The Role Of Ferric Oxide Particles As Sources And Sinks Of Reactive Oxygen Species During The Autoxidation Of Ferrous Iron

The oxic portion of the biosphere is a metastable mixture of different oxidation states of carbon, sulfur and oxygen energetically poised from equilibrium by the net rate differentials between photosynthetic carbon fixation and its metabolic or abiotic oxidation. The direct reaction of dioxygen with reduced carbon or sulfur is spin forbidden and therefore kinetically slow, but ferric and ferrous iron species serve as catalysts for enabling their oxidation and therefore play critical roles in the environment. This thesis reports exploratory and hypothesis driven research that seeks a better understanding of the physical and chemical limitations on the effectiveness of iron to catalyze interaction between the different oxidation states of these elements. These include studies of the relationship between iron speciation and its ability to generate reactive oxygen species (Chapter 1); the role of heterogeneous iron oxide suspensions in controlling reactive oxygen species yield during the spontaneous reaction of Fe(II) and O2 (Chapter 2), an exploration of the system of natural iron-containing soils, sulfide and oxygen and how they produce superoxide and hydrogen peroxide (Chapter 3) and a preliminary report of reactive oxygen species and antioxidant enzyme formation in the salt marsh muds (Chapter 4). The results are showing that ferric iron catalyzed oxidation of hydrogen sulfide is an important reservoir for the generation of reactive oxygen species except for the photoinduced processes. The ferrous iron oxidation in the presence of ferric oxides shows a faster oxidation rate and produces a higher yield of reactive oxygen species, which is indicating the catalysis of the process by removing ferric species from the iron cycle

Clinical characterization and proteomic profiling of lean nonalcoholic fatty liver disease

IntroductionObesity has been historically associated with nonalcoholic fatty liver disease (NAFLD), but it can also occur in lean individuals. However, limited data is available on this special group. To investigate the clinical and proteomic characteristics of lean subjects with NAFLD, and to identify potential clinical variables and plasma proteins for diagnosing NAFLD in lean individuals, we collected clinical data from a large cohort of 2,236 subjects.MethodsDiagnosis of NAFLD relied on detecting pronounced hepatic steatosis through abdominal ultrasonography. Participants were categorized into four groups based on body mass index: overweight NAFLD, overweight control, lean NAFLD, and lean control. Plasma proteomic profiling was performed on samples from 20 subjects in each group. The lean NAFLD group was compared to both lean healthy and obese NAFLD groups across all data.Results and discussionThe results indicated that the lean NAFLD group exhibited intermediate metabolic profiles, falling between those of the lean healthy and overweight NAFLD groups. Proteomic profiling of plasma in lean subjects with or without NAFLD revealed 45 statistically significant changes in proteins, of which 37 showed high diagnostic value (AUC > 0.7) for lean NAFLD. These potential biomarkers primarily involved lipid metabolism, the immune and complement systems, and platelet degranulation. Furthermore, AFM, GSN, CFH, HGFAC, MMP2, and MMP9 have been previously associated with NAFLD or NAFLD-related factors such as liver damage, insulin resistance, metabolic syndromes, and extracellular homeostasis. Overall, lean individuals with NAFLD exhibit distinct clinical profiles compared to overweight individuals with NAFLD. Despite having worse metabolic profiles than their healthy counterparts, lean NAFLD patients generally experience milder systemic metabolic disturbances compared to obese NAFLD patients. Additionally, the plasma proteomic profile is significantly altered in lean NAFLD, highlighting the potential of differentially expressed proteins as valuable biomarkers or therapeutic targets for diagnosing and treating NAFLD in this population

Photovoltaic potential of tin perovskites revealed through layer-by-layer investigation of optoelectronic and charge transport properties

Tin perovskites are the most promising environmentally friendly alternative to lead perovskites. Among tin perovskites, FASnI3 (CH4N2SnI3) shows optimum band gap, and easy processability. However, the performance of FASnI3 based solar cells is incomparable to lead perovskites for several reasons, including energy band mismatch between the perovskite absorber film and the charge transporting layers (CTLs) for both types of carriers, i.e., for electrons (ETLs) and holes (HTLs). However, the band diagrams in the literature are inconsistent, and the charge extraction dynamics are poorly understood. In this paper, we study the energy band positions of FASnI3 based perovskites using Kelvin probe (KP) and photoelectron yield spectroscopy (PYS) to provide a precise band diagram of the most used device stack. In addition, we analyze the defects within the current energetic landscape of tin perovskites. We uncover the role of bathocuproine (BCP) in enhancing the electron extraction at the fullerene C60/BCP interface. Furthermore, we used transient surface photovoltage (tr-SPV) for the first time for tin perovskites to understand the charge extraction dynamics of the most reported HTLs such as NiOx and PEDOT, and ETLs such as C60, ICBA, and PCBM. Finally, we used Hall effect, KP, and time-resolved photoluminescence (TRPL) to estimate an accurate value of the p-doping concentration in FASnI3 and showed a consistent result of 1.5 * 1017 cm-3. Our findings prove that the energetic system of tin halide perovskites is deformed and should be redesigned independently from lead perovskites to unlock the full potential of tin perovskites.Comment: 22 pages, 5 figure

Managing Excess Lead Iodide with Functionalized Oxo‐Graphene Nanosheets for Stable Perovskite Solar Cells

Stability issues could prevent lead halide perovskite solar cells (PSCs) from commercialization despite it having a comparable power conversion efficiency (PCE) to silicon solar cells. Overcoming drawbacks affecting their long-term stability is gaining incremental importance. Excess lead iodide (PbI2) causes perovskite degradation, although it aids in crystal growth and defect passivation. Herein, we synthesized functionalized oxo-graphene nanosheets (Dec-oxoG NSs) to effectively manage the excess PbI2. Dec-oxoG NSs provide anchoring sites to bind the excess PbI2 and passivate perovskite grain boundaries, thereby reducing charge recombination loss and significantly boosting the extraction of free electrons. The inclusion of Dec-oxoG NSs leads to a PCE of 23.7 % in inverted (p-i-n) PSCs. The devices retain 93.8 % of their initial efficiency after 1,000 hours of tracking at maximum power points under continuous one-sun illumination and exhibit high stability under thermal and ambient conditions

Adaptive Evolution of Virulence-Related Traits in a Susceptible-Infected Model with Treatment

Evolution problem is now a hot topic in the mathematical biology field. This paper investigates the adaptive evolution of pathogen virulence in a susceptible-infected (SI) model under drug treatment. We explore the evolution of a continuous trait, virulence of a pathogen, and consider virulence-dependent cure rate (recovery rate) that dramatically affects the outcome of evolution. With the methods of critical function analysis and adaptive dynamics, we identify the evolutionary conditions for continuously stable strategies, evolutionary repellers, and evolutionary branching points. First, the results show that a high-intensity strength drug treatment can result in evolutionary branching and the evolution of pathogen strains will tend towards a higher virulence with the increase of the strength of the treatment. Second, we use the critical function analysis to investigate the evolution of virulence-related traits and show that evolutionary outcomes strongly depend on the shape of the trade-off between virulence and transmission. Third, after evolutionary branching, the two infective species will evolve to an evolutionarily stable dimorphism at which they can continue to coexist, and no further branching is possible, which is independent of the cure rate function

Green city logistics path planning and design based on genetic algorithm

Effective logistics distribution paths are crucial in enhancing the fundamental competitiveness of an enterprise. This research introduces the genetic algorithm for logistics routing to address pertinent research issues, such as suboptimal scheduling of time-sensitive orders and reverse distribution of goods. It proposes an enhanced scheme integrating the Metropolis criterion. To address the limited local search ability of the genetic algorithm, this study combines the simulated annealing algorithm’s powerful local optimization capability with the genetic algorithm, thereby developing a genetic algorithm with the Metropolis criterion. The proposed method preserves the optimal chromosome in each generation population and accepts inferior chromosomes with a certain probability, thereby enhancing the likelihood of finding an optimal local solution and achieving global optimization. A comparative study is conducted with the Ant Colony Optimization, Artificial Bee Colony, and Particle Swarm Optimization algorithms, and empirical findings demonstrate that the proposed genetic algorithm effectively achieves excellent results over these algorithms

Dynamics of a novel nonlinear stochastic SIS epidemic model with double epidemic hypothesis

In this paper, we propose new mathematical models with nonlinear incidence rate and double epidemic hypothesis. Then we dedicate to develop a method to obtain the threshold of the stochastic SIS epidemic model. To this end, first, we investigate the stability of the equilibria of the deterministic system and obtain the conditions for the extinction and the permanence of two epidemic diseases. Second, we explore and obtain the threshold of a stochastic SIS system for the extinction and the permanence in mean of two epidemic diseases. The results show that a large stochastic disturbance can cause infectious diseases to go to extinction, in other words, the persistent infectious disease of a deterministic system can become extinct due to the white noise stochastic disturbance. This implies that the stochastic disturbance is conducive to epidemic diseases control. To illustrate the performance of the theoretical results, we present a series of numerical simulations of these cases with respect to different noise disturbance coefficients

Monthly Runoff Forecasting Based on Interval Sliding Window and Ensemble Learning

In recent years, machine learning, a popular artificial intelligence technique, has been successfully applied to monthly runoff forecasting. Monthly runoff autoregressive forecasting using machine learning models generally uses a sliding window algorithm to construct the dataset, which requires the selection of the optimal time step to make the machine learning tool function as intended. Based on this, this study improved the sliding window algorithm and proposes an interval sliding window (ISW) algorithm based on correlation coefficients, while the least absolute shrinkage and selection operator (LASSO) method was used to combine three machine learning models, Random Forest (RF), LightGBM, and CatBoost, into an ensemble to overcome the preference problem of individual models. Example analyses were conducted using 46 years of monthly runoff data from Jiutiaoling and Zamusi stations in the Shiyang River Basin, China. The results show that the ISW algorithm can effectively handle monthly runoff data and that the ISW algorithm produced a better dataset than the sliding window algorithm in the machine learning models. The forecast performance of the ensemble model combined the advantages of the single models and achieved the best forecast accuracy

Research on Frequency Adaptability of Photovoltaic Power Generation

In this paper, photovoltaic power generation(PV) and the asynchronously grid-connected power grid are taken as the research objects, and the frequency adaptability of PV to power grid is studied. The influence mechanism of grid frequency variation on PV is revealed, and it is proposed that the frequency tolerance range of PV is mainly determined by the setting value of inverter protection and PLL parameters. The whole process simulation of wind turbine adaptability under frequency change is realized on Matlab/Simulink, and the simulation results verify the correctness of the conclusion