Hybrid materials comprising of Pd, MCo2O4 (where M = Mn, Co or Ni) and graphene have been prepared for use as efficient bifunctional electrocatalysts in alkaline direct methanol fuel cells. Structural and electrochemical characterizations were carried out using X-ray diffraction, transmission electron microscopy, X-ray photoelectron spectroscopy, chronoamperometry and cyclic, CO stripping, and linear sweep voltammetries. The study revealed that all the three hybrid materials are active for both methanol oxidation (MOR) and oxygen reduction (ORR) reactions in 1 M KOH. However, the Pd-MnCo2O4/GNS hybrid electrode exhibited the greatest MOR and ORR activities. This active hybrid electrode has also outstanding stability under both MOR and ORR conditions, while Pt- and other Pd-based catalysts undergo degradation under similar experimental conditions. The Pd-MnCo2O4/GNS hybrid catalyst exhibited superior ORR activity and stability compared to even Pt in alkaline solutions.
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http://dx.doi.org/10.1039/c3cp53880j | DOI Listing |
ACS Appl Mater Interfaces
December 2024
TCS Research, Sahyadri Park 2, Rajiv Gandhi Infotech Park, Hinjewadi Phase 3, Pune 411057, India.
Realization of a sustainable hydrogen economy in the future requires the development of efficient and cost-effective catalysts for its production at scale. MXenes (MX) are a class of 2D materials with 'n' layers of carbon or nitrogen (X) interleaved by 'n+1' layers of transition metal (M) and have emerged as promising materials for various applications including catalysts for hydrogen evolution reaction (HER). Their properties are intimately related to both their composition and their atomic structure.
View Article and Find Full Text PDFACS Appl Mater Interfaces
December 2024
Central European Institute of Technology, Brno University of Technology, Purkynova 123, 612 00 Brno, Czech Republic.
The current study investigates and compares the biological effects of ultrathin conformal coatings of zirconium dioxide (ZrO) and vanadium pentoxide (VO) on osteoblastic MG-63 cells grown on TiO nanotube layers (TNTs). Coatings were achieved by the atomic layer deposition (ALD) technique. TNTs with average tube diameters of 15, 30, and 100 nm were fabricated on Ti substrates (via electrochemical anodization) and were used as primary substrates for the study.
View Article and Find Full Text PDFAdv Sci (Weinh)
December 2024
Key Laboratory for Photonic and Electronic Bandgap Materials, Ministry of Education, School of Physics and Electronic Engineering, Harbin Normal University, Harbin, Heilongjiang, 150025, China.
Lithium-sulfur batteries (LSBs) offer high energy density and environmental benefits hampered by the shuttle effect related to sluggish redox reactions of long-chain lithium polysulfides (LiPSs). However, the fashion modification of the d-band center in separators is still ineffective, wherein the mechanism understanding always relies on theoretical calculations. This study visibly probed the evolution of the Co 3d-band center during charge and discharge using advanced inverse photoemission spectroscopy/ultraviolet photoemission spectroscopy (IPES/UPS), which offers reliable evidence and are consistent well with theoretical calculations.
View Article and Find Full Text PDFBMC Oral Health
December 2024
Department of Restorative Dentistry, Faculty of Dentistry, Bolu Abant Izzet Baysal University, Bolu, Turkey.
Objective: To compare the translucency and contrast ratio of 13 different resin based restorative materials and to evaluate the effect of 2 different bleaching methods on the translucency and contrast ratio of these materials.
Methods: In this study, a total of 260 samples were prepared, 20 from each of 13 different dimethacrylate-based restorative materials. Then, each material group was divided into 4 subgroups.
Nat Comput Sci
December 2024
Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
Machine learning plays an important role in quantum chemistry, providing fast-to-evaluate predictive models for various properties of molecules; however, most existing machine learning models for molecular electronic properties use density functional theory (DFT) databases as ground truth in training, and their prediction accuracy cannot surpass that of DFT. In this work we developed a unified machine learning method for electronic structures of organic molecules using the gold-standard CCSD(T) calculations as training data. Tested on hydrocarbon molecules, our model outperforms DFT with several widely used hybrid and double-hybrid functionals in terms of both computational cost and prediction accuracy of various quantum chemical properties.
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