Benchmarking Semiempirical Methods To Compute Electrochemical Formal Potentials

Toward Modeling the Complexity of the Chemical Mechanism in SERS

Surface-enhanced Raman scattering (SERS) provides detailed information about the binding of molecules at interfaces and their interactions with the local environment due to the large enhancement of Raman scattering. This enhancement arises from a combination of the electromagnetic mechanism (EM) and chemical mechanism (CM). While it is commonly accepted that EM gives rise to most of the enhancement, large spectral changes originate from CM. To elucidate the rich information contained in SERS spectra about molecules at interfaces, a comprehensive understanding of the enhancement mechanisms is necessary. In this Perspective, we discuss the current understanding of the enhancement mechanisms and highlight their interplay in complex local environments. We will also discuss emerging areas where the development of computational and theoretical models is needed with specific attention given to how the CM contributes to the spectral changes. Future efforts in modeling should focus on overcoming the challenges presented in this review in order to capture the complexity of CM in SERS.

Linear Vibronic Coupling Approach for Surface-Enhanced Raman Scattering: Quantifying the Charge-Transfer Enhancement Mechanism

The outstanding amplification observed in surface-enhanced Raman scattering (SERS) is due to several enhancement mechanisms, and standing out among them are the plasmonic (PL) and charge-transfer (CT) mechanisms. The theoretical estimation of the enhancement factors of the CT mechanism is challenging because the excited-state coupling between bright plasmons and dark CT states must be properly introduced into the model to obtain reliable intensities. In this work, we aim at simulating electrochemical SERS spectra, considering models of pyridine on silver clusters subjected to an external electric field E⃗ that represents the effect of an electrode potential Vel. The method adopts quantum dynamical propagations of nuclear wavepackets on the coupled PL and CT states described with linear vibronic coupling models parametrized for each E⃗ through a fragment-based maximum-overlap diabatization. By presenting results at different values of E⃗, we show that indeed there is a relation between the population transferred to the CT states and the total scattered intensity. The tuning and detuning processes of the CT states with the bright PLs as a function of the electric field are in good agreement with those observed in experiments. Finally, our estimations for the CT enhancement factors predict values in the order of 105 to 106, meaning that when the CT and PL states are both in resonance with the excitation wavelength, the CT and PL enhancements are comparable, and vibrational bands whose intensity is amplified by different mechanisms can be observed together, in agreement with what was measured by typical experiments on silver electrodes.

Unlocking the Potential: Predicting Redox Behavior of Organic Molecules, from Linear Fits to Neural Networks

Redox-active organic molecules, i.e., molecules that can relatively easily accept and/or donate electrons, are ubiquitous in biology, chemical synthesis, and electronic and spintronic devices, such as solar cells and rechargeable batteries, etc. Choosing the best candidates from an essentially infinite chemical space for experimental testing in a target application requires efficient screening approaches. In this Review, we discuss modern in silico techniques for predicting reduction and oxidation potentials of organic molecules that go beyond conventional first-principles computations and thermodynamic cycles. Approaches ranging from simple linear fits based on molecular orbital energy approximation and energy difference approximation to advanced regression and neural network machine learning algorithms employing complex descriptors of molecular compositions, geometries, and electronic structures are examined in conjunction with relevant literature examples. We discuss the interplay between ab initio data and machine learning (ML), i.e., whether it is better to base predictions on low-level quantum-chemical results corrected with ML or to bypass first-principles computations entirely and instead rely on elaborate deep learning architectures. Finally, we list currently available data sets of redox-active organic molecules and their experimental and/or computed properties to facilitate the development of screening platforms and rational design of redox-active organic molecules.

Impact of the Hydrogen-Bonding Functional Group on Hydrogelation of Amphiphilic Naphthalene-diimide Derivatives and Nonspecific Protein Adsorption

This manuscript reports the effect of hydrogen-bonding functionality on the supramolecular assembly of naphthalene-diimide (NDI)-derived amphiphilic building blocks in water. All the molecules contain a central NDI chromophore, functionalized with a hydrophilic oligo-oxyethylene (OE) wedge in one arm and a phenyl group on the opposite arm. They differ by a single H-bonding functionality, which links the NDI chromophore and the phenyl moiety. The H-bonding functionalities are amide, thioamide, urea, and urethane in NDI-A, NDI-TA, NDI-U, and NDI-UT, respectively. All of these molecules exhibit π-stacking in water, as evident from their distinct UV/vis absorption spectra when compared to that of the monomeric dye in THF. However, among these four, only NDI-A and NDI-TA show hydrogelation, while the other two precipitate out of the medium. The NDI-A hydrogel also exhibits transient stability and leads to a crystalline precipitate within ∼5 h. Only NDI-TA produces stable transparent hydrogel with the entangled fibrillar morphology that is typical for gelators. Both NDI-A and NDI-TA showed a thermoresponsive property with a lower critical solution temperature of about 41-42 °C. Powder XRD studies show a parallel orientation for NDI-A and an antiparallel orientation for NDI-TA. Computational studies support this experimental observation and indicate that the NDI-A assembly is highly stabilized by strong H-bonding among the amide groups and π-stacking interaction in the parallel orientation. On the other hand, due to weak H-bonding among the thioamide groups, the binding energy of the parallelly oriented NDI-TA was significantly lower and the optimized structure was disordered. Instead, its antiparallel orientation was more stable, with criss-cross aligned H-bonding interactions and π-π interactions between adjacent aromatic rings. The NDI-TA hydrogel with less ordered OE chains on the surface showed prominent adsorption of serum protein BSA. In sharp contrast, NDI-A did not exhibit any notable interaction with BSA, as evident from the ITC studies.

Computational Model for Electrochemical Surface-Enhanced Raman Scattering: Key Role of the Surface Charges and Synergy between Electromagnetic and Charge-Transfer Enhancement Mechanisms

We present a computational model for electrochemical surface-enhanced Raman scattering (EC-SERS). The surface excess of charge induced by the electrode potential (V_el) was introduced by applying an external electric field to a set of clusters [Ag_n]^q with (n, q) of (19, ±1) or (20, 0) on which a molecule adsorbs. Using DFT/TD-DFT calculations, these metal-molecule complexes were classified by the adsorbate partial charge, and the main V_el-dependent properties were simultaneously studied with the aid of vibronic resonance Raman computations, namely, changes on the vibrational wavenumbers, relative intensities, and enhancement factors (EFs) for all SERS mechanisms: chemical or nonresonant, and resonance Raman with bright states of the adsorbate, charge-transfer (CT) states, and plasmon-like excitations on the metal cluster. We selected two molecules to test our model, pyridine, for which V_el has a remarkable effect, and 9,10-bis((E)-2-(pyridin-4-yl)vinyl)anthracene, which is almost insensitive to the applied bias. The results nicely reproduced most of the experimental observations, while the limitations of our approach were critically evaluated. We detected that accounting explicitly for the surface charges is key for EC-SERS models and that the highest calculated EFs, up to 10⁷ to 10⁸, are obtained by interstate coupling of bright local excitations of the metal cluster and CT states. These results highlight the importance of nonadiabatic effects in SERS and the capabilities of EC-SERS as a technique with potential to study excited-state coupling by tuning the CT and plasmon-like states by manipulating V_el.

Parametrization of the PM7 Semiempirical Quantum Mechanical Method for Silver Nanoclusters

Semiempirical quantum mechanical methods (SEQMs) are widely used in computational chemistry because of their low computational cost, but their accuracy depends on the quality of the parameters. The neglect of diatomic differential overlap method PM7 is among the few SEQMs that contain parameters for Ag, but the experimental reference data was insufficient to obtain reliable parameters in the original parametrization. In this work, we reparametrize the PM7 parameters for Ag to accurately reproduce the ground-state potential energy surfaces of Ag clusters. Since little experimental data is available, we use reference data obtained from the ab initio method CCSD(T). The resulting parameters significantly reduce the errors in binding energies, energies required to displace clusters along their normal modes, and relative energies of isomers compared to the default PM7 Ag parameters.

A map of mass spectrometry-based in silico fragmentation prediction and compound identification in metabolomics

Metabolomics, the comprehensive study of the metabolome, and lipidomics-the large-scale study of pathways and networks of cellular lipids-are major driving forces in enabling personalized medicine. Complicated and error-prone data analysis still remains a bottleneck, however, especially for identifying novel metabolites. Comparing experimental mass spectra to curated databases containing reference spectra has been the gold standard for identification of compounds, but constructing such databases is a costly and time-demanding task. Many software applications try to circumvent this process by utilizing cutting-edge advances in computational methods-including quantum chemistry and machine learning-and simulate mass spectra by performing theoretical, so called in silico fragmentations of compounds. Other solutions concentrate directly on experimental spectra and try to identify structural properties by investigating reoccurring patterns and the relationships between them. The considerable progress made in the field allows recent approaches to provide valuable clues to expedite annotation of experimental mass spectra. This review sheds light on individual strengths and weaknesses of these tools, and attempts to evaluate them-especially in view of lipidomics, when considering complex mixtures found in biological samples as well as mass spectrometer inter-instrument variability.

A new release of MOPAC incorporating the INDO/S semiempirical model with CI excited states

The semiempirical INDO/S Hamiltonian is incorporated into a new release of MOPAC2016. The MOPAC2016 software package has long been at the forefront of semiempirical quantum chemical methods (SEQMs) for small molecules, proteins, and solids and until this release has included only NDDO-type SEQMs. The new code enables the calculation of excited states using the INDO/S Hamiltonian combined with a configuration interaction (CI) approach using single excitations (CIS), single and double excitations (CISD), or multiple reference determinants (MRCI) where reference determinants are generated using a complete active space (CAS) approach. The capacity to perform excited-state calculations beyond the CIS level makes INDO/CI one of the few low-cost computational methods capable of accurately modeling states with substantial double-excitation character. Solvent corrections to the ground-state and excited-state energies can be computed using the COSMO implicit solvent model, incorporating state-specific corrections to the excited states based on the solvent refractive index. This code produces physically reasonable electronic structures, absorption spectra, and solvatochromic shifts at low computational costs for systems up to hundreds of atoms, and for both organic molecules and metal clusters.

Linear correlation models for the redox potential of organic molecules in aqueous solutions

In this study, we use the molecular orbital energy approximation (MOEA) and the energy difference approximation (EDA) to build linear correlation models for the redox potentials of 53 organic compounds in aqueous solutions. The molecules evaluated include nitroxides, phenols, and amines. Both the MOEA and EDA methods yield similar correlation models, however, the MOEA method is less computationally expensive. Correlation coefficients (R²) below 0.3 and mean absolute errors above 0.25 V were found for correlation models built without solvent effects. When explicit water molecules and a continuum solvent model are added to the calculations, correlation coefficients close to 0.8 are reached, and mean absolute errors below 0.18 V are obtained. The incorporation of solvent effects is necessary for good correlation models, particularly for redox processes of charged molecules in aqueous solutions. A comparison of the correlation models from different methodologies is provided. Graphical abstract.

Present and Future of Surface-Enhanced Raman Scattering

The discovery of the enhancement of Raman scattering by molecules adsorbed on nanostructured metal surfaces is a landmark in the history of spectroscopic and analytical techniques. Significant experimental and theoretical effort has been directed toward understanding the surface-enhanced Raman scattering (SERS) effect and demonstrating its potential in various types of ultrasensitive sensing applications in a wide variety of fields. In the 45 years since its discovery, SERS has blossomed into a rich area of research and technology, but additional efforts are still needed before it can be routinely used analytically and in commercial products. In this Review, prominent authors from around the world joined together to summarize the state of the art in understanding and using SERS and to predict what can be expected in the near future in terms of research, applications, and technological development. This Review is dedicated to SERS pioneer and our coauthor, the late Prof. Richard Van Duyne, whom we lost during the preparation of this article.

Mass Spectral Fragmentation of Pelargonium graveolens Essential Oil Using GC–MS Semi-Empirical Calculations and Biological Potential

The volatile constituents of the essential oil of local Pelargonium graveolens growing in Egypt was investigated by gas chromatography–mass spectrometry (GC–MS), and the main constituents were citronellol (27.67%), cis-Menthone (10.23%), linalool (10.05%), eudesmol (9.40%), geraniol formate 6.87%, and rose oxide (5.77%), which represent the major components in the obtained GC total ion chromatogram. The structural determination of the main constitutes based on their electron ionization mass spectra have been investigated. The MS of these compounds are absolutely identical in mass values of peaks of fragment ions, where their relative intensities have minor differences. In the spectra of all studied compounds, the observed characteristic ions were [M-H2O]+ and [M-CH3]+. The latter has a structure with m/z 69, 83. Different quantum parameters were obtained using Modified Neglect of Diatomic Overlap (MNDO) semi-empirical method as total energy, binding energy, heat of formations, ionization energy, the energy of highest occupied molecular orbital (HOMO), the energy of the lowest unoccupied molecular orbital (LUMO), energy gap Δ, and dipole moment. The antibacterial and antifungal activities of P. graveolens essential oil and identified compounds were tested against wide collection of organisms. The individual identified compounds in the essential oil—citronellol, cis-Menthone, and linalool (except eudesmol)—showed comparable activity to antibiotics. The most active isolated compound was the citronellol and the lowest MIC was found against E. coli. The essential oil showed high antifungal effects and this activity was attributed to cis-Menthone, eudesmol, and citronellol (excluding linalool). cis-Menthone was the most active compound against selected fungi followed by the eudesmol The study recommends local P. graveolens and identified active compounds for further applications in the pharmaceutical industries.