Potassium (as K) mg/L 1.0 1.0 Aluminum (as Al) mg/L 0.02 0.02 Lead (Pb) mg/L ND ND Cadmium (Cd) mg/L ND ND Arsenic (As) mg/L ND ND Mercury (Hg) mg/L ND ND Chromium (Cr) mg/L ND ND Nickel (Ni) mg/L ND ND Copper (Cu) mg/L ND ND Zinc (Zn) mg/L ND ND Iron (Fe) mg/L ND ND Manganese (Mn) mg/L ND ND Selenium (Se) mg/L ND ND Phenolic compounds mg/L ND ND A.M. K . L Abeykoon et al ., JSLAAS,Vol. 7, Issue 1 (2025 ) 40 - 48 47 heavy metals and toxic elements, including lead, arsenic, cadmium, mercury, and chromium, were not detected in both inlet and outlet samples. This indicates that the ionizer does not introduce any harmful heavy metal contaminants into the water. The aluminum concentration remained consistent at a low level of 0.02 mg/L, suggesting that the device’s components do not contribute to metal leaching. The consistent absence of heavy metals and toxic elements confirm that the device is safe and suitable for producing alkaline water drinking purpose. CONCLUTION This study successfully developed and automated a high-efficiency water ionizer, capable of producing ionized water with precisely controlled pH levels through an advanced electrolysis process. The ionizer's ability to generate both alkaline and acidic water makes it suitable for various applications, including sterilization, cleaning, and drinking. The integration of innovative graphite-based electrodes and selective ion-exchange membranes significantly enhanced the accuracy in pH regulation, providing a more reliable and user-friendly experience. Moreover, the inclusion of pH sensors and automated control mechanisms enabled real-time monitoring and adjustments, ensuring consistent output quality. The use of a solar-powered energy system further emphasizes the sustainability of the ionizer, reducing its reliance on traditional power sources and supporting environmental goals. ACKNOLOWLEDGEMENT Financial support from the Sri Lanka Treasury (Grant No. TG/21/196)is highly appreciated. REFERENCE [1] R.M.C. Ignacio, K.-B. Joo, K.-J. Lee, Clinical effect and mechanism of alkaline reduced water, J. Food Drug Anal. 20 (2012) 33. [2] F.S.L.G. Delos Reyes, A.C.C. Mamaril, T.J.P. Matias, M.K. V Tronco, G.R. Samson, N.D. Javier, A. Fadriquela, J.M. Antonio, M.E.J. V Sajo, The search for the elixir of life: On the therapeutic potential of alkaline reduced water in metabolic syndromes, Processes 9 (2021) 1876. [3] J.A. Koufman, N. Johnston, Potential Benefits of pH 8.8 Alkaline Drinking Water as an Adjunct in the Treatment of Reflux Disease, Ann. Otol. Rhinol. Laryngol. 121 (2012) 431– 434. https://doi.org/10.1177/000348941212100702. [4] S. Lal, A.K. Kakodia, S.K. Verma, Alkaline water and human health: Significant hypothesize, J. Appl. Sci. Educ. 2 (2022) 1–11. [5] B. Benelam, L. Wyness, Hydration and health: a review, Nutr. Bull. 35 (2010) 3–25. [6] Y.-R. Huang, Y.-C. Hung, S.-Y. Hsu, Y.-W. Huang, D.-F. Hwang, Application of electrolyzed water in the food industry, Food Control 19 (2008) 329–345. [7] J.-M. Kim, K. Yokoyama, Effects of Alkaline lonized Water on Spontaneously diabetic, Korean J. Lab Anim. Sci I3 2 (1997) 187–190. [8] C.N. Tango, M.S. Hussain, D.-H. Oh, Application of electrolyzed water on environment sterilization, Electrolyzed Water Food Fundam. Appl. (2019) 177–204. [9] T.W. LeBaron, R. Sharpe, K. Ohno, Electrolyzed–reduced water: Review ii: Safety concerns and effectiveness as a source of hydrogen water, Int. J. Mol. Sci. 23 (2022) 14508. [10] J. Chycki, A. Kurylas, A. Maszczyk, A. Golas, A. Zajac, Alkaline water improves exerciseinduced metabolic acidosis and enhances anaerobic exercise performance in combat sport athletes, PLoS One 13 (2018) e0205708. [11] S.M. Ostojic, M.D. Stojanovic, Hydrogen-rich water affected blood alkalinity in physically A.M.K.L Abeykoon et al., JSLAAS,Vol. 7, Issue 1 (2025) 40-48 48 active men, Res. Sport. Med. 22 (2014) 49–60. [12] J. Roller, Low platinum electrodes for proton exchange fuel cells manufactures by reactive spray deposition technology, (2009). [13] L.K. Abeykoon, H.-Y. Tan, C.-F. Yan, J. Bandara, Significant role of the initial precursor sulfur concentration in the photoelectrochemical hydrogen production of Cu2ZnSnS4 photocathode prepared by thermal evaporation, J. Nanophotonics 16 (2022) 1–15. https://doi.org/10.1117/1.jnp.16.016001. [14] K.R. Rasmi, S.C. Vanithakumari, R.P. George, U. Kamachi Mudali, Synthesis and characterization of nanostructured platinum coated titanium as electrode material, J. Mater. Eng. Perform. 23 (2014) 1673–1679. [15] P. Vadthya, N. Thummalapalli, S. Sundergopal, Ultrafiltration membrane assisted cost effective ionizer for production of therapeutic alkaline ionized water, J. Water Process Eng. 32 (2019) 100951. [16] H. Girault, B. Liu, L. Qiao, H. Bi, M. Prudent, N. Lion, M. Abonnenc, Electrochemical reactions and ionization processes, Eur. J. Mass Spectrom. 16 (2010) 341–349. [17] R.E. Panzer, P.J. Elving, Nature of the surface compounds and reactions observed on graphite electrodes, Electrochim. Acta 20 (1975) 635–647. [18] S. Laschi, I. Palchetti, G. Marrazza, M. Mascini, Innovative electrodes to control trace metal ionization used to treat pathogens in water distribution systems, in: Sensors Microsystems AISEM 2011 Proc., Springer, 2012: pp. 25–30. [19] A.M.K.L. Abeykoon, R.C.L. De Silva, L.D.C. Nayanajith, I.R.M. Kottegoda, A review on appropriate graphene synthesis methods for diverse applications, Sri Lankan J. Phys. 23 (2022) 125. https://doi.org/10.4038/sljp.v23i2.8116. [20] A. Abeykoon, G. Aponsu, H. Gunathilaka, H.A.V. Nadeera, Effect of temperature on the photovoltaic characteristics of polycrystalline silicon solar cells at hambantota solar power plant, Sol. Asia (2018). [21] C. Klaysom, B.P. Ladewig, G.Q.M. Lu, L. Wang, Preparation and characterization of sulfonated polyethersulfone for cation-exchange membranes, J. Memb. Sci. 368 (2011) 48– 53. [22] R. Guan, H. Zou, D. Lu, C. Gong, Y. Liu, Polyethersulfone sulfonated by chlorosulfonic acid and its membrane characteristics, Eur. Polym. J. 41 (2005) 1554–1560. A.M. K . L Abeykoon et al ., JSLAAS,Vol. 7, Issue 1 (2025 ) 40 - 48