Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userAutoionization of water: does it really occur?
62
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
The ionization constant of water Kw is currently determined on the proton conductivity sigma1 which is measured at frequencies lower than 10^7 Hz. Here, we develop the idea that the high frequency conductivity sigma2 (~10^11 Hz), rather than sigma1 represents a net proton dynamics in water, to evaluate the actual concentration c of H3O+ and OH- ions from sigma2. We find c to be not dependent on temperature to conclude that i) water electrodynamics is due to a proton exchange between H3O+ (or OH-) ions and neutral H2O molecules rather than spontaneous ionization of H2O molecules, ii) the common Kw (or pH) reflects the thermoactivation of the H3O+ and OH- ions from the potential of their interaction, iii) the lifetime of a target water molecule does not exceed parts of nanosecond.
rate research
Read More
In recent years, the lattice Boltzmann (LB) method has been widely employed to simulate boiling phenomena [A. Markus and G. Hazi, Phys. Rev. E 83, 046705 (2011); Biferale et al., Phys. Rev. Lett. 108, 104502 (2012); Li et al., Phys. Rev. E 96, 063303 (2017); Wu et al., Int. J. Heat Mass Transfer 126, 773 (2018)]. However, a very important issue still remains open, i.e., how does boiling occur in the LB simulations? For instance, the existing LB studies showed that the boiling on a hydrophobic surface begins at a lower wall superheat than that on a hydrophilic surface, which qualitatively agrees well with experimental studies, but no one has yet explained how this phenomenon appears in the LB simulations and what happened in the simulations after changing the wettability of the heating surface. In this paper, the LB boiling mechanism is revealed by analyzing boiling on a flat surface with mixed wettability and boiling on a structured surface with homogeneous wettability. Through a theoretical analysis, we demonstrate that, when the same wall superheat is applied, in the LB boiling simulations the fluid density near the heating surface decreases faster on a hydrophobic surface than that on a hydrophilic surface. Accordingly, a lower wall superheat can induce the phase transition from liquid to vapor on a hydrophobic surface than that on a hydrophilic surface. Furthermore, a similar theoretical analysis shows that the fluid density decreases fastest at concave corners in the case of a structured surface with homogeneous wettability, which explains why vapor bubbles are nucleated at concave corners in the LB simulations of boiling on structured surfaces.
Knowledge distillation is a popular technique for training a small student network to emulate a larger teacher model, such as an ensemble of networks. We show that while knowledge distillation can improve student generalization, it does not typically work as it is commonly understood: there often remains a surprisingly large discrepancy between the predictive distributions of the teacher and the student, even in cases when the student has the capacity to perfectly match the teacher. We identify difficulties in optimization as a key reason for why the student is unable to match the teacher. We also show how the details of the dataset used for distillation play a role in how closely the student matches the teacher -- and that more closely matching the teacher paradoxically does not always lead to better student generalization.
A single-particle multi-branched wave-function is studied. Usual which-path tests show that if the detector placed on one branch clicks, the detectors on the other branches remain silent. By the collapse postulate, after this click, the state of the particle is reduced to a single branch, the branch on which the detector clicked. The present article challenges the collapse postulate, claiming that when one branch of the wave-function produces a click in a detector, the other branches dont disappear. They cant produce clicks in detectors, but they are still there. An experiment different from which-path test is proposed, which shows that detectors are responsible for strongly decohering the wave-function, but not for making parts of it disappear. Moreover, one of the branches supposed to disappear may produce an interference pattern with a wave-packet of another particle.
Moderation is crucial to promoting healthy on-line discussions. Although several `toxicity detection datasets and models have been published, most of them ignore the context of the posts, implicitly assuming that comments maybe judged independently. We investigate this assumption by focusing on two questions: (a) does context affect the human judgement, and (b) does conditioning on context improve performance of toxicity detection systems? We experiment with Wikipedia conversations, limiting the notion of context to the previous post in the thread and the discussion title. We find that context can both amplify or mitigate the perceived toxicity of posts. Moreover, a small but significant subset of manually labeled posts (5% in one of our experiments) end up having the opposite toxicity labels if the annotators are not provided with context. Surprisingly, we also find no evidence that context actually improves the performance of toxicity classifiers, having tried a range of classifiers and mechanisms to make them context aware. This points to the need for larger datasets of comments annotated in context. We make our code and data publicly available.
We analyze various possible interpretations of the narrow state $D_{sJ}^+(2632)$ observed by SELEX Collaboration recently, which lies above threshold and has abnormal decay pattern. These interpretations include: (1) sever
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
