Efficient catalyst-free N2 fixation by water radical cations under ambient conditions.

The growth and sustainable development of humanity is heavily dependent upon molecular nitrogen (N2) fixation. Herein we discover ambient catalyst-free disproportionation of N2 by water plasma which occurs via the distinctive HONH-HNOH+• intermediate to yield economically valuable nitroxyl (HNO) and hydroxylamine (NH2OH) products. Calculations suggest that the reaction is prompted by the coordination of electronically excited N2 with water dimer radical cation, (H2O)2+•, in its two-center-three-electron configuration. The reaction products are collected in a 76-needle array discharge reactor with product yields of 1.14 µg cm-2 h-1 for NH2OH and 0.37 µg cm-2 h-1 for HNO. Potential applications of these compounds are demonstrated to make ammonia (for NH2OH), as well as to chemically react and convert cysteine, and serve as a neuroprotective agent (for HNO). The conversion of N2 into HNO and NH2OH by water plasma could offer great profitability and reduction of polluting emissions, thus giving an entirely look and perspectives to the problem of green N2 fixation.

The first HyDRA challenge for computational vibrational spectroscopy.

Vibrational spectroscopy in supersonic jet expansions is a powerful tool to assess molecular aggregates in close to ideal conditions for the benchmarking of quantum chemical approaches. The low temperatures achieved as well as the absence of environment effects allow for a direct comparison between computed and experimental spectra. This provides potential benchmarking data which can be revisited to hone different computational techniques, and it allows for the critical analysis of procedures under the setting of a blind challenge. In the latter case, the final result is unknown to modellers, providing an unbiased testing opportunity for quantum chemical models. In this work, we present the spectroscopic and computational results for the first HyDRA blind challenge. The latter deals with the prediction of water donor stretching vibrations in monohydrates of organic molecules. This edition features a test set of 10 systems. Experimental water donor OH vibrational wavenumbers for the vacuum-isolated monohydrates of formaldehyde, tetrahydrofuran, pyridine, tetrahydrothiophene, trifluoroethanol, methyl lactate, dimethylimidazolidinone, cyclooctanone, trifluoroacetophenone and 1-phenylcyclohexane-cis-1,2-diol are provided. The results of the challenge show promising predictive properties in both purely quantum mechanical approaches as well as regression and other machine learning strategies.

Interpolation and extrapolation problems of multivariate regression in analytical chemistry: benchmarking the robustness on near-infrared (NIR) spectroscopy data.

Modern analytical chemistry of industrial products is in need of rapid, robust, and cheap analytical methods to continuously monitor product quality parameters. For this reason, spectroscopic methods are often used to control the quality of industrial products in an on-line/in-line regime. Vibrational spectroscopy, including mid-infrared (MIR), Raman, and near-infrared (NIR), is one of the best ways to obtain information about the chemical structures and the quality coefficients of multicomponent mixtures. Together with chemometric algorithms and multivariate data analysis (MDA) methods, which were especially created for the analysis of complicated, noisy, and overlapping signals, NIR spectroscopy shows great results in terms of its accuracy, including classical prediction error, RMSEP. However, it is unclear whether the combined NIR + MDA methods are capable of dealing with much more complex interpolation or extrapolation problems that are inevitably present in real-world applications. In the current study, we try to make a rather general comparison of linear, such as partial least squares or projection to latent structures (PLS); "quasi-nonlinear", such as the polynomial version of PLS (Poly-PLS); and intrinsically non-linear, such as artificial neural networks (ANNs), support vector regression (SVR), and least-squares support vector machines (LS-SVM/LSSVM), regression methods in terms of their robustness. As a measure of robustness, we will try to estimate their accuracy when solving interpolation and extrapolation problems. Petroleum and biofuel (biodiesel) systems were chosen as representative examples of real-world samples. Six very different chemical systems that differed in complexity, composition, structure, and properties were studied; these systems were gasoline, ethanol-gasoline biofuel, diesel fuel, aromatic solutions of petroleum macromolecules, petroleum resins in benzene, and biodiesel. Eighteen different sample sets were used in total. General conclusions are made about the applicability of ANN- and SVM-based regression tools in the modern analytical chemistry. The effectiveness of different multivariate algorithms is different when going from classical accuracy to robustness. Neural networks, which are capable of producing very accurate results with respect to classical RMSEP, are not able to solve interpolation problems or, especially, extrapolation problems. The chemometric methods that are based on the support vector machine (SVM) ideology are capable of solving both classical regression and interpolation/extrapolation tasks.

Experimental thermodynamics of free glycine conformations: the first Raman experiment after twenty years of calculations.

Low-frequency, gas-phase vibrational (Raman) spectroscopy was used in conjunction with a jet-cooled technique and ab initio calculations to study the intrinsic thermodynamic properties of the free (gas-phase) amino acid--glycine (Gly, H(2)NCHRCOOH). The first experimental evaluation of the enthalpy differences between the Gly conformations in the vapor phase is presented. The enthalpy values were determined to be 0.33 ± 0.05 and 1.15 ± 0.07 kcal mol(-1) for the ccc and gtt rotamers, respectively; the corresponding relative entropy values were -2.86 ± 0.12 and -0.12 ± 0.16 cal mol(-1) K(-1), respectively. It was proven that the low-frequency Raman and infrared spectroscopy is capable of estimating intrinsic thermodynamic parameters of protein building blocks, such as intermolecular hydrogen bonds (ccc conformer) and rotation around one of the bonds (N-C, gtt conformer). The inaccuracy of the RRHO approximation to Gly conformers was experimentally confirmed. Benchmark data for quantum theory and molecular dynamics were provided.

What happens to hydrophobic interactions during transfer from the solution to the gas phase? The case of electrospray-based soft ionization methods.

The disappearance of the hydrophobic effect in the gas phase due to the absence of an aqueous surrounding raises a long-standing question: can noncovalent complexes that are exclusively bound by hydrophobic interactions in solution be preserved in the gas phase? Some reports of successful detection by mass spectrometry of complexes largely stabilized by hydrophobic effect are questionable by the presence of electrostatic forces that hold them together in the gas phase. Here, we report on the MS-based analysis of model supramolecular complexes with a purely hydrophobic association in solution, ß-cyclodextrin, and synthetic adamantyl-containing ligands with several binding sites. The stability of these complexes in the gas phase is investigated by quantum chemical methods (DFT-M06). Compared with the free interaction partners, the inclusion complex between ß-cyclodextrin and adamantyl-containing ligand is shown to be stabilized in the gas phase by ΔG = 9.6 kcal mol(-1). The host-guest association is mainly enthalpy-driven due to strong dispersion interactions caused by a large nonpolar interface and a high steric complementarity of the binding partners. Interference from other types of noncovalent binding forces is virtually absent. The complexes are successfully detected via electrospray ionization mass spectrometry, although a high dissociation yield is also observed. We attribute this pronounced dissociation of the complexes to the collisional activation of ions in the atmospheric interface of mass spectrometer. The comparison of several electrospray-based ionization methods reveals that cold spray ionization provides the softest ion generation conditions for these complexes.

Melamine detection by mid- and near-infrared (MIR/NIR) spectroscopy: a quick and sensitive method for dairy products analysis including liquid milk, infant formula, and milk powder.

Melamine (2,4,6-triamino-1,3,5-triazine) is a nitrogen-rich chemical implicated in the pet and human food recalls and in the global food safety scares involving milk products. Due to the serious health concerns associated with melamine consumption and the extensive scope of affected products, rapid and sensitive methods to detect melamine's presence are essential. We propose the use of spectroscopy data-produced by near-infrared (near-IR/NIR) and mid-infrared (mid-IR/MIR) spectroscopies, in particular-for melamine detection in complex dairy matrixes. None of the up-to-date reported IR-based methods for melamine detection has unambiguously shown its wide applicability to different dairy products as well as limit of detection (LOD) below 1 ppm on independent sample set. It was found that infrared spectroscopy is an effective tool to detect melamine in dairy products, such as infant formula, milk powder, or liquid milk. ALOD below 1 ppm (0.76±0.11 ppm) can be reached if a correct spectrum preprocessing (pretreatment) technique and a correct multivariate (MDA) algorithm-partial least squares regression (PLS), polynomial PLS (Poly-PLS), artificial neural network (ANN), support vector regression (SVR), or least squares support vector machine (LS-SVM)-are used for spectrum analysis. The relationship between MIR/NIR spectrum of milk products and melamine content is nonlinear. Thus, nonlinear regression methods are needed to correctly predict the triazine-derivative content of milk products. It can be concluded that mid- and near-infrared spectroscopy can be regarded as a quick, sensitive, robust, and low-cost method for liquid milk, infant formula, and milk powder analysis.

Support vector machine regression (LS-SVM)--an alternative to artificial neural networks (ANNs) for the analysis of quantum chemistry data?

A multilayer feed-forward artificial neural network (MLP-ANN) with a single, hidden layer that contains a finite number of neurons can be regarded as a universal non-linear approximator. Today, the ANN method and linear regression (MLR) model are widely used for quantum chemistry (QC) data analysis (e.g., thermochemistry) to improve their accuracy (e.g., Gaussian G2-G4, B3LYP/B3-LYP, X1, or W1 theoretical methods). In this study, an alternative approach based on support vector machines (SVMs) is used, the least squares support vector machine (LS-SVM) regression. It has been applied to ab initio (first principle) and density functional theory (DFT) quantum chemistry data. So, QC + SVM methodology is an alternative to QC + ANN one. The task of the study was to estimate the Møller-Plesset (MPn) or DFT (B3LYP, BLYP, BMK) energies calculated with large basis sets (e.g., 6-311G(3df,3pd)) using smaller ones (6-311G, 6-311G*, 6-311G**) plus molecular descriptors. A molecular set (BRM-208) containing a total of 208 organic molecules was constructed and used for the LS-SVM training, cross-validation, and testing. MP2, MP3, MP4(DQ), MP4(SDQ), and MP4/MP4(SDTQ) ab initio methods were tested. Hartree-Fock (HF/SCF) results were also reported for comparison. Furthermore, constitutional (CD: total number of atoms and mole fractions of different atoms) and quantum-chemical (QD: HOMO-LUMO gap, dipole moment, average polarizability, and quadrupole moment) molecular descriptors were used for the building of the LS-SVM calibration model. Prediction accuracies (MADs) of 1.62 ± 0.51 and 0.85 ± 0.24 kcal mol(-1) (1 kcal mol(-1) = 4.184 kJ mol(-1)) were reached for SVM-based approximations of ab initio and DFT energies, respectively. The LS-SVM model was more accurate than the MLR model. A comparison with the artificial neural network approach shows that the accuracy of the LS-SVM method is similar to the accuracy of ANN. The extrapolation and interpolation results show that LS-SVM is superior by almost an order of magnitude over the ANN method in terms of the stability, generality, and robustness of the final model. The LS-SVM model needs a much smaller numbers of samples (a much smaller sample set) to make accurate prediction results. Potential energy surface (PES) approximations for molecular dynamics (MD) studies are discussed as a promising application for the LS-SVM calibration approach.

Variable selection in near-infrared spectroscopy: benchmarking of feature selection methods on biodiesel data.

During the past several years, near-infrared (near-IR/NIR) spectroscopy has increasingly been adopted as an analytical tool in various fields from petroleum to biomedical sectors. The NIR spectrum (above 4000 cm(-1)) of a sample is typically measured by modern instruments at a few hundred of wavelengths. Recently, considerable effort has been directed towards developing procedures to identify variables (wavelengths) that contribute useful information. Variable selection (VS) or feature selection, also called frequency selection or wavelength selection, is a critical step in data analysis for vibrational spectroscopy (infrared, Raman, or NIRS). In this paper, we compare the performance of 16 different feature selection methods for the prediction of properties of biodiesel fuel, including density, viscosity, methanol content, and water concentration. The feature selection algorithms tested include stepwise multiple linear regression (MLR-step), interval partial least squares regression (iPLS), backward iPLS (BiPLS), forward iPLS (FiPLS), moving window partial least squares regression (MWPLS), (modified) changeable size moving window partial least squares (CSMWPLS/MCSMWPLSR), searching combination moving window partial least squares (SCMWPLS), successive projections algorithm (SPA), uninformative variable elimination (UVE, including UVE-SPA), simulated annealing (SA), back-propagation artificial neural networks (BP-ANN), Kohonen artificial neural network (K-ANN), and genetic algorithms (GAs, including GA-iPLS). Two linear techniques for calibration model building, namely multiple linear regression (MLR) and partial least squares regression/projection to latent structures (PLS/PLSR), are used for the evaluation of biofuel properties. A comparison with a non-linear calibration model, artificial neural networks (ANN-MLP), is also provided. Discussion of gasoline, ethanol-gasoline (bioethanol), and diesel fuel data is presented. The results of other spectroscopic techniques application, such as Raman, ultraviolet-visible (UV-vis), or nuclear magnetic resonance (NMR) spectroscopies, can be greatly improved by an appropriate feature selection choice.

Biodiesel classification by base stock type (vegetable oil) using near infrared spectroscopy data.

The use of biofuels, such as bioethanol or biodiesel, has rapidly increased in the last few years. Near infrared (near-IR, NIR, or NIRS) spectroscopy (>4000cm(-1)) has previously been reported as a cheap and fast alternative for biodiesel quality control when compared with infrared, Raman, or nuclear magnetic resonance (NMR) methods; in addition, NIR can easily be done in real time (on-line). In this proof-of-principle paper, we attempt to find a correlation between the near infrared spectrum of a biodiesel sample and its base stock. This correlation is used to classify fuel samples into 10 groups according to their origin (vegetable oil): sunflower, coconut, palm, soy/soya, cottonseed, castor, Jatropha, etc. Principal component analysis (PCA) is used for outlier detection and dimensionality reduction of the NIR spectral data. Four different multivariate data analysis techniques are used to solve the classification problem, including regularized discriminant analysis (RDA), partial least squares method/projection on latent structures (PLS-DA), K-nearest neighbors (KNN) technique, and support vector machines (SVMs). Classifying biodiesel by feedstock (base stock) type can be successfully solved with modern machine learning techniques and NIR spectroscopy data. KNN and SVM methods were found to be highly effective for biodiesel classification by feedstock oil type. A classification error (E) of less than 5% can be reached using an SVM-based approach. If computational time is an important consideration, the KNN technique (E=6.2%) can be recommended for practical (industrial) implementation. Comparison with gasoline and motor oil data shows the relative simplicity of this methodology for biodiesel classification.

Support vector machine regression (SVR/LS-SVM)--an alternative to neural networks (ANN) for analytical chemistry? Comparison of nonlinear methods on near infrared (NIR) spectroscopy data.

In this study, we make a general comparison of the accuracy and robustness of five multivariate calibration models: partial least squares (PLS) regression or projection to latent structures, polynomial partial least squares (Poly-PLS) regression, artificial neural networks (ANNs), and two novel techniques based on support vector machines (SVMs) for multivariate data analysis: support vector regression (SVR) and least-squares support vector machines (LS-SVMs). The comparison is based on fourteen (14) different datasets: seven sets of gasoline data (density, benzene content, and fractional composition/boiling points), two sets of ethanol gasoline fuel data (density and ethanol content), one set of diesel fuel data (total sulfur content), three sets of petroleum (crude oil) macromolecules data (weight percentages of asphaltenes, resins, and paraffins), and one set of petroleum resins data (resins content). Vibrational (near-infrared, NIR) spectroscopic data are used to predict the properties and quality coefficients of gasoline, biofuel/biodiesel, diesel fuel, and other samples of interest. The four systems presented here range greatly in composition, properties, strength of intermolecular interactions (e.g., van der Waals forces, H-bonds), colloid structure, and phase behavior. Due to the high diversity of chemical systems studied, general conclusions about SVM regression methods can be made. We try to answer the following question: to what extent can SVM-based techniques replace ANN-based approaches in real-world (industrial/scientific) applications? The results show that both SVR and LS-SVM methods are comparable to ANNs in accuracy. Due to the much higher robustness of the former, the SVM-based approaches are recommended for practical (industrial) application. This has been shown to be especially true for complicated, highly nonlinear objects.

The first step in glycine solvation: the glycine-water complex.

The jet-cooled spontaneous Raman spectrum of a glycine-water complex (Gly + H(2)O), the first step in amino acid hydration, is reported. The low-frequency vibrational spectrum (below 500 cm(-1)) of the solvated molecule is recorded and assigned using quantum chemical data calculated from ab initio (MP2) and DFT (B3LYP, BLYP, PBE0 = PBE1PBE). Anharmonic corrections or Raman and infrared (IR) active vibrations are calculated using second-order perturbation theory at the MP2/6-31+G(d) level. The acquired spectra at medium resolution (hwhm of ~4 cm(-1)) allow different conformers of the glycine-water heterodimer to be distinguished. Three different dimer conformations are observed and identified; selective collision-induced relaxation processes are used to estimate their relative stability. The results are compared with recent theoretical predictions and microwave (MW) spectroscopy data. The premise that the acidic character of the OH group of the carboxylic acid dominates the interaction between water and glycine is confirmed. The addition of a water molecule is found to greatly change the potential energy surface and conformational preferences of H(2)NCH(2)COOH. Water stabilizes conformations in which formation of a closed-ring, H-bonded structure is possible. Simultaneous participation by the carboxyl oxygen of the amino acid in two hydrogen bonds is found to be unfavorable. It may be expected that the addition of extra water molecules could ultimately lead to the stabilization of the Gly zwitterion.

Optical properties of protonated Rhodamine 19 isomers in solution and in the gas phase.

Visible light absorption and fluorescence of three positional isomers of protonated Rhodamine 19 (o-, m- and p-R19H(+)) were studied in solution and in the gas phase. In solution, strong solvatochromic effects lead to spectral shifts between rhodamine isomers. In contrast, in the gas phase, these species were found to exhibit very similar fluorescence, while pronounced differences were observed in the absorption spectra. The o-R19H(+) was found to have the largest Stokes shift in the gas phase (around 10 nm), suggesting that an intramolecular relaxation operates in the excited electronic state for this isomer. Several mechanisms for this relaxation are proposed, such as the change of the dihedral angle between the carboxyphenyl group and the xanthene chromophore or that between the carboxylic group and the phenyl ring.

Rhodamines in the gas phase: cations, neutrals, anions, and adducts with metal cations.

Optical spectroscopy of biological molecules in the gas phase has recently gained considerable attention, being able to provide complementary structural information in the absence of native matrix. Biomolecules can change their properties when brought into the gas phase, and so can chromophores associated with them. Understanding the photophysics of chromophore labels is central for the correct interpretation of experimental data. In this report, the structure and the optical properties of Rhodamine 19 (R19) in the gas phase were examined by a combination of Fourier-transform ion cyclotron resonance mass spectrometry and visible-light laser spectroscopy. While R19 in solution is found either in neutral (R19(n)) or protonated (R19+H(+)) forms, other structures can be generated in the gas phase, such as anions (R19-H(-)) and adducts with metal cations (R19+M(+)). Experimental evidence for the lactone structure of neutral gas-phase R19 is presented for the first time. The different properties of gas-phase compared to solution-phase R19 are discussed in view of structural analysis of labeled gas-phase biological molecules by optical spectroscopy.

Single-cell MALDI-MS as an analytical tool for studying intrapopulation metabolic heterogeneity of unicellular organisms.

Heterogeneity is a characteristic feature of all populations of living organisms. Here we make an attempt to validate a single-cell mass spectrometric method for detection of changes in metabolite levels occurring in populations of unicellular organisms. Selected metabolites involved in central metabolism (ADP, ATP, GTP, and UDP-Glucose) could readily be detected in single cells of Closterium acerosum by means of negative-mode matrix-assisted laser desorption/ionization (MALDI) mass spectrometry (MS). The analytical capabilities of this approach were characterized using standard compounds. The method was then used to study populations of individual cells with different levels of the chosen metabolites. With principal component analysis and support vector machine algorithms, it was possible to achieve a clear separation of individual C. acerosum cells in different metabolic states. This study demonstrates the suitability of mass spectrometric analysis of metabolites in single cells to measure cell-population heterogeneity.

Simultaneous sampling of volatile and non-volatile analytes in beer for fast fingerprinting by extractive electrospray ionization mass spectrometry.

By gently bubbling nitrogen gas through beer, an effervescent beverage, both volatile and non-volatile compounds can be simultaneously sampled in the form of aerosol. This allows for fast (within seconds) fingerprinting by extractive electrospray ionization mass spectrometry (EESI-MS) in both negative and positive ion mode, without the need for any sample pre-treatment such as degassing and dilution. Trace analytes such as volatile esters (e.g., ethyl acetate and isoamyl acetate), free fatty acids (e.g., caproic acid, caprylic acid, and capric acid), semi/non-volatile organic/inorganic acids (e.g., lactic acid), and various amino acids, commonly present in beer at the low parts per million or at sub-ppm levels, were detected and identified based on tandem MS data. Furthermore, the appearance of solvent cluster ions in the mass spectra gives insight into the sampling and ionization mechanisms: aerosol droplets containing semi/non-volatile substances are thought to be generated via bubble bursting at the surface of the liquid; these neutral aerosol droplets then collide with the charged primary electrospray ionization droplets, followed by analyte extraction, desolvation, ionization, and MS detection. With principal component analysis, several beer samples were successfully differentiated. Therefore, the present study successfully extends the applicability of EESI-MS to the direct analysis of complex liquid samples with high gas content.

Communications: Intramolecular basis set superposition error as a measure of basis set incompleteness: can one reach the basis set limit without extrapolation?

One of only two error sources in the solution of the electronic Schrodinger equation is addressed: The basis set convergence (incompleteness) error (BSIE). The results of ab initio (first principles) correlated methods, for which the Moller-Plesset second order perturbation theory (MP2) was chosen as an example, were extrapolated to the complete basis set (CBS) limit using a Dunning-type basis set series. Basis sets as large as cc-pV5Z and cc-pV6Z were used. A representative molecular set that included nitrogen (N(2)), acetylene (C(2)H(2)), ethylene (C(2)H(4)), carbon dioxide (CO(2)), water (H(2)O), ammonia (NH(3)), hydrogen cyanide (HCN), and ethanol (C(2)H(5)OH) molecules was used for the calculations. The intramolecular basis set superposition error (BSSE) was found to be correlated with BSIE, meaning that intramolecular BSSE can be used as a measure of basis set incompleteness. The BSIE dependence on BSSE could be qualitatively approximated (+/-25%) by a power-law dependence: BSIE = AxBSSE(p), where log(10)(A) = 1.45+/-0.21 and p = 1.27+/-0.09. This leads to the fact that CBS values at the MP2 theory level can be obtained using only one energy value and the corresponding intermolecular BSSE. The same power-law dependence was confirmed for all of the molecular systems studied. The universality of the BSIE versus BSSE dependence presented was checked using Pople-type basis sets. Even the results obtained with 6-311G, 6-311G(**), and 6-311G(2df,2pd) basis sets were found to be nicely described by the same (universal) power law. Benchmark studies of nitrogen and acetylene contraction (compaction) showed that BSIE can be decreased by up to 83% (at the cc-pVTZ level) using the CBS-BSSE strategy described. The presented BSIE versus BSSE dependence can greatly aid in obtaining CBS results for large molecular systems of chemical or biological interest.

Gasoline classification using near infrared (NIR) spectroscopy data: comparison of multivariate techniques.

Near infrared (NIR) spectroscopy is a non-destructive (vibrational spectroscopy based) measurement technique for many multicomponent chemical systems, including products of petroleum (crude oil) refining and petrochemicals, food products (tea, fruits, e.g., apples, milk, wine, spirits, meat, bread, cheese, etc.), pharmaceuticals (drugs, tablets, bioreactor monitoring, etc.), and combustion products. In this paper we have compared the abilities of nine different multivariate classification methods: linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), regularized discriminant analysis (RDA), soft independent modeling of class analogy (SIMCA), partial least squares (PLS) classification, K-nearest neighbor (KNN), support vector machines (SVM), probabilistic neural network (PNN), and multilayer perceptron (ANN-MLP) - for gasoline classification. Three sets of near infrared (NIR) spectra (450, 415, and 345 spectra) were used for classification of gasolines into 3, 6, and 3 classes, respectively, according to their source (refinery or process) and type. The 14,000-8000 cm(-1) NIR spectral region was chosen. In all cases NIR spectroscopy was found to be effective for gasoline classification purposes, when compared with nuclear magnetic resonance (NMR) spectroscopy or gas chromatography (GC). KNN, SVM, and PNN techniques for classification were found to be among the most effective ones. Artificial neural network (ANN-MLP) approach based on principal component analysis (PCA), which was believed to be efficient, has shown much worse results. We hope that the results obtained in this study will help both further chemometric (multivariate data analysis) investigations and investigations in the sphere of applied vibrational (infrared/IR, near-IR, and Raman) spectroscopy of sophisticated multicomponent systems.

Communications: Is quantum chemical treatment of biopolymers accurate? Intramolecular basis set superposition error (BSSE).

The accuracy of quantum chemical treatment of biopolymers by means of density functional theory is brought into question in terms of intramolecular basis set superposition error (BSSE). Secondary structure forms--beta-strands (C5; fully extended conformation), repeated gamma-turns (C7), 3(10)-helices (C10), and alpha-helices (C13)--of homopolypeptides (polyglycine and polyalanine) are used as representative examples. The studied molecules include Ace(Gly)(5)NH(2), Ace(Gly)(10)NH(2), Ace(Ala)(5)NH(2), and Ace(Ala)(10)NH(2). The counterpoise correction procedure was found to produce reliable estimations for the BSSE values (other methods of BSSE correction are discussed). The calculations reported here used the B3LYP, PBE0 (PBE1PBE), and BMK density functionals with different basis sets [from 6-31G(d) to 6-311+G(3df,3pd)] to estimate the influence of basis set size on intramolecular BSSE. Calculation of BSSE was used to determine the deviation of the current results from the complete basis set limit. Intramolecular BSSE was found to be nonadditive with respect to biopolymer size, in contrast to claims in recent literature. The error, which is produced by a basis set superposition, was found to exceed 4 kcal mol(-1) when a medium-sized basis set was used. This indicates that this error has the same order of magnitude as the relative energy differences of secondary structure elements of biopolymers. This result makes all recent reports on the gas-phase stability of homopolypeptides and their analogs questionable.

The identification of the two missing conformers of gas-phase alanine: a jet-cooled Raman spectroscopy study.

The jet-cooled spontaneous Raman spectrum of an amino acid-alanine (Ala, 2-aminopropanoic acid; H(2)NCH(CH(3))COOH)-is reported. The low-frequency vibrational spectrum (below 500 cm(-1)) was recorded and assigned using quantum chemical data: ab initio (MP2) and DFT (BLYP, B3LYP, and PBE0). Band polarization measurements were used to confirm the vibrational assignments. The acquired medium resolution spectra (HWHM of approximately 4 cm(-1)) allow the different alanine conformations to be distinguished. Four alanine conformers were observed and identified: two previously reported by microwave spectroscopy studies and two that were previously unreported. A set of reasons for why these conformers eluded previous studies are discussed. Selective collisional relaxation processes in the jet (associated with low interconversion barriers between different alanine conformations) that depopulate the high-energy conformers were experimentally demonstrated.

Rapid fingerprinting and classification of extra virgin olive oil by microjet sampling and extractive electrospray ionization mass spectrometry.

Microjet sampling in combination with extractive electrospray ionization (EESI) mass spectrometry (MS) was applied to the rapid characterization and classification of extra virgin olive oil (EVOO) without any sample pretreatment. When modifying the composition of the primary ESI spray solvent, mass spectra of an identical EVOO sample showed differences. This demonstrates the capability of this technique to extract molecules with varying polarities, hence generating rich molecular information of the EVOO. Moreover, with the aid of microjet sampling, compounds of different volatilities (e.g.E-2-hexenal, trans-trans-2,4-heptadienal, tyrosol and caffeic acid) could be sampled simultaneously. EVOO data was also compared with that of other edible oils. Principal Component Analysis (PCA) was performed to discriminate EVOO and EVOO adulterated with edible oils. Microjet sampling EESI-MS was found to be a simple, rapid (less than 2 min analysis time per sample) and powerful method to obtain MS fingerprints of EVOO without requiring any complicated sample pretreatment steps.