ترغب بنشر مسار تعليمي؟ اضغط هنا

Predictive model of surface adsorption in dissolution on transition metals and alloys

112   0   0.0 ( 0 )
 نشر من قبل Wang Gao
 تاريخ النشر 2021
  مجال البحث فيزياء
والبحث باللغة English




اسأل ChatGPT حول البحث

Surface adsorption, which is often coupled with surface dissolution, is generally unpredictable on alloys due to the complicated alloying and dissolution effects. Herein, we introduce the electronic gradient and cohesive properties of surface sites to characterize the effects of alloying and dissolution. This enables us to build a predictive model for the quantitative determination of the adsorption energy in dissolution, which holds well for transition metals, near-surface alloys, binary alloys, and high-entropy alloys. Furthermore, this model uncovers a synergistic mechanism between the d-band upper-edge ratio, d-band width and s-band depth in determining the alloying and dissolution effects on adsorption. Our study not only provides fundamental mechanistic insights into surface adsorption on alloys but also offers a long-sought tool for the design of advanced alloy catalysts.



قيم البحث

اقرأ أيضاً

75 - Xin Li , Bo Li , Zhiwen Chen 2021
Alloys present the great potential in catalysis because of their adjustable compositions, structures and element distributions, which unfortunately also limit the fast screening of the potential alloy catalysts. Machine learning methods are able to t ackle the multi-variable issues but still cannot yet predict the complex alloy catalysts from the properties of pure metals due to the lack of universal descriptors. Herein we propose a transferable machine-learning model based on the intrinsic properties of substrates and adsorbates, which can predict the adsorption energies of single-atom alloys (SAAs), AB intermetallics (ABs) and high-entropy alloys (HEAs), simply by training the properties of transition metals (TMs). Furthermore, this model builds the structure-activity relationship of the adsorption energies on alloys from the perspective of machine learning, which reveals the role of the surface atoms valence, electronegativity and coordination and the adsorbates valence in determining the adsorption energies. This transferable scheme advances the understanding of the adsorption mechanism on alloys and the rapid design of alloy catalysts.
Interplay between hydrogen and nanovoids, despite long-recognized as a central aspect in hydrogen-induced damages in structural materials, remains poorly understood. Focusing on tungsten as a model BCC system, the present study, for the first time, e xplicitly demonstrated sequential adsorption of hydrogen adatoms on Wigner-Seitz squares of nanovoids with distinct energy levels. Interaction between hydrogen adatoms on the nanovoid surface is shown to be dominated by pairwise power law repulsion. A predictive model was established for quantitative prediction of configurations and energetics of hydrogen adatoms in nanovoids. This model, further combined with equation of states of hydrogen gas, enables prediction of hydrogen molecule formation in nanovoids. Multiscale simulations based on the predictive model were performed, showing excellent agreement with experiments. This work clarifies fundamental physics and provides full-scale predictive model for hydrogen trapping and bubbling in nanovoids, offering long-sought mechanistic insights crucial for understanding hydrogen-induced damages in structural materials.
Measurements of the surface x-ray scattering from several pure liquid metals (Hg, Ga, and In) and from three alloys (Ga-Bi, Bi-In, and K-Na) with different heteroatomic chemical interactions in the bulk phase are reviewed. Surface-induced layering is found for each elemental liquid metal. The surface structure of the K-Na alloy resembles that of an elemental liquid metal. Bi-In displays pair formation at the surface. Surface segregation and a wetting film are found for Ga-Bi.
The interaction of CO with the Fe3O4(001)-(rt2xrt2)R45{deg} surface was studied using temperature programmed desorption (TPD), scanning tunneling microscopy (STM) and x-ray photoelectron spectroscopy (XPS), the latter both under ultrahigh vacuum (UHV ) conditions and in CO pressures up to 1 mbar. In general, the CO-Fe3O4 interaction is found to be weak. The strongest adsorption occurs at surface defects, leading to small TPD peaks at 115 K, 130 K and 190 K. Desorption from the regular surface occurs in two distinct regimes. For coverages up to 2 CO molecules per (rt2xrt2)R45{deg} unit cell, the desorption maximum shows a large shift with increasing coverage, from initially 105 K to 70 K. For coverages between 2 and 4 molecules per (rt2xrt2)R45{deg} unit cell, a much sharper desorption feature emerges at 50 K. Thermodynamic analysis of the TPD data suggests a phase transition from a dilute 2D gas into an ordered overlayer with CO molecules bound to surface Fe3+ sites. XPS data acquired at 45 K in UHV are consistent with physisorption. Some carbon-containing species are observed in the near-ambient-pressure XPS experiments at room temperature, but are attributed to contamination and/or reaction with CO with water from the residual gas. No evidence was found for surface reduction or carburization by CO molecules.
We present an extensive set of surface and chemisorption energies calculated using state of the art many-body perturbation theory. In the first part of the paper we consider ten surface reactions in the low coverage regime where experimental data is available. Here the random phase approximation (RPA) is found to yield high accuracy for both adsorption and surface energies. In contrast all the considered density functionals fail to describe both quantities accurately. This establishes the RPA as a universally accurate method for surface science. In the second part, we use the RPA to construct a database of 200 high quality adsorption energies for reactions involving OH, CH, NO, CO, N$_2$, N, O and H over a wide range of 3d, 4d and 5d transition metals. Due to the significant computational demand, these results are obtained in the high coverage regime where adsorbate-adsorbate interactions can be significant. RPA is compared to the more advanced renormalised adiabatic LDA (rALDA) method for a subset of the reactions and they are found to describe the adsorbate-metal bond as well as adsorbate-adsorbate interactions similarly. The RPA results are compared to a range of standard density functional theory methods typically employed for surface reactions representing the various rungs on Jacobs ladder. The deviations are found to be highly functional, surface and reaction dependent. Our work establishes the RPA and rALDA methods as universally accurate full ab-initio methods for surface science where accurate experimental data is scarce. The database is freely available via the Computational Materials Repository (CMR).
التعليقات
جاري جلب التعليقات جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا