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The surfactants mediated electropolymerized poly(aniline) (PANI)-reduced graphene oxide (rGO) composite counter electrode for dye-sensitized solar cell
No full textIn this work, poly(aniline) (PANI)-reduced graphene Oxide (rGO) low-cost nanocomposite used for counter electrode (CE) in dye-sensitized solar cells (DSSCs) for the replacement of the high-cost platinum (Pt) CE. The nanocomposite CEs were prepared with three different types of structural determine agents (cationic, non-ionic and anionic namely CTAB, Triton-X100, SDS) in aqueous medium and characterized by HR-SEM & TEM, XRD pattern analysis, FT-IR and Raman spectral studies. Electrochemical impedance spectroscopy (EIS) studies were used to determine the PANI-rGO-anionic surfactant in lower charge transfer resistance compared with, PANI, rGO and other type surfactants used in the preparation of CEs. DSSC assembled with a N719 dye sensitized TiO2 photo-anode, Poly(ethylene oxide) (PEO), iodide/triiodide redox couple containing electrolyte and PANI-rGO-anionic surfactant CE showed a sunlight-into-electrical energy conversion efficiency of 7.60%, which is higher than that of DSSC fabricated with pure Pt (6.40%), PANI (4.81%), rGO (4.30%) and PANI-rGO-CTAB (6.26%) and PANI-rGO-Triton X 100 (5.72%)
Molecular engineering of atomically dispersed Fe-N4 and Cu-N4 dual-sites in carbon nitride nanotubes for rechargeable zinc-air batteries
No full textMetal-nitrogen-carbon (M-N-C) electrocatalysts have emerged as promising oxygen electrocatalysts with the excessive catalytically active M-Nx sites. However, M-Nx sites are not easy to be preserved at elevated temperature of pyrolysis step. Here, we show that a supercritical fluid with a fast reaction kinetics allows us to synthesize a high-purity carbon nitride nanotube filled with the iron and copper phthalocyanine nanorods as a bifunctional oxygen electrocatalyst. The well-preserved Fe-N4 and Cu-N4 sites inside of carbon nitride nanotubes are clearly observed by the systematic analysis. In addition, we investigate the synergistic effect of atomically dispersed Fe-N4 and Cu-N4 dual-atom catalysts inside the carbon nitride nanotube. The prepared sample exhibits the half-wave potential of 0.94 V for oxygen reduction reaction and the potential of 1.65 V at 10 mA cm-2 for oxygen evolution reaction. Further, we fabricate rechargeable zinc-air batteries with the dual-atomic catalyst, which show better bi-functional activities than the mixture of Pt/C and IrO2 under high depth of discharge (DOD) of -32.6% (12 h per cycle) for the zinc-air batteries. Finally, the in-situ X-ray absorption spectroscopy analysis during ORR and OER reactions revealed the catalytic origin of the FCN4-CNNT, providing a new insight into the development of efficient oxygen electrocatalysts
Electrochemical lithium storage performance at high voltage and temperature of LiNi0.6Co0.2Mn0.2O2 cathode for Lithium-ion batteries by facile Mn3(PO4)2 dry coating
No full textHerein, we successfully synthesized the Mn3(PO4)2-coated LiNi0.6Co0.2Mn0.2O2 (MP-NCM) by the dry coating method, which is facile to apply to industrial applications. MP-NCM has improved electrochemical performance and storage properties compared to the bare sample under a high voltage cut-off condition of 4.55 V and a high -temperature environment of 60celcius. MP-NCM exhibited a discharge capacity of 198.5 mAh g-1 and capacity retention of 96.6 % after 50 cycles at 60 degrees C with an electrode loading level of 11 +/- 0.2 mg cm-2, and higher capacity retention of 70 % after storage. Remarkably, according to the direct current internal resistance (DCIR) measurement, MP-NCM showed an increased rate of resistance, which is 47 % and 40.2 % points lower than the bare sample in the charging and discharging modes respectively. The MP coating treatment introduced not only reduces cation-mixing by reconstructing the layered structure of the particle surface according to the doping of stable manganese ions but also serves to stabilize the surface according to the formation of Li3PO4
Phosphorylation of EIF2S1 (eukaryotic translation initiation factor 2 subunit alpha) is indispensable for nuclear translocation of TFEB and TFE3 during ER stress
No full textThere are diverse links between macroautophagy/autophagy pathways and unfolded protein response (UPR) pathways under endoplasmic reticulum (ER) stress conditions to restore ER homeostasis. Phosphorylation of EIF2S1/eIF2 alpha is an important mechanism that can regulate all three UPR pathways through transcriptional and translational reprogramming to maintain cellular homeostasis and overcome cellular stresses. In this study, to investigate the roles of EIF2S1 phosphorylation in regulation of autophagy during ER stress, we used EIF2S1 phosphorylation-deficient (A/A) cells in which residue 51 was mutated from serine to alanine. A/A cells exhibited defects in several steps of autophagic processes (such as autophagosome and autolysosome formation) that are regulated by the transcriptional activities of the autophagy master transcription factors TFEB and TFE3 under ER stress conditions. EIF2S1 phosphorylation was required for nuclear translocation of TFEB and TFE3 during ER stress. In addition, EIF2AK3/PERK, PPP3/calcineurin-mediated dephosphorylation of TFEB and TFE3, and YWHA/14-3-3 dissociation were required for their nuclear translocation, but were insufficient to induce their nuclear retention during ER stress. Overexpression of the activated ATF6/ATF6 alpha form, XBP1s, and ATF4 differentially rescued defects of TFEB and TFE3 nuclear translocation in A/A cells during ER stress. Consequently, overexpression of the activated ATF6 or TFEB form more efficiently rescued autophagic defects, although XBP1s and ATF4 also displayed an ability to restore autophagy in A/A cells during ER stress. Our results suggest that EIF2S1 phosphorylation is important for autophagy and UPR pathways, to restore ER homeostasis and reveal how EIF2S1 phosphorylation connects UPR pathways to autophagy
Solar Biomass Reforming and Hydrogen Production with Earth-Abundant Si-Based Photocatalysts
No full textEfficient electrochemical hydrogen production and biomass refinery are crucial for the decarbonization of various sectors. However, their energy-intensive nature and low efficiency have hindered their practical application. In this study, earth-abundant and non-toxic photocatalysts that can produce hydrogen and reform biomass efficiently, utilizing unlimited solar energy, are presented. The approach involves using low-bandgap Si flakes (SiF) for efficient light-harvesting, followed by modification with Ni-coordinated N-doped graphene quantum dots (Ni-NGQDs) to enable efficient and stable light-driven biomass reforming and hydrogen production. When using kraft lignin as a model biomass, SiF/Ni-NQGDs facilitate record-high hydrogen productivity at 14.2 mmol g(cat)(-1) h(-1) and vanillin yield of 147.1 mg g(lignin)(-1) under simulated sunlight without any buffering agent and sacrificial electron donors. SiF/Ni-NQGDs can be readily recycled without any noticeable performance degradation owing to the prevention of deactivation of Si via oxidation. This strategy provides valuable insights into the efficient utilization of solar energy and practical applications of electro-synthesis and biomass refinement
Tuning the Extracellular Vesicles Membrane through Fusion for Biomedical Applications
No full textMembrane fusion is one of the key phenomena in the living cell for maintaining the basic function of life. Extracellular vesicles (EVs) have the ability to transfer information between cells through plasma membrane fusion, making them a promising tool in diagnostics and therapeutics. This study explores the potential applications of natural membrane vesicles, EVs, and their fusion with liposomes, EVs, and cells and introduces methodologies for enhancing the fusion process. EVs have a high loading capacity, bio-compatibility, and stability, making them ideal for producing effective drugs and diagnostics. The unique properties of fused EVs and the crucial design and development procedures that are necessary to realize their potential as drug carriers and diagnostic tools are also examined. The promise of EVs in various stages of disease management highlights their potential role in future healthcare
Inland harmful algal blooms (HABs) modeling using internet of things (IoT) system and deep learning
No full textHarmful algal blooms (HABs) have been frequently occurred with releasing toxic substances, which typically lead to water quality degradation and health problems for humans and aquatic animals. Hence, accurate quantitative analysis and prediction of HABs should be implemented to detect, monitor, and manage severe algal blooms. However, the traditional monitoring required sufficient expense and labor while numerical models were restricted in terms of their ability to simulate the algae dynamic. To address the challenging issue, this study evaluates the applicability of deep learning to simulate chlorophyll-a (Chl-a) and phycocyanin (PC) with the internet of things(loT) system. Our research adopted LSTM models for simulating Chl-a and PC. Among LSTM models, the attention LSTM model achieved superior performance by showing 0.84 and 2.35 (g/L) of the correlation coefficient and root mean square error. Among preprocessing methods, the z-score method was selected as the optimal method to improve model performance. The attention mechanism highlighted the input data from July to October, indicating that this period was the most influential period to model output. Therefore, this study demonstrated that deep learning with loT system has the potential to detect and quantify cyanobacteria, which can improve the eutrophication management schemes for freshwater reservoirs
High-Performing Atomic Electrocatalyst for Chlorine Evolution Reaction
No full textElectrocatalysts facilitating chlorine evolution reaction (ClER) play a vital role in chlor???alkali industries. Owing to a huge amount of chlorine consumed worldwide, inexpensive high-performing catalysts for Cl2 production are highly demanded. Here, a superb ClER catalyst fabricated through uniform dispersion of Pt single atoms (SAs) in ???C2N2 moieties of N-doped graphene (denoted as Pt-1) is presented, which demonstrates near 100% exclusive ClER selectivity, long-term durability, extraordinary Cl2 production rate (3500 mmol h???1 gPt???1), and >140 000-fold increased mass activity over industrial electrodes in acidic medium. Excitingly, at the typical chlor???alkali industries??? operating temperature (80 ??C), Pt-1 supported on carbon paper electrode requires a near thermoneutral ultralow overpotential of 5 mV at 1 mA cm???2 current density to initiate the ClER, consistent with the predicted density functional theory (DFT) calculations. Altogether these results show the promising electrocatalyst of Pt-1 toward ClER
Improvement of the dust retrieval algorithm using GK-2A Geostationary satellite by updating Asian dust chemical composition
No full textDust storms often occur in the spring season and influence large areas of Korean peninsula. During a dust storm event, the concentration of dust particles in the atmosphere increases significantly. Satellite monitoring is a powerful tool for studying the properties of large-scale dust storms. however, amidst all uncertainties associated with aerosol properties, the inadequate information about the chemical composition of the dust also greatly affects the radiation field at the top of atmosphere (TOA). GEO-KOMPSAT-2A is a South Korean geostationary meteorological satellite for the meteorological mission and the space weather monitoring mission. It has been equipped with AMI (Advanced Meteorological Imager) and KSEM (Korean Space Environment Monitor) payloads. In this study, an algorithm will be investigated that uses four infrared channels: 8.6 ??m, 10.4 ??m, 11.2 ??m and 12.4 ??m, on the AMI. updating Asian dust components according to 25 samples collected during 14 Asian dust events occurring between 2005 and 2018 on the Korean Peninsula and compared them to 34 soil samples (<20?????m) obtained from the Mongolian Gobi Desert, which is a major source of Asian dust will be presented. We used the libRadtran radiative transfer model for simulation of the atmospheric condition, presence of the aerosols and radiance reaching TOA. according to the refractive index and size distribution dataset of new components and strong dependency of TIR wavelength bands to the optical properties of the dust we expect this method will increase the accuracy of the algorithm
Retrieval of hourly PM2.5 using top-of-atmosphere reflectance from geostationary ocean color imagers I and II
No full textTo produce real-time ground-level information on particulate matter with a diameter equal to or less than 2.5 mu m (PM2.5), many studies have explored the applicability of satellite data, particularly aerosol optical depth (AOD). However, many of the techniques used are computationally demanding; to overcome these challenges, machine learning(ML)-based research has been on the rise. Here, we used ML techniques to directly estimate ground-level PM2.5 concentrations over South Korea using top-of-atmosphere (TOA) reflectance from the Geostationary Ocean Color Imager I (GOCI-I) and its next generation GOCI-II with improved spatial, spectral, and temporal resolu-tions. Three ML techniques were used to estimate ground-level PM2.5 concentrations: random forest, light gradient boosting machine (LGBM), and artificial neural network. Three schemes were examined based on the input feature composition of the GOCI spectral bands: scheme 1 using all GOCI-I bands, scheme 2 using only GOCI-II bands that overlap with GOCI-I bands, and scheme 3 using all GOCI-II bands. The results showed that LGBM performed better than the other ML models. GOCI-II-based schemes 2 and 3 (determination of coefficient (R2) = 0.85 and 0.85 and root-mean-square-error (RMSE) = 7.69 and 7.82 mu g/m3, respectively) performed slightly better than GOCI-I-based scheme 1 (R2 = 0.83 and RMSE = 8.49 mu g/m3). In particular, TOA reflectance at a new channel (380 nm) of GOCI-II was identified as the most contributing variable, given its high sensitivity to aerosols. The long-term estimation of PM2.5 concentrations using the proposed models was examined for ground stations located in two major cities. GOCI-II-based models produced a more detailed spatial distribution of PM2.5 concentrations owing to their higher spatial resolution (i.e., 250 m). The use of TOA reflectance data, instead of AOD and other aerosol products commonly used in previous studies, reduced the missing rate of the estimated ground-level PM2.5 concentrations by up to 50%. Our results indicate that the proposed approach using TOA reflectance data from geostationary satellite sensors has great potential for estimating ground-level PM2.5 concentrations for operational purposes