Pearceking3977

By expressing the MP law as a function of estimated Fdiff after a certain period of time, we provide a uniform description of the changes in periodicity for both small and large reservoir volumes. Such modification should make the MP law a more robust tool for studying LP systems.Oxygen reduction reaction (ORR) is one of the most important electrochemical reactions. Starting from a common reaction intermediate *-O-OH, the ORR splits into two pathways, either producing hydrogen peroxide (H2O2) by breaking the *-O bond or leading to water formation by breaking the O-OH bond. However, it is puzzling why many catalysts, despite the strong thermodynamic preference for the O-OH breaking, exhibit high selectivity for hydrogen peroxide. read more Moreover, the selectivity is dependent on the potential and pH, which remain not understood. Here we develop an advanced first-principles model for effective calculation of the electrochemical reaction kinetics at the solid-water interface, which were not accessible by conventional models. Using this model to study representative catalysts for H2O2 production, we find that breaking the O-OH bond can have a higher energy barrier than breaking *-O, due to the rigidity of the O-OH bond. Importantly, we reveal that the selectivity dependence on potential and pH is rooted into the proton affinity to the former/later O in *-O-OH. For single cobalt atom catalyst, decreasing potential promotes proton adsorption to the former O, thereby increasing the H2O2 selectivity. In contrast, for the carbon catalyst, the proton prefers the latter O, resulting in a lower H2O2 selectivity in acid condition. These findings explain the experiments and highlight the kinetic origins of the selectivity. Our work improves the understanding of ORR by uncovering the proton affinity as a new factor and provides a new model to effectively simulate the atomic-level kinetics of heterogeneous electrochemistry.Over 80% of all chronic bacterial infections in humans are associated with biofilms, which are surface-associated bacterial communities encased within a secreted exopolysaccharide matrix that can provide resistance to environmental and chemical insults. Biofilm formation triggers broad adaptive changes in the bacteria, allowing them to be almost 1000-fold more resistant to conventional antibiotic treatments and host immune responses. The failure of antibiotics to eliminate biofilms leads to persistent chronic infections and can promote the development of antibiotic-resistant strains. Therefore, there is an urgent need to develop agents that effectively prevent biofilm formation and eradicate established biofilms. Herein, we present water-soluble synthetic peptidomimetic polyurethanes that can disrupt surface established biofilms of Pseudomonas aeruginosa, Staphylococcus aureus, and Escherichia coli, all of which show tolerance to the conventional antibiotics polymyxin B and ciprofloxacin. Furthermore, while these polyurethanes show poor antimicrobial activity against planktonic bacteria, they prevent surface attachment and stimulate bacterial surface motility to inhibit biofilm formation of both Gram-positive and Gram-negative bacteria at subinhibitory concentrations, without being toxic to mammalian cells. Our results show that these polyurethanes show promise as a platform for the development of therapeutics that target biofilms and modulate surface interactions of bacteria for the treatment of chronic biofilm-associated infections and as antibiofilm agents.Pyrrole-2-carbaldehyde (Pa) forms one of the unnatural nucleic acid bases, and as a base pair with 7-(2-thienyl)imidazo[4,5-b]pyridine (Ds), it has been known to be stable in DNA. The Ds-Pa pair is stabilized in DNA via van der Waals' interaction and shape fitting. There are some studies on the origin of its stability and reactivity in the ground state. However, for a successful unnatural base pair, it needs to be stable not only in the ground state but also upon irradiation with UV-visible light. To understand the photoinduced reactivity, we investigate the excited-state properties of the Pa base and understand the energetically feasible photoprocesses that can occur upon excitation in the UV region. Two distinct pathways are obtained. One of the pathways involves an out-of-plane mode and has some similarities with the deactivation channels in the natural pyrimidine bases. On the other hand, the second pathway involves an excited-state proton transfer.In this study, the rhodamine 6G hydrazide (R6GH) complex was synthesized to develop an "off-on" output platform for fluorescence and visual dual-mode analysis of lead(II) (Pb2+). The prepared R6GH complex using the heat to reflux reaction of rhodamine 6G (R6G) and hydrazine hydrate was characterized through FT-IR, MS, 1H NMR, and 13C NMR and demonstrated to have good fluorescence stability and reversibility. The microenvironment for Pb2+ detection has been optimized in detail. Under the optimal conditions, the "off-on" R6GH-based fluorescence output platform showed a good response to Pb2+ in the concentration range of 0.05-6.0 μM (R2 = 0.9851) with a limit of detection (LOD) of 0.02 μM. Furthermore, at three spiked Pb2+ levels in the selected agricultural (tap water, soil) and food (fish, shrimp) samples, the developed R6GH-based fluorescence assays obtained a significant recovery range of 84.0-102.0% (RSD less then 5.0%, n = 3), which had a good correlation with the results from ICP-MS (R2 = 0.9915). The developed R6GH immobilized paper-based array sensor can reach the lower LOD (2.5 μM) for Pb2+ through the naked eye. By combining with LAB analysis, a good linear response was obtained in the Pb2+ concentration range of 1.0-50.0 μM. These results indicated that the developed R6GH probe had great application potential in accurate detection of fluorescence and rapid visual and semiquantitative screening for Pb2+.Quantum machine learning algorithms, the extensions of machine learning to quantum regimes, are believed to be more powerful as they leverage the power of quantum properties. Quantum machine learning methods have been employed to solve quantum many-body systems and have demonstrated accurate electronic structure calculations of lattice models, molecular systems, and recently periodic systems. A hybrid approach using restricted Boltzmann machines and a quantum algorithm to obtain the probability distribution that can be optimized classically is a promising method due to its efficiency and ease of implementation. Here, we implement the benchmark test of the hybrid quantum machine learning on the IBM-Q quantum computer to calculate the electronic structure of typical two-dimensional crystal structures hexagonal-boron nitride and graphene. The band structures of these systems calculated using the hybrid quantum machine learning approach are in good agreement with those obtained by the conventional electronic structure calculations.