Synthesis and Electrochemical Evaluation of Multivalent Vanadium Hydride Gels for Lithium and Hydrogen Storage

A vanadium aryl hydride gel was prepared by thermal decomposition and subsequent hydrogenation of tetraphenyl vanadium and evaluated for electrochemical and hydrogen storage performance. Characterization by IR, XRD, XPS, nitrogen adsorption, and TGA suggests that the material consists predominantly of a mixture of vanadium centers in oxidation states of II–IV bound together by bridging hydride and phenyl groups. Electrochemical properties were explored to probe the reversible oxidation state behavior and possible applications to Li batteries, with the hypothesis that the low mass of the hydride ligand may lead to superior gravimetric performance relative to heavier vanadium oxides and phosphates. The material shows reversible redox activity and has a promising peak capacity of 131 mAh g–1, at a discharge rate of 1 mA cm–2, comparable to bulk VO2 samples also tested in this study. After repeated charge–discharge cycling for 50 cycles, the material retained 36% of its capacity. The material also shows improved hydrogen storage performance relative to previously synthesized VH3 based gels, reaching a reversible gravimetric storage capacity of 5.8 wt % at 130 bar and 25 °C. Based on the measured density, this corresponds to a volumetric capacity of 79.77 kg H2 m–3, demonstrating that the 2017 U.S. DOE system goals of 5.5 wt % and 40 kg H2 m–3 may be achievable upon containment in a Type 1 tank and coupling to a fuel cell

Exploring Mars at the nanoscale: applications of transmission electron microscopy and atom probe tomography in planetary exploration

The upcoming Mars Sample Return (MSR) mission aims to deliver small quantities of Martian rocks to the Earth. Investigating these precious samples requires the development and application of techniques that can extract the greatest amount of high quality data from the minimum sample volume, thereby maximising science return from MSR. Atom probe tomography (APT) and transmission electron microscopy (TEM) are two complementary techniques that can obtain nanoscale structural, geochemical and, in the case of atom probe, isotopic information from small sample volumes. Here we describe how both techniques operate, as well as review recent developments in sample preparation protocols. We also outline how APT has been successfully applied to extraterrestrial materials in the recent past. Finally, we describe how we have studied Martian meteorites using TEM and APT in close coordination in order to characterise the products of water/rock interactions in t h e cru st of Ma r s – a k ey sc ie n ce goal of MSR. Our results provide new insights into the Martian hydrosphere and the mechanisms of anhydrous-hydrous mineral replacement. In light of the unique results provided by these tools, APT and TEM should form a crucial part at the culmination of a correlative analytical pipeline for MSR mission materials

Biological Earth observation with animal sensors

Space-based tracking technology using low-cost miniature tags is now delivering data on fine-scale animal movement at near-global scale. Linked with remotely sensed environmental data, this offers a biological lens on habitat integrity and connectivity for conservation and human health; a global network of animal sentinels of environmen-tal change

Predicting leptomeningeal disease spread after resection of brain metastases using machine learning

OBJECTIVE:The incidence of leptomeningeal disease (LMD) has increased as treatments for brain metastases (BMs) have improved and patients with metastatic disease are living longer. Sample sizes of individual studies investigating LMD after surgery for BMs and its risk factors have been limited, ranging from 200 to 400 patients at risk for LMD, which only allows the use of conventional biostatistics. Here, the authors used machine learning techniques to enhance LMD prediction in a cohort of surgically treated BMs. METHODS:A conditional survival forest, a Cox proportional hazards model, an extreme gradient boosting (XGBoost) classifier, an extra trees classifier, and logistic regression were trained. A synthetic minority oversampling technique (SMOTE) was used to train the models and handle the inherent class imbalance. Patients were divided into an 80:20 training and test set. Fivefold cross-validation was used on the training set for hyperparameter optimization. Patients eligible for study inclusion were adults who had consecutively undergone neurosurgical BM treatment, had been admitted to Brigham and Women's Hospital from January 2007 through December 2019, and had a minimum of 1 month of follow-up after neurosurgical treatment. RESULTS:A total of 1054 surgically treated BM patients were included in this analysis. LMD occurred in 168 patients (15.9%) at a median of 7.05 months after BM diagnosis. The discrimination of LMD occurrence was optimal using an XGboost algorithm (area under the curve = 0.83), and the time to LMD was prognosticated evenly by the random forest algorithm and the Cox proportional hazards model (C-index = 0.76). The most important feature for both LMD classification and regression was the BM proximity to the CSF space, followed by a cerebellar BM location. Lymph node metastasis of the primary tumor at BM diagnosis and a cerebellar BM location were the strongest risk factors for both LMD occurrence and time to LMD. CONCLUSIONS:The outcomes of LMD patients in the BM population are predictable using SMOTE and machine learning. Lymph node metastasis of the primary tumor at BM diagnosis and a cerebellar BM location were the strongest LMD risk factors.</p