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•Continuum solvation fails to predict oxygen reduction onset potential on Pt (111).•Main issue is an underestimation of OH solvation energy.•Adding 1 or 2 explicit water molecules ...does not correct the OH solvation energy.
We present a comparison study between the implicit and explicit solvation approach for density functional theory (DFT) predictions of the oxygen reduction reaction (ORR) activity on Pt (111) and other metal surfaces under acidic conditions. DFT calculations with a self-consistent polarizable continuum model implement in VASPsol results in more accurate predictions of onset potentials for Pt(111) than vacuum DFT calculations due to the extra stabilization of the surface intermediates (OOH*, O*, OH*). Implicit solvation also preserves the scaling relationship among ORR intermediates and volcano-shape activity relationships that correlate ORR onset potential or activity to the free energy absorption of O* or OH*. Moreover, VASPsol predicts variation in the solvation energies across different surfaces, suggesting the use of universal solvation corrections may not be valid. However, VASPsol exhibits significant weaker OH* solvation energies (by ∼ 0.4 eV) on Pt (111) compared to literature values using an explicit water bilayer and therefore underestimates the onset potential on Pt (111). We attribute this lack of OH* solvation energies by VASPsol to its inability to address hydrogen bonds and the absence of intermediate stabilization of the bilayer structure. Strategies to mitigate this problem, including tuning VASPsol parameters and a hybrid approach incorporating one to two water molecules, are also examined but not found to alleviate the underestimation of the solvation of OH*.
Stromboli volcano (Italy), always active with low energy explosive activity, is a very attractive place for visitors, scientists, and inhabitants of the island. Nevertheless, occasional more intense ...eruptions can present a serious danger. This study focuses on the modeling and estimation of their inter-event time and temporal rate. With this aim we constructed a new historical catalog of major explosions and paroxysms through a detailed review of scientific literature of the last ca. 140 years. The catalog includes the calendar date and phenomena descriptions for 180 explosive events, of which 36 were paroxysms. We evaluated the impact of the main sources of uncertainty affecting the historical catalog. In particular, we categorized as uncertain 45 major explosions that reportedly occurred before 1985 and tested the effect of excluding these events from our analysis. Moreover, after analyzing the entire record in the period 1879, 2020, we separately considered, as sequences, events in 1879, 1960 and in 1985, 2020 because of possible under recording issues in the period 1960, 1985. Our new models quantify the temporal rate of major explosions and paroxysms as a function of time passed since the last event occurred. Recurrence hazard levels are found to be significantly elevated in the weeks and months following a major explosion or paroxysm, and then gradually decrease over longer periods. Computed hazard functions are also used to illustrate a methodology for estimating order-of-magnitude individual risk of fatality under certain basis conditions. This study represents a first quantitatively formal advance in determining long-term hazard levels at Stromboli.
Heteroatom‐doped Fe‐NC catalyst has emerged as one of the most promising candidates to replace noble metal‐based catalysts for highly efficient oxygen reduction reaction (ORR). However, delicate ...controls over their structure parameters to optimize the catalytic efficiency and molecular‐level understandings of the catalytic mechanism are still challenging. Herein, a novel pyrrole–thiophene copolymer pyrolysis strategy to synthesize Fe‐isolated single atoms on sulfur and nitrogen‐codoped carbon (Fe‐ISA/SNC) with controllable S, N doping is rationally designed. The catalytic efficiency of Fe‐ISA/SNC shows a volcano‐type curve with the increase of sulfur doping. The optimized Fe‐ISA/SNC exhibits a half‐wave potential of 0.896 V (vs reversible hydrogen electrode (RHE)), which is more positive than those of Fe‐isolated single atoms on nitrogen codoped carbon (Fe‐ISA/NC, 0.839 V), commercial Pt/C (0.841 V), and most reported nonprecious metal catalysts. Fe‐ISA/SNC is methanol tolerable and shows negligible activity decay in alkaline condition during 15 000 voltage cycles. X‐ray absorption fine structure analysis and density functional theory calculations reveal that the incorporated sulfur engineers the charges on N atoms surrounding the Fe reactive center. The enriched charge facilitates the rate‐limiting reductive release of OH* and therefore improved the overall ORR efficiency.
Fe‐isolated single atoms on S, N‐doped carbon (Fe‐ISA/SNC) electrocatalysts with tunable sulfur/nitrogen ratio are synthesized by a novel pyrrole‐co‐thiophene polymer pyrolysis strategy. The electronic modification of the FeNx center by adjacent S atoms enables optimized Fe‐ISA/SNC catalysts with superior activity (E1/2 = 0.896 V) and stability (durable for 15 000 CV cycles) for oxygen reduction reactions.
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The emerging non-noble metal two-dimensional (2D) catalyst, such as MoS2, for the hydrogen evolution reaction (HER) is known to have an inert basal plane unless being converted to a ...metastable metallic phase or defect engineered. In order to take advantage of the majority of the material in such layered catalysts, fast screening of 2D catalysts with superior basal plane activity is imperative. A local electrochemical measurement method assisted by the e-beam lithography patterning was developed and applied to quantify the activity of basal planes of different layered transition metal dichalcogenides (TMDs) toward HER. This local measurement offers a robust platform to discover active TMDs fast and precisely. The construction of HER volcano plot leads to the discovery of superior basal plane active group VB metal disulfides, especially 3R-NbS2. Interestingly, the trends found in the volcano plot imply distinctive differences in the mechanism of TMD catalysts compared to their metal counterparts. The intensive hydrogen evolution reaction in-between the basal planes drives self-nanostructuring in morphology of 3R-NbS2. The increase in the effective surface area, and decrease in the electron-transfer resistance across the substrate and basal plane interface induced by the self-nanostructuring in turn enhances the HER performance of 3R-NbS2. The 3R-NbS2 clearly stands out among non-noble metal catalysts for HER.
The construction of efficient, durable, and non‐noble metal electrocatalysts for oxygen evolution reaction (OER) is of great value but challenging. Herein, a facile method is developed to synthesize ...a series of trimetallic (W/Co/Fe) metal–organic frameworks (MOFs)‐derived carbon nanoflakes (CNF) with various Fe content, and an Fe‐dependent volcano‐type plot can be drawn out for WCoFex
‐CNF. The optimized WCoFe0.3‐CNF (when the feed ratio of Fe/Co is 0.3) demonstrates superior electrocatalytic performance with a low overpotential of only 254 mV@10 mA cm−2 and excellent durability of 100 h. Further researches show that appropriate amount of iron doping can regulate the electronic structure, resulting in a favorable synergistic environment. This method may stimulate the exploration of electrocatalysts by utilizing MOFs as precursors while realizing electronic modulation by multimetal doping.
Facile synthesis of W/Co/Fe trimetallic carbon nanoflake electrocatalysts which are derived from metal–organic frameworks (MOFs) is reported, and an Fe‐dependent volcano‐type relationship is discovered. Furthermore, the doping content of Fe can accurately modulate the electronic structure of the catalysts. The results highlight the great potential of utilizing multimetallic MOFs as promising templates while realizing electronic structure modulation.
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•Monodisperse Pd NPs were synthesized with a robust performance in EHDC of 2,4-DCP.•Efficient H∗ production and adsorption of 2,4-DCP over Pd are both crucial to EHDC.•Low pH promote ...the H∗ production, 2,4-DCP adsorption but H2 evolution over Pd NPs.•A solution of weak acid is ideal to improve EHDC efficiency and energy selectivity.
Electrocatalytic hydrodechlorination (EHDC) is a promising environmental technology that can produce highly active atomic hydrogen (H∗ad) to detoxify chlorinated hydrocarbon pollutants in water via the hydrogenolysis of C–Cl bonds. However, its efficiency is hampered by hydrogen evolution reaction (HER) in aqueous media which competes with EHDC by consuming H∗ad. Here we report that, by controlling the solution pH, we can facilely tune the relative kinetics of EHDC of 2,4-dichlorophenol (2,4-DCP) and HER over palladium nanoparticles (Pd NPs) for an optimized EHDC reaction. The batch EHDC experiments with different starting pHs find that both the EHDC efficiency (conversion rate of 2,4-DCP to phenol) and H∗ad utilization efficiency (HUE%, the molar percentage of H∗ad used by EHDC) present a volcano-shaped relationship vs. starting solution pH, with the peak EHDC efficiency of 66.4% and HUE% of 35–40% achieved at pHs of 2.12 and 3.49, respectively. The mechanism study finds that this volcano relationship originates from the dual effect of pH on both the H∗ad production rate and the adsorption behavior of 2,4-DCP over Pd NPs. The overfast generation of H∗ad at very low pHs and the poor adsorption of 2,4-DCP on electrode under alkaline conditions will both favor the HER over EHDC. A solution condition of weak acid is thus ideal to optimize the EHDC performance. This work presents an efficient approach to improve the EHDC efficiency and energy selectivity by tuning the solution pH, which should advance the EHDC application in environmental remediation.
The development of denitrification catalysts which can reduce nitrate and nitrite to dinitrogen is critical for sustaining the nitrogen cycle. However, regulating the selectivity has proven to be a ...challenge, due to the difficulty of controlling complex multielectron/proton reactions. Here we report that utilizing sequential proton–electron transfer (SPET) pathways is a viable strategy to enhance the selectivity of electrochemical reactions. The selectivity of an oxo-molybdenum sulfide electrocatalyst toward nitrite reduction to dinitrogen exhibited a volcano-type pH dependence with a maximum at pH 5. The pH-dependent formation of the intermediate species (distorted Mo(V) oxo species) identified using operando electron paramagnetic resonance (EPR) and Raman spectroscopy was in accord with a mathematical prediction that the pK a of the reaction intermediates determines the pH-dependence of the SPET-derived product. By utilizing this acute pH dependence, we achieved a Faradaic efficiency of 13.5% for nitrite reduction to dinitrogen, which is the highest value reported to date under neutral conditions.
Abstract
At Mount Etna volcano, the focus point of persistent tectonic extension is represented by the Summit Craters. A muographic telescope has been installed at the base of the North-East Crater ...from August 2017 to October 2019, with the specific aim to find time related variations in the density of volcanic edifice. The results are significant, since the elaborated images show the opening and evolution of different tectonic elements; in 2017, a cavity was detected months before the collapse of the crater floor and in 2018 a set of underground fractures was identified, at the tip of which, in June 2019, a new eruptive vent started its explosive activity, still going on (February, 2020). Although this is the pilot experiment of the project, the results confirm that muography could be a turning point in the comprehension of the plumbing system of the volcano and a fundamental step forward to do mid-term (weeks/months) predictions of eruptions. We are confident that an increment in the number of telescopes could lead to the realization of a monitoring system, which would keep under control the evolution of the internal dynamic of the uppermost section of the feeding system of an active volcano such as Mount Etna.
Computational catalyst screening has the potential to significantly accelerate heterogeneous catalyst discovery. Typically, this involves developing microkinetic reactor models that are based on ...parameters obtained from density functional theory and transition-state theory. To reduce the large computational cost involved in computing various adsorption and transition-state energies of all possible surface states on a large number of catalyst models, linear scaling relations for surface intermediates and transition states have been developed that only depend on a few, typically one or two descriptors, such as the carbon atom adsorption energy. As a result, only the descriptor values have to be computed for various active site models to generate volcano curves in activity or selectivity. Unfortunately, for more complex chemistries the predictability of linear scaling relations is unknown. Also, the selection of descriptors is essentially a trial and error process. Here, using a database of adsorption energies of the surface species involved in the decarboxylation and decarbonylation of propionic acid over eight monometalic transition-metal catalyst surfaces (Ni, Pt, Pd, Ru, Rh, Re, Cu, Ag), we tested if nonlinear machine learning (ML) models can outperform the linear scaling relations in prediction accuracy when predicting the adsorption energy for various species on a metal surface based on data from the rest of the metal surfaces. We found linear scaling relations to hold well for predictions across metals with a mean-absolute error of 0.12 eV, and ML methods being unable to outperform linear scaling relations when the training dataset contains a complete set of energies for all of the species on various metal surfaces. Only when the training dataset is incomplete, namely, contains a random subset of species’ energies for each metal, a currently unlikely scenario for catalyst screening, do kernel-based ML models significantly outperform linear scaling relations. We also found that simple coordinate-free species descriptors, such as bond counts, achieve as good results as sophisticated coordinate-based descriptors. Finally, we propose an approach for automatic discovery of appropriate metal descriptors using principal component analysis.
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