Computational Descriptors Research Articles

Uncovering Metal-Decorated TiO2 Photocatalysts for Ciprofloxacin Degradation-A Combined Experimental and DFT Study.

Optimization of the efficiency of the photocatalytic degradation of organic and pharmaceutical pollutants represents a matter of fundamental and practical interest. The present experimental and DFT study deals with evaluation of OH radical binding energy as a simple computational descriptor of the catalytic activity of d-metal-decorated TiO2 photocatalysts for the photodegradation of the widely used antibiotic ciprofloxacin. Five d-metals commonly used in catalytic materials (Zr, Pt, Pd, Fe, and Cu) were deposited on the TiO2 surface, and the obtained photocatalysts were characterized experimentally (XRPD, ICP-OES, and SEM) and theoretically (DFT). Attention was also paid to the mechanistic insights and degradation byproducts (based on UV-Vis spectrometry and LC/MS analysis) in order to obtain systematic insight into their structure/performance relationships and confirm the proposed model of the degradation process based on OH radical reactivity.

Computational descriptor for electrochemical currents of carbon dioxide reduction on Cu facets

Computation screening is crucial for designing efficient electrochemical catalysts for carbon dioxide (CO2R) reduction that produce valuable hydrocarbons and oxygenates. Herein, leveraging density functional theory calculations of the CO adsorption energy ΔECO on seventeen Cu terminations, we discover a strong linear correlation between ΔECO and the recently experimentally measured CO2R electrochemical currents (ACS Catal. 2022, 12, 11, 6578–6588). Examining the ab initio thermodynamics of the early critical intermediates CO*, COH*, and CHO*, we find that CO* → CHO* is the thermodynamically controlling step. Beyond the general CO adsorption energy that only shows a linear trend with CO2R activity, we show that the reaction free energy of CO* → CHO* is the descriptor for the overall CO2R activity for Cu facets, as it displays a volcano relationship with the experimental current. Importantly, we show that increasing the step and kink density of the Cu terminations not only enhances CO adsorption strength but also modulates the CO* → CHO* pathway, as respectively exemplified in the (941) and (741) facets. In addition, we explain that the high activity of (741) is due to its relatively low hydrogen evolution reaction activity compared with the other Cu surfaces.

Advanced computational modelling for biofuel catalyst optimization: enhancing beta zeolite acidity for oleic acid upgrading

We explore here, through reactive force field (ReaxFF) molecular dynamics simulations, oleic acid upgrading on beta zeolite (BEA) regulated with nine silica-to-aluminium ratios (SARs) to investigate their acid-catalyzed deoxygenation and coking susceptibility. The selected computational descriptors involved conversion, hydrodeoxygenation, and decarboxylation/decarbonylation selectivity, biofuels yield, and coke deposition characteristics. Simulations were validated by 20.3 SAR available experimental data and used for the systematic study. High deoxygenation selectivity was found to be related to the structural sensitivity of BEA(100) on the upgrading mechanism. ReaxFF simulations revealed that altering the Al-substitution could greatly promote biofuel formation. Specifically, SARs towards the mid-region (SAR 47) favored gasoline production, while 31 SAR exploited diesel-like hydrocarbons. An optimum ratio of 37.4 SAR achieved maximum oleic acid conversion (69.8%), with high yields of gasoline and diesel fuels (15.6 and 20.4 wt%, respectively) and moderate coking (8.6 wt%). Density functional theory screening of metallic dopants allowed investigating of their deoxygenation and coke susceptibility, obtaining that Cu-BEA structure favored the optimal carbon and O-moiety adsorption (-3.89 and -1.8 eV, respectively). Furthermore, the economic and environmental analysis showed that Cu-dopped BEA displayed the lowest market price and global warming potential (71 USD/kg and 2.8 kg CO2-eq/kg).

Role of Electrolyte pH on Water Oxidation for Iridium Oxides.

Understanding the effect of noncovalent interactions of intermediates at the polarized catalyst-electrolyte interface on water oxidation kinetics is key for designing more active and stable electrocatalysts. Here, we combine operando optical spectroscopy, X-ray absorption spectroscopy (XAS), and surface-enhanced infrared absorption spectroscopy (SEIRAS) to probe the effect of noncovalent interactions on the oxygen evolution reaction (OER) activity of IrOx in acidic and alkaline electrolytes. Our results suggest that the active species for the OER (Ir4.x+-*O) binds much stronger in alkaline compared with acid at low coverage, while the repulsive interactions between these species are higher in alkaline electrolytes. These differences are attributed to the larger fraction of water within the cation hydration shell at the interface in alkaline electrolytes compared to acidic electrolytes, which can stabilize oxygenated intermediates and facilitate long-range interactions between them. Quantitative analysis of the state energetics shows that although the *O intermediates bind more strongly than optimal in alkaline electrolytes, the larger repulsive interaction between them results in a significant weakening of *O binding with increasing coverage, leading to similar energetics of active states in acid and alkaline at OER-relevant potentials. By directly probing the electrochemical interface with complementary spectroscopic techniques, our work goes beyond conventional computational descriptors of the OER activity to explain the experimentally observed OER kinetics of IrOx in acidic and alkaline electrolytes.

Laying the experimental foundation for corrosion inhibitor discovery through machine learning

Creating durable, eco-friendly coatings for long-term corrosion protection requires innovative strategies to streamline design and development processes, conserve resources, and decrease maintenance costs. In this pursuit, machine learning emerges as a promising catalyst, despite the challenges presented by the scarcity of high-quality datasets in the field of corrosion inhibition research. To address this obstacle, we have created an extensive electrochemical library of around 80 inhibitor candidates. The electrochemical behaviour of inhibitor-exposed AA2024-T3 substrates was captured using linear polarisation resistance, electrochemical impedance spectroscopy, and potentiodynamic polarisation techniques at different exposure times to obtain the most comprehensive electrochemical picture of the corrosion inhibition over a 24-h period. The experimental results yield target parameters and additional input features that can be combined with computational descriptors to develop quantitative structure–property relationship (QSPR) models augmented by mechanistic input features.

Single metal-doped silicon (Si59X; X = Nb, Mo, Y, Zr) nanostructured as nanosensors for N-Nitrosodimethylamine (NDMA) pollutant: Intuition from computational study

The present work examines the efficacy of metal-doped silicon (Si59X; X = Nb, Mo, Y, Zr) nanocages as an efficient nanosensors for N-Nitrosodimethylamine (NDMA). A suitable computational approach employing the exchange-correlation functional (PBE) along the Lanl2DZ basis set was utilized to this end to investigate the potential of silicon nanostructure, doped transition metal as a detector for NDMA. To comprehend the nature of the interaction between the nanostructure and adsorbate (NDMA), diverse computational descriptors including the adsorption energy of interaction, electronic attributes of the surfaces, sensing mechanism and recovery time of the modelled nanostructures were examined. The results show that doping all Si60 fullerene nanostructure with transition metals (Nb, Mo, Y, and Zr) enhanced the sensing attributes of the nanostructure towards NDMA. The average adsorption energy was calculated to be − 169.43 kcal/mol. The doped metals were also observed to influence the surface conductivity and binding affinity; the obtained adsorption energy was found to decrease with the doping of Zr metal whereas an increase was observed in the case of Nb doping. Other molecular descriptors also pointed out favourable results and affirmed the suitability of the surfaces to detect NDMA. Moreover, the recovery of NDMA from the surfaces was calculated to be in the milliseconds’ range and therefore suggests the recyclability of the surfaces. Finally, classical MD simulations affirmed the stabilities of the surfaces and disclosed that adsorption the adsorbate does not result in considerable deformation of the modelled surfaces. It is, therefore, concluded that the modelled surfaces could be further explored as potential candidates for the construction of amperometric nano-sensors.

The Affective Regulation of Uncertainty: The Semiotic Dimensionality Model (SDM)

This paper presents a novel psychological model of the socio-cognitive management of uncertainty, the semiotic dimensional model (SDM). The SDM claims that uncertainty increases the momentum of affect-laden meanings in meaning-making. This is so because affective meanings provide a simplified interpretation of the world that restores the experience of being able to make sense of the reality destabilized by uncertainty. Moreover, the SDM models the affective meanings in terms of low-dimensional mental phase space (MPS). Each dimension of the MPS detects a facet of the context. The lower the MPS dimensionality, the lower the number of facets of the context processed, therefore, the more simplified the meaning-making is. We attained the first empirical validation of the SDM in a sample of 120 Italian people. First, the SDM assumption that the low-dimensional MPS is the computational descriptor of affective meaning was tested. Second, an experimental study was designed in which uncertainty was manipulated so as to assess its effects on the dimensionality of participants’ MPS. It was hypothesized that uncertainty induces a decrease in the MPS dimensionality. Results were consistent with both hypotheses. Theoretical implications of the SDM and its relationship with other theories are discussed and future research direction outlined.

Designing single-site alloy catalysts using a degree-of-isolation descriptor.

Geometrically isolated metal atoms in alloy catalysts can target efficient and selective catalysis. However, the geometric and electronic disturbance between the active atom and its neighbouring atoms, that is, diverse microenvironments, makes the active site ambiguous. Herein, we demonstrate a methodology to describe the microenvironment and determine the effectiveness of active sites in single-site alloys. A simple descriptor, degree-of-isolation, is proposed, considering both electronic regulation and geometric modulation within a PtM ensemble (M = transition metal). The catalytic performance of PtM single-site alloy is examined thoroughly using this descriptor for an industrially important reaction, propane dehydrogenation. The volcano-shaped isolation-selectivity plot reveals a Sabatier-type principle for designing selective single-site alloys. Specifically, for a single-site alloy with a high degree-of-isolation, alternation of the active centre has a great impact on tuning selectivity, validated by the outstanding consistency between experimental propylene selectivity and the computational descriptor.

Toward Improving the Selectivity of Organic Halide Electrocarboxylation with Mechanistically Informed Solvent Selection

The use of a liquid electrolyte is nearly ubiquitous in electrosynthetic systems and can have a significant impact on the selectivity and efficiency of electrochemical reactions. Solvent selection is thus a key step during optimization, yet this selection process usually involves trial-and-error. As a step toward more rational solvent selection, this work examines how the electrolyte solvent impacts the selectivity of electrocarboxylation of organic halides. For the carboxylation of a model alkyl bromide, hydrogenolysis is the primary side reaction. Isotope-labeling studies indicate the hydrogen atom in the hydrogenolysis product comes solely from the aprotic electrolyte solvent. Further mechanistic studies reveal that under synthetically relevant electrocarboxylation conditions, the hydrogenolysis product is formed via deprotonation of the solvent. Guided by these mechanistic findings, a simple computational descriptor based on the free energy to deprotonate a solvent molecule was shown to correlate strongly with carboxylation selectivity, overcoming limitations of traditional solvent descriptors such as pKa. Through careful mechanistic analysis surrounding the role of the solvent, this work furthers the development of selective electrocarboxylation systems and more broadly highlights the benefits of such analysis to electrosynthetic reactions.

Atroposelective Negishi Coupling Optimization Guided by Multivariate Linear Regression Analysis: Asymmetric Synthesis of KRAS G12C Covalent Inhibitor GDC-6036.

An efficient asymmetric synthesis of a potent KRAS G12C covalent inhibitor, GDC-6036 (1), is reported. The synthesis features a highly atroposelective Negishi coupling to construct the key C-C bond between two highly functionalized pyridine and quinazoline moieties by employing a Pd/Walphos catalytic system. Statistical modeling by comparing computational descriptors of a range of Walphos chiral bisphosphine ligands to a training set of experimental results was used to inform the selection of the best ligand, W057-2, which afforded the desired Negishi coupling product (Ra)-3 in excellent selectivity. A subsequent telescoped reaction sequence of alkoxylation, global deprotection, and acrylamide formation, followed by a final adipate salt formation, furnished GDC-6036 (1) in 40% overall yield from starting materials pyridine 5 and quinazoline 6.

Design and Synthesis of Fsp3-Enriched Spirocyclic-Based Biological Screening Compound Arrays via DOS Strategies and Their NNMT Inhibition Profiling.

Medicinal chemists are keen to explore tridimensional compounds, especially when it comes to small molecules. It has already been stressed that the majority of known drugs tend to be flat, whereas natural products tend to be more tridimensional and represent a good source of active compounds. 3D metrics have been implemented and computational descriptors are available to evaluate and prioritize compounds based on their 3D geometry. This is usually done by comparing the saturated carbon atoms in a molecule with the total number of its non-hydrogen atoms (the Fsp3 value). While this aspect is clear, still there are not enough synthetic tools that support the realization of novel chemotypes that conform to these criteria. Herein we describe a diversity oriented synthesis (DOS) synthetic cascade technology that starts from two simple reagents, and generates highly enriched Fsp3 novel and diverse spiro-scaffolds with pragmatic synthetic handles (points of diversity). The spiro nature of these scaffolds not only ensures high Fsp3 values but renders the compounds more rigid and therefore more effective in forming precise stereo-interactions with their potential biological targets. These compounds were also profiled for their drug-like properties and as potential modulators of the NNMT enzyme.

Effect of Iodide-Based Organic Salts and Ionic Liquid Additives in Dye-Sensitized Solar Cell Performance.

The use of ionic liquid and organic salts as additives for electrolyte systems in dye-sensitized solar cells have been widely described in recent years. The tunability of their physical-chemical properties according to the cation–anion selection contributes toward their high efficiencies. For this purpose, several iodide-based organic salts including imidazolium, picolinium, guanidinium and alkylammonium cations were tested using acetonitrile/valeronitrile electrolytes and their photovoltaic parameters were compared. A best efficiency of 4.48% (4.15% for the reference) was found for 1-ethyl-2,3-dimethylimidazolium iodide ([C2DMIM]I) containing electrolyte, reaffirming the effectiveness of these additives. 4-tertbutylpyridine was included into the formulation to further improve the performance while determining which iodide salts demonstrate the highest synergy with this additive. [C2DMIM]I once again proved to be the superior additive, achieving an efficiency of 6.48% (6% for the reference). Electrochemical impedance spectroscopy was employed to elucidate the effects of the various additives, demonstrating the relevance of the counter electrode resistance on device performance. Finally, several computational descriptors for the cationic structures were calculated and correlated with the photovoltaic and resistance parameters, showing that properties related to polarity, namely relative positive charge, molecular polarizability and partition coefficient are in good agreement with the counter-electrode resistance.

Immobilized artificial membrane-chromatographic and computational descriptors in studies of soil-water partition of environmentally relevant compounds

Chromatographic retention factor log kIAM obtained from immobilized artificial membrane (IAM) HPLC with buffered, aqueous mobile phases and calculated molecular descriptors (molecular weight — log MW; molar volume — VM; polar surface area — PSA; total count of nitrogen and oxygen atoms -(N + O); count of freely rotable bonds — FRB; H-bond donor count — HD; H-bond acceptor count — HA; energy of the highest occupied molecular orbital — EHOMO; energy of the lowest unoccupied orbital — ELUMO; dipole moment — DM; polarizability — α) obtained for a group of 175 structurally unrelated compounds were tested in order to generate useful models of solutes’ soil-water partition coefficient normalized to organic carbon log Koc. It was established that log kIAM obtained in the conditions described in this study is not sufficient as a sole predictor of the soil-water partition coefficient. Simple, potentially useful models based on log kIAM and a selection of readily available, calculated descriptors and accounting for over 88% of total variability were generated using multiple linear regression (MLR) and artificial neural networks (ANN). The models proposed in the study were tested on a group of 50 compounds with known experimental log Koc values by plotting the calculated vs. experimental values. There is a good close similarity between the calculated and experimental data for both MLR and ANN models for compounds from different chemical families (R2 ≥ 0.80, n = 50) which proves the models’ reliability.

Bridging Database and Experimental Analysis to Reveal Super-hydrodynamic Conductivity Scaling Regimes in Ionic Liquids.

Ion transport through electrolytes critically impacts the performance of batteries and other devices. Many frameworks used to model ion transport assume hydrodynamic mechanisms and focus on maximizing conductivity by minimizing viscosity. However, solid-state electrolytes illustrate that non-hydrodynamic ion transport can define device performance. Increasingly, selective transport mechanisms, such as hopping, are proposed for concentrated electrolytes. However, viscosity-conductivity scaling relationships in ionic liquids are often analyzed with hydrodynamic models. We report data-centric analyses of hydrodynamic transport models of viscosity-conductivity scaling in ionic liquids by merging three databases to bridge physical properties and computational descriptors. With this expansive database, we constrained scaling analyses using ion sizes defined from simulated volumes, as opposed to estimating sizes from activity coefficients. Remarkably, we find that many ionic liquids exhibit positive deviations from the Nernst-Einstein model, implying ions move faster than hydrodynamics should allow. We verify these findings using microrheology and conductivity experiments. We further show that machine learning tools can improve predictions of conductivity from molecular properties, including predictions from solely computational features. Our findings reveal that many ionic liquids exhibit super-hydrodynamic viscosity-conductivity scaling, suggesting mechanisms of correlated ion motion, which could be harnessed to enhance electrochemical device performance.

A charge transfer framework that describes supramolecular interactions governing structure and properties of 2D perovskites

The elucidation of structure-to-function relationships for two-dimensional (2D) hybrid perovskites remains a primary challenge for engineering efficient perovskite-based devices. By combining insights from theory and experiment, we describe the introduction of bifunctional ligands that are capable of making strong hydrogen bonds within the organic bilayer. We find that stronger intermolecular interactions draw charge away from the perovskite layers, and we have formulated a simple and intuitive computational descriptor, the charge separation descriptor (CSD), that accurately describes the relationship between the Pb-I-Pb angle, band gap, and in-plane charge transport with the strength of these interactions. A higher CSD value correlates to less distortion of the Pb-I-Pb angle, a reduced band gap, and higher in-plane mobility of the perovskite. These improved material properties result in improved device characteristics of the resulting solar cells.

RP-18 TLC and Computational Descriptors of Skin Permeability of Sunscreens

The relationships between the reversed-phase thin layer chromatographic retention parameters obtained on octadecyl-modified silica (RP-18) sorbent for mobile phases containing water and one of six water-miscible organic modifiers (acetone, methanol, acetonitrile, tetrahydrofurane, N,N-dimethylformamide, 1,4-dioxane) and skin permeability coefficients were studied for a group of 21 cosmetic raw materials, mainly organic sunscreens and preservatives. The correlations between the skin permeability coefficients log K_d calculated in silico using EpiSuite software and the RP-18 thin layer chromatographic retention parameters are mostly linear, especially for compounds of lower-to-medium lipophilicity. It was established that skin permeability coefficient models based on retention parameters collected for mobile phases containing acetone or dioxane (75% v/v), proposed for structurally unrelated cosmetic raw materials are also applicable to other actives, as shown using a test set of compounds whose in vivo log K_d data are available. Skin permeability models developed in this study have the benefit of being based on easily obtained, chromatographic descriptors and their applicability extends beyond cosmetic chemistry.

Computational descriptor analysis on excited state behaviours of a series of TADF and non-TADF compounds.

The thermally activated delayed fluorescence (TADF) behaviours of seventeen organic TADF emitters and two non-TADF chromophores bearing various donor and acceptor moieties were investigated, focusing on their torsion angles, singlet-triplet gap (ΔEST), spin orbit couplings (SOC) and topological ΦS index. Electronic structure calculations were performed in the framework of the Tamm-Dancoff approximation (TDA) allowing the possible reverse intersystem crossing (RISC) pathways to be characterized. The electronic density reorganization of the excited states was checked also with respect to the different exchange-correlation functional and absorption spectra were obtained by considering vibrational and dynamical effects through Wigner sampling of the ground state equilibrium regions. Examining all the parameters obtained in our computational study, we rationalized the influence of electron-donating and electron-accepting groups and the effects of geometrical factors, especially torsion angles, on a wide class of diverse compounds ultimately providing an easy and computationally effective protocol to assess TADF efficiencies.

Effective Screening Route for Highly Active and Selective Metal-Nitrogen-Doped Carbon Catalysts in CO2 Electrochemical Reduction.

To identify high-efficiency metal-nitrogen-doped (M-N-C) electrocatalysts for the electrochemical CO2 -to-CO reduction reaction (CO2 RR), a method that uses density functional theory calculation is presented to evaluate their selectivity, activity, and structural stability. Twenty-three M-N4 -C catalysts are evaluated, and three of them (M= Fe, Co, or Ni) are identified as promising candidates. They are synthesized and tested as proof-of-concept catalysts for CO2 -to-CO conversion. Different key descriptors, including the maximum reaction energy, differences of the *H and *CO binding energy (ΔG*H -ΔG*CO ), and *CO desorption energy (ΔG*CO→CO( g ) ), are used to clarify the reaction mechanism. These computational descriptors effectively predict the experimental observations in the entire range of electrochemical potential. The findings provide a guideline for rational design of heterogeneous CO2 RR electrocatalysts.

Chemometric analysis of bio-inspired micellar electrokinetic chromatographic systems – modelling of retention mechanism and prediction of biological properties using bile salts surfactants

The development of screening methods for the estimation of physicochemical, biological and pharmacokinetic properties of new pharmaceutical agents at early stage of the drug discovery and development process is highly desirable. Due to inadequate properties many of drug candidates are rejected during clinical trials. Among available screening methods, both chromatographic and electrophoretic approaches have a well-established position. This work is continuation of our research focused on developing new micellar electro-kinetic chromatographic methods for rapid assessment of complex biological properties. In this work an extended set of 45 compounds was used in order to realize two main goals. The first one was the characterization of molecular mechanism of retention in sodium cholate and sodium deoxycholate MEKC systems using quantitative structure-retention relationships (QSRR) approach. The second goal was evaluation of the possibilities to use MEKC with sodium cholate and sodium deoxycholate pseudo-stationary phases in order to predict human intestinal absorption (HIA). The proposed quantitative retention-activity relationships (QRAR) model was additionally supported by theoretical descriptors and support vector machine (SVM) regression. Additionally, the prediction ability of obtained QRAR models were compared with PREADME software. The obtained results suggest that SVM model has better predictive abilities and is better suited to separate noise which accompanies the experimental and computational descriptors.

Self-Association in EO-BO-EO Triblock Copolymers as a Nanocarrier Template for Sustainable Release of Anticancer Drugs.

Ethylene oxide (EO)-butylene oxide (BO)-ethylene oxide (EO)-based triblock copolymers with varying hydrophilic-hydrophobic ratios in arrangement, generally referred to as EBE, were scrutinized in an aqueous environment. Various self-associative (micellization) physicochemical properties of these EBEs were examined at different temperatures unified with a quantum chemical study. The salting-out effect on 5%w/v EBE was examined by observing their aqueous solution behavior where the clear transparent solution/turbidity suggested the probable presence of spherical or ellipsoidal micelles, which was confirmed from the scattering outline. The hydrodynamic radius (Dh) of the formed micellar geometry as a function of temperature and electrolyte (2 M NaCl) was inspected from dynamic light scattering and further supported by small-angle neutron scattering, where the Q-range prototype and scattering parameters were evaluated by the best fitting of the structure factor. Furthermore, these micelles were employed as potential nanocarriers for anticancer (curcumin and quercetin) drugs, where its release profile at a particular time interval was estimated using UV-vis spectroscopy. Different kinetic models were employed to fit the release profile data that enabled this study to act as an ideal platform for drug delivery. Also, the plausible interactions between EO-BO-EO blocks and the anticancer drugs were inferred from the evaluated computational descriptors.