This study demonstrated a rapid and simple method for the determination of seven anions including halides and oxyhalides from the KURT underground water sample by capillary electrophoresis with UV detection. In nuclear waste disposal, some anions such as iodine, selenium, and technetium have been of great concern due to its high mobility and toxicity with a long half-life. It has been needed for a reliable analysis of anionic speciation because the high mobility of anions is easily affected by environmental conditions especially pH and salinity of underground water. Here this project is to develop a fast separation of seven anions including iodide, iodate, and selenite using capillary electrophoresis. The electroosmotic flow (EOF) was suppressed using a poly (ethyleneglycol) -coated capillary (DB-WAX capillary). As a result, anions migrated depending on their mobility under a reverse polarity condition (-15 kV) and the analysis time was within 15 min. UV detection was used at 200 nm. The RSDs for migration time were between 0.7% and 1.3% except for selenite of 5.1%. The RSDs for peak area were obtained between 2.9% and 7.4%. The calibration curves were linear from 10 to 200 mg/L with correlation coefficients greater than 0.9952. The LODs were 7.3, 10.9, 11.3, 12.9, 13.0, 13.9, and 17.4 mg/L for iodide, nitrate, bromide, selenite, bromate, tungstate and iodate. The KURT underground water sample spiked with seven anions at 50 mg/L were analyzed. The recoveries of spiked KURT sample ranged from 93.4% to 99.3%. The proposed method was successfully applied to determine seven anions in underground water sample.

Korea Atomic Energy Research Institute (“KAERI”) has been developing various studies related to the nuclear fuel cycle. Among them, KAERI was focusing on the pyroprocess, which recycles some useful elements white reducing the volume and toxicity of spent nuclear fuel (SNF). Pyroprocess involves the handling of SNF, which cannot be handled directly by the facility worker. Therefore, SNF is handled and processed through remote handling device within a shielded facility such as a hot cell. Nuclear Facilities with such hot cells are called nuclear fuel cycle facilities, and unlike other facilities, heating, ventilating, and air conditioning (HVAC) system are particularly important in nuclear fuel cycle facilities to maintain the atmosphere in the hot cell and remove radioactive materials. In addition, due to the nature of the pyroprocess, which uses molten salt, corrosion is a problem in air atmosphere, so the process can only be carried out in an inert gas atmosphere. KAERI has a nuclear fuel cycle facility called the Irradiation Material Examination Facility (IMEF), and has built and operated the ACPF inside the IMEF, which operates an inert atmosphere hot cell for the demonstration of the pyroprocess. For efficient process development of the pyroprocess, it is necessary to put the developed equipment into the hot cell, which is a radiationcontrolled area, after sufficient verification in a mock-up facility. For this purpose, the ACPF mock-up facility, which simulates the system, space, and remote handling equipment of the ACPF, is operated separately in the general laboratory area. The inert gas conditioning system of the ACPF consists of very complex piping, blowers, and valves, requires special attention to maintenance. In addition, if there is a small leak in the piping within these valves or piping, radioactive materials can be directly exposed to facility workers, so continuous monitoring and maintenance are required to prevent accident. In this study, the applicability of acoustic emission technology and ultrasonic technology for leak detection in the inert gas conditioning system of ACPF mock-up facility was investigated. For this purpose, new bypass pipes and valves were installed in the existing system to simulate the leakage of pipes and valves. Acoustic emission sensors are attached directly to pipes or valves to detect signals, while ultrasonic sensors are installed at a distance to detect signals. The optimal parameters of each technology to effectively suppress background noise were derived and, and the feasibility of identifying normal and abnormal scenarios in the system was analyzed.

In the nuclear fuel cycle (NFC) facilities, the failure of Heating Ventilation and Air Conditioning (HVAC) system starts with minor component failures and can escalate to affecting the entire system, ultimately resulting in radiological consequences to workers. In the field of air-conditioning and refrigerating engineering, the fault detection and diagnosis (FDD) of HVAC systems have been studied since faults occurring in improper routine operations and poor preventive maintenance of HVAC systems result in excessive energy consumption. This paper aims to provide a systematic review of existing FDD methods for HVAC systems therefore explore its potential application in nuclear field. For this goal, typical faults and FDD methods are investigated. The commonly occurring faults of HVAC are identified through various literature including publications from International Energy Agency (IEA) and American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE). However, most literature does not explicitly addresses anomalies related to pressure, even though in nuclear facilities, abnormal pressure condition need to be carefully managed, particularly for maintaining radiological contamination differently within each zone. To build simulation model for FDD, the whole-building energy system modeling is needed because HVAC systems are major contributors to the whole building’s energy and thermal comfort, keeping the desired environment for occupants and other purposes. The whole-building energy modeling can be grouped into three categories: physics-based modeling (i.e., white-box models), hybrid modeling (i.e., grey-box models), and data-driven modeling (i.e., black-box models). To create a white-box FDD model, specialized tools such as EnergyPlus for modeling can be used. The EnergyPlus is open source program developed by US-DOE, and features heat balance calculation, enabling the dynamic simulation in transient state by heat balance calculation. The physics based modeling has the advantage of explaining clear cause-and-effect relationships between inputs and outputs based on heat and mass transfer equations, while creating accurate models requires time and effort. Creating a black-box FDD model requires a sufficient quantity and diverse types of operational data for machine learning. Since operation data for HVAC systems in existing nuclear cycle facilities are not fully available, so efforts to establish a monitoring system enabling the collection, storage, and management of sensor data indicating the status of HVAC systems and buildings should be prioritized. Once operational data are available, well-known machine learning methods such as linear regression, support vector machines, random forests, artificial neural networks, and recurrent neural networks (RNNs) can be used to classify and diagnose failures. The challenge with black-box models is the lack of access to failure data from operating facilities. To address this, one can consider developing black-box models using reference failure data provided by IEA or ASHRAE. Given the unavailability of operation data from the operating NFC facilities, there is a need for a short to medium-term plan for the development of a physics-based FDD model. Additionally, the development of a monitoring system to gather useful operation data is essential, which could serve both as a means to validate the physics-based model and as a potential foundation for building data-driven model in the long term.

The high-level radioactive waste repository must ensure its performance for a long period of time enough to sufficiently reduce the potential risk of the waste, and for this purpose, multibarrier systems consisting of engineered and natural barrier systems are applied. If waste nuclides leak, the dominating mechanisms facilitating their movement toward human habitats include advection, dispersion and diffusion along groundwater flows. Therefore, it is of great importance to accurately assess the hydrogeological and geochemical characteristics of the host rock because it acts as a flow medium. Normally, borehole investigations were used to evaluate the characteristics and the use of multi-packer system is more efficient and economical compared to standpipes, as it divides a single borehole into multiple sections by installing multiple packers. For effective analyses and groundwater sampling, the entire system is designed by preselecting sections where groundwater flow is clearly remarkable. The selection is based on the analyses of various borehole and rock core logging data. Generally, sections with a high frequency of joints and evident water flow are chosen. Analyzing the logging data, which can be considered continuous, gives several local points where the results exhibit significant local changes. These clear deviations can be considered outliers within the data set, and machine learning algorithms have been frequently applied to classify them. The algorithms applied in this study include DBSCAN (density based spatial clustering of application with noise), OCSVM (one class support vector method), KNN (K nearest neighbor), and isolation forest, of which are widely used in many applications. This paper aims to evaluate the applicability of the aforementioned four algorithms to the design of multi-packer system. The data used for this evaluation were obtained from DB-2 borehole logging data, which is a deep borehole locates near KURT.

To address the pressing societal concern in Korea, characterized by the imminent saturation of spent nuclear fuel storage, this study was undertaken to validate the fundamental reprocessing process capable of substantially mitigating the accumulation of spent nuclear fuel. Reprocessing is divided into dry processing (pyro-processing) and wet reprocessing (PUREX). Within this context, the primary focus of this research is to elucidate the foundational principles of PUREX (Plutonium Uranium Redox Extraction). Specifically, the central objective is to elucidate the interaction between uranium (U) and plutonium (Pu) utilizing an organic phase consisting of tributyl phosphate (TBP) and dodecane. The objective was to comprehensively understand the role of HNO3 in the PUREX (Plutonium Uranium Redox Extraction) process by subjecting organic phases mixed with TBPdodecane to various HNO3 concentrations (0.1 M, 1.0 M, 5.0 M). Subsequently, the introduction of Strontium (Sr-85) and Europium (Eu-152) stock solutions was carried out to simulate the presence of fission products typically contented in the spent nuclear fuel. When the operation proceeds, the complex structure takes the following form. 􀜷􀜱􀬶 􀬶􀬾(􀜽􀝍) + 2􀜰􀜱􀬷 􀬿(􀜽􀝍) + 2􀜶􀜤􀜲(􀝋􀝎􀝃) ↔ 􀜷􀜱􀬶(􀜰􀜱􀬷)􀬶 ∙ 2􀜶􀜤􀜲(􀝋􀝎􀝃) Subsequently, separate samples were gathered from both the organic and aqueous phases for the quantification of gamma-rays and alpha particles. Alpha particle measurements were conducted utilizing the Liquid Scintillation Counter (LSC) system, while gamma-ray measurements were carried out using the High-Purity Germanium Detector (HPGe). The distribution ratio for U, Eu (Eu-152), and Sr (Sr-84) was ascertained by quantifying their activity through LSC and HPGe. Through the experiments conducted within this program, we have gained a comprehensive understanding of the selective solvent extraction of actinides. Specifically, uranium has been effectively separated from the aqueous phase into the organic phase using a combination of tributyl phosphate (TBP) and dodecane. Subsequently, samples containing U(VI), Eu(III), and Sr(II) underwent thorough analysis utilizing LSC and HPGe detectors. Our radiation measurements have firmly established that the concentration of nitric acid enhances the selective separation of uranium within the process.

The CTBTO is the Comprehensive Test Ban Treaty Organization to ban all forms of nuclear testing (underwater, air, and underground) worldwide and was adopted at the UN’s 50th annual general meeting in September 1996. As of September 2023, 187 out of 196 countries signed and 178 ratified. The Republic of Korea signed it in 1996 and ratified it in 1999. Several major Annex 2 countries still need to ratify it, and certain countries have not even signed it, so it has not come entry into force. The CTBTO has three verification systems for nuclear tests and consists of the International Monitoring System (IMS), the International Data Center (IDC), and On-Site Inspections (OSI). IMS consists of seismic, hydroacoustic, infrasound, and radionuclide monitoring. The measured data are delivered to IDC, analyzed by CTBTO headquarters, distributed raw data, and analyzed forms to member states. The final means of verification is in the field of OSI and will be operated when CTBT takes effect. Based on the IMS data, inspectors will be dispatched to the Inspected State Party (ISP) to check for nuclear tests. KINAC is attending the Working Group B, OSI technology development verification along with KINS and KIGAM. Since OSI is a means for final verification, integrated capabilities such as seismic and data interpretation and nuclides detection are required. CTBTO continues its efforts to foster integrated talent and modernize OSI equipment. Types of equipment include measurement, flight simulation equipment, and geographic information monitoring systems etcetera. KINAC is also developing equipment to detect contaminated areas using drones and probes. Development equipment is the nuclides detection and measurement of contaminated areas, and it is the equipment that prepares a control center and drops probes into suspected contamination areas to find a location of the radiation source. The probe can be used to track the location where the dose is most substantial through Bayesian estimation and source measurement.

As remote sensing measures, satellite imagery has played an essential role in verifying nuclear activities for decades. Starting with the first artificial satellite, Sputnik 1, in 1957, thousands of satellites are currently missioning in space. Since the 2000s, the level of detail in pixels of an image (spatial resolution) has been significantly improving, thereby identifying objects less than one meter, even tens of centimetres. The more things are identifiable, the wider regions become targets for observation. With the increasing number of satellites, computer vision technology is required to explore the applicability of algorithm-based automation. This paper aims to investigate the R&D publications worldwide from the 1990s to the present, which have tried to apply algorithms to verify any clandestine nuclear activities or detect anomalies at the site. The versatile open-source publications, including the IAEA, ESARDA, US-DOE national laboratories, and universities, are extensively reviewed from the perspective of nuclear nonproliferation (or counter-proliferation). Thus, target objects for applications are essentially located in nuclearrelated sites, and the source type of satellite sensors focuses on electro-optical images with high spatial resolution. The research trend over time by groups is discussed with limitations at the time in order to contemplate the role of algorithms in the field and to present recommendations on a way forward.

Over the past decades, particle physics has made significant progress in characterizing neutrinos even if neutrinos have extremely small cross-section (~10-44 cm2), allowing them to penetrate any object. More recently, neutrino detection and analysis have indeed become valuable tools in various aspects of nuclear science and technology. Neutrinos are detected using various methods, including Inverse Beta Decay (IBD), Neutrino-electron scattering, and Coherent Neutrino-Nucleus Scattering (CNNS). For the detection of anti-neutrinos from nuclear reactor, the Inverse Beta Decay (IBD) is commonly considered with scintillators. Notable experiments in Korea, such as RENO and NEOS, have been conducted using the IBD method at the Hanbit Nuclear Power Plant since 2006. Additionally, the NEON experiment, which employs CNNS, which has a significantly larger reaction cross-section than IBD but its low-energy signal detection difficulty, has been ongoing since 2021. Based on the results of NEOS (2015-2020) the signal to noise is ~30 and IBD detection rate is ~2000 counts per day. The IBD event in nuclear power plants provides valuable information about reactor behavior. IBD count rates are in good agreement with the thermal power of the reactor. Furthermore, the neutrino energy spectrum can be used to estimate the fission isotope ratio of the reactor core, showing promise for obtaining reactor core information from antineutrino detection techniques. Neutrino detection in nuclear facilities provides valuable information about reactor behavior. However, as a surveillance technology neutrino detection faces challenges due to the very low cross-section, requiring efforts to overcome limitations related to detector size and signal acquisition time. In 2008, the International Atomic Energy Agency (IAEA) included neutrino detection in its Research and Development (R&D) program for reactor safeguards. In January 2023, the IAEA organized a “Technical Meeting on Nuclear Data Needs for Antineutrino Spectra Applications” to discuss the latest developments and research results in this field. In summary, the use of neutrino detection in the nuclear field, particularly for reactor monitoring and safeguarding, has advanced significantly. Ongoing research and collaboration are expected to enhance our understanding of neutrinos and their applications in nuclear science and technology.

MicroRNAs (miRNAs) are emerging materials as ideal biomarkers for noninvasive cancer detection in the early phase. In this article, a simple and label-free electrochemical miRNA biosensor was developed. A single-stranded DNA (ss-DNA) probes were successfully mapped to f-MWCNT and hybridized with the target miR-141 sequence. The optimum peak points of the obtained hybridization were determined using Cyclic Voltammetry (CV) and Differential Pulse Voltammetry (DPV) methods. Significant peaks were observed in the results, depending on miR-141 at different concentrations. The linear relationship (ν) between redox peak currents (Ip) and scanning rate indicated that electron transfer (ET) between miR-141 and the electrode surface was accomplished successfully. In DPV measurements, miR-141 was measured with a low detection limit (LOD) in the 1.3–12 nM concentration range, and the LOD and limit of quantification (LOQ) results were found to be 3 and 9.1 pM, respectively. Besides, selectivity test was investigated for the biosensor using different target analytes and a significant difference in value was observed between the peak currents of miR-141, and other target molecules. This developed strategy has been found to detect miR-141 sensitively, selectively and without tags, and its integration into mobile devices has been successfully carried out.

In this work, norepinephrine (NE) was determined by an electrochemical sensor represented by a carbon paste electrode boosted using nitrogen-doped porous carbon (NDPC) derived from Spirulina Platensis microalga anchored CoFe2O4@ NiO and 1-Ethyl-3-methylimidazolium acetate (EMIM Ac) ionic liquid. The morphological characteristics of the catalyst were recorded by field emission scanning electron microscope (FE-SEM) images. Moreover, the electrochemical behavior of norepinephrine on the fabricated electrode was checked using various voltammetric methods. All tests were done at pH 7.0 as the optimized condition in phosphate buffer solution. The results from linear sweep voltammetry revealed that the electro-oxidation of norepinephrine was diffusion, and the diffusion coefficient value was obtained by chronoamperometry (D⁓6.195 × 10– 4). The linear concentration of the modified electrode was obtained from 10 to 500 μM with a limit of detection of 2.26 μM using the square wave voltammetry (SWV) method. The sensor selectivity was investigated using various species, and the results from stability and reproducibility tests showed acceptable values. The sensor's efficiency was tested in urine and pharmaceutical as real samples with recovery percentages between 97.1% and 102.82%.

Graphene-based sensors have emerged as significant tools for biosensing applications due to their unique electrical, mechanical, and thermal properties. In this study, we have developed an innovative and sensitive aptasensor based on the surfacemodified graphene for the detection of lung cancer biomarker CA125. The sensor leverages the combination of graphene surface and gold nanoparticles (AuNPs) electrodeposition to achieve a high level of sensitivity and selectivity for the biomarker detection. A noticeable decrease in electron transfer resistance was observed upon the AuNPs deposition, demonstrating the enhancement of electrochemical performance. Our experimental findings showed a strong linear relationship between the sensor response and CA125 concentrations, ranging from 0.2 to 15.0 ng/mL, with a detection limit of 0.085 ng/ mL. This study presents a novel approach to lung cancer detection, surpassing the traditional methods in terms of invasiveness, cost, and accuracy. The results from this work could pave the way for the development of graphene-based sensors in various other biosensing applications.

Graphene is a suitable transducer for wearable sensors because of its high conductivity, large specific surface area, flexibility, and other unique considerable features. Using a simple, fast galvanic pulse electrodeposition approach, a unique nonenzymatic glucose amperometric electrode was successfully developed based on well-distributed fine Cu nanoparticles anchored on the surface of 3D structure laser-induced graphene. The fabricated electrode allows glucose detection with a sensitivity of 2665 μA/mM/cm2, a response time of less than 5 s, a linear range of 0.03–4.5 mM, and a LOD of 0.023 μM. It also detects glucose selectively in the presence of interfering species such as ascorbic acid and urea. These provide the designed electrode the advantages for glucose sensing in saliva with 97% accuracy and present it among the best saliva-range non-enzymatic glucose sensors reported to date for real-life diagnostic applications.

This work describes Ni–Ce–Cu metallic–organic framework (MOF) for the detection of non-essential amino acid l-cysteine. The tri-metallic Ni–Ce–Cu MOF was synthesized via a solvothermal method. The cyclic voltammetry and the differential pulse voltammetry techniques were used to examine the electrochemical detection of l-cysteine. The Ni–Ce–Cu MOF shows an oxidation peak in PB solution at pH 3.0 between the potential range of 0.0 and 0.7 V and strong electro-catalytic activity toward the oxidation of l-cysteine across a wide linear range of 0.1 to 250 nM and low detection limit (LOD) was calculated of 1.56 nM. The analysis of l-cysteine in milk and egg yolk samples showed with recovery range of 96.75–103.5% and 97.78–99.43% with RSD% of 2.3–3.2% and 2.7–7.2%, respectively. These results show the Ni–Ce–Cu MOF has high selectivity for l-cysteine detection in milk and egg samples.