Composition Engineering of Amorphous Nickel Boride Nanoarchitectures Enabling Highly Efficient Electrosynthesis of Hydrogen Peroxide

Abstract

Developing advanced electrocatalysts with exceptional two electron (2e−) selectivity, activity, and stability is crucial for driving the oxygen reduction reaction (ORR) to produce hydrogen peroxide (H2O2). Herein, a composition engineering strategy is proposed to flexibly regulate the intrinsic activity of amorphous nickel boride nanoarchitectures for efficient 2e− ORR by oriented reduction of Ni2+ with different amounts of BH4−. Among borides, the amorphous NiB2 delivers the 2e− selectivity close to 99% at 0.4 V and over 93% in a wide potential range, together with a negligible activity decay under prolonged time. Notably, an ultrahigh H2O2 production rate of 4.753 mol gcat−1 h−1 is achieved upon assembling NiB2 in the practical gas diffusion electrode. The combination of X‐ray absorption and in situ Raman spectroscopy, as well as transient photovoltage measurements with density functional theory, unequivocally reveal that the atomic ratio between Ni and B induces the local electronic structure diversity, allowing optimization of the adsorption energy of Ni toward *OOH and reducing of the interfacial charge‐transfer kinetics to preserve the OO bond.

Sulfur Vacancy-Rich Amorphous Rh Metallene Sulfide for Electrocatalytic Selective Synthesis of Aniline Coupled with Efficient Sulfion Degradation

“…The amorphous metallene structure is conducive to providing abundant active sites and facilitating the mass transfer process of active species. 44,46 Besides, the formed S vacancies can optimize the species adsorption and lower the activation barriers on the catalyst surface to facilitate the conversion of reactants. 50,51 The anodic SOR performance of a-RhS 2−x metallene was investigated by a three-electrode system in 1 M KOH containing Na 2 S solutions.…”

“…The abundant active sites of a-RhS 2−x metallene are derived from the ultrathin metallene structure, amorphization, and rich S vacancies. 34,44,51 Given the attractive electrocatalytic performance of a-RhS 2−x metallene for cathodic Ph-NO 2 ERR and anodic SOR, a directed two-electrolysis system can be assembled by utilizing a-RhS 2−x metallene as both cathode and anode, where the Ph-NO 2 ERR is performed at the cathode and the SOR is performed at the anode (Figure 6a), which can maximize energy efficiency and generate directed high value-added chemicals at both the cathode and anode. Figure 6b displays the LSV curves of a-RhS 2−x metallene||a-RhS 2−x metallene in the two-electrode coupled system (cathodic Ph-NO 2 ERR and anodic SOR) and the water splitting system.…”

Lattice Strained B‐Doped Ni Nanoparticles for Efficient Electrochemical H2O2 Synthesis

“…The strong interaction between the 2p electrons of B and the 2s electrons of O 2 is beneficial to the preservation of the OO bond. [28,29] The high resolution N 1s spectra of the Ni 1 -B 2 @BNC, Ni 4 -B 1 @BNC, Ni 7 -B 1 @BNC, Ni@NC, and BNC catalysts in Figure 4b. The N 1s spectrum of Ni 4 -B 1 @BNC is divided into four peaks at 397.9, 398.8, 400.6, and 401.7 eV, ascribed to NB, pyridinic-N (N6), pyrrolic-N (N5), and quaternary-N, respectively.…”

Porous Nanoarchitectures of Nonprecious Metal Borides: From Controlled Synthesis to Heterogeneous Catalyst Applications

“…The typical M–B in TMBs is often strongly combined with M–M and B–B bonds, which endows TMBs with rich compositional tunability and complex electronic structures. Therefore, the synthesis of TMBs with the same or similar morphology yet adjustable B content helps explore the real role of B . However, due to the complexity of the composition, proper compositions and structures are extremely challenging to experimentally determine under the synthesis conditions based on only chemical intuition from a human .…”

“…Therefore, the synthesis of TMBs with the same or similar morphology yet adjustable B content helps explore the real role of B. 150 However, due to the complexity of the composition, proper compositions and structures are extremely challenging to experimentally determine under the synthesis conditions based on only chemical intuition from a human. 151 An efficient strategy is to combine experiments with data science and artificial intelligence through machine learning, which has great potential for accelerating catalysis research and the rapid exploration of materials chemistry.…”