Advances, challenges and perspectives of quantum chemical approaches in molecular spectroscopy of the condensed phase

Electronic interaction between dimethyl carbonate and Li+ studied by attenuated total reflectance far-ultraviolet spectroscopy

Organic electrolytes with Li+ were analyzed by far-ultraviolet (≤200 nm) spectroscopy, achieved by an attenuated total reflectance setup. The spectra showed a redshift with Li+ addition, attributed to the charge transfer, as revealed by quantum chemical calculations. Multivariate analysis successfully decomposed the spectra into pure solvent and Li-coordinated solvent components.

Accelerating Molecular Vibrational Spectra Simulations with a Physically Informed Deep Learning Model

In recent years, machine learning (ML) surrogate models have emerged as an indispensable tool to accelerate simulations of physical and chemical processes. However, there is still a lack of ML models that can accurately predict molecular vibrational spectra. Here, we present a highly efficient multitask ML surrogate model termed Vibrational Spectra Neural Network (VSpecNN), to accurately calculate infrared (IR) and Raman spectra based on dipole moments and polarizabilities obtained on-the-fly via ML-enhanced molecular dynamics simulations. The methodology is applied to pyrazine, a prototypical polyatomic chromophore. The VSpecNN-predicted energies are well within the chemical accuracy (1 kcal/mol), and the errors for VSpecNN-predicted forces are only half of those obtained from a popular high-performance ML model. Compared to the ab initio reference, the VSpecNN-predicted frequencies of IR and Raman spectra differ only by less than 5.87 cm-1, and the intensities of IR spectra and the depolarization ratios of Raman spectra are well reproduced. The VSpecNN model developed in this work highlights the importance of constructing highly accurate neural network potentials for predicting molecular vibrational spectra.

Deep Learning-Assisted Spectrum-Structure Correlation: State-of-the-Art and Perspectives

Spectrum-structure correlation is playing an increasingly crucial role in spectral analysis and has undergone significant development in recent decades. With the advancement of spectrometers, the high-throughput detection triggers the explosive growth of spectral data, and the research extension from small molecules to biomolecules accompanies massive chemical space. Facing the evolving landscape of spectrum-structure correlation, conventional chemometrics becomes ill-equipped, and deep learning assisted chemometrics rapidly emerges as a flourishing approach with superior ability of extracting latent features and making precise predictions. In this review, the molecular and spectral representations and fundamental knowledge of deep learning are first introduced. We then summarize the development of how deep learning assist to establish the correlation between spectrum and molecular structure in the recent 5 years, by empowering spectral prediction (i.e., forward structure-spectrum correlation) and further enabling library matching and de novo molecular generation (i.e., inverse spectrum-structure correlation). Finally, we highlight the most important open issues persisted with corresponding potential solutions. With the fast development of deep learning, it is expected to see ultimate solution of establishing spectrum-structure correlation soon, which would trigger substantial development of various disciplines.

Advances in Computational Methods for Modeling Photocatalytic Reactions: A Review of Recent Developments

Photocatalysis is a fascinating process in which a photocatalyst plays a pivotal role in driving a chemical reaction when exposed to light. Its capacity to harness light energy triggers a cascade of reactions that lead to the formation of intermediate compounds, culminating in the desired final product(s). The essence of this process is the interaction between the photocatalyst's excited state and its specific interactions with reactants, resulting in the creation of intermediates. The process's appeal is further enhanced by its cyclic nature-the photocatalyst is rejuvenated after each cycle, ensuring ongoing and sustainable catalytic action. Nevertheless, comprehending the photocatalytic process through the modeling of photoactive materials and molecular devices demands advanced computational techniques founded on effective quantum chemistry methods, multiscale modeling, and machine learning. This review analyzes contemporary theoretical methods, spanning a range of lengths and accuracy scales, and assesses the strengths and limitations of these methods. It also explores the future challenges in modeling complex nano-photocatalysts, underscoring the necessity of integrating various methods hierarchically to optimize resource distribution across different scales. Additionally, the discussion includes the role of excited state chemistry, a crucial element in understanding photocatalysis.

ATR-far-ultraviolet spectroscopy: a challenge to new σ chemistry

This review reports the recent progress on ATR-far ultraviolet (FUV) spectroscopy in the condensed phase. ATR-FUV spectroscopy for liquids and solids enables one to explore various topics in physical chemistry, analytical chemistry, nanoscience and technology, materials science, electrochemistry, and organic chemistry. In this review, we put particular emphasis on the three major topics: (1) studies on electronic transitions and structures of various molecules, which one cannot investigate via ordinary UV spectroscopy. The combined use of ATR-FUV spectroscopy and quantum chemical calculations allows for the investigation of various electronic transitions, including σ, n-Rydberg transitions. ATR-FUV spectroscopy may open a new avenue for σ-chemistry. (2) ATR-FUV spectroscopy enables one to measure the first electronic transition of water at approximately 160 nm without peak saturation. Using this band, one can study the electronic structure of water, aqueous solutions, and adsorbed water. (3) ATR-FUV spectroscopy has its own advantages of the ATR method as a surface analysis method. ATR-FUV spectroscopy is a powerful technique for exploring a variety of top surface phenomena (∼50 nm) in adsorbed water, polymers, graphene, organic materials, ionic liquids, and so on. This review briefly describes the principles, characteristics, and instrumentation of ATR-FUV spectroscopy. Next, a detailed description about quantum chemical calculation methods for FUV and UV regions is given. The recent application of ATR-FUV-UV spectroscopy studies on electronic transitions from σ orbitals in various saturated molecules is introduced first, followed by a discussion on the applications of ATR-FUV spectroscopy to studies on water, aqueous solutions, and adsorbed water. Applications of ATR-FUV spectroscopy in the analysis of other materials such as polymers, ionic liquids, inorganic semiconductors, graphene, and carbon nanocomposites are elucidated. In addition, ATR-FUV-UV-vis spectroscopy focusing on electrochemical interfaces is outlined. Finally, FUV-UV-surface plasmon resonance studies are discussed.

Surface functionalization of graphene-like boron arsenide monolayer: a first-principles study

In this work, the effects of hydrogen (H) and oxygen (O) adsorption on the electronic and magnetic properties of graphene-like boron arsenide (BAs) monolayer are investigated using first-principles calculations. Pristine monolayer is a non-magnetic two-dimensional (2D) material, exhibiting direct gap semiconductor character with band gap of 0.75 (1.18) eV as calculated by generalized gradient approximation with Perdew-Burke-Ernzerhof (HSE06) functional. Four high-symmetry adsorption sites are considered, including on-top of B atom (TB), on-top of As atom (TAs), on-top of hollow site (TH), and on-top of bridge site (Tbridge). Using the criterion of adsorption energy, it is found thatTBandTbridgesites are favorable adsorption sites for H and O adatom, respectively. The analysis of electronic interactions indicate the charge transfer from host BAs monolayer to both adatoms. H adsorption conducts to the emergence of magnetic semiconductor nature in BAs monolayer with a total magnetic moment of 1.00 μB. Herein, the magnetism is originated mainly from H adatom and its neighbor As atoms. In contrast, the non-magnetic nature of BAs monolayer is preserved upon absorbing O atoms. In this case, the energy gap exhibits a slight reduction of 4%. Further, the effects of adatom coverage are also analyzed. The presented results suggest an effective modification of ground state electronic properties, as well as induction of new feature-rich properties to make new multifunctional 2D materials from non-magnetic BAs monolayer.

Dual-Site Metal Catalysts for Electrocatalytic CO2 Reduction Reaction

Electrocatalytic CO2 reduction reaction (CO2 RR) is a promising and green approach for reducing atmospheric CO2 concentration and achieving high-valued conversion of CO2 under the carbon-neutral policy. In CO2 RR, the dual-site metal catalysts (DSMCs) have received wide attention for their ingenious design strategies, abundant active sites, and excellent catalytic performance attributed to the synergistic effect between dual-site in terms of activity, selectivity and stability, which plays a key role in catalytic reactions. This review provides a systematic summary and detailed classification of DSMCs for CO2 RR, describes the mechanism of synergistic effects in catalytic reactions, and also introduces in situ characterization techniques commonly used in CO2 RR. Finally, the main challenges and prospects of dual-site metal catalysts and even multi-site catalysts for CO2 recycling are analyzed. It is believed that based on the understanding of bimetallic site catalysts and synergistic effects in CO2 RR, well-designed high-performance, low-cost electrocatalysts are promising for achieving CO2 conversion, electrochemical energy conversion and storage in the future.

Internal reflectance cell fluorescence measurement combined with multi-way analysis to detect fluorescence signatures of undiluted honeys and a fusion of fluorescence and NIR to enhance predictability

Honey is a complex food matrix that contains diverse polyphenolic compounds. Some phenolics exhibit fluorescence signatures which can be used to evaluate honey quality, and authenticity and to determine botanical origin. Mānuka honey contains two unique fluorescence markers: Leptosperin (MM1) and Lepteridine^TM (MM2) that are derived from Leptospermum scoparium nectar. Fluorescence measurement of supersaturated solutions such as undiluted honeys can be challenged by complex inner filter effects. The current study shows the ability of internal reflectance cell fluorescence measurement and multi-way analysis to detect fluorophores in undiluted honeys. This study scanned honeys from different geographic districts generating excitation emission matrices (250-400/300-600 nm), and by near infrared (NIR) hyperspectral camera (547-1701 nm). PARAFAC and tri-PLS could track two fluorescence markers: MM1 (R² = 0.82 & RMSEP = 138.65) and MM2 (R² = 0.82 & RMSEP = 2.75) from undiluted honey fluorescence data with > 80 % accuracy. Classification of mono-floral, multi-floral and non-mānuka honeys achieved 90 % overall accuracy. Fusion of fluorescence data at ƛ_ex 270 & 330 nm and NIR hyperspectral data combined with multi-block PLS analysis enhances predictability of fluorescence markers further. The study revealed the potential of internal reflectance cell fluorescence measurement combined with chemometrics and data fusion for rapid evaluation of honey quality and botanical origin.

First ternary tungsten tellurate(IV) WTe2O7 with unique crystal structure type

At multianvil high-pressure/high-temperature conditions of 10 GPa and 1273 K, the first ternary tungsten tellurate WTe₂O₇ is formed, starting from a stoichiometric mixture of WO₃ and TeO₂. The compound crystallizes triclinic in a hitherto unknown crystal structure type with the space group P1̄; (no. 2), and was refined from single-crystal X-ray diffractometer data: a = 538.3(1), b = 687.5(1), c = 802.3(1) pm, α = 72.4(1)°, β = 85.7(1)°, γ = 68.1(1)°, wR₂ = 0.0323, GooF = 1.048, 3157 F² values, and 106 variables. The main motifs of the crystal structure are pairs of edge-linked [WO₆]^6- octahedra and fourfold oxygen-coordinated Te⁴⁺ atoms. The oxidation state of W⁶⁺ and Te⁴⁺ was further verified by measuring the characteristic binding energy values for the W 4f and the Te 3d core levels via X-ray photoelectron spectroscopy (XPS). In addition, DFT calculations of the structure, the associated electron localisation functions (ELF) and vibrational spectra have been carried out. The theoretical data clearly demonstrates the impact of the residual electron density located at the Te⁴⁺ ions, which can be directly interpreted as the presence of lone electron pairs within the solid structure.

Improving the IR spectra alignment algorithm with spectra deconvolution and combination with Raman or VCD spectroscopy

The relative stereochemistry of organic molecules can be determined by comparing theoretical and experimental infrared (IR) spectra of all isomers and assessing the best match. For this purpose, we have recently developed the IR spectra alignment (IRSA) algorithm for automated optimal alignment. IRSA provides a set of quantitative metrics to identify the candidate structure that agrees best with the experimental spectrum. While the correct diastereomer could be determined for the tested sets of rigid and flexible molecules, two issues were identified with more complex compounds that triggered further development. First, strongly overlapping peaks in the IR spectrum are not treated adequately in the current IRSA implementation. Second, the alignment of multiple spectra from different sources (e.g. IR and VCD or Raman) can be improved. In this study, we present an in-depth discussion of these points, followed by the description of modifications to the IRSA algorithm to address them. In particular, we introduce the concept of deconvolution of the experimental and theoretical spectra with a set of pseudo-Voigt bands. The pseudo-Voigt bands have a set of parameters, which can be employed in the alignment algorithm, leading to improved scoring functions. We test the modified algorithm on two data sets. The first set contains compounds with IR and Raman spectra measured in this study, and the second set contains compounds with IR and VCD spectra available in the literature. We show that the algorithm is able to determine the correct diastereomer in all cases. The results highlight that vibrational spectroscopy can be a valuable alternative or complementary method to inform about the stereochemistry of compounds, and the performance of the updated IRSA algorithm suggests that it is a powerful tool for quantitative-based spectral assignments in academia and industry.

ResNet and MaxEnt modeling for quality assessment of Wolfiporia cocos based on FT-NIR fingerprints

As a fungus with both medicinal and edible value, Wolfiporia cocos (F. A. Wolf) Ryvarden & Gilb. has drawn more public attention. Chemical components' content fluctuates in wild and cultivated W. cocos, whereas the accumulation ability of chemical components in different parts is different. In order to perform a quality assessment of W. cocos, we proposed a comprehensive method which was mainly realized by Fourier transform near-infrared (FT-NIR) spectroscopy and ultra-fast liquid chromatography (UFLC). A qualitative analysis means was built a residual convolutional neural network (ResNet) to recognize synchronous two-dimensional correlation spectroscopy (2DCOS) images. It can rapidly identify samples from wild and cultivated W. cocos in different parts. As a quantitative analysis method, UFLC was used to determine the contents of three triterpene acids in 547 samples. The results showed that a simultaneous qualitative and quantitative strategy could accurately evaluate the quality of W. cocos. The accuracy of ResNet models combined synchronous FT-NIR 2DCOS in identifying wild and cultivated W. cocos in different parts was as high as 100%. The contents of three triterpene acids in Poriae Cutis were higher than that in Poria, and the one with wild Poriae Cutis was the highest. In addition, the suitable habitat plays a crucial role in the quality of W. cocos. The maximum entropy (MaxEnt) model is a common method to predict the suitable habitat area for W. cocos under the current climate. Through the results, we found that suitable habitats were mostly situated in Yunnan Province of China, which accounted for approximately 49% of the total suitable habitat area of China. The research results not only pave the way for the rational planting in Yunnan Province of China and resource utilization of W. cocos, but also provide a basis for quality assessment of medicinal fungi.

In silico NIR spectroscopy - A review. Molecular fingerprint, interpretation of calibration models, understanding of matrix effects and instrumental difference

Quantum mechanical calculations are routinely used as a major support in mid-infrared (MIR) and Raman spectroscopy. In contrast, practical limitations for long time formed a barrier to developing a similar synergy between near-infrared (NIR) spectroscopy and computational chemistry. Recent advances in theoretical methods suitable for calculation of NIR spectra opened the pathway to modeling NIR spectra of various molecules. Accurate theoretical reproduction of NIR spectra of molecules reaching the size of long-chain fatty acids was accomplished so far. In silico NIR spectroscopy, where the spectra are calculated ab initio, provides substantial improvement in our understanding of the overtones and combination bands that overlap in staggering numbers and create complex lineshape typical for NIR spectra. This improves the comprehension of the spectral information enabling access to rich and detail molecular footprint, essential for fundamental research and useful in routine analysis by NIR spectroscopy and chemometrics. This review article summarizes the most recent accomplishments in the emerging field with examples of simulated NIR spectra of molecules reaching long-chain fatty acids and polymers. In addition to detailed NIR band assignments and new physical insights, simulated spectra enable innovative support in applications. Understanding of the difference in the performance observed between miniaturized NIR spectrometers and chemical interpretation of the chemometric models are noteworthy here. These new elements integrated into NIR spectroscopy framework enable a knowledge-based design of the analysis with comprehension of the processed chemical information.

Practical Qualitative Evaluation and Screening of Potential Biomarkers for Different Parts of Wolfiporia cocos Using Machine Learning and Network Pharmacology

Wolfiporia cocos is a widely used traditional Chinese medicine and dietary supplement. Artificial intelligence algorithms use different types of data based on the different strategies to complete multiple tasks such as search and discrimination, which has become a trend to be suitable for solving massive data analysis problems faced in network pharmacology research. In this study, we attempted to screen the potential biomarkers in different parts of W. cocos from the perspective of measurability and effectiveness based on fingerprint, machine learning, and network pharmacology. Based on the conclusions drawn from the results, we noted the following: (1) exploratory analysis results showed that differences between different parts were greater than those between different regions, and the partial least squares discriminant analysis and residual network models were excellent to identify Poria and Poriae cutis based on Fourier transform near-infrared spectroscopy spectra; (2) from the perspective of effectiveness, the results of network pharmacology showed that 11 components such as dehydropachymic acid and 16α-hydroxydehydrotrametenolic acid, and so on had high connectivity in the “component-target-pathway” network and were the main active components. (3) From a measurability perspective, through orthogonal partial least squares discriminant analysis and the variable importance projection > 1, it was confirmed that three components, namely, dehydrotrametenolic acid, poricoic acid A, and pachymic acid, were the main potential biomarkers based on high-performance liquid chromatography. (4) The content of the three components in Poria was significantly higher than that in Poriae cutis. (5) The integrated analysis showed that dehydrotrametenolic acid, poricoic acid A, and pachymic acid were the potential biomarkers for Poria and Poriae cutis. Overall, this approach provided a novel strategy to explore potential biomarkers with an explanation for the clinical application and reasonable development and utilization in Poria and Poriae cutis.

Delving into guest-free and He-filled sI and sII clathrate hydrates: a first-principles computational study

The dynamics of the formation of a specific clathrate hydrate as well as its thermodynamic transitions depend on the interactions between the trapped molecules and the host water lattice. The molecular-level understanding of the different underlying processes benefits not only the description of the properties of the system, but also allows the development of multiple technological applications such as gas storage, gas separation, energy transport, etc. In this work we investigate the stability of periodic crystalline structures, such as He@sI and He@sII clathrate hydrates by first-principles computations. We consider such host water networks interacting with a guest He atom using selected density functional theory approaches, in order to explore the effects on the encapsulation of a light atom in the sI/sII crystals, by deriving all energy components (guest-water, water-water, guest-guest). Structural properties and energies were first computed by structural relaxations of the He-filled and empty sI/sII unit cells, yielding lattice and compressibility parameters comparable to experimental and theoretical values available for those hydrates. According to the results obtained, the He enclathration in the sI/sII unit cells is a stabilizing process, and both He@sI and He@sII clathrates, considering single cage occupancy, are predicted to be stable whatever the XDM or D4 dispersion correction applied. Our results further reveal that despite the weak underlying interactions the He encapsulation has a rather notable effect on both lattice parameters and energetics, with the He@sII being the most energetically favorable in accord with recent experimental observations.

Miniaturized NIR Spectroscopy in Food Analysis and Quality Control: Promises, Challenges, and Perspectives

The ongoing miniaturization of spectrometers creates a perfect synergy with the common advantages of near-infrared (NIR) spectroscopy, which together provide particularly significant benefits in the field of food analysis. The combination of portability and direct onsite application with high throughput and a noninvasive way of analysis is a decisive advantage in the food industry, which features a diverse production and supply chain. A miniaturized NIR analytical framework is readily applicable to combat various food safety risks, where compromised quality may result from an accidental or intentional (i.e., food fraud) origin. In this review, the characteristics of miniaturized NIR sensors are discussed in comparison to benchtop laboratory spectrometers regarding their performance, applicability, and optimization of methodology. Miniaturized NIR spectrometers remarkably increase the flexibility of analysis; however, various factors affect the performance of these devices in different analytical scenarios. Currently, it is a focused research direction to perform systematic evaluation studies of the accuracy and reliability of various miniaturized spectrometers that are based on different technologies; e.g., Fourier transform (FT)-NIR, micro-optoelectro-mechanical system (MOEMS)-based Hadamard mask, or linear variable filter (LVF) coupled with an array detector, among others. Progressing technology has been accompanied by innovative data-analysis methods integrated into the package of a micro-NIR analytical framework to improve its accuracy, reliability, and applicability. Advanced calibration methods (e.g., artificial neural networks (ANN) and nonlinear regression) directly improve the performance of miniaturized instruments in challenging analyses, and balance the accuracy of these instruments toward laboratory spectrometers. The quantum-mechanical simulation of NIR spectra reveals the wavenumber regions where the best-correlated spectral information resides and unveils the interactions of the target analyte with the surrounding matrix, ultimately enhancing the information gathered from the NIR spectra. A data-fusion framework offers a combination of spectral information from sensors that operate in different wavelength regions and enables parallelization of spectral pretreatments. This set of methods enables the intelligent design of future NIR analyses using miniaturized instruments, which is critically important for samples with a complex matrix typical of food raw material and shelf products.

Infrared spectroscopic monitoring of solid-state processes

We put a spotlight on IR spectroscopic investigations in materials science by providing a critical insight into the state of the art, covering both fundamental aspects, examples of its utilisation, and current challenges and perspectives focusing on the solid state.

Anharmonicity and Spectra-Structure Correlations in MIR and NIR Spectra of Crystalline Menadione (Vitamin K3)

Mid-infrared (MIR) and near-infrared (NIR) spectra of crystalline menadione (vitamin K3) were measured and analyzed with aid of quantum chemical calculations. The calculations were carried out using the harmonic approach for the periodic model of crystal lattice and the anharmonic DVPT2 calculations applied for the single molecule model. The theoretical spectra accurately reconstructed the experimental ones permitting for reliable assignment of the MIR and NIR bands. For the first time, a detailed analysis of the NIR spectrum of a molecular system based on a naphthoquinone moiety was performed to elucidate the relationship between the chemical structure of menadione and the origin of the overtones and combination bands. In addition, the importance of these bands during interpretation of the MIR spectrum was demonstrated. The overtones and combination bands contribute to 46.4% of the total intensity of menadione in the range of 3600-2600 cm-1. Evidently, these bands play a key role in shaping of the C-H stretching region of MIR spectrum. We have shown also that the spectral regions without fundamentals may provide valuable structural information. For example, the theoretical calculations reliably reconstructed numerous overtones and combination bands in the 4000-3600 and 2800-1800 cm-1 ranges. These results, provide a comprehensive origin of the fundamentals, overtones and combination bands in the NIR and MIR spectra of menadione, and the relationship of these spectral features with the molecular structure.