During electrorefining, fission products, such as Sr and Cs, accumulate in a eutectic LiCl-KCl molten salt and degrade the efficiency of the separation process by generating high heat and decreasing uranium capture. Thus, the removal of the fission products from the molten salt bath is essential for reusing the bath, thereby reducing the additional nuclear waste. While many studies focus on techniques for selective separation of fission products, there are few studies on processing monitoring of those techniques. In-situ monitoring can be used to evaluate separation techniques and determine the integrity of the bath. In this study, laser-induced breakdown spectroscopy (LIBS) was selected as the monitoring technique to measure concentrations of Sr and Cs in 550°C LiCl-KCl molten salt. A laser spectroscopic setup for analyzing high-temperature molten salts in an inert atmosphere was established by coupling an optical path with a glove box. An air blower was installed between the sample and lenses to avoid liquid splashes on surrounding optical products caused by laser-liquid interaction. Before LIBS measurements, experimental parameters such as laser pulse energy, delay time, and gate width were optimized for each element to get the highest signal-to-noise ratio of characteristic elemental peaks. LIBS spectra were recorded with the optimized conditions from LiCl-KCl samples, including individual elements in a wide concentration range. Then, the limit of detections (LODs) for Sr and Cs were calculated using calibration curves, which have high linearity with low errors. In addition to the univariate analysis, partial least-squares regression (PLSR) was employed on the data plots to obtain calibration models for better quantitative analysis. The developed models show high performances with the regression coefficient R2 close to one and root-mean-square error close to zero. After the individual element analysis, the same process was performed on samples where Sr and Cs were dissolved in molten salt simultaneously. The results also show low-ppm LODs and an excellent fitted regression model. This study illustrates the feasibility of applying LIBS to process monitoring in pyroprocessing to minimize nuclear waste. Furthermore, this high-sensitive spectroscopic system is expected to be used for coolant monitoring in advanced reactors such as molten salt reactors.

Molten salt reactors and pyroprocessing are widely considered for various nuclear applications. The main challenges for monitoring these systems are high temperature and strong radiation. Two harsh environments make the monitoring system needs to measure nuclides at a long distance with sufficient resolution for discriminating many different elements simultaneously. Among available methodologies, laser-induced breakdown spectroscopy (LIBS) has been the most studied. The LIBS method can provide the required stand-off and desired multi-elemental measurable ability. However, the change of the level for molten salts induces uncertainty in measuring the concentration of the nuclides for LIBS analysis. The spectra could change by focusing points due to the different laser fluence and plasma shape. In this study, to prepare for such uncertainties, we evaluated a LIBS monitoring system with machine learning technology. While the machine learning technology cannot use academic knowledge of the atomic spectrum, this technique finds the new variable as a vector from any data including the noise, target spectrum, standard deviation, etc. Herein, the partial least squares (PLS) and artificial neural network (ANN) were studied because these methods represent linear and nonlinear machine learning methods respectively. The Sr (580–7200 ppm) and Mo (480–4700 ppm) as fission products were investigated for constructing the prediction model. For acquiring the data, the experiments were conducted at 550°C in LiCl-KCl using a glassy carbon crucible. The LIBS technique was used for accumulating spectra data. In these works, we successfully obtained a reasonable prediction model and compared each other. The high linearities of the prediction model were recorded. The R2 values are over 0.98. In addition, the root means square of the calibration and cross-validation were used for evaluating the prediction model quantitatively.

연구에서 나노 알루미나와 마그네지아의 첨가에 의한 304 스테인레스 스틸에 170 ℃ 2시간 열 경화시켰다. 레이저유도 분광학에 의한 코팅된 시료를 전하결합 장치와 SEM을 활용한 장치를 설계 하여 시험 측정하였다. 이 결과 나노 알루미나와 마그네지아가 함유된 세라믹 코팅이 나노 무기화합물이 함유되지 않은 시료보다 부착성, 내스크래치성이 우수하였으며, 또한 산용액속에서 시료의 질량감소의 변화가 매우 작았다. 그리하여 본 연구는 304 스테인레스 스틸의 내부식성을 개선하기 위해 시료가 코 팅되었으며, 분석공정이 설계되어 고분해능 CCD와 함께 분석되었다. 요즈음, 스테인레스 스틸의 코팅은 산업에 특이응용이 발전됨에 따라 위생학, 우주항공, 기기장치, 관측 등의 분야 등에 산업적 요구가 증 가되고 있다.

시간분해 레이저 유도 형광 분광학을 이용하여 UO22+, UO2(OH)+, (UO2)2(OH)22+, (UO2)3(OH)5+와 같은 우라늄(VI) 화학종 규명 연구를 수행하였다. 들뜸 파장의 변화에 따른 화학종 규명 감도를 조사하였다. 266 nm의 들뜸 파장을 이용할 경우, 나노 몰 농도의 U(VI) 화합물을 구분할 수 있는 화학종 규명 감도를 얻었다. 이온 세기가 0.1 M, pH가 1인 조건에서 UO22+ 이온의 형광 스펙트럼과 형광 수명을 측정하였다. 488, 509, 533, 559 nm 파장의 특징적인 형광 봉우리를 관측하였고, 측정한 형광 수명은 1.92±0.17 ㎲ 이었다. U(VI) 가수분해 화합물의 형광 스펙트럼과 형광 수명의 변화를 이 값을 기준으로 비교하였다. 장파장 방향으로 이동한 형광 봉우리와 길어진 형광 수명을 가진 가수분해 화합물의 특징적인 양상을 보고한다.

This study aims to find a correlation between XRD and Raman result of the oxidized high modulus carbon fibers as a function of its oxidation degrees, and compare with the isotropic carbon fiber reported early. La of the high modulus carbon fiber prepared by oxidation in carbon dioxide gas have been observed using laser Raman spectroscopy. The basic structural parameters of the fibers were evaluated by XRD as well. The La of the original high modulus carbon fibers were measured to be 144 a from Raman analysis and 135 a from XRD analysis. La of the 92% oxidized fiber were 168 a by using Raman and 182 a by using XRD. There was some correlation between the La value obtained from Raman and XRD. However the La value changes of the high modulus carbon fiber through whole oxidation process showed opposite tendency compare with the isotropic carbon fiber because of the fiber structure basically.

This study aims to find a correlation between XRD and Raman result of the activated carbon fibers as a function of its activation degrees. La of the isotropic carbon fiber prepared by oxidation in carbon dioxide gas have been observed using laser Raman spectroscopy. The basic structural parameters of the fibers were evaluated by XRD as well, and compared with Raman result. The La of the carbon fibers were measured to be 25.5 a from Raman analysis and 23.6 a from XRD analysis. La of the ACFs were 23.6 a and 20.4 a, respectively, representing less ordered through activation process. It seems that the ID/IG of Raman spectra were related to crystallite size(La). Raman spectroscopy has demonstrated its unique ability to detect structural changes during the activation of the fibers. There was good correlation between the La value obtained from Raman and XRD.

매년 많은 양의 플라스틱 폐기물이 발생되면서 폐플라스틱을 순환 자원화하기 위해 여러 공공기관, 연구소에서는 폐플라스틱 자동선별 시스템을 구축하기 위한 노력을 하고 있다. 이미 국내 지자체 재활용 선별장 등에서는 근적외선 분광법(NIR)을 활용한 자동선별 시스템을 구축 및 활용하고 있지만 검정색 플라스틱 제품군의 물리적 성상인 근적외선 파장의 과도한 흡수로 인한 스펙트럼 분석이 어려워 자동분류가 힘든 실정이다. 이러한 문제들을 해결하기 위해 NIR 분광장비가 아닌 LIBS 분광기를 사용하여 데이터를 구축 및 분석하고 지능형 알고리즘을 이용하여 자동 선별이 가능한 흑색 플라스틱의 재질별 선별 분류기 구축하고자 한다. LIBS분광장비는 시료가 기체, 액체 및 고체 상태와 관계없이 주기율표 상의 거의 모든 원소에 대하여 정성 정량 분석이 가능한 장비로 시료의 전처리 과정이 필요 없으며, 분석 시간이 매우 짧기 때문에 실시간 분석이 가능하다는 장점을 가진다. 이러한 LIBS 분광장비를 이용하여 데이터를 추출하고 이를 분석하여 인공지능 알고리즘을 이용한 분류기를 설계하고자 한다. 검은색 플라스틱을 인공지능 알고리즘을 통하여 재질별 자동 선별하도록 설계하여 산업적･경제적인 효율의 향상을 기대할 수 있다.