Oxygen transport in nanostructured lanthanum manganites

Methods and models describing oxygen diffusion and desorption in oxides have been developed for slightly defective and well crystallised bulky materials. Does nanostructuring change the mechanism of oxygen mobility? In such a case, models should be properly checked and adapted to take into account new material properties. In order to do so, temperature programmed oxygen desorption and thermogravimetric analysis, either in isothermal or ramp mode, have been used to investigate some nanostructured La1\u2013xAxMnO3 d samples (A = Sr and Ce, 20\u201360 nm particle size) with perovskite-like structure. The experimental data have been elaborated by means of different models to define a set of kinetic parameters able to describe oxygen release properties and oxygen diffusion through the bulk. Different rate-determining steps have been identified, depending on the temperature range and oxygen depletion of the material. In particular, oxygen diffusion was shown to be rate-limiting at low temperature and at low defect concentration, whereas oxygen recombination at the surface seems to be the rate-controlling step at high temperature. However, the oxygen recombination step is characterised by an activation energy much lower than that for diffusion. In the present paper oxygen transport in nanosized materials is quantified by making use of widely diffused experimental techniques and by critically adapting to nanoparticles suitably chosen models developed for bulk materials

Effect of sulphur poisoning on perovskite catalysts prepared by flame-pyrolysis

ABO(3) perovskite-like catalysts are known to be sensitive to sulphur-containing compounds. Possible solutions to increase resistance to sulphur are represented by either catalyst bed protection with basic guards or catalyst doping with different transition or noble metals. In the present work La((1-x))A(x)'CoO(3), La((1-x))A(x)'MnO(3) and La((1-x))A(x)'FeO(3), with A' = Ce, Sr and x = 0, 0.1, 0.2, either pure or doped with noble metals (0.5 wt% Pt or Pd), were prepared in nano-powder form by flame-pyrolysis. All the catalysts were tested for the catalytic flameless combustion of methane, monitoring the activity by on-line mass spectrometry. The catalysts were then progressively deactivated in operando with a new procedure, consisting of repeated injection of some doses of tetrahydrothiophene (THT), usually employed as odorant in the natural gas grid, with continuous analysis of the transient response of the catalyst. The activity tests were then repeated on the poisoned catalyst. Different regenerative treatments were also tried, either in oxidising or reducing atmosphere. Among the unsubstituted samples, higher activity and better resistance to poisoning have been observed in general with manganites with respect to the corresponding formulations containing Co or Fe at the B-site. The worst catalyst showed LaFeO(3), from both the points of view of activity and of resistance to sulphur poisoning. La(0.9)Sr(0.1)MnO(3) showed, the best results, exhibiting very high activity and good resistance even after the addition of up to 8.4 mg of THT/g of catalyst. Interesting results were attained also by adding Sr to Co-based perovskites. Sr showed a first action by forcing Mn or Co in their highest oxidation state, but, in addition, it could also act as a sulphur guard, likely forming stable sulphates due to its basicity. Among noble metals, Pt doping proved beneficial in improving the activity of both the fresh and the poisoned catalyst

Self-supervised learning for few-shot medical image segmentation

Fully-supervised deep learning segmentation models are inflexible when encountering new unseen semantic classes and their fine-tuning often requires significant amounts of annotated data. Few-shot semantic segmentation (FSS) aims to solve this inflexibility by learning to segment an arbitrary unseen semantically meaningful class by referring to only a few labeled examples, without involving fine-tuning. State-of-the-art FSS methods are typically designed for segmenting natural images and rely on abundant annotated data of training classes to learn image representations that generalize well to unseen testing classes. However, such a training mechanism is impractical in annotation-scarce medical imaging scenarios. To address this challenge, in this work, we propose a novel self-supervised FSS framework for medical images, named SSL-ALPNet, in order to bypass the requirement for annotations during training. The proposed method exploits superpixel-based pseudo-labels to provide supervision signals. In addition, we propose a simple yet effective adaptive local prototype pooling module which is plugged into the prototype networks to further boost segmentation accuracy. We demonstrate the general applicability of the proposed approach using three different tasks: organ segmentation of abdominal CT and MRI images respectively, and cardiac segmentation of MRI images. The proposed method yields higher Dice scores than conventional FSS methods which require manual annotations for training in our experiments

Excitons and their Fine Structure in Lead Halide Perovskite Nanocrystals from Atomistic GW/BSE Calculations

Atomistically detailed computational studies of nanocrystals, such as those derived from the promising lead-halide perovskites, are challenging due to the large number of atoms and lack of symmetries to exploit. Here, focusing on methylammonium lead iodide nanocrystals, we combine a real-space tight binding model with the GW approximation to the self-energy and obtain exciton wavefunctions and absorption spectra via solutions of the associated Bethe-Salpeter equation. We find that the size dependence of carrier confinement, dielectric contrast, electron-hole exchange, and exciton binding energies has a strong impact on the lowest excitation energy, which can be tuned by almost 1 eV over the diameter range of 2-6 nm. Our calculated excitation energies are about 0.2 eV higher than experimentally measured photoluminescence, and they display the same qualitative size dependence. Focusing on the fine structure of the band-edge excitons, we find that the lowest-lying exciton is spectroscopically dark and about 20-30 meV lower in energy than the higher-lying triplet of bright states, whose degeneracy is slightly broken by crystal field effects.Comment: 8 pages, 4 figure

La-Ag-Co perovskites for the catalytic flameless combustion of methane

Ag represents an interesting dopant for the highly active LaCoO3 perovskites used for the catalytic flameless combustion (CFC) of methane, due to its ability to adsorb and activate oxygen and to the possibility of incorporation into the framework as Ag+ or Ag2+, with formation of oxygen vacancies. In the present work we compared the catalytic activity and resistance to sulphur poisoning of a series of LaCoO3, x%Ag/LaCoO3, La1-xAgxCoO3 samples (nominal composition), the latter two notations indicating post-synthesis Ag loading or direct incorporation during the synthesis, respectively. The samples were prepared by flame pyrolysis (FP) and by the sot-gel (SG) method, leading to different particle size and possibly to different incorporation degree of the dopant, quantified by Rietveld refinement of XRD patterns. Higher activity was observed, in general, with fresh catalysts synthesised by FP. The SG samples demonstrated a slightly better resistance to sulphur poisoning when considering the conversion decrease between the fresh and the poisoned samples, due to lower surface exposure. However, interesting data have been obtained with some of the Ag-doped poisoned FP samples, performing even better than the fresh SG-prepared ones. Ag addition led to a complex change of activity and resistance to poisoning. The activity of FP-prepared samples doped with a small amount of Ag (e.g. 5 mol%) was indeed lower than that of the undoped LaCoO3. By contrast, a further increase of Ag concentration led to increasing catalytic activity, mainly when big extra framework Ag particles were present. By contrast, for SG samples a low Ag amount was beneficial for activity, due to an increased reducibility of Co3+

Cool Core Clusters from Cosmological Simulations

We present results obtained from a set of cosmological hydrodynamic simulations of galaxy clusters, aimed at comparing predictions with observational data on the diversity between cool-core (CC) and non-cool-core (NCC) clusters. Our simulations include the effects of stellar and AGN feedback and are based on an improved version of the smoothed particle hydrodynamics code GADGET-3, which ameliorates gas mixing and better captures gas-dynamical instabilities by including a suitable artificial thermal diffusion. In this Letter, we focus our analysis on the entropy profiles, the primary diagnostic we used to classify the degree of cool-coreness of clusters, and on the iron profiles. In keeping with observations, our simulated clusters display a variety of behaviors in entropy profiles: they range from steadily decreasing profiles at small radii, characteristic of cool-core systems, to nearly flat core isentropic profiles, characteristic of non-cool-core systems. Using observational criteria to distinguish between the two classes of objects, we find that they occur in similar proportions in both simulations and in observations. Furthermore, we also find that simulated cool-core clusters have profiles of iron abundance that are steeper than those of NCC clusters, which is also in agreement with observational results. We show that the capability of our simulations to generate a realistic cool-core structure in the cluster population is due to AGN feedback and artificial thermal diffusion: their combined action allows us to naturally distribute the energy extracted from super-massive black holes and to compensate for the radiative losses of low-entropy gas with short cooling time residing in the cluster core.Comment: 6 pages, 4 figures, accepted in ApJL, v2 contains some modifications on the text (results unchanged

5kWe+5kWt reformer-PEMFC energy generator from bioethanol first data on the fuel processor from a demonstrative project

A power unit constituted by a reformer section, a H 2 purification section and a fuel cell stack is being tested c/o the Dept. of Physical Chemistry and Electrochemistry of Universit\ue0 degli Studi di Milano, on the basis of a collaboration with HELBIO S.A. Hydrogen and Energy Production Systems, Patras (Greece), supplier of the unit, and some sponsors (Linea Energia S.p.A., Parco Tecnologico Padano and Provincia di Lodi, Italy). The system size allows to co-generate 5 kW e (220 V, 50 Hz a.c.) + 5 kW t (hot water at 65\ub0C) as peak output. Bioethanol, obtainable by different non-food-competitive biomass, is transformed into syngas by a pre-reforming and reforming reactors couple and the reformate is purified from CO to a concentration below 20 ppmv, suitable to feed a proton exchange membrane fuel cell (PEMFC) stack that will be integrated in the fuel processor in a second step of the experimentation. This result is achieved by feeding the reformate to two water gas shift reactors, connected in series and operating at high and low temperature, respectively. CO concentration in the outcoming gas is ca. 0.4 vol% and the final CO removal to meet the specifications is accomplished by two methanation reactors in series. The second methanation step acts merely as a guard, since ca. 15 ppmv of CO are obtained already after the first reactor. The goals of the present project are to test the integrated fuel processor, to check the effectiveness of the proposed technology and to suggest possible adequate improvements

Hot isostatic pressing and heat treatments of LPBFed CoCuFeMnNiTi0.13 high-entropy alloy: microstructure and mechanical properties

The present work explores the possibility of processing a CoCuFeMnNiTi0.13 high-entropy alloy by laser powder bed fusion (LPBF). The alloy, produced under optimised processing conditions, presents good densification but also hot cracks, caused by the liquation of an inter-dendritic Cu-rich phase. Microstructure of the as-built alloy is characterised by face centred cubic (FCC) columnar grains, containing Cu-poor dendrites and Cu-rich inter-dendritic areas. The alloy, which was designed to be strengthened by spinodal decomposition and precipitation, was subjected to different thermo-mechanical treatments to try and improve its properties. Direct ageing and solution treatment and ageing produced a strong but brittle material (tensile strength of 683 MPa and elongation to failure of 1.3%), whereas hot isostatic pressing followed by controlled cooling was able to heal pores and cracks while triggering the desired microstructural transformations (spinodal decomposition and precipitation). This resulted into a balanced set of mechanical properties (tensile strength of 473 MPa and elongation to failure of 7.6%). This work shows that proper post-processing can mitigate the issues typically affecting LPBF fabricated HEAs, producing tailored microstructures with satisfactory mechanical performances

Explainable Anatomical Shape Analysis through Deep Hierarchical Generative Models

Quantification of anatomical shape changes currently relies on scalar global indexes which are largely insensitive to regional or asymmetric modifications. Accurate assessment of pathology-driven anatomical remodeling is a crucial step for the diagnosis and treatment of many conditions. Deep learning approaches have recently achieved wide success in the analysis of medical images, but they lack interpretability in the feature extraction and decision processes. In this work, we propose a new interpretable deep learning model for shape analysis. In particular, we exploit deep generative networks to model a population of anatomical segmentations through a hierarchy of conditional latent variables. At the highest level of this hierarchy, a two-dimensional latent space is simultaneously optimised to discriminate distinct clinical conditions, enabling the direct visualisation of the classification space. Moreover, the anatomical variability encoded by this discriminative latent space can be visualised in the segmentation space thanks to the generative properties of the model, making the classification task transparent. This approach yielded high accuracy in the categorisation of healthy and remodelled left ventricles when tested on unseen segmentations from our own multi-centre dataset as well as in an external validation set, and on hippocampi from healthy controls and patients with Alzheimer's disease when tested on ADNI data. More importantly, it enabled the visualisation in three-dimensions of both global and regional anatomical features which better discriminate between the conditions under exam. The proposed approach scales effectively to large populations, facilitating high-throughput analysis of normal anatomy and pathology in large-scale studies of volumetric imaging

Virtual Reality Social Prediction Improvement and Rehabilitation Intensive Training (VR-SPIRIT) for paediatric patients with congenital cerebellar diseases: study protocol of a randomised controlled trial

Background: Patients with cerebellar malformations exhibit not only movement problems, but also important deficits in social cognition. Thus, rehabilitation approaches should not only involve the recovery of motor function but also of higher-order abilities such as processing of social stimuli. In keeping with the general role of the cerebellum in anticipating and predicting events, we used a VR-based rehabilitation system to implement a social cognition intensive training specifically tailored to improve predictive abilities in social scenarios (VR-Spirit). Methods/design: The study is an interventional randomised controlled trial that aims to recruit 42 children, adolescents and young adults with congenital cerebellar malformations, randomly allocated to the experimental group or the active control group. The experimental group is administered the VR-Spirit, requiring the participants to compete with different avatars in the reaching of recreational equipment and implicitly prompting them to form expectations about their playing preference. The active control group participates in a VR-training with standard games currently adopted for motor rehabilitation. Both trainings are composed by eight 45-min sessions and are administered in the GRAIL VR laboratory (Motekforce Link, Netherlands), an integrated platform that allows patients to move in natural and attractive VR environments. An evaluation session in VR with the same paradigm used in the VR-Spirit but implemented in a different scenario is administered at the beginning (T0) of the two trainings (T1) and at the end (T2). Moreover, a battery of neurocognitive tests spanning different domains is administered to all participants at T0, T2 and in a follow-up session after 2 months from the end of the two trainings (T3). Discussion: This study offers a novel approach for rehabilitation based on specific neural mechanisms of the cerebellum. We aim to investigate the feasibility and efficacy of a new, intensive, social cognition training in a sample of Italian patients aged 7-25 years with congenital cerebellar malformations. We expect that VR-Spirit could enhance social prediction ability and indirectly improve cognitive performance in diverse domains. Moreover, through the comparison with a VR-active control training we aim to verify the specificity of VR-Spirit in improving social perception skills. Trial registration: ISRCTN, ID: ISRCTN 22332873. Retrospectively registered on 12 March 2018