Effect of Annealing Temperature on Electrochemical Properties of Zr56Cu19Ni11Al9Nb5 in PBS Solution.

The electrochemical properties of as-cast Zr56Cu19Ni11Al9Nb5 metallic glass and samples annealed at different temperatures were investigated using potentiodynamic polarization tests and electrochemical impedance spectroscopy (EIS) in phosphate buffer saline (PBS) solution. It was shown that passivation occurred for the as-cast sample and the samples annealed at 623-823 K, indicating good corrosion resistance. At higher annealing temperature, the corrosion resistance first increased, and then decreased. The sample annealed at 823 K exhibited the best corrosion resistance, with high spontaneous corrosion potential Ecorr at -0.045 VSCE, small corrosion current density icorr at 1.549 × 10-5 A·cm-2, high pitting potential Epit at 0.165 VSCE, the largest arc radius, and the largest sum of Rf and Rct at 5909 Ω·cm2. For the sample annealed at 923 K, passivation did not occur, with low Ecorr at -0.075 VSCE, large icorr at 1.879 × 10-5 A·cm-2, the smallest arc radius, and the smallest sum of Rf and Rct at 2173 Ω·cm2, which suggested the worst corrosion resistance. Proper annealing temperature led to improved corrosion resistance due to structural relaxation and better stability of the passivation film, however, if the annealing temperature was too high, the corrosion resistance deteriorated due to the chemical inhomogeneity between the crystals and the amorphous matrix. Optical microscopy and scanning electron microscopy (SEM) examinations indicated that localized corrosion occurred. Results of energy dispersive X-ray spectroscopy (EDS) and X-ray photoelectron spectroscopy (XPS) illustrated that the main corrosion products were ZrO2, CuO, Cu2O, Ni(OH)2, Al2O3, and Nb2O5.

Arsenic speciation transformation in soils with high geological background: New insights from the governing role of Fe.

In soils, the speciation transformation of As were inherently related to the behaviors of iron (oxyhydr) oxides. It is poorly understood that the effects of the transformation of iron (oxyhydr) oxides coupled with As speciation transformation during dissimilatory Fe(III) reduction (DIR) involving with humic substances (HS) as electron donor or shuttle in soils with high arsenic geological background. In this study, the relationships between the transformation of iron (oxyhydr)oxides and As speciation transformation were investigated according to the response between continuously As speciation monitoring and iron (oxyhydr) oxides identification during DIR in the soils. The results showed that F4 (arsenic incorporated with amorphous iron (oxyhydr)oxides including ferrihydrite and schwertmannite) and F5 (arsenic incorporated with crystalline iron (oxyhydr)oxides including hematite and magnetite) were the main source and sink for As(III)Dissolved during DIR. During the incubation period, Fe(II) was the dominant driving force for the reduction of As(V) in the water-soil system. The XRD analysis indicated the changes of iron oxides such as ferrihydrite, schwertmannite, hematite and magnetite were closely related to the release and reduction of As, and those iron oxides could play governing roles for As speciation transformation during DIR in soils. Different from the known mechanism in low As concentrations, a limiting effect of As concentration on iron oxides transformation was found in our incubation experiments using soils with high As geological background (∼1000 mg/kg). This work provides new insights for Fe as governing role in As speciation transformation in soils with high arsenic geological background by firstly identifying the corresponding iron (oxyhydr)oxides in operationally defined arsenic speciation incorporated with iron oxides.

Development and validation of a deep-learning model for detecting brain metastases on 3D post-contrast MRI: a multi-center multi-reader evaluation study.

BACKGROUND: Accurate detection is essential for brain metastasis (BM) management, but manual identification is laborious. This study developed, validated, and evaluated a BM detection (BMD) system. METHODS: Five hundred seventy-three consecutive patients (10 448 lesions) with newly diagnosed BMs and 377 patients without BMs were retrospectively enrolled to develop a multi-scale cascaded convolutional network using 3D-enhanced T1-weighted MR images. BMD was validated using a prospective validation set comprising an internal set (46 patients with 349 lesions; 44 patients without BMs) and three external sets (102 patients with 717 lesions; 108 patients without BMs). The lesion-based detection sensitivity and the number of false positives (FPs) per patient were analyzed. The detection sensitivity and reading time of three trainees and three experienced radiologists from three hospitals were evaluated using the validation set. RESULTS: The detection sensitivity and FPs were 95.8% and 0.39 in the test set, 96.0% and 0.27 in the internal validation set, and ranged from 88.9% to 95.5% and 0.29 to 0.66 in the external sets. The BMD system achieved higher detection sensitivity (93.2% [95% CI, 91.6-94.7%]) than all radiologists without BMD (ranging from 68.5% [95% CI, 65.7-71.3%] to 80.4% [95% CI, 78.0-82.8%], all P < .001). Radiologist detection sensitivity improved with BMD, reaching 92.7% to 95.0%. The mean reading time was reduced by 47% for trainees and 32% for experienced radiologists assisted by BMD relative to that without BMD. CONCLUSIONS: BMD enables accurate BM detection. Reading with BMD improves radiologists' detection sensitivity and reduces their reading times.