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Amine???Rich Hydrogels Enhance Solar Water Oxidation via Boosting Proton???Coupled Electron Transfer
Get PDFPhotoelectrochemical (PEC) water oxidation is a highly challenging task that acts as a bottleneck for efficient solar hydrogen production. It is because each cycle of water oxidation is composed of four proton-coupled electron transfer (PCET) processes and conventional photoanodes and cocatalysts have limited roles in enhancing the charge separation and storage rather than in enhancing catalytic activity. In this study, a simple and generally applicable strategy to improve the PEC performance of water oxidation photoanodes through their modification with polyethyleneimine (PEI) hydrogel is reported. The rich amine groups of PEI not only allow the facile and stable modification of photoanodes by crosslinking but also contribute to improving the kinetics of PEC water oxidation by boosting the PCET. Consequently, the PEC performance of various photoanodes, such as BiVO4, Fe2O3, and TiO2, is significantly enhanced in terms of photocurrent densities and onset potentials even in the presence of notable cocatalyst, cobalt phosphate. The present study provides new insights into and strategies for the design of efficient photoelectrodes and PEC devices
Edge Reconstruction-Dependent Growth Kinetics of MoS2
No full textUnderstanding the growth mechanisms of multielement two-dimensional (2D) crystals is challenging because of the unbalanced stoichiometry and possible reconstruction of their edges. Here, we present a systematic theoretical study on the chemical vapor deposition (CVD) growth mechanism of MoS2. We found that the growth kinetics of MoS2 highly depends on its edge reconstruction determined by concentrations of Mo and S in the growth environment. Based on the calculated energies of nucleation and propagation of various MoS2 edges, we predicted the transition of a MoS2 island growth from a regime of a triangle enclosed by Mo-terminated zigzag edges that are passivated by 50% S (Mo-II edges), to a regime of continuous evolution within a triangle, hexagon, and inverted triangle with 75%-S-terminated edges (S-III edges) and Mo-II edges, and finally to a regime of triangles with Mo-terminated zigzag edges that are passivated by 100% S (Mo-III edges) by tuning the growth condition from Mo-rich to S-rich, which provides a reasonable explanation to many experimental observations. This study provides a general guideline on theoretical studies of 2D crystals' growth mechanisms, deepens our understanding on the growth mechanism of multielement 2D crystals, and is beneficial for the controllable synthesis of various 2D crystals
Tocopherol-assisted magnetic Ag-Fe3O4-TiO2 nanocomposite for photocatalytic bacterial-inactivation with elucidation of mechanism and its hazardous level assessment with zebrafish model
No full textIn recent years, many endeavours have been prompted with photocatalytic nanomaterials by the need to eradicate pathogenic microorganisms from water bodies. Herein, a tocopherol-assisted Ag-Fe3O4-TiO2 nano -composite (TAFTN) was synthesized for photocatalytic bacterial inactivation. The prepared TAFTN became active under sunlight due to its narrowed bandgap, inactivating the bacterial contaminants via photo-induced ROS stress. The ROS radicals destroy bacteria by creating oxidative stress, which damages the cell membrane and cellular components such as nucleic acids and proteins. For the first time, the nano-LC-MS/MS-based quantitative proteomics reveals that the disrupted proteins are involved in a variety of cellular functions; the most of these are involved in the metabolic pathway, eventually leading to bacterial death during TAFTN-photocatalysis under sunlight. Furthermore, the toxicity analysis confirmed that the inactivated bacteria seemed to have no detrimental impact on zebrafish model, showing that the disinfected water via TAFTN- photocatalysis is enormously safe. Furthermore, the TAFTN-photocatalysis successfully killed the bacterial cells in natural seawater, indicating the consistent photocatalytic efficacy when recycled repeatedly. The results of this work demonstrate that the produced nanocomposite might be a powerful recyclable and sunlight-active photocatalyst for environmental water treatment
Revealing Causes for False-Positive and False-Negative Calling of Gene Essentiality in Escherichia coli Using Transposon Insertion Sequencing
No full textTransposon mutagenesis is an efficient way to explore gene essentiality of a bacterial genome. However, there was a discrepancy between the essential gene set determined by transposon mutagenesis and that determined using single-gene knockout strains. The massive sequencing of transposon insertion mutant libraries (Tn-Seq) represents a commonly used method to determine essential genes in bacteria. Using a hypersaturated transposon mutant library consisting of 400,096 unique Tn insertions, 523 genes were classified as essential in Escherichia coli K-12 MG1655. This provided a useful genome-wide gene essentiality landscape for rapidly identifying 233 of 301 essential genes previously validated by a knockout study. However, there was a discrepancy in essential gene sets determined by conventional gene deletion methods and Tn-Seq, although different Tn-Seq studies reported different extents of discrepancy. We have elucidated two causes of this discrepancy. First, 68 essential genes not detected by Tn-Seq contain nonessential subgenic domains that are tolerant to transposon insertion, which leads to the false assignment of an essential gene as a nonessential or dispensable gene. These genes exhibited a high level of transposon insertion in their subgenic nonessential domains. In contrast, 290 genes were additionally categorized as essential by Tn-Seq, although their knockout mutants were available. The comparative analysis of Tn-Seq and high-resolution footprinting of nucleoid-associated proteins (NAPs) revealed that a protein-DNA interaction hinders transposon insertion. We identified 213 false-positive genes caused by NAP-genome interactions. These two limitations have to be considered when addressing essential bacterial genes using Tn-Seq. Furthermore, a comparative analysis of high-resolution Tn-Seq with other data sets is required for a more accurate determination of essential genes in bacteria.IMPORTANCE Transposon mutagenesis is an efficient way to explore gene essentiality of a bacterial genome. However, there was a discrepancy between the essential gene set determined by transposon mutagenesis and that determined using single-gene knockout strains. In this study, we generated a hypersaturated Escherichia coli transposon mutant library comprising approximately 400,000 different mutants. Determination of transposon insertion sites using next-generation sequencing provided a high-resolution essentiality landscape of the E. coli genome. We identified false negatives of essential gene discovery due to the permissive insertion of transposons in the C-terminal region. Comparisons between the transposon insertion landscape with binding profiles of DNA-binding proteins revealed interference of nucleoid-associated proteins to transposon insertion, generating false positives of essential gene discovery. Consideration of these findings is required to avoid the misinterpretation of transposon mutagenesis results
Selectively Enhanced Electrocatalytic Oxygen Evolution within Nanoscopic Channels Fitting a Specific Reaction Intermediate for Seawater Splitting
No full textAbundant availability of seawater grants economic and resource-rich benefits to water electrolysis technology requiring high-purity water if undesired reactions such as chlorine evolution reaction (CER) competitive to oxygen evolution reaction (OER) are suppressed. Inspired by a conceptual computational work suggesting that OER is kinetically improved via a double activation within 7 ??-gap nanochannels, RuO2 catalysts are realized to have nanoscopic channels at 7, 11, and 14 ?? gap in average (dgap), and preferential activity improvement of OER over CER in seawater by using nanochanneled RuO2 is demonstrated. When the channels are developed to have 7 ?? gap, the OER current is maximized with the overpotential required for triggering OER minimized. The gap value guaranteeing the highest OER activity is identical to the value expected from the computational work. The improved OER activity significantly increases the selectivity of OER over CER in seawater since the double activation by the 7 ??-nanoconfined environments to allow an OER intermediate (*OOH) to be doubly anchored to Ru and O active sites does not work on the CER intermediate (*Cl). Successful operation of direct seawater electrolysis with improved hydrogen production is demonstrated by employing the 7 ??-nanochanneled RuO2 as the OER electrocatalyst
Production of Highly Charged Ions using Electron Beam Ion Source Charge Breeder for RAON Accelerator Facility
Get PDFDepartment of PhysicsSeveral heavy ion accelerator facilities worldwide are constructed and utilized for nuclear physics experiments, exotic nuclear research, astronomical research, etc. The Rare Isotope Science Project (RISP) of the Institute for Basic Science (IBS) in Korea is designing and constructing the Rare isotope Accelerator complex for ON-line experiments (RAON), the Korean heavy ion accelerator, for the basic science research using rare-isotope (RI) beams. ISAC and ARIEL of TRIUMF, CARIBU of ANL, LBNL, and ISOLDE of CERN, and so on, use an ISOL system that accelerates light ions and collides them against heavy element targets to generate RI beams. On the other hand, FRIB, GSI???s FAIR, RIBF of RIKEN, and GANIL???s Spiral, etc., use an IF method that accelerates heavy ions and collides with light element targets. However, the RAON is designed to use a combination of the ISOL and the IF systems, resulting in a wider range of RI beams than when either is used. That is, accelerating the heavy RI beam made through the ISOL system to the superconducting linac, SCL3, of the RAON and generating a new RI beam after incident into the IF system is used in fundamental experiments and various applications.
An important injection condition for accelerating the RI beam produced in this ISOL system to the SCL3 is 10 keV/u. Therefore, the charge state of the ion beam is adjusted and accelerated through the Electron Beam Ion Source (EBIS) charge breeder to match the RI beam generated by the Target Ion Source (TIS) of the ISOL by 10 keV/u. The charge evolution method of the EBIS involves trapping a single charge ion in a high magnetic field and then removing electrons from the ion using an electron beam. Depending on the mass-to-charge ratio (A/q) of the highly charged ions produced by the EBIS, the above conditions are satisfied using a voltage for the acceleration, and then an ion beam is transmitted. At this time, in the EBIS, ions in various charge states are generated, so ions satisfying the condition are selected and transported through the A/q separator to the post-accelerator.
This dissertation introduces the current status of the EBIS charge breeder, which is essential for the operation of the ISOL system in the RAON facilities. It is designed and manufactured for the superconducting magnet, an essential element for the electron beam transmission and trapping ion beam, as well as the construction of the high-voltage platforms for the acceleration of the charge-bred ions and the beam optics for their stable transportation. On-site installation for the charge breeding experiment of the EBIS, and the construction of the electrical and control systems necessary for the experiment were conducted. Experiments for the stable electron beam transmission were performed achieving a beam current of 2 A. Additionally, charge breeding tests using a couple of stable beams extracted from the test ion source before using the RI beam, and a linkage test using stable beams from the ISOL beamline were conducted producing the highly charged ions with A/q < 6 and 10 keV/u. With the completion of the online commissioning using a stable beam, the performance for the charge breeding and the transmission of an RI beam using the EBIS charge breeder was verified.ope
Neural Processes ??? ?????? Meta-Reinforcement Learning ?????? ??????
No full textGraduate School of Artificial IntelligenceReinforcement learning (RL) is a methodology to solve various sequential decision-making problems ranging from simulation tasks to real-world applications. RL can learn a policy to determine the optimal action in a given state despite the absence of supervision through the interaction between the agent and the environments corresponding to the target task. However, due to their nature, a vast number of data samples are required to obtain an effective agent, so this limitation is a significant obstacle in introducing it to real-world applications. To overcome these restrictions, the meta-reinforcement learning (meta-RL) field is being actively studied to transfer the knowledge of agents learned in various simulation environments to real-world problems. Learning distribution over tasks rather than a single task is a challenging objective. To do this effectively, some research employs the probabilistic models such as Gaussian process (GP) or Bayesian neural network (BNN) for solving meta-RL. Recently, to overcome the high time complexity of GP, an approach for learning stochastic processes with a neural network has been proposed: Neural process (NP). Miscellaneous studies suggest that NP can be a suitable replacement for GP in sequential decision-making problems. Therefore, this thesis applies NP to meta-RL problems and investigates whether NP can be utilized in these complex and challenging tasks. Specifically, this thesis presents a strategy to solve the model-based learning among meta-RL problems with the NP-based approach and allows the NP to learn the distribution over dynamics of the target environments. NP models that are well-trained on the dynamics of various tasks are expected to be able to adapt sample efficiently to unexplored problems at the test phase. However, this optimistic expectation does not correspond to all NPs through evaluation in various environments, such as classic control or Mujoco locomotion benchmarks. This thesis demonstrates that only NeuBANP achieves promising results among many NP models introduced to solve model-based meta-RL and thus empirically suggests the potential of a NeuBANP-based approach to complicated sequential decision-making problems.ope
SUMOylation of ZNF212 by PIAS1 and PIAS3 contribute to DNA damage response
Get PDFDepartment of Biological SciencesSmall ubiquitin like modifiers (SUMO) proteins are small polypeptide that bind to lysine residue of target proteins to regulate functions. SUMOylation is a post-translational modification that regulate various biological process such as transcriptional regulation, cell cycle progression, nuclear-cytosol translocation and DNA damage response. In this study, I found that Zinc Finger Protein 212 (ZNF212) can be SUMOylated by interaction with Protein Inhibitor of Activated STAT 1(PIAS1) and Protein Inhibitor of Activated STAT3 (PIAS3) which are E3-type SUMO ligases. In addition, I identified that K217 of ZNF212 is a major SUMOylation site. Interestingly, the mutant at the SUMOylation site of ZNF212 was weakly translocated to the DNA damage site and showed hypersensitivity to camptothecin(CPT) treatment compared with the wild type. Combined, these results support the potential that SUMOylation of ZNF212 regulates the DNA damage response.ope
Systematic Fault Injection Scenario Generation for the Safety Monitoring of the Autonomous Vehicle
Get PDFDepartment of Mechanical EngineeringThe Object and Event Detection and Response (OEDR) assessment of Automated Vehicles(AVs) must be thoroughly conducted on the entire Operational Design Domain(ODD) to prevent any potential safety risk caused by corner cases. In response to these challenges, AVs must be tested over hundreds of millions of kilometers before deployment to convince its OEDR capabilities. However, claiming safety through years of testing on the entire ODD is not practically sound. Therefore, many studies have addressed this problem, focusing on efficiently and effectively finding corner cases within high-fidelity simulation environment. In particular, one of key OEDR functionalities is a collision risk assessment system alarming the driver about an impending collision in advance. In AV ODD context, the collision risk assessment is confronting challenging situations such as incorrect sensor information and unexpected algorithmic errors derived from uncertain environments (weather, traffic flow, road conditions, obstacles). Whereas the widely employed collision risk assessment methods relies on the first principle, e.g., Time-To-Collision (TTC), the aforementioned situations cannot be properly assessed without appropriate scene understanding toward the each situation. To this end, AI-based research that leverages previous experience and sensor information (especially camera image) to assess collision risk through visual cues has been developed in recent years. Inspired by the above research trends, this paper aims to develop: 1) systematic corner case generation using a scenario-based falsification simulationand 2) an AI-based safety monitoring system applicable in complex driving scenarios. The implemented simulation is shown to competently find the corner case scenarios, through which the developed system is validated that it can be used as an alternative to an existing collision risk indicator in complex AV driving scenarios.ope
Threshold-aware Learning to Generate Feasible Solutions of Mixed Integer Programs
Get PDFGraduate School of Artificial IntelligenceFinding a high-quality feasible solution to a combinatorial optimization (CO) problem in a limited time is challenging due to its discrete nature. Neural diving is a learning-based approach to generating partial assignments for the discrete variables in Mixed Integer Programs (MIP). We find that there is a specific range of variable assignment rates (coverage) that lead to high-quality feasible solutionswhen too many variables are assigned, the solution space is too restricted to find a feasible solutionwhen too few variables are assigned, the solution space is too wide to efficiently find a high-quality feasible solution. Therefore, choosing the proper coverage is critical for the Neural diving performance. One problem in the learning-based method to generate a feasible MIP solution is a large discrepancy between the supervised learning (SL) objective, i.e., accuracy or loss function, and the MIP objective, i.e., primal bound. Here, we present theoretical insights that threshold functions exist in the ML-generated MIP solution feasibility and quality over the variable assignment coverage. The threshold functions are criteria that bridge the gap between the SL and MIP objectives by providing theoretical guarantees for the ML model???s actual MIP performance. Based on the theoretical foundations, we introduce a post-hoc method and a learning-based approach to optimize the coverage. A key idea is to jointly learn to restrict the coverage search space and to predict the coverage in the learned search space that leads to a high-quality feasible solution. We leverage the MIP solver in the end-to-end learning framework to evaluate the feasibility and quality of the solution derived from the Neural diving model. We suggest that learning a deep neural network to generate threshold-aware coverage is an effective method to find high-quality feasible solutions in a shorter time. Experimental results demonstrate that our method achieves state-of-the-art performance in NeurIPS ML4CO datasets. In the workload apportionment dataset, our method achieves the optimality gap of 0.45%, which is around 10?? better than SCIP, at the one-minute time limit.ope