Energetics of hydrogen coverage on group VIII transition metal surfaces and a kinetic model for adsorption/desorption

We determined the binding energy of hydrogen to the closest packed surface for all nine group VIII transition metals as a function of surface coverage using quantum mechanics (density functional theory with the generalized gradient approximation) with periodic boundary conditions. The study provides a systematic comparison of the most stable surfaces of the nine group VIII transition metals, leading to results consistent with available surface science studies. We then use these to develop a simple thermodynamic model useful in estimating the surface coverage under typical heterogeneous catalysis conditions and compare these results to temperature programmed desorption experiments

Medical Database for Detecting Neoplastic Lesions in Human Colorectal Cancer with Deep Learning

Medical databases are fundamental for developing new techniques for early detection of neoplastic cells. They are however difficult to obtain, since the labelling of the images is often operator dependent, requires specialized skills and the written informed consent of the patient. The variability of structures in biological tissue poses a challenge to both manual and automated analysis of histopathology slides. Although some authors showed moderate to good agreement among expert pathologists, and satisfactory results on their intra-observer reliability, other studies found that even experienced pathologists frequently disagree on tissue classification, which may lead to the conclusion that solely using expert scoring as gold standard for histopathological assessment could be insufficient. Hence, there is a growing demand for robust computational methods in order to increase reproducibility of diagnoses. In this note we present a database containing images of preneoplastic and neoplastic colorectal tissues and in a forthcoming paper we will describe our proposed DL algorithm to classify them into the following categories: normal mucosa, early preneoplastic lesions, adenomas, cancer

Synthetic Data Generation for Automatic Segmentation of X-ray Computed Tomography Reconstructions of Complex Microstructures

The greatest challenge when using deep convolutional neural networks (DCNNs) for automatic segmentation of microstructural X-ray computed tomography (XCT) data is the acquisition of sufficient and relevant data to train the working network. Traditionally, these have been attained by manually annotating a few slices for 2D DCNNs. However, complex multiphase microstructures would presumably be better segmented with 3D networks. However, manual segmentation labeling for 3D problems is prohibitive. In this work, we introduce a method for generating synthetic XCT data for a challenging six-phase Al-Si alloy composite reinforced with ceramic fibers and particles. Moreover, we propose certain data augmentations (brightness, contrast, noise, and blur), a special in-house designed deep convolutional neural network (Triple UNet), and a multi-view forwarding strategy to promote generalized learning from synthetic data and therefore achieve successful segmentations. We obtain an overall Dice score of 0.77. Lastly, we prove the detrimental effects of artifacts in the XCT data on achieving accurate segmentations when synthetic data are employed for training the DCNNs. The methods presented in this work are applicable to other materials and imaging techniques as well. Successful segmentation coupled with neural networks trained with synthetic data will accelerate scientific output

Factors affecting cyclic durability of all-solid-state lithium batteries using poly(ethylene oxide)-based polymer electrolytes and recommendations to achieve improved performance

A detailed experimental analysis of the factors affecting cyclic durability of all-solid-state lithium batteries using poly(ethylene oxide)-based polymer electrolytes was published in EES by Nakayama et al. We use quantum mechanics to interpret these results, identifying processes involved in the degradation of rechargeable lithium batteries based on polyethylene oxide (PEO) polymer electrolyte with LiTFSI. We consider that ionization of the electrolyte near the cathode at the end of the recharge step is probably responsible for this degradation. We find that an electron is likely removed from PEO next to a TFSI anion, triggering a sequence of steps leading to neutralization of a TFSI anion and anchoring of another TFSI to the PEO. This decreases the polymer conductivity near the cathode, making it easier to ionize additional PEO and leading to complete degradation of the battery. We refer to this as the Cathode Overpotential Driven Ionization of the Solvent (CODIS) model. We suggest possible ways to confirm experimentally our interpretation and propose modifications to suppress or reduce electrolyte degradation

A population-based study on myelodysplastic syndromes in the Lazio Region (Italy), medical miscoding and 11-year mortality follow-up. The Gruppo Romano-Laziale Mielodisplasie experience of retrospective multicentric registry

Data on Myelodysplastic Syndromes (MDS) are difficult to collect by cancer registries because of the lack of reporting and the use of different classifications of the disease. In the Lazio Region, data from patients with a confirmed diagnosis of MDS, treated by a hematology center, have been collected since 2002 by the Gruppo Romano-Laziale Mielodisplasie (GROM-L) registry, the second MDS registry existing in Italy. This study aimed at evaluating MDS medical miscoding during hospitalizations, and patients' survival. For these purposes, we selected 644 MDS patients enrolled in the GROM-L registry. This cohort was linked with two regional health information systems: the Hospital Information System (HIS) and the Mortality Information System (MIS) in the 2002-2012 period. Of the 442 patients who were hospitalized at least once during the study period, 92% had up to 12 hospitalizations. 28.5% of patients had no hospitalization episodes scored like MDS, code 238.7 of the International Classification of Disease, Ninth Revision, Clinical Modification (ICD-9-CM). The rate of death during a median follow-up of 46 months (range 0.9-130) was 45.5%. Acute myeloid leukemia (AML) was the first cause of mortality, interestingly a relevant portion of deaths is due to cerebro-cardiovascular events and second tumors. This study highlights that MDS diagnosis and treatment, which require considerable healthcare resources, tend to be under-documented in the HIS archive. Thus we need to improve the HIS to better identify information on MDS hospitalizations and outcome. Moreover, we underline the importance of comorbidity in MDS patients' survival

Role of solvent-anion charge transfer in oxidative degradation of battery electrolytes

Electrochemical stability windows of electrolytes largely determine the limitations of operating regimes of lithium-ion batteries, but the degradation mechanisms are difficult to characterize and poorly understood. Using computational quantum chemistry to investigate the oxidative decomposition that govern voltage stability of multi-component organic electrolytes, we find that electrolyte decomposition is a process involving the solvent and the salt anion and requires explicit treatment of their coupling. We find that the ionization potential of the solvent-anion system is often lower than that of the isolated solvent or the anion. This mutual weakening effect is explained by the formation of the anion-solvent charge-transfer complex, which we study for 16 anion-solvent combinations. This understanding of the oxidation mechanism allows the formulation of a simple predictive model that explains experimentally observed trends in the onset voltages of degradation of electrolytes near the cathode. This model opens opportunities for rapid rational design of stable electrolytes for high-energy batteries