Magnéli-Phase Ti₄O₇ Nanosphere Electrocatalyst Support for Carbon-Free Oxygen Electrodes in Lithium–Oxygen Batteries

Lithium–oxygen batteries have been considerably researched due to their potential for high energy density compared to some rechargeable batteries. However, it is known that the stability of a carbon-based oxygen electrode is insufficient owing to the promotion of carbonate formation, which results in capacity fading and large overpotential in lithium–oxygen batteries. To improve the chemical stability in organic-based electrolytes, alternative electrocatalyst support materials are required. The Ti–O crystal system appears to provide a good compromise between electrochemical performance and cost and is thus an interesting material for further investigation. Here, we investigate a carbon-free electrode with the goal of identifying routes for its successful optimization. To replace carbon materials as an electrocatalyst support, Magnéli Ti₄O₇ nanospheres were synthesized from anatase TiO₂ nanospheres via a controlled thermochemical reduction. The Magnéli Ti₄O₇ nanospheres demonstrated effective overpotential characteristics (1.53 V) compared to the anatase TiO₂ nanospheres (1.91 V) during charge–discharge cycling at a current rate of 100 mA g^–1. Additionally, RuO₂@Magnéli-Ti₄O₇ nanospheres were prepared as a bifunctional catalyst-containing oxygen electrode for lithium–oxygen batteries, providing a remarkably reduced overpotential (0.9 V)

Highly Reversible Li Storage in Hybrid NiO/Ni/Graphene Nanocomposites Prepared by an Electrical Wire Explosion Process

NiO/Ni/graphene nanocomposites were prepared using a simple and environmentally friendly method comprising an electrical wire pulse technique in oleic acid containing graphenes and subsequent annealing to form anodes for Li ion batteries. The control product of NiO/Ni nanocomposite was prepared under the same conditions and characterized by structural and electrochemical analysis. The obtained NiO/Ni/graphene nanocomposite particles had sizes of 5–12 nm and a high surface area of 137 m² g^–1. In comparison to NiO/Ni, NiO/Ni/graphene exhibited improved cycling performance and good rate capability. Reversible capacity was maintained at over 600 mA h g^–1 at 0.2 C and was attributed to the alleviation in volume change and improved electrical conductivity of NiO/Ni/graphene nanocomposites

Heteroepitaxy-Induced Rutile VO₂ with Abundantly Exposed (002) Facets for High Lithium Electroactivity

Research on VO₂ cathodes for lithium ion batteries has been mainly focused on the VO₂ (B) phase. However, rutile VO₂ (M/R) has rarely been studied because of the intrinsically low lithium activity resulting from the highly anisotropic nature of lithium accommodation. Here, we demonstrate that heteroepitaxial engineering can be an effective strategy for activating the anisotropic electrode and developing kinetically superior electrodes. Appropriate lattice mismatch between the active material (VO₂) and conductive support (Sb:SnO₂) yields a coherent interface, where tensile strain aids preferential growth along the rutile <i>c</i>-axis as well as expansion in the <i>ab</i> plane and thereby the exposure of reactive (002) facets. The VO₂–Sb:SnO₂ electrode exhibits high reversible capacity (350 mA h g^–1 at 100 mA g^–1) and ultrafast rate capability (196 mA h g^–1 at 2000 mA g^–1) with structural stability, which represents record-high performance compared with previous VO₂ reports, including those on other polymorphs such as VO₂ (A) and VO₂ (B)

The prognostic value of CT radiomic features for patients with pulmonary adenocarcinoma treated with EGFR tyrosine kinase inhibitors

<div><p>Purpose</p><p>To determine if the radiomic features on CT can predict progression-free survival (PFS) in epidermal growth factor receptor (<i>EGFR</i>) mutant adenocarcinoma patients treated with first-line EGFR tyrosine kinase inhibitors (TKIs) and to identify the incremental value of radiomic features over conventional clinical factors in PFS prediction.</p><p>Methods</p><p>In this institutional review board–approved retrospective study, pretreatment contrast-enhanced CT and first follow-up CT after initiation of TKIs were analyzed in 48 patients (M:F = 23:25; median age: 61 years). Radiomic features at baseline, at 1^st first follow-up, and the percentage change between the two were determined. A Cox regression model was used to predict PFS with nonredundant radiomic features and clinical factors, respectively. The incremental value of radiomic features over the clinical factors in PFS prediction was also assessed by way of a concordance index.</p><p>Results</p><p>Roundness (HR: 3.91; 95% CI: 1.72, 8.90; P = 0.001) and grey-level nonuniformity (HR: 3.60; 95% CI: 1.80, 7.18; P<0.001) were independent predictors of PFS. For clinical factors, patient age (HR: 2.11; 95% CI: 1.01, 4.39; P = 0.046), baseline tumor diameter (HR: 1.03; 95% CI: 1.01, 1.05; P = 0.002), and treatment response (HR: 0.46; 95% CI: 0.24, 0.87; P = 0.017) were independent predictors. The addition of radiomic features to clinical factors significantly improved predictive performance (concordance index; combined model = 0.77, clinical-only model = 0.69, P<0.001).</p><p>Conclusions</p><p>Radiomic features enable PFS estimation in <i>EGFR</i> mutant adenocarcinoma patients treated with first-line EGFR TKIs. Radiomic features combined with clinical factors provide significant improvement in prognostic performance compared with using only clinical factors.</p></div

Kaplan-Meier plots demonstrating the performance of each estimation model.

<p>Patients were divided in to low- and high-probability groups for progression-free survival according to the median value of output from (A) the radiomic model (HR: 5.34, 95% CI: 2.42, 11.76; P<0.001, (B) the clinical-factor model (HR: 2.51, 95% CI: 1.37, 4.59; P = 0.003), and (C) the combined model (HR: 5.49, 95% CI: 2.77, 10.89; P<0.001). CI, confidence interval; HR, hazard ratio.</p

Spearman’s rank correlation coefficients of textural parameters compared to the value of co-occurrence entropy.

<p>Abbreviations are listed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0189766#pone.0189766.s002" target="_blank">S1 Table</a>.</p

Patient characteristics, clinical factors, and TKI treatment.

<p>Patient characteristics, clinical factors, and TKI treatment.</p

Cox proportional regression analysis of the optimal cutoff value calculated from the exploratory dataset.

<p>Cox proportional regression analysis of the optimal cutoff value calculated from the exploratory dataset.</p

Schematic flow of textural analysis.

<p><b>(A) FDG-PET/CT scan acquisition. (B) Placement of a volume of interest on the primary tumor. (C) Tumor segmentation by isocontour with SUV of 3.5 (D) Gray scale resampling and texture feature extraction in global, local, and regional scales.</b> Abbreviations: Co = Co-occurrence, NID = Neighborhood intensity difference, VA = Voxel alignment, ISZ = intensity size zone.</p

Characteristics of the study population.

<p>Characteristics of the study population.</p