The removal of three chemotherapy drugs including irinotecan, tamoxifen, and cyclophosphamide in water was examined by both physical and chemical treatment processes. Powdered activated carbon (PAC) absorption tests and oxidation by ozone or an advanced oxidation process (hydrogen peroxide and ultraviolet irradiation) were conducted on de-ionized water samples spiked with these drugs. Liquid chromatography coupled with tandem mass spectroscopy was successfully applied for the selective and sensitive determination of these three compounds. Removal of the three selected drugs in water samples by PAC was found to be dependant on PAC dosage. The Freundlich isotherm coefficients revealed the PAC absorption capacity sequence as tamoxifen > irinotecan > cyclophosphamide. Ozone was found to be effective in removing tamoxifen and irinotecan but not able to destroy cyclophosphamide in water. Advanced oxidation processes such as UV/H
Plasma molding over deep trenches and the resulting ion and energetic neutral distributionsA simplified two-dimensional Monte Carlo simulation is performed to estimate the charging potential fluctuations caused by strong binary Coulomb interactions between discrete charged particles in nanometer scale trenches. It is found that the discrete charge effect can be an important part of the nanoscale trench research, inducing scattering of ion trajectories in a nanoscale trench by a fluctuating electric field. The effect can enhance the ion deposition on the side walls and disperse the material contact energy of the incident ions, among others.
Among various edge localized mode (ELM) crash control methods, only non-axisymmetric magnetic perturbations (NAMPs) yield complete suppression of ELM crashes beyond their mitigation, and thus attract more attention than others. No other devices except KSTAR, DIII-D, and recently EAST have successfully achieved complete suppression with NAMPs. The underlying physics mechanisms of these successful ELM crash suppressions in a non-axisymmetric field environment, however, still remain uncertain. In this work, we investigate the ELM crash suppression characteristics of the KSTAR ELMy H-mode discharges in a controlled multi-spectral field environment, created by both n = 2 middle reference and n = 1 top/bottom proxy in-vessel control coils. Interestingly, the attempts have produced a set of contradictory findings, one expected (ELM crash suppression enhancement with the addition of n = 1 to the n = 2 field at relatively low heating discharges) and another unexpected (ELM crash suppression degradation at relatively high heating discharges) from the earlier findings in DIII-D. This contradiction indicates the dependence of the ELM crash suppression characteristics on the heating level and the associated kink-like plasma responses. Preliminary linear resistive MHD plasma response simulation shows the unexpected suppression performance degradation to be likely caused by the dominance of kink-like plasma responses over the island gap-filling effects.
Suppression or mitigation of edge-localized mode (ELM) crashes is necessary for ITER. The strategy to suppress all the ELM crashes by the resonant magnetic perturbation (RMP) should be applied as soon as the first low-to-high confinement (L-H) transition occurs. A control algorithm based on real-time machine learning (ML) enables such an approach: it classifies the H-mode transition and the ELMy phase in real-time and automatically applies the preemptive RMP. This paper reports the algorithm design, which is now implemented in the KSTAR plasma-control system, and the corresponding experimental demonstration of typical high-δ KSTAR H-mode plasmas. As a result, all initial ELM crashes are suppressed with an acceptable safety factor at the edge (q95) and with RMP field adjustment. Moreover, the ML-driven ELM-crash-suppression discharges remain stable without further degradation due to the regularization of the plasma pedestal.
A tokamak relies on the axisymmetric magnetic fields to confine fusion plasmas and aims to deliver sustainable and clean energy. However, misalignments arise inevitably in the tokamak construction, leading to small asymmetries in the magnetic field known as error fields (EFs). The EFs have been a major concern in the tokamak approaches because a small level EFs, even less than 0.1 %, can drive a plasma disruption. Contrary to conventional wisdom, we report that the EFs in a tokamak can be favorably used for controlling plasma instabilities, such as edge-localized modes (ELMs), while maintaining a hot fusion plasma at a temperature of 100 million kelvin. A novel optimization tailors the EFs to maintain an edge 3D response for ELM control with a minimized core 3D response to avoid plasma disruption and unnecessary degradation. We design and demonstrate such an edge-localized 3D response at the Korean Superconducting Tokamak Advanced Research facility, benefiting from its unique flexibility to change many degrees of freedom in the 3D coil space for the various fusion plasma regimes. This favorable control of the tokamak EF represents a notable advance for designing intrinsically 3D tokamaks to optimize stability and confinement for the next-step fusion reactors.
The integrated resonant magnetic perturbation (RMP)-based ELM-crash-control process aims to enhance the plasma performance during the RMP-driven ELM crash suppression, where the RMP induces an unwanted confinement degradation. In this study, the normalized beta (βN) is introduced as a metric for plasma performance. The integrated process incorporates the latest achievements in the RMP technique to enhance βN efficiently. The integrated process triggers the n = 1 edge-localized RMP (ERMP) at the L-H transition timing using the real-time machine learning (ML) classifier. The pre-emptive RMP onset can reduce the required external heating power for achieving the same βN by over 10 % compared to the conventional RMP onset. During the RMP phase, the adaptive feedback RMP ELM controller, demonstrating its performance in previous experiments, plays a crucial role in maximizing βN during the suppression phase and sustaining the βN-enhanced suppression state by optimizing the RMP strength. The integrated process achieves βN up to ~2.65 during the suppression phase, which is ~10 % higher than the previous KSTAR record but ~6 % lower than the target of the K-DEMO first phase (βN = 2.8), and maintains the suppression phase above the lower limit of target βN (= 2.4) for ~4 s (~60 τE). In addition to βN enhancement, the integrated process demonstrates quicker restoration of the suppression phase and recovery of βN compared to the adaptive control with the n = 1 conventional RMP (CRMP). The post-analysis of the experiment shows the localized effect of the ERMP spectrum in radial and the close relationship between the evolution of βN and the electron temperature.
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