Reduction potential tuning of first row transition metal MIII/MII (M = Cr, Mn, Fe, Co, Ni) hexadentate complexes for viable aqueous redox flow battery catholytes: A DFT study

High sensitivity atomic fluorescence spectroscopy for the detection of AsIII by selective electrolysis of arsenic on nanoflowers-like Fe/NFE

Modified electrosynthetic sample introduction technique is a reliable means of solving the problem of high sensitivity analysis of trace arsenite. This article attempts to achieve selective electroreduction of AsIII through the construction of electrode surfaces with different structures and materials from the perspective of interface reactions. Among the four transition metal modifiers, the iron modified nickel foam electrode with nano-flower structure documented higher efficiency in inducing arsenic reduction and better species selectivity. Systematic electrochemical and spectroscopic tests suggest that strong adsorption effect between Fe and AsIII, appropriate hydrogen evolution potential, and catalytic activity jointly promote efficient electroreduction of AsIII. Optimization based on electrode materials and electrolysis conditions, with high sensitivity, wide linear range (0.1-50 μg L-1), and excellent species selectivity, this paper offers an efficient and economic sample introduction method for trace AsIII/V selective atomic spectroscopy direct determination.

Precise Fingerprint Determination of Vibrational Infrared Spectra in a Series of Co(II) Clathrochelates through Experimental and Theoretical Analyses

The energetic demands of modern society for clean energy vectors, such as H2, have caused a surge in research associated with homogeneous and immobilized electrocatalysts that may replace Pt. In particular, clathrochelates have shown excellent electrocatalytic properties for the hydrogen evolution reaction (HER). However, the actual mechanism for the HER catalyzed by these d-metal complexes remains an open debate, which may be addressed via Operando spectroelectrochemistry. The prediction of electrochemical properties via density functional theory (DFT) needs access to thermodynamic functions, which are only available after Hessian calculations. Unfortunately, there is a notable lack in the current literature regarding the precise evaluation of vibrational spectra of such complexes, given their structural complexity and the associated tangled IR spectra. In this work, we have performed a detailed theoretical and experimental analysis in a family of Co(II) clathrochelates, in order to establish univocally their IR pattern, and also the calculation methodology that is adequate for such predictions. In summary, we have observed the presence of multiple common bands shared by this clathrochelate family, using the B3LYP functional, the LANL2DZ basis, and effective core potentials (ECP) for heavy atoms. The most important issue addressed in this article was therefore related to the detailed assignment of the fingerprint associated with cobalt(II) clathrochelates, which is a challenging endeavor due to the crowded nature of their spectra.

Molecular Engineering of Quinone-Based Nickel Complexes and Polymers for All-Organic Li-Ion Batteries

All-organic Li-ion batteries appear to be a sustainable and safer alternative to the currently-used Li-ion batteries but their application is still limited due to the lack of organic compounds with high redox potentials toward Li⁺/Li⁰. Herein, we report a computational design of nickel complexes and coordination polymers that have redox potentials spanning the full voltage range: from the highest, 4.7 V, to the lowest, 0.4 V. The complexes and polymers are modeled by binding low- and high-oxidized Ni ions (i.e., Ni(II) and Ni(IV)) to redox-active para-benzoquinone molecules substituted with carboxyl- and cyano-groups. It is found that both the nickel ions and the quinone-derived ligands are redox-active upon lithiation. The type of Ni coordination also has a bearing on the redox potentials. By combining the complex of Ni(IV) with 2-carboxylato-5-cyano-1,4-benzoquinones as a cathode and Ni(II)-2,5-dicarboxylato-3,6-dicyano-1,4-benzoquinone coordination polymer as an anode, all-organic Li-ion batteries could be assembled, operating at an average voltage exceeding 3.0 V and delivering a capacity of more than 300 mAh/g.

Theoretical determination of half-wave potentials for phenanthroline-, bipyridine-, acetylacetonate-, and glycinate-containing copper (II) complexes

We report a protocol for the evaluation of theoretical half-wave potential (E_1/2) using a set of 22 mixed chelate copper (II) complexes containing 1,10-phenanthroline and 2,2'-bipyridine derivatives as primary ligands, and acetylacetonate or glycinate as secondary ligands (formally from the Casiopeínas® family) for which accurate experimental values were determined in a 2/5 mixture of ethanol/water. We have calibrated the BP86, PBE, PBE0, B3LYP, M06-2X, and ω-B97XD functionals, using the Los Alamos LANL2DZ and Stuttgart-Köln SDDAll effective core potentials for the Cu and Fe atoms and the 6-311+G* basis set for the C, H, O, and N atoms. To address the solvent effects, we have saturated the first solvation shell with up to 9 water molecules for the explicit model and compared it with the Continuum Like-Polarizable Continuum Model (CPCM) implicit solvent scheme. We found that the PBE/LANL2DZ-6-311+G* protocol (with the CPCM implicit solvent scheme with an effective dielectric constant ε = 64.9121 for the 2/5 mixture of ethanol/water) yields the overall best performance. The theoretical values are compared with experimental data, three of which are reported here for the first time. We find good correlations between the theoretical and experimental E_1/2 values for the 2,2'-bipyridine derivatives (R² = 0.987, MAE = 86 mV) and 1,10-phenanthroline derivatives (R² = 0.802, MAE = 58.4 mV). The correlation trends have been explained in terms of the copper atom's ability to be reduced in the presence of the ligands. The Gibbs free energy differences at 298 K obtained for the redox reactions show that the more flexible secondary ligands (acetylacetonate) lead to larger entropic contributions which, as expected, increase the average MAE values as compared with the more rigid ligands (glycine). The present protocol yields lower MAEs as compared with previous approaches for similar mixed and flexible Cu(II) complexes.

Accurate Multiobjective Design in a Space of Millions of Transition Metal Complexes with Neural-Network-Driven Efficient Global Optimization

The accelerated discovery of materials for real world applications requires the achievement of multiple design objectives. The multidimensional nature of the search necessitates exploration of multimillion compound libraries over which even density functional theory (DFT) screening is intractable. Machine learning (e.g., artificial neural network, ANN, or Gaussian process, GP) models for this task are limited by training data availability and predictive uncertainty quantification (UQ). We overcome such limitations by using efficient global optimization (EGO) with the multidimensional expected improvement (EI) criterion. EGO balances exploitation of a trained model with acquisition of new DFT data at the Pareto front, the region of chemical space that contains the optimal trade-off between multiple design criteria. We demonstrate this approach for the simultaneous optimization of redox potential and solubility in candidate M(II)/M(III) redox couples for redox flow batteries from a space of 2.8 M transition metal complexes designed for stability in practical redox flow battery (RFB) applications. We show that a multitask ANN with latent-distance-based UQ surpasses the generalization performance of a GP in this space. With this approach, ANN prediction and EI scoring of the full space are achieved in minutes. Starting from ca. 100 representative points, EGO improves both properties by over 3 standard deviations in only five generations. Analysis of lookahead errors confirms rapid ANN model improvement during the EGO process, achieving suitable accuracy for predictive design in the space of transition metal complexes. The ANN-driven EI approach achieves at least 500-fold acceleration over random search, identifying a Pareto-optimal design in around 5 weeks instead of 50 years.