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Hierarchically cactus-like nickel sulfide–lanthanum carbonate hydroxide composite for urea-assisted water splitting
Hydrogen production from water electrolysis is mainly affected by the high overpotential and slow reaction kinetics of the anodic oxygen evolution reaction (OER). Replacing slow oxygen evolution reaction (OER) with thermodynamically favorable urea oxidation reaction (UOR) is one of the feasible strategies for achieving energy-saving hydrogen production. In this work, a cactus-like nickel sulfide–lanthanum carbonate hydroxide (NiSx–LaCH) composite was synthesized on the surface of Ni foam by a hydrothermal method and sulfurization process for the first time. This cactus-like structure can significantly improve the specific surface area of the catalyst and the electrolyte accessibility. The coupling of NiSx and LaCH brought about the electron structure redistribution and enhanced the stability and conductivity. The generated synergistic effect of NiSx–LaCH/NF improved the adsorption of urea/water molecules and the intrinsic catalytic activity. It exhibited outstanding electrocatalytic performance for UOR and hydrogen evolution reaction, which can drive the current density of 100 mA cm−2 at low potentials of 1.36 V and −0.26 V, respectively. Assembling NiSx–LaCH/NF into urea electrolysis system can reduce the cell voltage by 0.22 V compared to water electrolysis. This indicates that using UOR instead of OER could achieve energy-saving hydrogen production and has promising prospects in treating urea-rich wastewater.
Development of the Korean enhanced recovery after surgery audit program
Electron spin, kinetic energy, and stereodynamics control of the reaction between 9-methyl-8-oxoguanine radical cation and nitric oxide
8-oxoguanine (OG) is a prevalent DNA lesion and exhibits a significantly lower oxidation potential than natural nucleic acid components, making the formation of OG•+ radical cation the most efficient hole trap in the one-electron oxidation of DNA. Nitric oxide (•NO) is a precursor to reactive nitrogen species and plays multiple roles in biological activities, including DNA base nitrosation and enhancement of DNA radiosensitivity in radiotherapy. Herein, we report the reaction of •NO with 9-methyl-8-oxoguanine radical cation (9MOG•+), a model compound for OG•+ nucleoside. 9MOG•+ was generated via redox dissociation of [CuII(9MOG)3]•2+ and its reaction with •NO was investigated using electrospray ionization guided-ion beam mass spectrometry as a function of kinetic energy. Multiple coupled reaction potential energy surfaces were computed using spin-projected ωB97XD, DLPNO-CCSD(T), and CASPT2 methods, with theoretical results benchmarked against experimentally determined reaction thermodynamics. The synergistic experiment and computation revealed distinct reaction mechanisms and dynamics across the open-shell singlet, close-shell singlet, and triplet states formed in radical–radical collisions. Comparison with the reaction of •NO with guanine radical cation (G•+) [Benny and Liu, J. Chem. Phys. 159, 085102 (2023) and Benny et al., J. Chem. Phys. 161, 125101 (2024)] addressed the resemblances and distinctions between •NO reaction dynamics with OG•+ vs G•+. On the one hand, both systems present spin–orbit charge transfer, forming vibrationally excited NO+(ν+ = 1) product ions. On the other hand, OG•+ demonstrates lower nitrosation efficiency than G•+ due to few pathways, less favorable thermodynamics, and constrained stereodynamics. Only the closed-shell singlet [5-NO-9MOG]+ product was detected. This study provides new insights into •NO-mediated DNA damage.
Evaluating crop yield prediction models in illinois using aquacrop, semi-physical model and artificial neural networks
Abstract Crop yield is important for agricultural productivity and the country’s economy. While crop yield estimation is an essential aspect of modern agriculture, it continues to be one of the most challenging tasks to manage effectively. Corn and soybean are the important crops in Illinois, USA, considerably enhancing the region’s agricultural output and economy. The present study integrates semi-physical model, AquaCrop and Artificial Neural Network (ANN) Models for estimating corn and soybean yields. Data of different meteorological parameters including precipitation, maximum and minimum temperature, relative humidity, wind speed, solar radiation, photosynthetically active radiation and fraction of photosynthetically active radiation, land surface water index were collected for a period of 25 years from 2000 to 2024 from NASA POWER, USDA and NASS. The observed yield of soybean and corn was ranges from 2.49 to 4.37 ton/ha and 7.06 to 14.66 ton/ha. The predicted corn yield using the AquaCrop, semi-physical, and ANN models ranged from 7.60 to 14.42 ton/ha, 9.01 to 13.42 ton/ha, and 6.81 to 15.63 ton/ha, respectively. For soybean, the predicted yield ranged from 2.80 to 4.34 ton/ha, 2.92 to 3.84 ton/ha, and 2.45 to 4.43 ton/ha, respectively. The ANN model achieves the highest coefficient of determination (R² = 0.96) in predicting soybean yield, while the semi-physical model records the lowest R² value of 0.42, indicating the superior predictive capability of the ANN model. For both corn and soybean yields, the ANN model showed the highest prediction accuracy among the other models. Thus, the study underscores the significance of employing the ANN model for crop yield estimation, particularly in the regions that share similar physiographic and meteorological conditions with Illinois.
Bonding and the dynamics of glassy network liquids
The Random First Order Transition (RFOT) theory of glasses provides a unified framework for explaining the observed correlations of the kinetic and thermodynamic behaviors of glass-forming liquids having a wide variety of chemical compositions and interactions. The theory also provides a solid starting point for calculating glassy dynamics starting from the microscopic forces. Network liquids, which interact via long-lived, geometrically constraining interactions, such as covalent bonding, have competing energy scales for bond breaking events and for collective particle rearrangement events. In this paper, we show microscopic calculations via the RFOT theory can predict how glassy dynamics depends on the degree of bonding, focusing on mixtures of network-forming particles with non-bonding impurities as in familiar window glass. By introducing soft-core nonbonding interactions, we show that the viscosity and fragility of the network liquid model can be computed as a function of composition, temperature, and density or pressure. We find that the fragility in the strong-bond limit depends only on composition and not on the bond breaking energy and describes well corresponding measurements in sodium or potassium silicates. The model predicts that materials with weaker bonds may show a non-monotonic trend in the fragility as a function of composition.
Elevated SLC4A11 expression promotes OV progression via interaction with EGFR
Unraveling the mechanisms of charge-separation in a dibenzo[<i>b</i>,<i>d</i>]thiophene sulfone polymer photocatalyst using time-resolved electronic absorption spectroscopy
Organic polymer photocatalysts have gained much interest in recent years, largely because of their photocatalytic activity toward sacrificial hydrogen production from water. Time-resolved electronic absorption spectroscopy is commonly employed to understand the photophysical processes occurring following photon absorption, which in turn is used to rationalize photocatalytic activities. The homopolymer of dibenzo[b,d]thiophene sulfone (P10) is a well-studied and high performing photocatalyst for sacrificial hydrogen evolution from water. While sacrificial reagents are well documented as a prerequisite for this reaction, their roles in the picosecond–nanosecond photodynamics have yet to be determined using transient electronic signatures. By employing lifetime density analysis of time-resolved electronic absorption spectra of P10 in a variety of solvent mixtures, we show that the electron polaron (the required charge for hydrogen evolution) is produced on the 0.5–100 and 50–800 ps timescales via excitonic quenching by triethylamine and methanol, respectively, two common sacrificial electron donors. We conclude that there is significant pre-association of triethylamine with the P10 polymer, resulting in efficient excitonic quenching. This mechanism competes effectively with radiative excitonic relaxation, which occurs on similar timescales, reducing exciton losses and improving polaron yields.
Impact of temporal tear meniscus height on the tear osmolarity measurements
Multireference configuration interaction study of potential energy curves of gold monotelluride (AuTe) and energy levels of Te atom incorporating spin–orbit coupling
The potential energy curves (PECs) for the ground and low-lying excited states of AuTe are calculated using multireference configuration interaction with the Davidson correction, incorporating spin–orbit coupling (SOC). The ground state of AuTe is identified as the Ω = 3/2[(1)2Π3/2] state. Spectroscopic constants for the ground and low-lying excited states are derived from the computed PECs and generally exhibit excellent agreement with available experimental results. In addition, the experimental energy level ordering of the Te atom, in which the 3P0 state lies below the 3P1 state, is successfully reproduced by incorporating both the second-order SOC and dynamic electron correlation effects. The analysis indicates that while second-order SOC contributes to lowering the energy of the 3P0 state, including the dynamic electron correlation is essential for achieving the correct energy ordering. The spin–orbit (SO) effect in the ground state of AuTe is found to be negligible, resulting from the mutual cancellation of SO contributions from Au and Te atoms, which is rationalized through the natural bond orbital analysis. These results offer valuable insights into the fundamental nature of gold–chalcogen bonding and serve as a benchmark for future experimental and theoretical investigations of AuTe and related systems.
Multi-objective optimal scheduling of islands considering offshore hydrogen production
Quantum dynamics of dissipative two-level systems and intradimer excitation energy transfer in the presence of static disorder
We use the numerically exact, fully quantum mechanical small matrix path integral (SMatPI) methodology to investigate the time evolution of the reduced density matrix (RDM) following photoexcitation of model molecular dimers in the presence or absence of static disorder. The dimer is modeled in terms of a two-level system that represents the excited electronic states of the monomers, which are coupled to a dissipative bath of vibrational modes with an Ohmic spectral density under diverse conditions that correspond to homo- or heterodimers, weak or moderately strong exciton–vibration coupling, high- or low-frequency vibrations, and high or low temperature. Through the equivalence class path integral algorithm, the averaging with respect to static disorder is performed with computational effort comparable to that of a single SMatPI calculation. We find that static disorder alters the dynamics and equilibrium properties of the RDM in significant and often subtle ways, which can mimic effects associated with stronger or weaker dissipation. The impact of disorder is most pronounced at low temperatures, where it tends to suppress coherence and often induces upward shifts in the population of the higher-lying state, while the effects on the off-diagonal RDM element and the eigenstate populations depend nonmonotonically on the asymmetry parameter. At high temperatures, the population shift is weaker and reversed for some parameters.
Durable formulations of quorum quenching enzymes
Abstract Enzymes with industrial potential often face limitations due to stability and longevity constraints. Thermostable quorum quenching lactonases are appealing biotechnology tools for controlling microbial pathogenicity and biofilm formation via the interference of quorum sensing. However, the effective formulation of these enzymes remains a challenge. Here, we evaluate the resistance and activity of two thermostable quorum quenching lactonase enzymes (SsoPox and GcL) across diverse formulations relevant to industrial applications. We systematically tested these enzymes with 16 different crop adjuvants (including oils, an anti-foaming agent, surfactants, deposition aids, a water conditioner, and a sticking agent) over a 210-day period, demonstrating broad compatibility except with oil-based adjuvants. Additionally, both enzymes maintained their activity when incorporated into five different coating bases (acrylic, silicone, polyurethane, epoxy, and latex) with activity levels varying according to polymer type. Further investigation of enzymatic acrylic coating characterized the effects of salt water and temperature on enzyme activity levels. Functionalized coatings maintained remarkable stability over 250 days in both wet and dry conditions. These findings establish a practical demonstration and framework for integrating quorum quenching lactonases into industrial materials and formulations, significantly advancing their potential for ‘real-world’ applications for microbial control across multiple sectors.
Kinetics of seeded protein aggregation: Theory and application
“Seeding” is the addition of preformed fibrils to a solution of monomeric protein to accelerate its aggregation into new fibrils. It is a versatile and widely used tool for scientists studying protein aggregation kinetics, as it enables the isolation and separate study of discrete reaction steps contributing to protein aggregation, specifically elongation and secondary nucleation. However, the seeding levels required to achieve dominating effects on each of these steps separately have been established largely by trial-and-error due in part to the lack of availability of integrated rate laws valid for moderate to high seeding levels and generally applicable to all common underlying reaction mechanisms. Here, we improve on a recently developed mathematical method based on Lie symmetries for solving differential equations and with it derive such an integrated rate law. We subsequently develop simple expressions for the amounts of seed required to isolate each step. We rationalize the empirical observation that fibril seeds must often be broken up into small pieces to successfully isolate elongation. We also derive expressions for average fibril lengths at different times in the aggregation reaction and explore different methods to break up fibrils. This paper will provide an invaluable reference for future experimental and theoretical studies in which seeding techniques are employed and should enable more sophisticated analyses than have been performed to date.
Application of 3D Slicer for preoperative planning in upper cervical posterior fixation with vertebral artery variations
Nonadiabatic ring-polymer instanton rate theory: A generalized dividing-surface approach
Constructing an accurate approximation to nonadiabatic rate theory that is valid for arbitrary values of the electronic coupling has been a long-standing challenge in theoretical chemistry. Ring-polymer instanton theories offer a very promising approach to solve this problem, since they can be rigorously derived using semiclassical approximations and can capture nuclear quantum effects such as tunneling and zero-point energy at a cost similar to that of a classical calculation. A successful instanton rate theory already exists within the Born–Oppenheimer approximation, for which the optimal tunneling pathway is located on a single adiabatic surface. A related instanton theory has also been developed for nonadiabatic reactions using two weakly coupled diabatic surfaces within the framework of Fermi’s golden rule. However, many chemical reactions do not satisfy the conditions of either limit. By employing a tunable dividing surface that measures the flux both along nuclear coordinates and between electronic states, we develop a generalized nonadiabatic instanton rate theory that bridges between these two limits. The resulting theory approximates the quantum-mechanically exact rates well for the systems studied and, in addition, offers a novel mechanistic perspective on nonadiabatic reactions.
Genetic map of the carotid body stem cell niche with focus on the O2-sensing chemoreceptor cell lineage
Interpretable machine learned predictions of adsorption energies at the metal–oxide interface
The conversion of CO2 to value-added compounds is an important part of the effort to store and reuse atmospheric CO2 emissions. Here, we focus on CO2 hydrogenation over so-called inverse catalysts: transition metal oxide clusters supported on metal surfaces. The conventional approach for computational screening of such candidate catalyst materials involves a reliance on density functional theory (DFT) to obtain accurate adsorption energies at a significant computational cost. Here, we present a machine learning (ML)-accelerated workflow for obtaining adsorption energies at the metal–oxide interface. We enumerate possible binding sites at the clusters and use DFT to sample a subset of these with diverse local adsorbate environments. The dataset is used to explore interpretable and black-box ML models with the aim of revealing the electronic and structural factors controlling adsorption at metal–oxide interfaces. Furthermore, the explored ML models can be used for low-cost prediction of adsorption energies on structures outside of the original training dataset. The workflow presented here, along with the insights into trends in adsorption energies at metal–oxide interfaces, will be useful for identifying active sites, predicting parameters required for microkinetic modeling of reactions on complex catalyst materials, and accelerating data-driven catalyst design.
Knowledge attitudes and practices of healthcare workers on respirator fit testing and PAPR use at a university medical center
Abstract Particularly during an epidemic of infectious diseases, worker safety in healthcare depends critically on respirator fit testing and the usage of powered air-purifying respirators (PAPR). Reducing hazards requires ensuring healthcare professionals’ (HCW) knowledge, attitudes, and behaviors as well as their compliance with respiratory protection programs. There is little information on these factors in Saudi Arabian healthcare environments, which calls for targeted research. This study aimed to assess healthcare workers’ (HCW) knowledge, attitudes, and practices (KAP) regarding respirator fit testing and powered air-purifying respirator (PAPR) use at King Saud University Medical City (KSUMC) which is referred to as ‘the medical center’ throughout the paper. Specifically, it sought to identify gaps in policy understanding and training, evaluate compliance and confidence levels, and examine how demographic variables influence these outcomes. A total of 204 HCWs from different departments and hospitals around the medical center participated in cross-sectional research. Structured surveys measuring demographic variables, knowledge, attitudes, training experience, and compliance with fit testing and PAPR use gathered data. While chi-square tests and correlation analysis look at relationships between variables, descriptive statistics compile the demographic traits and survey answers. With SPSS, version 27, all the statistical tests were run with a significance threshold of α = 0.05. With respirator fit testing, the results revealed a high compliance rate—93.4%. Nurses had the best rates of compliance and confidence. However, demonstrating a large knowledge gap, only 6.9% (N-36) of the respondents knew about quantitative fit assessment techniques. Among the 82.2% (N-168) of HCWs who reported PAPR usage training, 48% (of N-168) received consistent instructions. While 14.8% (of N-168) of the respondents reported poor confidence, suggesting room for development, PAPR use was rather high—85.2% (N-204). Significant correlations were found between demographic variables and compliance, training, and confidence levels ( p < 0.05). In particular, a negative connection between PAPR usage ( r = -0.287, p = 0.01) and confidence in fit testing indicated possible specialized effects. This study highlights the need for thorough and consistent respiratory protection training courses for different HCW profiles. Respiratory protection measures at KSUMC may be strengthened even further by addressing knowledge gaps, increasing hands-on training, and strengthening policy communication to guarantee HCW safety and preparedness.
Unraveling the facet dependent activity and surface reactive species in ketonization of acetic acid on CeO2(111) and (110)
A combined density functional theory and microkinetic study of the ketonization of acetic acid on facets of CeO2 has been performed to understand the reaction mechanism by identifying the key reactive intermediates and active surface structures. The overall Gibbs free energies of activation, i.e., the difference between the transition state of the C–C coupling step and the surface-bound acetates, were determined to be 2.08 and 1.81 eV on CeO2(111) and 2.01 and 1.52 eV on CeO2(110) involving bidentate and monodentate acetates, respectively. Micro-kinetic analysis revealed that monodentate acetate (minor surface species) is more reactive than bidentate one (major surface species), and the (110) surface is more active than the (111) surface. The α-H abstraction step is mainly controlled by the basicity of the surface O sites, while the configuration of the adjacent Ce–O pairs determines the C–C coupling step, and together, they dictate the overall ketonization activity. Compared with CeO2(111), a stronger basicity of surface O3c on CeO2(110) facilitates efficient α-H abstraction, whereas a matching configuration of the adjacent Ce–O pairs enables facile C–C coupling, resulting in a higher ketonization activity. Detailed structural analysis revealed that the two adjacent Ce–O pairs in a rhombus configuration on the same Ce–O–Ce chain of the CeO2(110) surface form the most active ensemble for the ketonization of carboxylic acids via monodentate carboxylates. The understanding and insights will benefit the design of efficient ketonization catalysts based on transition metal oxides.