Galaxy evolution studies require the measurement of the physical properties of galaxies at different redshifts. In this work, we build supervised machine learning models to predict the redshift and physical properties (gas-phase metallicity, stellar mass, and star formation rate) of star-forming galaxies from the broad-band and medium-band photometry covering optical to near-infrared wavelengths, and present an evaluation of the model performance. Using 55 magnitudes and colors as input features, the optimized model can predict the galaxy redshift with an accuracy of σ(Δz/1+z) = 0.008 for a redshift range of z < 0.4. The gas-phase metallicity [12 + log(O/H)], stellar mass [log(Mstar)], and star formation rate [log(SFR)] can be predicted with the accuracies of σNMAD = 0.081, 0.068, and 0.19 dex, respectively. When magnitude errors are included, the scatter in the predicted values increases, and the range of predicted values decreases, leading to biased predictions. Near-infrared magnitudes and colors (H, K, and H −K), along with optical colors in the blue wavelengths (m425–m450), are found to play important roles in the parameter prediction. Additionally, the number of input features is critical for ensuring good performance of the machine learning model. These results align with the underlying scaling relations between physical parameters for star-forming galaxies, demonstrating the potential of using medium-band surveys to study galaxy scaling relations with large sample of galaxies.

We investigate the evolution of initial fractal clusters at 3 kpc from the Galactic Center (GC) of the MilkyWay and show how red supergiant clusters (RSGCs)-like objects, which are considered to be the result of active star formation in the Scutum complex, can form by 16 Myr. We find that initial tidal filling and tidal over-filling fractals are shredded by the tidal force, but some substructures can survive as individual subclusters, especially when the initial virial ratio is ≤0.5. These surviving subclusters are weakly mass segregated and show a top-heavy mass function. This implies the possibility that a single substructured star cluster can evolve into multiple ‘star clusters’.

A force-free field (FFF) is determined solely by the normal components of magnetic field and current density on the entire boundary of the domain. Methods employing three components of magnetic field suffer from overspecification of boundary conditions and/or a nonzero divergence-B problem. A vector potential formulation eliminates the latter issue, but introduces difficulties in imposing the normal component of current density at the boundary. This paper proposes four different boundary treatment methods within the vector potential formulation. We conduct a comparative analysis of the vector potential FFF solvers that we have developed incorporating these methods against other FFF codes in different magnetic field representations. Although the vector potential solvers with the new boundary treatments do not outperform our poloidal-toroidal formulation code, they demonstrate comparable or superior performance compared to the optimization code in SolarSoftWare. The methods developed here are expected to be readily applied not only to force-free field computations but also to time-dependent data-driven simulations.

Biomass-derived porous carbon is an excellent scientific and technologically interesting material for supercapacitor applications. In this study, we developed biomass-derived nitrogen-doped porous carbon nanosheets (BDPCNS) from cedar cone biomass using a simple KOH activation and pyrolysis method. The BDPCNS was effectively modified at different temperatures of 600 °C, 700 °C, and 800 ℃ under similar conditions. The as-prepared BDPCNS-700 electrode exhibited a high BET surface area of 2883 m2 g− 1 and a total pore volume of 1.26 cm3 g− 1. Additionally, BDPCNS-700 had the highest electrical conductivity (11.03 cm− 1) and highest N-doped content among the different electrode materials. The BDPCNS-700 electrode attained a specific capacitance of 290 F g− 1 at a current density of 1 A g− 1 in a 3 M KOH electrolyte and an excellent longterm electrochemical cycling stability of 93.4% over 1000 cycles. Moreover, the BDPCNS-700 electrode had an excellent energy density (40.27 Wh kg− 1) vs power density (208.19 W kg− 1). These findings indicate that BDPCNS with large surface areas are promising electrode materials for supercapacitors and energy storage systems.

본 연구에서는 정삼투 중공사막 모듈에서 중공사막의 가닥을 비틀어 배치하였을 때의 효과를 알아보기 위해 CFD 전산 유체 역학 프로그램을 통해 5개의 다른 각도로 비틀린 중공사막 모듈을 설계하고 시뮬레이션하여 비틀리지 않은 모듈과 비교하였다. 실험 결과, 중공사막이 비틀렸을 때, 모듈 내부의 유도 용액의 농도가 비틀리지 않을 때에 비해 고르게 분포하였 다. 모듈 입구의 압력은 중공사막의 비틀림과 관계없이 일정한 값을 보였지만 출구의 압력은 중공사막이 비틀린 정도가 커질 수록 증가하는 추세를 보였다. 출구의 압력이 높아짐에 따라 막 내부의 유체 속도가 감소하고 모듈 체류 시간이 증가하여 막 사이의 물질 교환이 원활하게 이루어질 것으로 예측된다. 이는 결과적으로 막이 비틀려 있을 때의 모듈 플럭스가 투과 수량 이 차지하는 비율이 그렇지 않을 때에 비해 2배 증가하였다.

본 연구에서는 한외여과 polysulfone (PSf) 중공사막에 첨가제를 섞는 방법을 통해 친수성 증가에 따른 분리막 특성 및 성능을 향상하고자 하였다. 15 nm 크기의 fumed silica (FS)를 0.1, 0.3, 0.5 wt%로 방사 용액에 분산시켜 혼합 매트릭스 분리막을 제조하였다. 단면 및 표면상태를 확인하기 위해 SEM 분석을 진행하였으며, FS가 함유될수록 중공사막의 평균 기공 반경이 4 nm 이상 증가하는 것을 확인하였다. 또한, 분리막의 친수성 분석을 위해 접촉각 측정을 진행하였으며, FS 함유로 분리막의 친수성이 높아진 것을 확인하였다. 수투과도의 경우 FS가 섞인 분리막은 91~96 LMH 수준을 보였으며 PSf 분리막보다 5~11%의 증가율을 보였다. 내오염성 평가에서도 친수도가 상승한 FS 혼합 중공사막 표면에 소수성을 띄는 BSA가 흡착되지 못하여 상대 유량 감소율이 PSf 단일막 보다 낮아졌음을 확인하였다.