Seoul National University of Science&Technology
Recent publications
Grazing management is an important factor affecting the delivery of ecosystem services at the watershed scale. Moreover, characterizing the impacts of climate variation on water resources is essential in managing range-lands. In this study, the effects of alternative grazing management scenarios on provisioning, regulating, and supporting services were assessed in two watersheds with contrasting climates; the Lower Prairie Dog Town Fork Red River (LPDTFR) Watershed in North Texas and the Apple Watershed in South Dakota. The impacts of heavy stocking continuous grazing, light stocking continuous grazing, Adaptive Multi-Paddock (AMP) grazing, and an ungrazed exclosure were compared using the Soil and Water Assessment Tool (SWAT) model. Our results indicate that the quantity of snow and timing of snow melt substantially influenced grazing management effects on ecosystem services in the Apple Watershed. In contrast, precipitation was the main factor influencing these effects in the LPDTFR Watershed because it highly affected the variation in water cycling, streamflow, sediment, and nutrient controls. Simulated results indicated that AMP grazing was the optimal grazing management approach for enhancing water conservation and ecosystem services in both watersheds regardless of climatic conditions. The Apple Watershed, which is a snow-dominated watershed, exhibited greater ecosystem service improvements under AMP grazing (50.6%, 58.7%, 74.4%, 61.5% and 72.6% reduction in surface runoff, streamflow, and sediment, total nitrogen (TN) and total phosphorus (TP) losses, respectively as compared to HC grazing) than the LPDTFR Watershed (46.0%, 22.8%, 34.1%, 18.9% and 38.4% reduction in surface runoff, streamflow, and sediment, TN and TP losses, respectively). Our results suggest that improved grazing management practices enhance ecosystem services and water catchment functions in rangeland-dominated areas, especially in colder climates.
The development of organic‐based optoelectronic technologies for the indoor Internet of Things market, which relies on ambient energy sources, has increased, with organic photovoltaics (OPVs) and photodetectors (OPDs) considered promising candidates for sustainable indoor electronic devices. However, the manufacturing processes of standalone OPVs and OPDs can be complex and costly, resulting in high production costs and limited scalability, thus limiting their use in a wide range of indoor applications. This study uses a multi‐component photoactive structure to develop a self‐powering dual‐functional sensory device with effective energy harvesting and sensing capabilities. The optimized device demonstrates improved free‐charge generation yield by quantifying charge carrier dynamics, with a high output power density of over 81 and 76 μW/cm ² for rigid and flexible OPVs under indoor conditions (LED 1000 lx (5200 K)). Furthermore, we demonstrate a single‐pixel image sensor as a feasible prototype for practical indoor operating in commercial settings by leveraging the excellent OPD performance with a linear dynamic range of over 130 dB in photovoltaic mode (no external bias). This apparatus with high‐performance OPV‐OPD characteristics provides a roadmap for further exploration of the potential which can lead to synergistic effects for practical multifunctional applications in the real world by their mutual relevance. This article is protected by copyright. All rights reserved
There is an urgent demand to reduce the plastic mass as it has become a serious environmental concern. Plastic bottles made of PET (polyethylene terephthalate) have been widely used for water, milk, and other beverages packaging. PET blow molding process has sought researchers’ attention for the fabrication of light weight PET bottles with reduced cost. In this study, lightweight PET bottles were fabricated by reducing the weight of PET, usually used for manufacturing PET bottle in industry. Here, initially computer simulation was performed for designing the preform with reduced weight and the stretch blow molding process was used to fabricate carbonated soft drink PET bottles. The computer simulation was performed under the same conditions as the experiment using non-isothermal models to analyze the blowing phenomena, velocity, temperature, thickness distributions, and stretch ratio through stretching path of PET bottles. Experimental and simulation results were compared with existing PET bottle to confirm that the stretch blow molding simulation was significant for designing and fabricating of weight reduced PET bottle through the stretch blow molding process.
We propose a minimal extension of the Standard Model by incorporating sterile neutrinos and a QCD axion to account for the mass and mixing hierarchies of quarks and leptons and to solve the strong CP problem and by introducing GSM×ΓN×U(1)X symmetry. We demonstrate that the Kähler transformation corrects the weight of modular forms in the superpotential and show that the model is consistent with the modular and U(1)X anomaly-free conditions. This enables a simple construction of a modular-independent superpotential for scalar potential. Using minimal supermultiplets, we demonstrate a level-3 modular form-induced superpotential. Sterile neutrinos explain small active neutrino masses via the seesaw mechanism and provide a well-motivated U(1)X-breaking scale, whereas gauge singlet scalar fields play crucial roles in generating the QCD axion, heavy neutrino mass, and fermion mass hierarchy. The model predicts a range for the U(1)X-breaking scale from 1013 to 1015 GeV for 1 TeV<m3/2<106 TeV. In the supersymmetric limit, all Yukawa coefficients in the superpotential are given by complex numbers with an absolute value of unity, implying a democratic distribution. Performing numerical analysis, we study how model parameters are constrained by current experimental results. In particular, the model predicts that the value of the quark Dirac CP phase falls between 38° to 87°, which is consistent with experimental data, and the favored value of the neutrino Dirac CP phase is around 250°. Furthermore, the model can be tested by ongoing and future experiments on axion searches, neutrino oscillations, and 0νββ decay.
Bioglasses are used in applications related to bone rehabilitation and repair. The mechanical and bioactive properties of polysaccharides like alginate and agarose can be modulated or improved using bioglass nanoparticles. Further essential metal ions used as crosslinker have the potential to supplement cultured cells for better growth and proliferation. In this study, the alginate bioink is modulated for fabrication of tissue engineering scaffolds by extrusion-based 3D bioprinting using agarose, bioglass nanoparticles and combination of essential trace elements such as iron, zinc, and copper. Homogeneous bioink was obtained by in situ mixing and bioprinting of its components with twin screw extruder (TSE) based 3D bioprinting, and then distribution of metal ions was induced through post-printing diffusion of metal ions in the printed scaffolds. The mechanical and 3d bioprinting properties, microscopic structure, biocompatibility of the crosslinked alginate/agarose hydrogels were analyzed for different concentrations of bioglass. The adipose derived mesenchymal stem cells (ADMSC) and osteoblast cells (MC3T3) were used to evaluate this hydrogel’s biological performances. The porosity of hydrogels significantly improves with the incorporation of the bioglass. More bioglass concentration results in improved mechanical (compressive, dynamic, and cyclic) and 3D bioprinting properties. Cell growth and extracellular matrix are also enhanced with bioglass concentration. For bioprinting of the bioinks, the advanced TSE head was attached to 3D bioprinter and in situ fabrication of cell encapsulated scaffold was obtained with optimized composition considering minimal effects on cell damage. Fabricated bioinks demonstrate a biocompatible and noncytotoxic scaffold for culturing MC3T3 and ADMSC, while bioglass controls the cellular behaviors such as cell growth and extracellular matrix formation.
Direct methane protonic ceramic fuel cells are promising electrochemical devices that address the technical and economic challenges of conventional ceramic fuel cells. However, Ni, a catalyst of protonic ceramic fuel cells exhibits sluggish reaction kinetics for CH4 conversion and a low tolerance against carbon-coking, limiting its wider applications. Herein, we introduce a self-assembled Ni-Rh bimetallic catalyst that exhibits a significantly high CH4 conversion and carbon-coking tolerance. It enables direct methane protonic ceramic fuel cells to operate with a high maximum power density of ~0.50 W·cm⁻² at 500 °C, surpassing all other previously reported values from direct methane protonic ceramic fuel cells and even solid oxide fuel cells. Moreover, it allows stable operation with a degradation rate of 0.02%·h⁻¹ at 500 °C over 500 h, which is ~20-fold lower than that of conventional protonic ceramic fuel cells (0.4%·h⁻¹). High-resolution in-situ surface characterization techniques reveal that high-water interaction on the Ni-Rh surface facilitates the carbon cleaning process, enabling sustainable long-term operation.
Prediction of solar energy data is very crucial for the effective utilization of freely available renewable energy abundantly in nature. Solar energy data are widely available which must be carefully prepared and arranged for modelling. In this work, typical meteorological year (TMY) data made available by the Korea institute of energy research (KIER) and the National renewable energy laboratory (NREL) are used for modelling in different phases. TMY data at single-point location and multiple locations from KIER are initially used for training of machine learning (ML) algorithms. Later, the TMY data from NREL and KIER are combined and then modelled using radius nearest neighbour (RNN), decision tree regressor (DTR), random forest regressor (RFR), and X-gradient boosting (XGB) algorithms. The solar energy parameters modelled in this work are dew point temperature (DPT), dry bulb temperature (DBT), relative humidity (RH), surface pressure (SP), windspeed (WS), and solar insolation of horizontal plane (IHP). Quantitative analysis of the algorithms is also performed in each stage of the work. The modelling indicates that the DBT, DPT, RH, and SP are able to be predicted with a minimum accuracy of over 90% in each stage. The WS and IHP data when modelled from a single-source TMY data provide superior accuracy than when they are combined. RFR and XGB have outperformed overall as they provide good accuracy for WS and IHP data as well. RNN and DTR achieved 100% accuracy in training, while RFR and XGB showed slightly lower training accuracy due to their avoidance of overfitting. There are errors in testing for RNN/DTR. Using RNN/DTR, the training errors are 0% in all cases, while in some cases like DTP the error by RFR/XGB up to 3%, whereas RNN/DTR testing errors go up to 5% and in case of RFR/XGB they are up to 7.5%. For RH modelling RFR/XGB, training errors are max 6%. RNN/DTR testing errors go up to 11%, while for RFR/XGB up to 7.5% which indicates their robustness. It is observed that many solar parameters, when combined with different source data, can be predicted easily with good accuracy, while WS and IHP become a little bit challenging to model.
Agarperoxinols A and B (1–2), two naturally occurring humulene-type sesquiterpenoids with an unprecedented tricyclic 6/6/7 ring, were discovered from the agarwood of Aquilaria malaccensis. Their structures were unambiguously determined by various spectroscopic data, experimental ECD calculations, and single-crystal X-ray diffraction analysis. Agarperoxinol B showed significant and dose-dependent neuroinflammatory inhibitory effects on various proinflammatory mediators, including NO, TNF-α, IL-6, and IL-1β, and suppressed iNOS and COX-2 enzymes in LPS-activated microglial cells. A mechanistic study demonstrated that agarperoxinol B remarkably inhibited the phosphorylation of the Akt and JNK signaling pathways. Agarperoxinol B also significantly reduced the expression of the microglial markers Iba-1, COX-2, and TNF-α in the mouse cerebral cortex. Our findings introduce a bioactive compound from natural products that decreases proinflammatory factor production and has application for the treatment of neurodegenerative diseases.
A gold nanourchin (AuNU) probe with a novel sensing mechanism for monitoring H2S was developed as a feasible colorimetric sensor. In this study, AuNUs that are selectively responsive to H2S were fabricated in the presence of trisodium citrate and 1,4-hydroquinone using a seed-mediated approach. Upon exposure of the AuNU solution to H2S, the hydrosulfide ions (HS⁻) in the solution are converted into oligomeric sulfides by 1,4-hydroquinone used as a reducing agent during the synthesis of AuNUs. The oligomeric sulfides formed in the AuNU solution upon the addition of H2S were found to coat the surface of the AuNUs, introducing a blue shift in absorption accompanied by a color change in the solution from sky blue to light green. This colorimetric alteration by the capping of oligomeric sulfides on the surface of AuNUs is unique compared to well-known color change mechanisms, such as aggregation, etching, or growth of nanoparticles. The novel H2S sensing mechanism of the AuNUs was characterized using UV-Vis spectroscopy, high-resolution transmission microscopy, X-ray photoelectron spectroscopy, surface-enhanced Raman spectroscopy, secondary ion mass spectroscopy, liquid chromatography-tandem mass spectrometry, and atom probe tomography. H2S was reliably monitored with two calibration curves comprising two sections with different slopes according to the low (0.3–15 μM) and high (15.0–300 μM) concentration range using the optimized AuNU probe, and a detection limit of 0.29 μM was obtained in tap water.
We present a theoretical and experimental analysis of the use of a reversed uneven power splitting (RUPS) technique for asymmetric Doherty power amplifiers (PAs). The RUPS technique utilizes an uneven power splitter that drives more input power into the carrier amplifier, enabling shallow class-C operation of the peaking amplifier. Although the RUPS technique has played a significant role in achieving high-performance Doherty PAs, there has been a lack of comprehensive research examining the fundamental factors that contribute to its effectiveness. We conducted numerical and experimental investigations to demonstrate that the RUPS Doherty PA exhibits significant improvements in efficiency, gain, and linearity compared to conventional Doherty PAs with even power splitting (EPS). For the experiments, the EPS and RUPS networks were developed using lumped-element directional couplers. The fabricated RUPS Doherty PA, based on a 0.25-μm GaN HEMT monolithic microwave integrated circuit (MMIC) process, achieves superior overall performance at 2.14 GHz compared to the conventional EPS Doherty PA, without requiring any additional circuitry. The results verify that the RUPS technique can enhance the performance of asymmetric Doherty PAs.
A bstract The A → Z (*) h decay signature has been highlighted as possibly being the first testable probe of the Standard Model (SM) Higgs boson discovered in 2012 ( h ) interacting with Higgs companion states, such as those existing in a 2-Higgs Doublet Model (2HDM), chiefly, a CP-odd one ( A ). The production mechanism of the latter at the Large Hadron Collider (LHC) takes place via $$ b\overline{b} $$ b b ¯ -annihilation and/or gg -fusion, depending on the 2HDM parameters, in turn dictated by the Yukawa structure of this Beyond the SM (BSM) scenario. Among the possible incarnations of the 2HDM, we test here the so-called Type-II, for a twofold reason. On the one hand, it intriguingly offers two very distinct parameter regions compliant with the SM-like Higgs measurements, i.e., where the so-called ‘SM limit’ of the 2HDM can be achieved. On the other hand, in both configurations, the AZh coupling is generally small, hence the signal is strongly polluted by backgrounds, so that the exploitation of Machine Learning (ML) techniques becomes extremely useful. In this paper, we show that the application of advanced ML implementations can be decisive in establishing such a signal. This is true for all distinctive kinematical configurations involving the A → Z (*) h decay, i.e., below threshold ( m A < m Z + m h ), at its maximum ( m Z + m h < m A < 2 m t ) and near the onset of $$ t\overline{t} $$ t t ¯ pair production ( m A ≈ 2 m t ), for which we propose Benchmark Points (BPs) for future phenomenological analyses.
The quantity of data collected has expanded tremendously as the world becomes more dependent on technology and digitization, offering organizations and sectors chances for innovation and development. Concerns regarding the effects of such fast expansion on the environment have been expressed as a result of this increase, however. Prioritizing environmental sustainability is now more crucial than ever in this situation. This research develops a theoretical model to assess the relative advantages Big Data have on R&D via “substitution effects” and “complementary effects,” and further leads to directed technological change and its influence on environmental quality. We examine various effects on environmental quality while taking into account and without taking into account the extensive data application to understand the function of Big Data better. Big Data and environmental sustainability may be used to provide major commercial advantages, such as cost savings, higher productivity, and enhanced reputation. Big Data in environmental monitoring may be used to better detect and solve sustainability challenges including resource depletion, waste management, and air and water pollution. Big Data analytics may facilitate better forecasting and decision-making, enabling businesses and industries to develop more sustainable practices and strategies. Big Data management needs a dedication to sustainability principles and practices, as well as a thorough awareness of the environmental impact of the company or sector. In the era of Big Data, cooperation between enterprises, industries, governments, and other stakeholders is crucial for advancing environmental sustainability. The paper also addresses some of the difficulties in using Big Data for environmental sustainability, including issues with data privacy, quality, and security. In conclusion, the fusion of Big Data with environmental sustainability presents both enormous potential and formidable difficulties for enterprises and sectors. Addressing these issues and fostering stakeholder engagement are crucial if Big Data is to achieve its full promise for environmental sustainability.
A deconstruction-reconstruction strategy for the synthesis of multisubstituted polycyclic aromatic hydrocarbons (PAH) is delineated herein. The deconstruction step enables the synthesis of o-cyanomethylaroyl fluorides that are bifunctional substrates holding both...
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243 members
Jongyoul Park
  • Department of Applied Artificial Intelligence
Gwang-Pil Jung
  • Mechanical&Automotive Engineering
Vinayagam Mariappan
Parthiban Anburajan
  • Department of Environmental Engineering
Hyung-Jun Song
  • Department of Safety Engineering
Seoul, South Korea