Direct in-situ imaging of electrochemical corrosion of Pd-Pt core-shell electrocatalysts.

Corrosion of electrocatalysts during electrochemical operations, such as low potential - high potential cyclic swapping, can cause significant performance degradation. However, the electrochemical corrosion dynamics, including structural changes, especially site and composition specific ones, and their correlation with electrochemical processes are hidden due to the insufficient spatial-temporal resolution characterization methods. Using electrochemical liquid cell transmission electron microscopy, we visualize the electrochemical corrosion of Pd@Pt core-shell octahedral nanoparticles towards a Pt nanoframe. The potential-dependent surface reconstruction during multiple continuous in-situ cyclic voltammetry with clear redox peaks is captured, revealing an etching and deposition process of Pd that results in internal Pd atoms being relocated to external surface, followed by subsequent preferential corrosion of Pt (111) terraces rather than the edges or corners, simultaneously capturing the structure evolution also allows to attribute the site-specific Pt and Pd atomic dynamics to individual oxidation and reduction events. This work provides profound insights into the surface reconstruction of nanoparticles during complex electrochemical processes.

A study on the differential of solid lung adenocarcinoma and tuberculous granuloma nodules in CT images by Radiomics machine learning.

To study the classification efficiency of using texture feature machine learning method in distinguishing solid lung adenocarcinoma (SADC) and tuberculous granulomatous nodules (TGN) that appear as solid nodules (SN) in non-enhanced CT images. 200 patients with SADC and TGN who underwent thoracic non-enhanced CT examination from January 2012 to October 2019 were included in the study, 490 texture eigenvalues of 6 categories were extracted from the lesions in the non-enhanced CT images of these patients for machine learning, the classification prediction model is established by using relatively the best classifier selected according to the fitting degree of learning curve in the process of machine learning, and the effectiveness of the model was tested and verified. The logistic regression model of clinical data (including demographic data and CT parameters and CT signs of solitary nodules) was used for comparison. The prediction model of clinical data was established by logistic regression, and the classifier was established by machine learning of radiologic texture features. The area under the curve was 0.82 and 0.65 for the prediction model based on clinical CT and only CT parameters and CT signs, and 0.870 based on Radiomics characteristics. The machine learning prediction model developed by us can improve the differentiation efficiency of SADC and TGN with SN, and provide appropriate support for treatment decisions.

Design of Highly Durable Core-Shell Catalysts by Controlling Shell Distribution Guided by In-Situ Corrosion Study.

Most degradations in electrocatalysis are caused by corrosion in operation, for example the corrosion of the core in a core-shell electrocatalyst during the oxygen reduction reaction (ORR). Herein, according to the in-situ study on nanoscale corrosion kinetics via liquid cell transmission electron microscopy (LC-TEM) in the authors' previous work, they sequentially designed an optimized nanocube with the protection of more layers on the corners by adjusting the Pt atom distribution on corners and terraces. This modified nanocube (MNC) is much more corrosion resistant in the in-situ observation. Furthermore, in the practical electrochemical stability testing, the MNC catalyst also showed the best stability performance with the 0.37% and 9.01% loss in specific and mass activity after 30 000 cycles accelerated durability test (ADT). This work also demonstrates that how an in-situ study can guide the design of desired materials with improved properties and build a bridge between in-situ study and practical application.

A Large-Scalable, Surfactant-Free, and Ultrastable Ru-Doped Pt3Co Oxygen Reduction Catalyst.

Developing a large-scale method to produce platinum (Pt)-based electrocatalysts for the oxygen reduction reaction (ORR) is highly desirable to propel the commercialization of the membrane electrode assembly (MEA). Here, we successfully report the large-scale production of surfactant-free ruthenium-doped Pt-cobalt octahedra grown on carbon (Ru-Pt3Co/C), which display a much higher ORR activity and stability and MEA stability than Pt3Co/C and Pt/C. Significantly, the in-situ X-ray absorption fine structure result reveals that Ru can drive the reduced Pt atoms to reverse to their initial state after the ORR by transferring a redundant electron from Pt to Ru, preventing the over-reduction of Pt active sites and boosting the chemical stability. Theory investigations further confirm that the doped Ru can accelerate the breach and desorption of oxygen intermediates, making it active and durable for the ORR. The present work sheds light on the exploration of a large-scale strategy for producing advanced Pt-based nanocatalysts, which may offer significant advantages for practical fuel cell applications in the future.

Understanding of Strain-Induced Electronic Structure Changes in Metal-Based Electrocatalysts: Using Pd@Pt Core-Shell Nanocrystals as an Ideal Platform.

While metal-based electrocatalysts have garnered extensive attention owing to the large variety of enzyme-mimic properties, the search for such highly-efficient catalysts still relies on empirical explorations, owing to the lack of predictive indicators as well as the ambiguity of structure-activity relationships. Notably, surface electronic structures play a crucial role in metal-based catalysts yet remain unexplored in enzyme-mimics. Herein, the authors investigate the electronic structure as a possible indicator of electrocatalytic activities of H2 O2 decomposition and glucose oxidation using Pd@Pt core-shell nanocrystals as a well-defined platform. The electron densities of the Pd@Pt are modulated with the correlation of strain through precise control of surface orientation and the number of atomic layers. The close relationships between the electrocatalytic activities and the surface charge accumulation are found, in which the increase of the electron accumulation can enhance both the enzyme-mimic activities. As a result, the Pd@Pt3L icosahedra with compressive strain in Pt shells exhibit the highest electrocatalytic activities for H2 O2 decomposition and glucose oxidation. Such systematic and comprehensive study provides the structure-activity relationships and paves a new way for the rational design of metal-based electrocatalysts. Especially, the charge accumulation degrees may serve as a general performance indicator for metal-based catalysts.

Plasmonic-Enhanced Oxygen Reduction Reaction of Silver/Graphene Electrocatalysts.

Oxygen reduction reaction (ORR) is of paramount importance in polymer electrolyte membrane fuel cells due to its sluggish kinetics. In this work, a plasmon-induced hot electrons enhancement method is introduced to enhance ORR property of the silver (Ag)-based electrocatalysts. Three types of Ag nanostructures with differently localized surface plasmon resonances have been used as electrocatalysts. The thermal effect of plasmonic-enhanced ORR can be minimized in our work by using graphene as the support of Ag nanoparticles. By tuning the resonance positions and laser power, the enhancement of ORR properties of Ag catalysts has been optimized. Among these catalysts, Ag nanotriangles after excitation show the highest mass activity and reach 0.086 mA/µgAg at 0.8 V, which is almost 17 times that of a commercial Pt/C catalyst after the price is accounted. Our results demonstrate that the hot electrons generated from surface plasmon resonance can be utilized for electrochemical reaction, and tuning the resonance positions by light is a promising and viable approach to boost electrochemical reactions.