Zhao, Zelin | Search NASA

DOE OSTI2024

Epitaxial heterointerfacial electron bridge synchronizes oxygen evolution activity and stability on a layered double hydroxide surface

Scalable green hydrogen production via electrocatalytic water splitting is largely restricted by the insufficient activity and stability of oxygen evolution reaction (OER) catalysts at the anode. As a class of the most active OER catalysts in alkaline electrolyzers, the application of layered double hydroxides (LDHs) remains a main challenge owing to the unstable lattice oxygen dissolution due to the dominant lattice oxygen-involving OER mechanism during long-term operation. Herein, we found that using an epitaxial hetero-interfacing nickel hydroxide (namely Ni(OH) 2 ) as an electron bridge between an active FeCo LDH and Ni foam support to form an LDH*/NFO catalyst, the electronic storage capacity around the Fermi level (-0.5 to +0.5 eV, e-D FE ) sharply increases from 0.93 per cell to 1.51 per cell. Subsequently, we demonstrate that this high e-D FE enables ceaseless and fast power injection into the kinetic process of intermediate species conversion and inhibits lattice oxygen dissolution in the active FeCo LDH. Consequently, it demonstrated a low OER overpotential of 246 mV at a current density of 100 mA cm -2 and ultrahigh stability for up to 3500 hours with an ultraslow overpotential increase rate of 9.4 × 10 -3 mV h -1 . Therefore, we developed an epitaxial hetero-interfacial electron bridging strategy to synchronize the activity and stability of available catalysts for scalable green hydrogen production via electrocatalytic water splitting.

25 ENERGY STORAGE↗

DOE OSTI2024

Novel symmetry-preserving neural network model for phylogenetic inference

Abstract Motivation Scientists world-wide are putting together massive efforts to understand how the biodiversity that we see on Earth evolved from single-cell organisms at the origin of life and this diversification process is represented through the Tree of Life. Low sampling rates and high heterogeneity in the rate of evolution across sites and lineages produce a phenomenon denoted “long branch attraction” (LBA) in which long nonsister lineages are estimated to be sisters regardless of their true evolutionary relationship. LBA has been a pervasive problem in phylogenetic inference affecting different types of methodologies from distance-based to likelihood-based. Results Here, we present a novel neural network model that outperforms standard phylogenetic methods and other neural network implementations under LBA settings. Furthermore, unlike existing neural network models in phylogenetics, our model naturally accounts for the tree isomorphisms via permutation invariant functions which ultimately result in lower memory and allows the seamless extension to larger trees. Availability and implementation We implement our novel theory on an open-source publicly available GitHub repository: https://github.com/crsl4/nn-phylogenetics.

59 BASIC BIOLOGICAL SCIENCES↗