A quantitative metric for organic radical stability and persistence using thermodynamic and kinetic features.

Long-lived organic radicals are promising candidates for the development of high-performance energy solutions such as organic redox batteries, transistors, and light-emitting diodes. However, "stable" organic radicals that remain unreactive for an extended time and that can be stored and handled under ambient conditions are rare. A necessary but not sufficient condition for organic radical stability is the presence of thermodynamic stabilization, such as conjugation with an adjacent π-bond or lone-pair, or hyperconjugation with a σ-bond. However, thermodynamic factors alone do not result in radicals with extended lifetimes: many resonance-stabilized radicals are transient species that exist for less than a millisecond. Kinetic stabilization is also necessary for persistence, such as steric effects that inhibit radical dimerization or reaction with solvent molecules. We describe a quantitative approach to map organic radical stability, using molecular descriptors intended to capture thermodynamic and kinetic considerations. The comparison of an extensive dataset of quantum chemical calculations of organic radicals with experimentally-known stable radical species reveals a region of this feature space where long-lived radicals are located. These descriptors, based upon maximum spin density and buried volume, are combined into a single metric, the radical stability score, that outperforms thermodynamic scales based on bond dissociation enthalpies in identifying remarkably long-lived radicals. This provides an objective and accessible metric for use in future molecular design and optimization campaigns. We demonstrate this approach in identifying Pareto-optimal candidates for stable organic radicals.

Real-time prediction of 1H and 13C chemical shifts with DFT accuracy using a 3D graph neural network.

Nuclear magnetic resonance (NMR) is one of the primary techniques used to elucidate the chemical structure, bonding, stereochemistry, and conformation of organic compounds. The distinct chemical shifts in an NMR spectrum depend upon each atom's local chemical environment and are influenced by both through-bond and through-space interactions with other atoms and functional groups. The in silico prediction of NMR chemical shifts using quantum mechanical (QM) calculations is now commonplace in aiding organic structural assignment since spectra can be computed for several candidate structures and then compared with experimental values to find the best possible match. However, the computational demands of calculating multiple structural- and stereo-isomers, each of which may typically exist as an ensemble of rapidly-interconverting conformations, are expensive. Additionally, the QM predictions themselves may lack sufficient accuracy to identify a correct structure. In this work, we address both of these shortcomings by developing a rapid machine learning (ML) protocol to predict 1H and 13C chemical shifts through an efficient graph neural network (GNN) using 3D structures as input. Transfer learning with experimental data is used to improve the final prediction accuracy of a model trained using QM calculations. When tested on the CHESHIRE dataset, the proposed model predicts observed 13C chemical shifts with comparable accuracy to the best-performing DFT functionals (1.5 ppm) in around 1/6000 of the CPU time. An automated prediction webserver and graphical interface are accessible online at http://nova.chem.colostate.edu/cascade/. We further demonstrate the model in three applications: first, we use the model to decide the correct organic structure from candidates through experimental spectra, including complex stereoisomers; second, we automatically detect and revise incorrect chemical shift assignments in a popular NMR database, the NMRShiftDB; and third, we use NMR chemical shifts as descriptors for determination of the sites of electrophilic aromatic substitution.

Treatment of high-strength sewage by textile fibers-based sequencing batch biofilm reactor for simultaneous removal of organics and nutrients.

This study investigates the effectiveness of SBBR with low-cost textile fibers-based bio-carrier namely polypropylene fibers for the treatment of real sewage. The influent loading rates of COD, TN, and TP were averaged at 0.2780, 0.0170, and 0.0077 kg/m3.d, respectively. The removal efficiencies of BOD, COD, TN, and TP recorded in SBBR were 98%, 93%, 82%, and 44%, respectively at an aeration time of 4 h. The TN and TP removal achieved in SBBR were 2.05 and 2.75 times, respectively higher than SBR. The COD removal efficiency was more than 90% under all SRT conditions (10, 14, 18, 22, and 26 d) in SBBR, and the highest efficiency of 93% was obtained at an SRT of 22 days. As the SRT increased, the nitrogen and phosphorus removal decreased, because the denitrification rate and phosphorus release and uptake rate decreased at longer SRT. Simultaneous nitrification and denitrification (SND) efficiency was 85% in SBBR and 44% in SBR, indicating the co-existence of aerobic nitrifiers and anoxic denitrifiers in the biofilm reactor. In SBBR, the nitrogen mass balance showed 74% of nitrogen removed by denitrification, 9% was removed through sludge wasting process, and 13% was removed in effluent at an SRT of 22 days and DO concentration of 3 mg/L. The t-test results suggest that the performance of SBBR was better than SBR in nitrogen and phosphorus removal at a 95% confidence interval.

A quantification scheme for non-covalent interactions in the enantio-controlling transition states in asymmetric catalysis.

The origin of enantioselectivity in asymmetric catalysis is attributed to the energy difference between lower and higher energy diastereomeric transition states, which are respectively responsible for the formation of major and minor enantiomers. Although the increase in the number of transition state models emphasizes the role of weak non-covalent interactions in asymmetric induction, the strength of such interactions is seldom quantified. Through this article, we propose a simple and effective method of quantifying the total non-covalent interaction in stereocontrolling transition states belonging to a group of three representative asymmetric catalytic reactions involving chiral phosphoric acids. Our method relies on rational partitioning of a given transition state into two (or three) sub-units, such that the complex network of intramolecular interactions can be ameliorated to a set of intermolecular interactions between two sub-units. The computed strength of interaction obtained using the counterpoise (CP) method on suitably partitioned transition states provides improved estimates of non-covalent interactions, which are also devoid of basis set superposition error (BSSE). It has been noted that catalysts decorated with larger aromatic arms provide cumulative non-covalent interactions (C-Hπ, N-Hπ and ππ) to the tune of 10 to 15 kcal mol-1. Fine-tuning of the magnitude and nature of these interactions can provide valuable avenues in the design of asymmetric catalysts.

Performance of Property-Optimized Basis Sets for Optical Rotation with Coupled Cluster Theory.

The effectiveness of the optical rotation prediction (ORP) basis set for computing specific rotations at the coupled cluster (CC) level has been evaluated for a test set of 14 chiral compounds. For this purpose, the ORP basis set has been developed for the second-row atoms present in the investigated systems (that is, for sulfur, phosphorus, and chlorine). The quality of the resulting set was preliminarily evaluated for seven molecules using time-dependent density-functional theory (TD-DFT). Rotations were calculated with the coupled cluster singles and doubles method (CCSD) as well as the second-order approximate coupled cluster singles and doubles method (CC2) with the correlation-consistent aug-cc-pVDZ and aug-cc-pVTZ basis sets and extrapolated to estimate the complete basis-set (CBS) limit for comparison with the ORP basis set. In the compounds examined here, the ORP calculations on molecules containing only first-row atoms compare favorably with results from the larger aug-cc-pVTZ basis set, in some cases lying closer to the estimated CBS limit, while results for molecules containing second-row atoms indicate that larger correlation-consistent basis sets are necessary to obtain reliable estimates of the CBS limit.

Isolation, characterization, and in vitro evaluation of bovine rumen submucosa films of collagen or chitosan-treated collagen.

Bovine rumen is hitherto considered as an inedible waste of meat industry. The rumen tissues can be used as an alternative source of collagen to produce biocompatible materials for clinical application. In an effort to develop a functional biomaterial from the inedible mammalian tissues, this study aims to isolate and characterize bovine rumen submucosa. Initially, the rumen tissue was sequentially processed using chemical and enzymatic treatment to decellularize, neutralize, stabilize, and to produce a native collagen matrix which is referred as collagen film (COL-F). Thus, prepared matrix was treated with 1% (w/v) chitosan solution to produce a hybrid film which is referred as collagen-chitosan film (COL/CS-F). The comparative study includes the evaluation of physical, chemical, and biological properties of the biofilms prepared. The surface topology of COL-F exhibited a continuous collagenous network with fibrous nature, while the chitosan treatment provided smooth plain surface to the parent film. Incorporation of chitosan in COL-F increased the tensile properties, as well as the thermal stability and durability of the films. The Fourier Transform Infrared spectroscopy results revealed the presence of respective amide peaks, which corresponds to protein (collagen), and the evidence of collagen-chitosan interlinking. The submucosa layer was electrophoretically found to have type I collagen. The X-ray diffraction data showed the presence of amorphous and crystalline peak which attributes to the triple helical structure of collagen in the films. Cytotoxicity studies on the films were performed in vitro using human keratinocytes. The results of cell viability and proliferation demonstrated that COL-F and COL/CS-F exhibit good biocompatibility and therefore can augment cell infiltration and proliferation. However, enhanced cellular activity was observed on the chitosan treated COL-F. These observations demonstrate that the biofilms prepared in this study can be used as an alternative functional biomaterial in tissue engineering.