Solvent induced shifts in the UV spectrum of amides

Reactive Oxygen Species Generated in Situ During Carbamazepine Photodegradation at 222 nm Far-UVC: Unexpected Role of H2O Molecules

When 222 nm far-UVC is used to drive AOPs, photolysis emerges as a critical pathway for the degradation of numerous organic micropollutants (OMPs). However, the photodegradation mechanisms of the asymmetrically polarized OMPs at 222 nm remain unclear, potentially posing a knowledge barrier to the applications of far-UVC. This study selected carbamazepine (CBZ), a prevalent aquatic antiepileptic drug that degrades negligibly at 254 nm, to investigate its photodegradation mechanisms at 222 nm. Accelerated CBZ treatment by 222 nm far-UVC was mainly attributed to in situ ROS generation via self-sensitized photodegradation of CBZ. By quenching experiments and EPR tests, •OH radicals were identified as the major contributor to the CBZ photodegradation, whereas O2•- played a minor role. By deoxygenation and solvent exchange experiments, the H2O molecules were demonstrated to play a crucial role in deactivating the excited singlet state of CBZ (1CBZ*) at 222 nm: generating •OH radicals via electron transfer interactions with 1CBZ*. In addition, 1CBZ* could also undergo a photoionization process. The transformation products and pathways of CBZ at 222 nm were proposed, and the toxicities of CBZ's products were predicted. These findings provide valuable insights into OMPs' photolysis with 222 nm far-UVC, revealing more mechanistic details for far-UVC-driven systems.

Self-aggregation, H-bonding, and photoresponse in film and solution states of azobenzene containing polyurea

We critically understand the hydrogen bonding interactions and electronic transitions occurring in a thin film as well as in solution of a photo-responsive polymer, azo-polyurea (azo-PU). We synthesize azo-PU by covalent attachment of the azobenzene chromophore to the main chain of polyurea. Azo-PU shows reversible photoisomerization between trans and cis states upon light exposure, the occurrence of which is typically analysed using the π-π* and n-π* electronic transition peaks in the UV-visible absorption spectrum. We find that the π-π* and n-π* bands undergo a redshift and blueshift respectively on dissolving azo-PU in DMF solvent, resulting in a single overlapped peak in the spectrum. However, upon UV irradiation, these bands split into two independent transitions that are characteristic of azo-PU solid films. These observations are explained based on the changes in polymer-polymer and polymer-solvent interactions through hydrogen bonding and self-aggregation tendency. The experimental findings are corroborated using DFT simulations which provide useful insights into electronic orbital transitions, electron distribution, and hydrogen bonding interaction through IR vibrational modes.

A Machine-Learning Protocol for Ultraviolet Protein-Backbone Absorption Spectroscopy under Environmental Fluctuations

Ultraviolet (UV) absorption spectra are commonly used for characterizing the global structure of proteins. However, the theoretical interpretation of UV spectra is hindered by the large number of required expensive ab initio calculations of excited states spanning a huge conformation space. We present a machine-learning (ML) protocol for far-UV (FUV) spectra of proteins, which can predict FUV spectra of proteins with comparable accuracy to density functional theory (DFT) calculations but with 3-4 orders of magnitude reduced computational cost. It further shows excellent predictive power and transferability that can be used to probe structural mutations and protein folding pathways.

Quinazoline Derivatives Designed as Efflux Pump Inhibitors: Molecular Modeling and Spectroscopic Studies

Multidrug resistance of bacteria is a worrying concern in the therapeutic field and an alternative method to combat it is designing new efflux pump inhibitors (EPIs). This article presents a molecular study of two quinazoline derivatives, labelled BG1189 and BG1190, proposed as EPIs. In silico approach investigates the pharmacodynamic and pharmacokinetic profile of BG1189 and BG1190 quinazolines. Molecular docking and predicted ADMET features suggest that BG1189 and BG1190 may represent attractive candidates as antimicrobial drugs. UV-Vis absorption spectroscopy was employed to study the time stability of quinazoline solutions in water or in dimethyl sulfoxide (DMSO), in constant environmental conditions, and to determine the influence of usual storage temperature, normal room lighting and laser radiation (photostability) on samples stability. The effects of irradiation on BG1189 and BG1190 molecules were also assessed through Fourier-transform infrared (FTIR) spectroscopy. FTIR spectra showed that laser radiation breaks some chemical bonds affecting the substituents and the quinazoline radical of the compounds.

A neural network protocol for electronic excitations of N-methylacetamide

UV absorption is widely used for characterizing proteins structures. The mapping of UV spectra to atomic structure of proteins relies on expensive theoretical simulations, circumventing the heavy computational cost which involves repeated quantum-mechanical simulations of excited-state properties of many fluctuating protein geometries, which has been a long-time challenge. Here we show that a neural network machine-learning technique can predict electronic absorption spectra of N-methylacetamide (NMA), which is a widely used model system for the peptide bond. Using ground-state geometric parameters and charge information as descriptors, we employed a neural network to predict transition energies, ground-state, and transition dipole moments of many molecular-dynamics conformations at different temperatures, in agreement with time-dependent density-functional theory calculations. The neural network simulations are nearly 3,000× faster than comparable quantum calculations. Machine learning should provide a cost-effective tool for simulating optical properties of proteins.

Sticking to (first) principles: quantum molecular dynamics and Bayesian probabilistic methods to simulate aquatic pollutant absorption spectra

This work explores the relationship between theoretically predicted excitation energies and experimental molar absorption spectra as they pertain to environmental aquatic photochemistry. An overview of pertinent Quantum Chemical descriptions of sunlight-driven electronic transitions in organic pollutants is presented. Second, a combined molecular dynamics (MD), time-dependent density functional theory (TD-DFT) analysis of the ultraviolet to visible (UV-Vis) absorption spectra of six model organic compounds is presented alongside accurate experimental data. The functional relationship between the experimentally observed molar absorption spectrum and the discrete quantum transitions is examined. A rigorous comparison of the accuracy of the theoretical transition energies (ΔES0→Sn) and oscillator strength (fS0→Sn) is afforded by the probabilistic convolution and deconvolution procedure described. This method of deconvolution of experimental spectra using a Gaussian Mixture Model combined with Bayesian Information Criteria (BIC) to determine the mean (μ) and standard deviation (σ) as well as the number of observed singlet to singlet transition energy state distributions. This procedure allows a direct comparison of the one-electron (quantum) transitions that are the result of quantum chemical calculations and the ensemble of non-adiabatic quantum states that produce the macroscopic effect of a molar absorption spectrum. Poor agreement between the vertical excitation energies produced from TD-DFT calculations with five different functionals (CAM-B3LYP, PBE0, M06-2X, BP86, and LC-BLYP) suggest a failure of the theory to capture the low energy, environmentally important, electronic transitions in our model organic pollutants. However, the method of explicit-solvation of the organic solute using the quantum Effective Fragment Potential (EFP) in a density functional molecular dynamics trajectory simulation shows promise as a robust model of the hydrated organic pollutant. Furthermore, the described protocol can be extended using higher-level equilibration and vertical excitation methods to increase the numerical accuracy and describe multi-reference electronic transitions. Finally, a measure of the accuracy of theoretically derived absorption spectra is discussed as a tool to further develop our capacity to produce accurate a priori simulations of sunlight-driven photochemistry in natural waters.