Effects of Oxide Roughness at Metal Oxide Interface: MgO on Ag(001)

Defects in thin oxide films on metal substrates affect metal work function and determine the chemical and physical properties of an oxide. However, accurately predicting properties of these heterogeneous systems is still challenging. Here we use a new approach to treat a mixed metal/metal oxide system within density functional theory, which is based on the application of the auxiliary density matrix method (J. Chem. Theory Comput.2010, 6, 2348) to calculate the exchange interaction at a sharp interface between the two materials, as implemented in the CP2K code. This method is used to calculate the shift of the Ag work function in the MgO/Ag(001) system as a function of the MgO film morphology as well as charge state, position, and density of oxygen vacancies. An accurate band alignment between metal and oxide allows us to predict the relative stabilities of different charge states of oxygen vacancies in MgO as a function of their position with respect to the interface with Ag. Our results confirm that F+ centers are the most stable defects at terrace sites of MgO clusters and show that F0 and F+ centers can have comparable energies at low-coordinated sites, such as steps and corners. They show how thin oxide film roughness as well as oxygen deficiency can affect the metal work function

Relation between image charge and potential alignment corrections for charged defects in periodic boundary conditions

Charged defects are often studied within the periodic density functional theory (DFT), but this introduces strong finite-size artifacts. In this work, we develop an electrostatic image interaction correction (IIC) method based on the direct solution of the Poisson equation for charge models constructed directly from DFT calculations. These IICs are found to be detail-insensitive, depending almost entirely on bulk dielectric properties. As these IICs are not able to fully explain the observed finite-size scaling, we explore potential alignment in detail and introduce a novel decomposition to separate out different contributions. We find that the two main sources of potential alignment are defect image interactions and changes in the number of atoms present in the supercell. This first effect is accurately predicted by the periodic part of our IIC. The second contribution is unrelated to the IIC and justifies the common observation that the magnitude of finite-size dependence can strongly vary between vacancy and interstitial defects. It can be approximately predicted using atomic radius, but is strongly sensitive to the pseudopotential employed. Combined, these developments provide a new justification for known finite-size scaling rules. Our results suggest that for cubic supercells, the Lany-Zunger IIC, combined with simplified potential alignment between neutral systems, can yield accurate corrections in spite of the simplicity of the approach

Effect of electric field on migration of defects in oxides: Vacancies and interstitials in bulk MgO

Dielectric layers composed of metal oxides are routinely subjected to external electric fields during the course of normal operation of electronic devices. Many phenomenological theories suggest that electric fields strongly affect the properties and mobilities of defects in oxide films and can even facilitate the creation of new defects. Although defects in metal oxides have been studied extensively both experimentally and theoretically, the effect of applied electric fields on their structure and migration barriers is not well understood and still remains subject to speculations. Here, we investigate how static, homogeneous electric fields affect migration barriers of canonical defects—oxygen vacancies and interstitial ions—in a prototypical oxide, MgO. Using the modern theory of polarization within density functional theory (DFT), we apply electric fields to defect migration pathways in three different charge states. The effect of the field is characterized by the change of the dipole moment of the system along the migration pathway. The largest changes in the calculated barriers are observed for charged defects, while those for the neutral defects are barely significant. We show that by multiplying the dipole moment difference between the initial and the transition states, which we define as the effective dipole moment, by the field strength, one can obtain an estimate of the barrier change in excellent agreement with the DFT calculated values. These results will help to assess the applicability of phenomenological models and elucidate linear and nonlinear effects of field application in degradation of microelectronic devices, electrocatalysis, batteries, and other applications

The Role of Cation-Vacancies for the Electronic and Optical Properties of Aluminosilicate Imogolite Nanotubes: A Non-local, Linear-Response TDDFT Study

We report a combined non-local (PBE-TC-LRC) Density Functional Theory (DFT) and linear-response time-dependent DFT (LR-TDDFT) study of the structural, electronic, and optical properties of the cation-vacancy based defects in aluminosilicate (AlSi) imogolite nanotubes (Imo-NTs) that have been recently proposed on the basis of Nuclear Magnetic Resonance (NMR) experiments. Following numerical determination of the smallest AlSi Imo-NT model capable of accommodating the defect-induced relaxation with negligible finite-size errors, we analyse the defect-induced structural deformations in the NTs and ensuing changes in the NTs' electronic structure. The NMR-derived defects are found to introduce both shallow and deep occupied states in the pristine NTs' band gap (BG). These BG states are found to be highly localized at the defect site. No empty defect-state is modeled for any of the considered systems. LR-TDDFT simulation of the defects reveal increased low-energy optical absorbance for all but one defects, with the appearance of optically active excitations at energies lower than for the defect-free NT. These results enable interpretation of the low-energy tail in the experimental UV-vis spectra for AlSi NTs as being due to the defects. Finally, the PBE-TC-LRC-approximated exciton binding energy for the defects' optical transitions is found to be substantially lower (up to 0.8 eV) than for the pristine defect-free NT's excitations (1.1 eV)

Hydrogen-induced rupture of strained Si─O bonds in amorphous silicon dioxide

Using ab initio modeling we demonstrate that H atoms can break strained Si─O bonds in continuous amorphous silicon dioxide (a-SiO(2)) networks, resulting in a new defect consisting of a threefold-coordinated Si atom with an unpaired electron facing a hydroxyl group, adding to the density of dangling bond defects, such as E' centers. The energy barriers to form this defect from interstitial H atoms range between 0.5 and 1.3 eV. This discovery of unexpected reactivity of atomic hydrogen may have significant implications for our understanding of processes in silica glass and nanoscaled silica, e.g., in porous low-permittivity insulators, and strained variants of a-SiO(2)

Towards more sustainable urban transportation for NetZero cities: assessing air quality and risk for e-scooter users using sensor fusion and artificial intelligence

The need to develop smart and NetZero cities and reduce carbon emission is driving innovation in cities around the world to use electric transportation technologies. Among that the use of e-scooters. Nottingham (UK) is one of the cities that has an e-scooter scheme where people could rent e-scooters to travel around the city. However, in the current situation, to ensure pedestrian safety e-scooters need to be ridden on the road amongst cars, most of them are fossil fuelled. This gives rise to two potential risks for e-scooter users: the air quality that they breathe and the physical risk of being near cars, where drivers may not be familiar with seeing e-scooters on the road. This paper uses a mixed methods approach by conducting surveys to drivers and e-scooter users, jointly with an experimental work to monitor the journey of e-scooter users combining air quality, GPS data and 360 degrees camera footage to assess the risk to e-scooter riders using sensor fusion and artificial intelligence. The results indicate that the suggested novel methodology is effective in understanding the current limitations and the potential air quality and physical risks to e-scooter users

Robotic ubiquitous cognitive ecology for smart homes

Robotic ecologies are networks of heterogeneous robotic devices pervasively embedded in everyday environments, where they cooperate to perform complex tasks. While their potential makes them increasingly popular, one fundamental problem is how to make them both autonomous and adaptive, so as to reduce the amount of preparation, pre-programming and human supervision that they require in real world applications. The project RUBICON develops learning solutions which yield cheaper, adaptive and efficient coordination of robotic ecologies. The approach we pursue builds upon a unique combination of methods from cognitive robotics, machine learning, planning and agent- based control, and wireless sensor networks. This paper illustrates the innovations advanced by RUBICON in each of these fronts before describing how the resulting techniques have been integrated and applied to a smart home scenario. The resulting system is able to provide useful services and pro-actively assist the users in their activities. RUBICON learns through an incremental and progressive approach driven by the feed- back received from its own activities and from the user, while also self-organizing the manner in which it uses available sensors, actuators and other functional components in the process. This paper summarises some of the lessons learned by adopting such an approach and outlines promising directions for future work

Client abuse to public welfare workers: theoretical framework and critical incident case study

We analyse a case study of workers’ experience of client abuse in a Danish public welfare organisation. We make an original contribution by putting forward two different theoretical expectations of the case. One expectation is that the case follows a pattern of customer abuse processes in a social market economy – in which worker are accorded power and resources, in which workers tend to frame the abuse as the outcome of a co-citizen caught in system failure, and in which workers demonstrate some resilience to abuse. Another expectation is that New Public Management reforms push the case to follow patterns of customer abuse associated with a liberal market economy – in which the customer is treated as sovereign against the relatively powerless worker, and in which workers bear heavy emotional costs of abuse. Our findings show a greater match to the social processes of abuse within a social market economy

Building collaboration in multi-agent systems using reinforcement learning

© Springer Nature Switzerland AG 2018. This paper presents a proof-of concept study for demonstrating the viability of building collaboration among multiple agents through standard Q learning algorithm embedded in particle swarm optimisation. Collaboration is formulated to be achieved among the agents via competition, where the agents are expected to balance their action in such a way that none of them drifts away of the team and none intervene any fellow neighbours territory, either. Particles are devised with Q learning for self training to learn how to act as members of a swarm and how to produce collaborative/collective behaviours. The produced experimental results are supportive to the proposed idea suggesting that a substantive collaboration can be build via proposed learning algorithm

A qualitative examination of inappropriate hospital admissions and lengths of stay

<p>Abstract</p> <p>Background</p> <p>Research has shown that a number of patients, with a variety of diagnoses, are admitted to hospital when it is not essential and can remain in hospital unnecessarily. To date, research in this area has been primarily quantitative. The purpose of this study was to explore the perceived causes of inappropriate or prolonged lengths of stay and focuses on a specific population (i.e., patients with long term neurological conditions). We also wanted to identify interventions which might avoid admission or expedite discharge as periods of hospitalisation pose particular risks for this group.</p> <p>Methods</p> <p>Two focus groups were conducted with a convenience sample of eight primary and secondary care clinicians working in the Derbyshire area. Data were analysed using a thematic content approach.</p> <p>Results</p> <p>The participants identified a number of key causes of inappropriate admissions and lengths of stay, including: the limited capacity of health and social care resources; poor communication between primary and secondary care clinicians and the cautiousness of clinicians who manage patients in community settings. The participants also suggested a number of strategies that may prevent inappropriate admissions or reduce length of stay (LoS), including: the introduction of new sub-acute care facilities; the introduction of auxiliary nurses to support specialist nursing staff and patient held summaries of specialist consultations.</p> <p>Conclusion</p> <p>Clinicians in both the secondary and primary care sectors acknowledged that some admissions were unnecessary and some patients remain in hospital for a prolonged period. These events were attributed to problems with the current capacity or structuring of services. It was noted, for example, that there is a shortage of appropriate therapeutic services and that the distribution of beds between community and sub-acute care should be reviewed.</p