Toward predicting vertical detachment energies for superhalogen anions exclusively from 2-D structures

Design and Investigation of Superatoms for Redox Applications: First-Principles Studies

A superatom is a cluster of atoms that acts like a single atom. Two main groups of superatoms are superalkalis and superhalogens, which mimic the chemistry of alkali and halogen atoms, respectively. The ionization energies of superalkalis are smaller than those of alkalis (<3.89 eV for cesium atom), and the electron affinities of superhalogens are larger than that of halogens (>3.61 eV for chlorine atom). Exploring new superalkali/superhalogen aims to provide reliable data and predictions of the use of such compounds as redox agents in the reduction/oxidation of counterpart systems, as well as the role they can play more generally in materials science. The low ionization energies of superalkalis make them candidates for catalysts for CO2 conversion into renewable fuels and value-added chemicals. The large electron affinity of superhalogens makes them strong oxidizing agents for bonding and removing toxic molecules from the environment. By using the superatoms as building blocks of cluster-assembled materials, we can achieve the functional features of atom-based materials (like conductivity or catalytic potential) while having more flexibility to achieve higher performance. This feature paper covers the issues of designing such compounds and demonstrates how modifications of the superatoms (superhalogens and superalkalis) allow for the tuning of the electronic structure and might be used to create unique functional materials. The designed superatoms can form stable perovskites for solar cells, electrolytes for Li-ion batteries of electric vehicles, superatomic solids, and semiconducting materials. The designed superatoms and their redox potential evaluation could help experimentalists create new materials for use in fields such as energy storage and climate change.

Versatile in silico modelling of microplastics adsorption capacity in aqueous environment based on molecular descriptor and machine learning

To comprehensively evaluate the hazards of microplastics and their coexisting organic pollutants, the sorption capacity of microplastics is a major issue that is quantified through the microplastic-aqueous sorption coefficient (K_d). Almost all quantitative structure-property relationship (QSPR) models that describe K_d apply only to narrow, relatively homogeneous groups of reactants. Herein, non-hybrid QSPR-based models were developed to predict PE-water (K_PE-w), PE-seawater (K_PE-sw), PVC-water (K_PVC-w) and PP-seawater (K_PP-sw) sorption coefficients at different temperatures, with eight machine learning algorithms. Moreover, novel hybrid intelligent models for predicting K_d more accurately were innovatively developed by applying GA, PSO and AdaBoost algorithms to optimize MLP and ELM models. The results indicated that all three optimization algorithms could improve the robustness and predictability of the standalone MLP and ELM models. In all models trained with K_PE-w, K_PE-sw, K_PVC-w and K_PP-sw data sets, GBDT-1 and XGBoost-1 models, MLP-GA-2 and MLP-PSO-2 models, MLR-3 and MLR-4 models performed better in terms of goodness of fit (R_adj²: 0.907-0.999), robustness (Q_BOOT²: 0.900-0.937) and predictability (R_ext²: 0.889-0.970), respectively. Analyzing the descriptors revealed that temperature, lipophilicity, ionization potential and molecular size were correlated closely with the adsorption capacity of microplastics to organic pollutants. The proposed QSPR models may assist in initial environmental exposure assessments without imposing heavy costs in the early experimental phase.

Prediction models with multiple machine learning algorithms for POPs: The calculation of PDMS-air partition coefficient from molecular descriptor

Polydimethylsiloxane-air partition coefficient (K_PDMS-air) is a key parameter for passive sampling to measure POPs concentrations. In this study, 13 QSPR models were developed to predict K_PDMS-air, with two descriptor selection methods (MLR and RF) and seven algorithms (MLR, LASSO, ANN, SVM, kNN, RF and GBDT). All models were based on a data set of 244 POPs from 13 different categories. The diverse model evaluation parameters calculated from training and test set were used for internal and external verification. Notably, the R_adj², Q_BOOT² and Q_ext² are 0.995, 0.980 and 0.951 respectively for GBDT model, showing remarkable superiority in fitting, robustness and predictability compared with other models. The discovery that molecular size, branches and types of the bonds were the main internal factors affecting the partition process was revealed by mechanism explanation. Different from the existing QSPR models based on single category compounds, the models developed herein considered multiple classes compounds, so that its application domain was more comprehensive. Therefore, the obtained models can fill the data gap of missing experimental K_PDMS-air values for compounds in the application range, and help researchers better understand the distribution behavior of POPs from the perspective of molecular structure.

In vivo toxicity of nitroaromatic compounds to rats: QSTR modelling and interspecies toxicity relationship with mouse

Nitroaromatic compounds (NACs) in the environment can cause serious public health and environmental problems due to their potential toxicity. This study established quantitative structure-toxicity relationship (QSTR) models for the acute oral toxicity of NACs towards rats following the stringent OECD principles for QSTR modelling. All models were assessed by various internationally accepted validation metrics and the OECD criteria. The best QSTR model contains seven simple and interpretable 2D descriptors with defined physicochemical meaning. Mechanistic interpretation indicated that van der Waals surface area, presence of C-F at topological distance 6, heteroatom content and frequency of C-N at topological distance 9 are main factors responsible for the toxicity of NACs. This proposed model was successfully applied to a true external set (295 compounds), and prediction reliability was analysed and discussed. Moreover, the rat-mouse and mouse-rat interspecies quantitative toxicity-toxicity relationship (iQTTR) models were also constructed, validated and employed in toxicity prediction for true external sets consisting of 67 and 265 compounds, respectively. These models showed good external predictivity that can be used to rapidly predict the rat oral acute toxicity of new or untested NACs falling within the applicability domain of the models, thus being beneficial in environmental risk assessment and regulatory purposes.

Magnesium-Based Clusters as Building Blocks of Electrolytes in Lithium-Ion Batteries

Superhalogens, owing to their large electron affinity (EA, exceeding those of any halogen atom), play an essential role in physical chemistry as well as new material design. They have applications in hydrogen storage and lithium-ion batteries. Owing to the unique geometries and electronic features of magnesium-based clusters, their potential to form a new class of lithium salts has been investigated here theoretically. The idea is assessed by conducting ab initio computations on Li⁺ /Mg_n F_2n+1-2m O_m ^- compounds (n=2, 3; m=0-3) and analyzing their performance as potential Li-ion battery electrolytes. The Mg₃ F₇ ^- cluster, with large electron binding energy (EA of 7.93 eV), has been proven to serve as a building block for lithium salts. It is shown that, apart from high electronic stability, the new superhalogen-based electrolytes exhibit a set of desirable properties, including a large band gap, high electrolyte stability window, easy mobility of the Li⁺ , and favorable insensitivity to water.

Magnesium-Based Oxyfluoride Superatoms: Design, Structure, and Electronic Properties

The ability of mixed ligands to form stable dinuclear and trinuclear magnesium-based superatoms has been investigated theoretically. The Mg₂F_{5-2 m}O _m and Mg₃F_{7-2 m}O _m systems (where m = 1-3) were found able to form stable and strongly bound anionic clusters, and those assumptions were validated by (i) the analysis of the geometrical stability; (ii) the estimated Gibbs free energies for the most probable disproportion paths these clusters might be vulnerable to (which allows examining their thermodynamic stabilities); (iii) the localization of the electron density; and (iv) the adiabatic electron affinity (AEA), vertical electron detachment energy (VDE), and adiabatic electron detachment energy (ADE) values calculated for the studied systems. It is demonstrated that the stability of the anionic daughters of these clusters increases with the number of electronegative ligands, and Mg _nF_{2 n+1-2 m}O _m^- ( n = 2, 3; m = 1-3) clusters are stable against electron emission. The largest electron binding energy was found for the Mg₃F₅O^- anion (VDE = 6.826 eV). The strong VDE dependence on (i) the geometrical structure, (ii) the number of central atoms, (iii) ligand type, and (iv) bonding/antibonding character of the highest molecular orbital (HOMO) was also remarked upon and discussed.

The Acid Strength of the Lewis-Brønsted Superacids - A QSPR Study

The acidity of Lewis-Brønsted superacids can be derived from the theoretical calculations as the Gibbs free energy of the deprotonation reaction (ΔG_acid ), which describes the tendency of a studied compound to donate a proton. This paper presents the first Quantitative Structure - Property Relationship (QSPR) model that correlates the ΔG_acid of superacid (HF/MeX₃ formula (X=F, Cl, Br)) with their structure. Developed model is well fitted, roubustness, has good predictive abilities, fulfills all OECD recommendation for good model. Obtained results provide the insight into the relation of structural features of superacids, which are responsible for their acid strength - the structures characterized by strong F-Me dative bond (with relatively large vibrational frequency), small positive partial atomic charge on Me central atom, possibly large polarity exhibit large acid strength. Such assumption can be used in the future as valuable information in the process of the designing new, stronger, more effective superacids.

Oxidizing Metal Oxides with Polynuclear Superhalogen: An ab Initio Study

The ability of metal oxides (CoO, CuO, MgO, MnO₂, NiO, SiO_2, TiO₂, and ZnO) to form stable systems with polynuclear superhalogen (i.e., Mg₃F₇) is examined on the basis of theoretical considerations supported by ab initio calculations. It is demonstrated that the MeO _n ( n = 1, 2) molecules (such as CoO, CuO, MgO, MnO₂, NiO, TiO₂, ZnO) should form stable and strongly bound (MeO _n)⁺(superhalogen)^- salts when combined with the Mg₃F₇ superhalogen radical (acting as an oxidizing agent). This conclusion is supported by providing: (i) structural deformation of superhalogen upon ionization, (ii) predicted charge flow between each MeO _n and superhalogen (which allows estimating the amount of electron density withdrawn from MeO _n molecule during the ionization process), (iii) the localization of the spin density distribution, and (iv) the interaction energies and vertical ionization potentials (VIPs) for the compounds obtained at the CCSD(T)/6-311+G(d) level of theory. On the other hand, the Mg₃F₇ superhalogen was found to be incapable of ionizing molecules whose adiabatic ionization potentials (AIPs) exceed 12 eV (e.g., SiO₂). I believe that the results provided in this contribution may likely be of prospective relevance in the future studies on the issue of binding and preventing metal oxide nanoparticles aggregation in the environment before they occur harmful to human health and environment.

Development of a novel in silico model of zeta potential for metal oxide nanoparticles: a nano-QSPR approach

Once released into the aquatic environment, nanoparticles (NPs) are expected to interact (e.g. dissolve, agglomerate/aggregate, settle), with important consequences for NP fate and toxicity. A clear understanding of how internal and environmental factors influence the NP toxicity and fate in the environment is still in its infancy. In this study, a quantitative structure-property relationship (QSPR) approach was employed to systematically explore factors that affect surface charge (zeta potential) under environmentally realistic conditions. The nano-QSPR model developed with multiple linear regression (MLR) was characterized by high robustness [Formula: see text] and external predictivity [Formula: see text] The results clearly showed that zeta potential values varied markedly as functions of the ionic radius of the metal atom in the metal oxides, confirming that agglomeration and the extent of release of free MexOy largely depend on their intrinsic properties. A developed nano-QSPR model was successfully applied to predict zeta potential in an ionized solution of NPs for which experimentally determined values of response have been unavailable. Hence, the application of our model is possible when the values of zeta potential in the ionized solution for metal oxide nanoparticles are undetermined, without the necessity of performing more time consuming and expensive experiments. We believe that our studies will be helpful in predicting the conditions under which MexOy is likely to become problematic for the environment and human health.

When a nanoparticle meets a superhalogen: a case study with C60 fullerene

The ability of a selected nanoparticle to form stable systems with superhalogens (i.e. AlF4, AlCl4, MgF3, MgCl3, LiF2, LiCl2, and LiI2) is examined on the basis of theoretical considerations supported by ab initio calculations. It is demonstrated that the C60 fullerene molecule should form stable and strongly bound (C60)˙(+)(superhalogen)(-) radical cation salts when combined with an appropriately chosen superhalogen radical (acting as an oxidizing agent). The conclusion is supported by providing: (i) the structural deformation of superhalogens and C60 nanoparticles upon ionization, (ii) predicted charge flow between the fullerene and each superhalogen (which allows estimating the amount of electron density withdrawn from the C60 molecule during the ionization process), (iii) the localization of the spin density distribution, and (iv) the interaction energies for the compounds obtained both at the B3LYP/6-31+G(d) level and at the B3LYP-D3/6-31+G(d) level. Solvent effects have been considered in the present study by means of the polarizable continuum model. It is found that the stability of C60/superhalogen species can be improved in solvents. We believe that the results provided in this contribution may likely be of prospective relevance in the future studies on the issue of binding and removal of this potentially risky nanoparticle.

Are noble gas molecules able to exhibit a superhalogen nature?

The NgF_6n+1⁻ (Ng = Xe, Rn) anions exhibit much larger vertical detachment energies than the EA of halogen elements, confirming their superhalogen identities.