Electronic Effects Research Articles

Where is the hidden intramolecular H-bonding vibrational signal in the proline matrix IR spectrum?

The assignment of the hydrogen bonded O-H stretch vibration in the proline matrix IR spectrum has sparked controversy. Employing constrained nuclear electronic orbital methods, we provide a clear assignment that the vibrational frequency drops to near 3000cm-1 as a result of the interplay between electronic effects, nuclear quantum effects, and matrix effects.

Hydrodynamic simulation of laser ablation with electronic entropy effects included

Hydrodynamic simulation of laser ablation with electronic entropy effects included

Data-Efficient Active Learning for Thermodynamic Integration: Acidity Constants of BiVO4 in Water.

The protonation state of molecules and surfaces is pivotal in various disciplines, including (electro-)catalysis, geochemistry, biochemistry, and pharmaceutics. Accurately and efficiently determining acidity constants is critical yet challenging, particularly when explicitly considering the electronic structure, thermal fluctuations, anharmonic vibrations, and solvation effects. In this research, we employ thermodynamic integration accelerated by com- mittee Neural Network potentials, training a single machine learning model that accurately describes the relevant protonated, deprotonated, and intermediate states. We investigate two deprotonation reactions at the BiVO4 (010)-water interface, a promising candidate for efficient photocatalytic water splitting. Our results illustrate the convergence of the required ensemble averages over simulation time and of the final acidity constant as a function of the Kirkwood coupling parameter. We demonstrate that simulation times on the order of nanoseconds are required for statistical convergence. This time scale is currently unachievable with explicit ab-initio molecular dynamics simulations at the hybrid DFT level of theory. In contrast, our machine learning workflow only requires a few hundred DFT single point calculations for training and testing. Exploiting the extended time scales accessible, we furthermore asses the effect of commonly applied bias potentials. Thus, our study significantly advances calculating free energy differences with ab-initio accuracy.

Effect of Halogen Substituents on Charge Transport Properties of n-type Organic Semiconductors: A Theoretical Study.

Organic semiconductors (OSCs) have received significant attention as promising materials for electronic devices. In this study, we investigate the impact of halogen groups on the charge transport properties of n-type OSC-6,13 bis((triisopropylsilyl)ethynyl)-5,7,12,14-tetraazapentacene (TIPS-TAP). The computed mobilities for TIPS-TAPs substituted with F and Cl exhibit excellent agreement with the experimental values, while the simulation overestimates the electron mobility for TIPS-TAP. Interestingly, the mobility of TIPS-TAP-4F is significantly lower than that of TIPS-TAP-4Cl/Br, despite their similar packing structures. This discrepancy can be attributed to the strong electron-withdrawing effect of fluoride, which reduces the electron transfer integral and increases the reorganization energy. While molecular packing is widely recognized as a dominant factor in charge transport in OSCs, our study highlights the crucial role of electronic effects in charge transport in OSCs. This study provides new insights into the mechanisms underlying charge transport in OSCs.

Machine learning analysis of a large set of homopolymers to predict glass transition temperatures

Glass transition temperature of polymers, Tg, is an important thermophysical property, which sometimes can be difficult to measure experimentally. In this regard, data-driven machine learning approaches are important alternatives to assess Tg values, in a high-throughput way. In this study, a large dataset of more than 900 polymers with reported glass transition temperature (Tg) was assembled from various public sources in order to develop a predictive model depicting the structure-property relationships. The collected dataset was curated, explored via cluster analysis, and then split into training and test sets for validation purposes and then polymer structures characterized by molecular descriptors. To find the models, several machine learning techniques, including multiple linear regression (MLR), k-nearest neighbor (k-NN), support vector machine (SVM), random forest (RF), gaussian processes for regression (GPR), and multi-layer perceptron (MLP) were explored. As result, a model with the subset of 15 descriptors accurately predicting the glass transition temperatures was developed. The electronic effect indices were determined to be important properties that positively contribute to the Tg values. The SVM-based model showed the best performance with determination coefficients (R2) of 0.813 and 0.770, for training and test sets, respectively. Also, the SVM model showed the lowest estimation error, RMSE = 0.062. In addition, the developed structure-property model was implemented as a web app to be used as an online computational tool to design and evaluate new homopolymers with desired glass transition profiles.

Constructing ultralow-loading Cu single atoms/Fe2O3 particles on Nb2C MXenes for efficient utilization of atomic H to boost electrochemical debromination

Bromate is a commonly identified carcinogenic and genotoxic disinfection byproduct in water. Electrochemical debromination is an environmentally friendly and effective approach but it often suffers the limited reducing atomic H (H*) ability and high metal catalyst dosage. In this study, a unique composite catalyst comprised of ultra-low loading Cu single atoms (0.016 wt%) and Fe2O3 nanoparticles supported on a Nb2C MXene (denoted as Cu0.2/Fe0.1/Nb2C) was synthesized by one-step high-temperature molten salt method. The Cu0.2/Fe0.1/Nb2C significantly outperformed the Nb2C, Cu0.2/Nb2C and Fe0.1/Nb2C counterparts in the electrochemical debromination with a 94.2 % removal efficiency of bromate. It is revealed that the introducing of Cu single atoms, efficiently promotes the adsorption of H* and inhibition of competitive H2 evolution. The incorporation of Fe2O3 nanoparticles significantly increased the electronic metal-support interaction effect between the metal components and Nb2C, thereby forming a noticeable electronic cloud bridge to facilitate efficient charge transfer. Ultimately, the synergistic interplay between Cu single atoms and Fe2O3 nanoparticles plays a crucial role in achieving the remarkable removal performance of bromate. This work not only presents a significant instance of a synergistic catalyst involving metal single atoms and metal nanoparticles for effective electrochemical debromination but also advances the understanding of the electronic metal-support interaction.

Alloying effects on the reactivity of Pd are ensemble dominated

In this paper the geometric (“ensemble”) and electronic (“ligand”) effects of alloying on surface reactivity and catalysis are considered. The effect of alloying on the behaviour of Pd, both in single crystal form, and as a nanoparticulate catalyst is discussed. The first case concerns Pd alloyed with Cu, and here the reactivity with formic acid and ethanol is modified by the presence of Cu. However, both Cu and Pd maintain their elemental integrity for the reactions, and it is shown that the main alloying effect is one of dilution of Pd atoms, rather than by global electronic factors such as d-band shifting and filling. Similarly, when Pd is alloyed with Au, then the adsorption characteristics (sticking probability and uptake) for CO, O2, ethene and acetaldehyde are dominated by changes in the surface arrangement of the two atoms. Au mainly acts as an adsorption blocker, but to different degrees depending upon the nature of the adsorbing molecule and its demand for particular ensemble sizes. Finally, nanoparticulate Pd is considered, and the effect of alloying on high pressure methanol synthesis from CO2 and H2 is outlined. Pd on its own is not very selective, instead it mainly produces CO and methane. However, by supporting on oxides such as ZnO, Ga2O3 and In2O3 and by reducing in hydrogen, the Pd forms alloys, which then results in high selectivity to methanol. Again, this is ascribed to the dilution of the Pd ensembles at the surface, which are the cause of methane production.

On the secondary promotion effect of Al and Ga on Cu/ZnO methanol synthesis catalysts

The structural and electronic effect of Al and Ga as ternary metals in Cu/ZnO catalysts for the methanol synthesis was examined. For this purpose, three zincian malachite-derived catalysts with the nominal Cu:Zn ratio of 70:30 were synthesized: an unpromoted binary catalyst (CZ) and two ternary catalysts with either 3 mol% Al (CZA) or Ga (CZG). Both Al and Ga showed a strong impact on the catalyst’s structure. Alongside the catalyst’s evolution from the co-precipitated precursors phase to the activated and reduced state, an improved microstructure and an increased BET surface area were found for the secondary promotor (Al or Ga) containing catalysts. Moreover, a sequence of chemisorption experiments allowed us to quantify and differentiate between Cusurf and Znred surface species in the activated catalysts. Considering the specific copper surface areas, DRIFTS data and catalytic results, an additional electronic promotion of Al and Ga is proposed. As demonstrated by Ga K-edge XANES, this promotion effect is related to doping of the ZnO support and enhances the reducibility of ZnO to form more Znred sites. This effect is stronger for Al leading to a more pronounced Znred overlayer on the Cu surface due to SMSI. In methanol synthesis, this results in a performance order CZA > CZG > CZ.

Electrical Conduction and Optical Characteristics of Li2O and Bi2O3 Co-doped Zinc-Phosphate Glassy Nanocomposites: A Critical Observation of Mixed Ionic Electronic Effect

Electrical Conduction and Optical Characteristics of Li2O and Bi2O3 Co-doped Zinc-Phosphate Glassy Nanocomposites: A Critical Observation of Mixed Ionic Electronic Effect

Tailoring Ni-Fe-B Electronic Effects in Layered Double Hydroxides for Enhanced Oxygen Evolution Activity.

NiFe layered double hydroxides (LDHs) are state-of-the-art catalysts for the oxygen evolution reaction (OER) in alkaline media, yet they still face significant overpotentials. Here, quantitative boron (B) doping is introduced in NiFe LDHs (ranging from 0% to 20.3%) to effectively tailor the Ni-Fe-B electronic interactions for enhanced OER performance. The co-hydrolysis synthesis approach synchronizes the hydrolysis rates of Ni and Fe precursors with the formation rate of B─O─M (M: Ni, Fe) bonds, ensuring precise B doping into the NiFe LDHs. It is demonstrated that B, as an electron-deficient element, acts as an "electron sink" at doping levels from 0% to 13.5%, facilitating the transition of Ni2+ to the active Ni3+δ, thereby accelerating OER kinetics. However, excessive B doping (13.5-20.3%) effectively generates oxygen vacancies in the LDHs, which increases electron density at Ni2+ sites and hinders their transition to Ni3+δ, thereby reducing OER activity. Optimal OER performance is achieved at a B doping level of 13.5%, with an overpotential of only 208mV to reach a current density of 500mA cm-2, placing it among the most effective OER catalysts to date. This Ni-Fe-B electronic engineering opens new avenues for developing highly efficient anode catalysts for water-splitting hydrogen production.

Vibrational Fermi Resonance in Atomically Thin Black Phosphorus.

Fermi resonance is a phenomenon involving the hybridization of two coincidentally quasi-degenerate states that is observed in the vibrational or electronic spectra of molecules. Despite numerous examples in molecular systems, vibrational Fermi resonances in dispersive semiconducting systems remain largely unexplored due to the rarity of occurrence. Here we report a vibrational Fermi resonance in atomically thin black phosphorus. The Fermi resonance arises via anharmonic mixing of a fundamental Raman mode and a Davydov component of an infrared mode, leading to a doublet with mixed character. The extent of Fermi coupling can be modulated by the application of external biaxial strain. The consequences of Fermi hybridization are revealed by electronic resonance effects in the thickness-dependent and excitation-wavelength-dependent Raman spectrum, which is predicted by ab initio hybrid functional simulations including excitonic interactions. This work reveals new insight into electron-phonon coupling in black phosphorus and demonstrates a novel method for modulating Fermi resonances in 2D semiconductors.

Atomically Permeated MoP‐WOx Core‐Shell Catalysts for Efficient and Selective Oxidation of Thiophenic Sulfides in Fuels via Direct Electron Transfer Mechanism

AbstractHeterogeneous interfaces designed rationally in catalysts can induce geometrical, compositional, and electronic effects that can be applied to modulate catalytically active sites and consequently accelerate reaction kinetics. Here, a core‐shell heterostructure catalyst consisting of crystalline molybdenum phosphide (MoP) cores and amorphous tungsten oxide (WOx) shells on porous carbon nanosheets (PCN) is developed for oxidative desulfurization (ODS) of fuels. The strongly coupled core‐shell heterogeneous interface triggers a distinctive atomic permeation effect that induces substantial electron transfer from MoP to WOx and subsequent generation of electron‐rich W sites, consequently optimizing the adsorption of intermediates and substrates. The resulting catalyst (WOx@MoP/PCN) demonstrates a turnover frequency as high as 768.1 h−1 for ODS at 60 °C, exceeding that of almost all the state‐of‐the‐art ODS catalysts by 1–2 orders of magnitude. Moreover, a novel surface oxidation pathway based on direct electron transfer is identified in the WOx@MoP/PCN‐H2O2 system, which bypasses the formation of reactive oxygen species, consequently endowing the system with a moderate redox potential. Thus, WOx@MoP/PCN exhibits unprecedented selectivity, achieving 100% removal of thiophenic sulfides from real diesel with minimal consumption of oxidant.

A Quantum Mechanical MP2 Study of the Electronic Effect of Nonplanarity on the Carbon Pyramidalization of Fullerene C60

Among C60’s diverse functionalities, its potential application in CO2 sequestration has gained increasing interest. However, the processes involved are sensitive to the molecule’s electronic structure, aspects of which remain debated and require greater precision. To address this, we performed structural optimization of fullerene C60 using the QM MP2/6–31G* method. The nonplanarity of the optimized icosahedron is characterized by two types of dihedral angles: 138° and 143°. The 120 dihedrals of 138° occur between two hexagons intersecting at C–C bonds of 1.42 Å, while the 60 dihedrals of 143° are observed between hexagons and pentagons at C–C bonds of 1.47 Å. NBO analysis reveals less pyramidal sp1.78 hybridization for carbons at the 1.42 Å bonds and more pyramidal sp2.13 hybridization for the 1.47 Å bonds. Electrostatic potential charges range from −0.04 a.u. to 0.04 a.u. on the carbon atoms. Second-order perturbation analysis indicates that delocalization interactions in the C–C bonds of 1.42 Å (143.70 kcal/mol) and 1.47 Å (34.98 kcal/mol) are 22% and 38% higher, respectively, than those in benzene. MP2/Def2SVP calculations yield a correlation energy of 13.49 kcal/mol per electron for C60, slightly higher than the 11.68 kcal/mol for benzene. However, the results from HOMO-LUMO calculations should be interpreted with caution. This study may assist in the rational design of fullerene C60 derivatives for CO2 reduction systems.

A Smart Design of Non-noble Catalysts for Sustainable Propane Dehydrogenation.

Propane dehydrogenation (PDH), an important process for propylene synthesis, relies on expensive noble metals or highly toxic oxides as catalysts. In a recent publication in Science, Gong and coworkers report a breakthrough discovery for PDH by introducing a sustainable catalyst composed of titanium oxide overlayers encapsulating nickel nanoparticles, termed Ni@TiOx. This innovative catalyst showcases exceptional performance in PDH, exhibiting high propylene selectivity and stability under industrially relevant conditions. The study elucidates the role of defective TiOx overlayers and the electronic promotional effect of subsurface Ni in enhancing catalytic activity, translating a traditional model catalyst system into a sustainable industrial catalyst for low-carbon energy and the chemical industry.

Structure and Dynamics of ATP and the ATP-Zn2+ Complex in Solution.

Despite the crucial role of ATP in life and artificial life-like applications, fundamental aspects relevant to its function, such as its conformational properties and its interaction with water and ions, remain unclear. Here, by integrating linear and two-dimensional infrared spectroscopy with ab initio molecular dynamics, we provide a detailed characterization of the vibrational spectra of the phosphate groups in ATP and in its complex with Zn2+ in water. Our study highlights the role of conformational disorder and solvation dynamics, beyond the harmonic normal-mode analysis, and reveals a complex scenario in which electronic and environmental effects tune the coupling between phosphate vibrations. We identify βγ-bidentate and αβγ-tridentate modes as the preferential coordination modes of Zn2+, as was proposed in the literature for Mg2+, although this conclusion is reached by a different spectral interpretation.

Atomically Isolated Pd Sites Promote Electrochemical CO Reduction to Acetate through a Protonation-Regulated Mechanism.

Electrochemical CO reduction reaction (CORR) offers a promising approach for sustainable acetate production, the promotion of which requires the control of multiple protonation steps. This paper describes the synthesis of atomically isolated Pd sites onto Cu nanoflakes to regulate the protonation of key intermediates. The Pd sites with moderate water activation capability are found to enhance the protonation of *CO at the neighboring Cu site to *COH, which is confirmed to be the rate-determining step through kinetic isotope effect studies. The formation of *COH-*CO is therefore promoted. Additionally, the Pd sites would preferentially protonate the C-OH group in *COH-*CO due to the spatial approximability and electronic modulation effects, generating *CCO for the selective formation of acetate. An acetate Faradaic efficiency of 59.5% is achieved at -0.78 V vs reversible hydrogen electrode (RHE), with a maximum partial current density of 286 mA cm-2 at -0.86 V vs RHE. The optimized catalyst also exhibits long-term stability for 500 h at 100 mA cm-2 in a membrane electrode assembly. This work reveals a new promoting mechanism for selective CORR with simultaneous tuning of the structural and electronic properties of the proton-supplying sites.

The Key Descriptors for Predicting the Exciton Binding Energy of Organic Photovoltaic Materials

Exciton binding energy (Eb) is a key parameter to determine the mechanism and performance of organic optoelectronic devices. Small Eb benefits to reduce the interfacial energy offset and the energy loss of organic solar cells. However, quantum‐chemical calculations of the Eb in solid state with considering electronic polarization effects are extremely time‐consuming. Furthermore, current studies lack critical descriptors. Here, we use data‐driven machine learning (ML) to accelerate the computation and identify the key descriptors most relevant to the solid‐state Eb. The results verify two key descriptors associated with molecular and aggregation‐state properties for efficient prediction of the solid‐state Eb. Moreover, a very high accuracy is achieved by using the extreme gradient boosting algorithm, with the Pearson’s correlation coefficient of 0.92. Finally, we use this ML model to predict the Eb of thin films, which is difficult to achieve using the current quantum‐chemical calculations due to the large structural disorder. Remarkably, the predicted thin‐film Eb values are fully consistent with the results of temperature‐dependent photoluminescence spectra. Therefore, our work provides an accurate and efficient approach to predict the solid‐state Eb and would be helpful to accelerate the exploitation of novel promising organic photovoltaic materials.

The Key Descriptors for Predicting the Exciton Binding Energy of Organic Photovoltaic Materials.

Exciton binding energy (Eb) is a key parameter to determine the mechanism and performance of organic optoelectronic devices. Small Eb benefits to reduce the interfacial energy offset and the energy loss of organic solar cells. However, quantum-chemical calculations of the Eb in solid state with considering electronic polarization effects are extremely time-consuming. Furthermore, current studies lack critical descriptors. Here, we use data-driven machine learning (ML) to accelerate the computation and identify the key descriptors most relevant to the solid-state Eb. The results verify two key descriptors associated with molecular and aggregation-state properties for efficient prediction of the solid-state Eb. Moreover, a very high accuracy is achieved by using the extreme gradient boosting algorithm, with the Pearson's correlation coefficient of 0.92. Finally, we use this ML model to predict the Eb of thin films, which is difficult to achieve using the current quantum-chemical calculations due to the large structural disorder. Remarkably, the predicted thin-film Eb values are fully consistent with the results of temperature-dependent photoluminescence spectra. Therefore, our work provides an accurate and efficient approach to predict the solid-state Eb and would be helpful to accelerate the exploitation of novel promising organic photovoltaic materials.

Towards Efficient White-light Emission in Sulfur Dots through Surface Charge Engineering.

Sulfur dots (SDs) have emerged as promising photoluminescence (PL) materials owing to their intrinsic merits such as abundant electronic effects, outstanding biocompatibility and available photocatalytic activity. Typically based on quantum confinement effects, SDs are reported usually confined emission in blue-to-green region. However, it is challenging to achieve their broad emission tunability in the visible region, restricted by inherent band gap of bulk sulfur (ca. 2.79 eV). Herein, we present white-light-emitting SDs achieved by surface charge engineering that hybridizes the surface of SDs with oleylamine. The resulting SDs exhibit broadband emissions (full width at half maximum of 187 nm) with PL quantum yields of up to 12.1% and Commission International de I'Eclairage color coordinates of (0.27, 0.32). Detailed experimental and calculation results reveal that the strong orbital coupling between oleylamine and sulfur on the hybrid surfaces of the SDs causes electron delocalization, leading to the generation of low-energy charge transfer (CT) states. These CT states are highly sensitive to sulfur-oleylamine hybrid structures, which complicate the transition dynamics and promote multi-energy emission, accounting for efficient white-light emission. The demonstration of white-light SDs based on surface charge engineering is an important step towards the development of sulfur-based PL materials.

Strengthened Delocalized Electronic Effect in Nano-Nickel@Carbon with High Pyrrolic Nitrogen for Selective Hydrogenation of Substituted Nitrobenzene Hydrogenation

Strengthened Delocalized Electronic Effect in Nano-Nickel@Carbon with High Pyrrolic Nitrogen for Selective Hydrogenation of Substituted Nitrobenzene Hydrogenation