Bioactivities and chemical profiling comparison and metabolomic variations of polyphenolics and steroidal glycoalkaloids in different parts of Solanum nigrum L.

INTRODUCTION: Solanum nigrum L. is a traditional medicinal herb and edible plant. Many studies provide evidence that S. nigrum L. is a nutritious vegetable. Polyphenols and steroidal glycoalkaloids are the main components. OBJECTIVES: This study aimed to systemically evaluate the phytochemical profile, quantification, and bioactivities of polyphenolics and glycoalkaloids in different parts of S. nigrum L. RESULTS: Total polyphenols (TPC) and total glycoalkaloids (TGK) were determined using the Folin-Ciocalteu and acid dye colorimetric methods, respectively. A total of 55 polyphenolic constituents (including 22 phenolic acids and 33 flavonoids) and 24 steroidal glycoalkaloids were identified from different parts using ultrahigh-performance liquid chromatography Q-exactive high-resolution mass spectrometry (UHPLC-QE-HRMS), of which 40 polyphenols (including 15 phenolic acids and 25 flavonoids) and one steroidal glycoalkaloid were characterised for the first time in S. nigrum L. Moreover, typical polyphenols and glycoalkaloids were determined using HPLC-UV and HPLC-evaporative light-scattering detector (ELSD), respectively. In addition, the TPC and TGK and their typical constituents were compared in different anatomical parts. Finally, the antioxidant capacities of polyphenolic extracts from different parts of S. nigrum L. were evaluated by ·OH, 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging and ferric-reducing antioxidant power (FRAP) assay in vitro. In addition, the antitumour effects of TGK from different parts of S. nigrum L. on the proliferation of PC-3 cells were investigated using 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. Polyphenolic and glycoalkaloid extracts from different parts of S. nigrum L. showed different antioxidant and cytotoxic capacities in vitro. CONCLUSION: This is the first study to systematically differentiate between polyphenolic and glycoalkaloid profiles from different parts of S. nigrum L.

N-Heterocyclic Carbene and Manganese Synergistic Catalysis: A Three-Component Radical Acylmonofluoroalkylation of Alkenes.

Despite the importance of monofluoroalkyl groups in pharmaceutically relevant molecules, catalytic protocols for their incorporation into alkenes remain limited. We describe herein a three-component acylmonofluoroalkylation of alkenes for the introduction of such moieties through an unprecedented cooperativity between the N-heterocyclic carbene catalyst and earth-abundant Mn(II) complex. This general method can be applied to a variety of alkenes, including styrenes, 1,3-enynes, and allenes, as well as complex substrates containing natural product and drug motifs.

Experimental and Modeling Study on the Ignition Kinetics of Nitromethane behind Reflected Shock Waves.

Nitromethane (NM) is the simplest nitroalkane fuel and has demonstrated potential usage as propellant and fuel additive. Thus, understanding the combustion characteristics and chemistry of NM is critical to the development of hierarchical detailed kinetic models of nitro-containing energetic materials. Herein, to further investigate the ignition kinetics of NM and supplement the experimental database for kinetic mechanism development, an experimental and kinetic modeling analysis of the ignition delay times (IDTs) of NM behind reflected shock waves at high fuel concentrations is reported against previous studies. Specifically, the IDTs of NM are measured via a high-pressure shock tube within the temperature from 900 to 1150 K at pressures of 5 and 10 bar and equivalence ratios of 0.5, 1.0, and 2.0. Brute-force sensitivity analysis and chemical explosive mode analysis in combination with reaction path analysis are employed to reveal the fundamental ignition kinetics of NM. Finally, a skeletal mechanism for NM is derived via the combination of directed relation graph-based methods, which demonstrates good prediction accuracy of NM ignition and flame speeds. The present work should be valuable for understanding the combustion chemistry of NM and the development of the fundamental reaction mechanism of nitroalkane fuels.

Pentacyclic triterpene-amino acid derivatives induced apoptosis and autophagy in tumor cells, affected the JNK and PI3K/AKT/mTOR pathway.

A series of pentacyclic triterpene-amino acid derivatives were synthesized and tested for anti-proliferative activity. The results showed that most of the target compounds had good anti-proliferative activity. 2c did not contain protecting groups and hydrochloride, had excellent cytotoxicity, so it had been selected for further study in the mechanism of action in T24 cells. The data from transcriptome sequencing indicated that 2c was found to be closely related to apoptosis and autophagy. Observation of fluorescence staining and analysis from flow cytometry demonstrated that 2c induced apoptosis and cause cell cycle arrest in S/G2 phase in T24 cells. Molecular mechanism studies exhibited that 2c induced apoptosis in the intrinsic and extrinsic pathways. 2c also induced cellular autophagy in T24 cells. Results from Western Blotting showed that 2c could activate JNK pathway and inhibit PI3K/AKT/mTOR pathway. In conclusion, 2c was deserved further investigation in the field of anti-tumor.

The application of PROTAC in HDAC.

Inducing protein degradation by proteolysis targeting chimera (PROTAC) has provided great opportunities for scientific research and industrial applications. Histone deacetylase (HDAC)-PROTAC has been widely developed since the first report of its ability to induce the degradation of SIRT2 in 2017. To date, ten of the eighteen HDACs (HDACs 1-8, HDAC10, and SIRT2) have been successfully targeted and degraded by HDAC-PROTACs. HDAC-PROTACs surpass traditional HDAC inhibitors in many aspects, such as higher selectivity, more potent antiproliferative activity, and the ability to disrupt the enzyme-independent functions of a multifunctional protein and overcome drug resistance. Rationally designing HDAC-PROTACs is a main challenge in development because slight variations in chemical structure can lead to drastic effects on the efficiency and selectivity of the degradation. In the future, HDAC-PROTACs can potentially be involved in clinical research with the support of the increased amount of in vivo data, pharmacokinetic evaluation, and pharmacological studies.

Pyrazolone derivatives as potent and selective small-molecule SIRT5 inhibitors.

Sirtiun 5 (SIRT5) is a NAD+-dependent protein lysine deacylase. It is emerging as a promising target for the development of drugs to treat cancer and metabolism-related diseases. In this study, we screened 5000 compounds and identified a hit compound 14 bearing a pyrazolone functional group as a novel SIRT5-selective inhibitor. Structure-based optimization of 14 resulted in compound 47 with an IC50 value of 0.21 ± 0.02 µM and a 100-fold improved potency. Compound 47 showed substantial selectivity for SIRT5 over SIRT1-3 and SIRT6. Biochemical studies suggest that 47 does not occupy the NAD + -binding pocket and acts as a substrate-competitive inhibitor. The identified potent and selective SIRT5 inhibitors allow further studies as research tools and therapeutic agents.

(4-Picolylamino)-17ß-Estradiol derivative and analogues induce apoptosis with death receptor trail R2/DR5 in MCF-7.

In order to discover more effective and less toxic drugs in the field of anti-tumor, the backbone structure of 17ß-estradiol was modified, and 11 target compounds were synthesized. Compounds 5 and 10, which exhibited better anti-tumor activity and higher selectivity (more than 10-fold), were chosen for further biological investigation. Flow cytometry results indicated that 5 and 10 could arrest MCF-7 cells in the G2 phase and induce apoptosis. Immunohistochemical analysis revealed that 5 and 10 could bind to the estradiol receptor alpha in MCF-7 cells. Western blotting and real-time PCR assays were performed to detect the effects of compounds on apoptosis-related targets at the protein and gene levels. These results showed that both 5 and 10 could dosed-dependently increase the expression of Apaf-1, Bax, caspase-3,8,9 and reduce the expression levels of the anti-apoptotic factors Bcl-2 and Bcl-xL. Besides, the Human apoptosis array assay demonstrated the expression level of death receptor Trail R2/DR5 was upregulated obviously while the expression of TNF R1, IAPs and Hsp27/60/70 were downregulated. On the whole, 5 induced MCF-7 cell death through the endogenous pathway in mitochondria and the exogenous pathway with death receptor Trail R2/DR5.

Experimental and Kinetic Modeling Study on High-Temperature Autoignition of Cyclohexene.

Cyclohexene is an important intermediate during the oxidation of cycloalkanes, which comprise a significant portion of real fuels. Thus, experimental data sets and kinetic models of cyclohexene play an important role in the understanding of the combustion of cycloalkanes and real fuels. In this work, an experimental and kinetic modeling study of the high-temperature ignition of cyclohexene is performed. Ignition delay time (IDT) measurements are carried out in a high-pressure shock tube (HPST). The studied pressures are 5, 10, and 20 bar; the equivalence ratios are 0.5, 1.0, and 2.0; and the temperatures range from 980 to 1400 K for IDT in HPST. It is shown that the IDTs of cyclohexene exhibit Arrhenius behaviors as a function of temperature, and the IDTs decrease as the equivalence ratio and pressure increase. The experimental results are simulated using three previous detailed kinetic mechanisms and an updated detailed mechanism in this work. The updated detailed kinetic mechanism exhibits good agreement with experimental results. Reaction path analysis and sensitivity analysis are performed to provide insights into the chemical kinetics controlling the ignition of cyclohexene. The results demonstrate that different detailed kinetic mechanisms are significantly different, and there are still no unified conclusions about the major reaction path for cyclohexene oxidation. However, it is worth noting that the abstraction reaction by oxygen at the allylic site and the submechanism of cyclopentene are of significant importance for the accurate prediction of IDTs of cyclohexene. The present experimental data set and kinetic model should be valuable to improve our understanding of the combustion chemistry of cycloalkanes.

Organocatalytic Three-Component Acyldifluoromethylation of Vinylarenes via N-Heterocyclic Carbene-Catalyzed Radical Relay.

We herein describe an N-hetercyclic carbene-catalyzed three-component acyldifluoromethylation of vinylarenes, aldehydes, and NaSO2CF2H. This organocatalytic approach provides a practical route for the synthesis of pharmaceutically relevant α-aryl-ß-difluormethyl ketones without the need for transition metals or photocatalysts. The late-stage acyldifluoromethylation of drug analogues was also demonstrated. The reaction design employs NaSO2CF2H as the source of the CF2H radical in the presence of an oxidant for the radical relay.

A Hierarchical Theoretical Study of the Hydrogen Abstraction Reactions of H2/C1-C4 Molecules by the Methyl Peroxy Radical and Implications for Kinetic Modeling.

The hydrogen atom abstraction by the methyl peroxy radical (CH3O2) is an important reaction class in detailed chemical kinetic modeling of the autoignition properties of hydrocarbon fuels. Systematic theoretical studies are performed on this reaction class for H2/C1-C4 fuels, which is critical in the development of a base model for large fuels. The molecules include hydrogen, alkanes, alkenes, and alkynes with a carbon number from 1 to 4. The B2PLYP-D3/cc-pVTZ level of theory is employed to optimize the geometries of all of the reactants, transition states, and products and also the treatments of hindered rotation for lower frequency modes. Accurate benchmark calculations for abstraction reactions of hydrogen, methane, and ethylene with CH3O2 are performed by using the coupled cluster method with explicit inclusion of single and double electron excitations and perturbative inclusion of triple electron excitations (CCSD(T)), the domain-based local pair-natural orbital coupled cluster method (DLPNO-CCSD(T)), and the explicitly correlated CCSD(T)-F12 method with large basis sets. Reaction rate constants are computed via conventional transition state theory with quantum tunneling corrections. The computed rate constants are compared with literature values and those employed in detailed chemical kinetic mechanisms. The calculated rate constants are implemented into the recently developed NUIGMECH1.1 base model for kinetic modeling of ignition properties.

Lubricity and Rheological Properties of Highly Dispersed Graphite in Clay-Water-Based Drilling Fluids.

Improving the tribological characteristics of water-based drilling fluids by adding graphene-based lubricants has garnered attention because of the potential for a range of inorganic-material-based additives at high temperature. In this study, we constructed a green and simple adsorption approach to prepare highly dispersed graphite using a cationic surfactant for graphite modification. The findings demonstrated that the prepared graphite was highly dispersed in water and had a low sedimentation rate and small contact angle in distilled water. The concentration dosage of cetyltrimethylammonium chloride (CTAC) on graphite was 0.02 g/g. We evaluated the performance of the modified graphite as a lubricated additive in water-based drilling through a rheological study and viscosity coefficient measurement. The results showed that the viscosity coefficient of drilling fluid with 0.05% modified graphite was reduced by 67% at 180 °C. We proved that the modified graphite can significantly improve the lubrication performance of drilling fluid. Furthermore, we revealed the lubrication mechanism by analyzing the chemical structural and crystalline and morphological features of graphite through a particle size test, zeta potential test, Fourier transform infrared (FTIR) spectroscopy, X-ray powder diffraction (XRD), and scanning electron microscopy (SEM) measurements. The results indicated that the modification of graphite by CTAC only occurs through physical adsorption, without changing the crystal structure. These findings provide a reference for the development of high-performance water-based drilling fluids.

Pharmacokinetics, oral bioavailability and metabolic analysis of solasodine in mice by dried blood spot LC-MS/MS and UHPLC-Q-Exactive MS.

Solasodine, a major ingredient in Solanaceae family, has various biological functions such as inducing neurogenesis, anticonvulsant and anti-tumor. Its risk assessment has also drawn public attention. However, little is known about its oral bioavailability and metabolic process. In this study, an liquid chromatography-tandem mass spectrometry (LC-MS/MS) method was developed for the quantification of solasodine in mice dried blood spot (DBS) samples. To block nonspecific adsorption, DBS samples were pretreated with bovine serum albumin (BSA) and then extracted with ethyl acetate. This method was applied to a pharmacokinetic and bioavailability study of solasodine. The absolute bioavailability was only 1.28%. Thereafter, its metabolites in mice were characterized using an ultra-performance liquid chromatography Q-Exactive high-resolution mass spectrometer (UHPLC-QE-HRMS). Several isomeric metabolites were well separated and differentiated using their retention time, fragmentation pathways and correspondingly fragmentation rules of solasodine. As a result, 21 metabolites were characterized including 16 phase I and 5 phase II metabolites. The proposed metabolic pathways showed that solasodine mainly experienced oxidation, dehydration, dehydrogenation and sulfation. These results could help us to better understand the efficacy and safety of solasodine.

Machine Learning Prediction of the Exfoliation Energies of Two-Dimension Materials via Data-Driven Approach.

Exfoliation energy is one of the fundamental parameters in the science and engineering of two-dimensional (2D) materials. Traditionally, it was obtained via indirect experimental measurement or first-principles calculations, which are very time- and resource-consuming. Herein, we provide an efficient machine learning (ML) method to accurately predict the exfoliation energies for 2D materials. Toward this end, a series of simple descriptors with explicit physical meanings are defined. Regression trees (RT), support vector machines (SVM), multiple linear regression (MLR), and ensemble trees (ET) are compared to develop the most suitable model for the prediction of exfoliation energies. It is shown that the ET model can efficiently predict the exfoliation energies through extensive validations and stability analysis. The influence of the defined features on the exfoliation energies is analyzed by sensitivity analysis to provide novel physical insight into the affecting factors of the exfoliation energies.

Single-Pulse Shock Tube Experimental and Kinetic Modeling Study on Pyrolysis of a Direct Coal Liquefaction-Derived Jet Fuel and Its Blends with the Traditional RP-3 Jet Fuel.

A basic understanding of the high-temperature pyrolysis process of jet fuels is not only valuable for the development of combustion kinetic models but also critical to the design of advanced aeroengines. The development and utilization of alternative jet fuels are of crucial importance in both military and civil aviation. A direct coal liquefaction (DCL) derived liquid fuel is an important alternative jet fuel, yet fundamental pyrolysis studies on this category of jet fuels are lacking. In the present work, high-temperature pyrolysis studies on a DCL-derived jet fuel and its blend with the traditional RP-3 jet fuel are carried out by using a single-pulse shock tube (SPST) facility. The SPST experiments are performed at averaged pressures of 5.0 and 10.0 bar in the temperature range around 900-1800 K for 0.05% fuel diluted by argon. Major intermediates are obtained and quantified using gas chromatography analysis. A flame-ionization detector and a thermal conductivity detector are used for species identification and quantification. Ethylene is the most abundant product for the two fuels in the pyrolysis process. Other important intermediates such as methane, ethane, propyne, acetylene, and 1,3-butadiene are also identified and quantified. The pyrolysis product distributions of the pure RP-3 jet fuel are also performed. Kinetic modeling is performed by using a modern detailed mechanism for the DCL-derived jet fuel and its blends with the RP-3 jet fuel. Rate-of-production analysis and sensitivity analysis are conducted to compare the differences of the chemical kinetics of the pyrolysis process of the two jet fuels. The present work is not only valuable for the validation and development of detailed combustion mechanisms for alternative jet fuels but also improves our understanding of the pyrolysis characteristics of alternative jet fuels.

Data-driven machine learning model for the prediction of oxygen vacancy formation energy of metal oxide materials.

Metal oxides are widely used in the fields of chemistry, physics and materials science. Oxygen vacancy formation energy is a key parameter to describe the chemical, mechanical, and thermodynamic properties of metal oxides. How to acquire quickly and accurately oxygen vacancy formation energy remains a challenge for both experimental and theoretical researchers. Herein, we propose a machine learning model for the prediction of oxygen vacancy formation energy via data-driven analysis and the definition of simple descriptors. Starting with the database containing oxygen vacancy formation energies for 1750 metal oxides with enough structural diversity, new descriptors that effectively avoid the defects of molecular fingerprints, molecular graphic descriptors and site descriptors are defined. The descriptors have obvious physical meanings and wide practicability. Multiple linear regression analysis is then used to screen important features for machine learning model development, and two strongly associated features are obtained. The selected descriptors are used as input for the training of 21 machine learning models to select and develop the most accurate machine learning model. Finally, it is shown that the least squares support vector regression method exhibits the best performance for accurate prediction of the targeted oxygen vacancy formation energy through systematic error analysis, and the prediction accuracy is also verified by the external dataset. Our work establishes a novel and simple computational approach for accurate prediction of the oxygen vacancy formation energy of metal oxides and highlights the availability of data-driven analysis for metal oxide material research.

Accelerating the optimization of enzyme-catalyzed synthesis conditions via machine learning and reactivity descriptors.

Enzyme-catalyzed synthesis reactions are of crucial importance for a wide range of applications. An accurate and rapid selection of optimal synthesis conditions is crucial and challenging for both human knowledge and computer predictions. In this work, a new scenario, which combines a data-driven machine learning (ML) model with reactivity descriptors, is developed to predict the optimal enzyme-catalyzed synthesis conditions and the reaction yield. Fourteen reactivity descriptors in total are constructed to describe 125 reactions (classified into five categories) included in different reaction mechanisms. Nineteen ML models are developed to train the dataset and the Quadratic support vector machine (SVM) model is found to exhibit the best performance. The Quadratic SVM model is then used to predict the optimal reaction conditions, which are subsequently used to obtain the highest yield among 109 200 reaction conditions with different molar ratios of substrates, solvents, water contents, enzyme concentrations and temperatures for each reaction. The proposed protocol should be generally applicable to a diverse range of chemical reactions and provides a black-box evaluation for optimizing the reaction conditions of organic synthesis reactions.

Single-Pulse Shock Tube Pyrolysis Study of RP-3 Jet Fuel and Kinetic Modeling.

A single-pulse shock tube study of the pyrolysis of two different concentrations of Chinese RP-3 jet fuel at 5 bar in the temperature range of 900-1800 K has been performed in this work. Major intermediates are obtained and quantified using gas chromatography analysis. A flame-ionization detector and a thermal conductivity detector are used for species identification and quantification. Ethylene is the most abundant product in the pyrolysis process. Other important intermediates such as methane, ethane, propyne, acetylene, butene, and benzene are also identified and quantified. Kinetic modeling is performed using several detailed, semidetailed, and lumped mechanisms. It is found that the predictions for the major species such as ethylene, propene, and methane are acceptable. However, current kinetic mechanisms still need refinement for some important species. Different kinetic mechanisms exhibit very different performance in the prediction of certain species during the pyrolysis process. The rate of production (ROP) is carried out to compare the differences among these mechanisms and to identify major reaction pathways to the formation and consumption of the important species, and the results indicate that further studies on the thermal decomposition of 1,3-butadiene are needed to optimize kinetic models. The experimental data are expected to contribute to a database for the validation of mechanisms under pyrolytic conditions for RP-3 jet fuel and should also be valuable to a better understanding of the combustion behavior of RP-3 jet fuel.

Research on BOLD-fMRI Data Denoising Based on Bayesian Estimation and Adaptive Wavelet Threshold.

The acquisition of functional magnetic resonance imaging (fMRI) images of blood oxygen level-dependent (BOLD) effect and the signals to be analyzed is based on weak changes in the magnetic field caused by small changes in blood oxygen physiological levels, which are weak signals and complex in noise. In order to model and analyze the pathological and hemodynamic parameters of BOLD-fMRI images effectively, it is urgent to use effective signal analysis techniques to reduce the interference of noise and artifacts. In this paper, the noise characteristics of functional magnetic resonance imaging and the traditional signal denoising methods are analyzed. The Bayesian decision criterion takes into account the probability of the total occurrence of all kinds of references and the loss caused by misjudgment and has strong discriminability. So, an improved adaptive wavelet threshold denoising method based on Bayesian estimation is proposed. By using the correlation characteristics of multiscale wavelet coefficients, the corresponding wavelet components of useful signals and noises are processed differently; while retaining useful frequency information, the noise is weakened to the greatest extent. The new adaptive threshold wavelet denoising method based on Bayesian estimation is applied to the actual experiment, and the results of OEF (oxygen extraction fraction) are optimized. A series of simulation experiments are carried out to verify the effectiveness of the proposed method.

Benchmark calculations for bond dissociation energies and enthalpy of formation of chlorinated and brominated polycyclic aromatic hydrocarbons.

Thermodynamic properties, i.e., bond dissociation energies and enthalpy of formation, of chlorinated and brominated polycyclic aromatic hydrocarbons play a fundamental role in understanding their formation mechanisms and reactivity. Computational electronic structure calculations routinely used to predict thermodynamic properties of various species are limited for these compounds due to large computational cost to obtain accurate results by employing high-level wave function theory methods. In this work, a number of composite model chemistry methods (CBS-QB3, G3MP2, G3, and G4) are used to compute bond dissociation energies and enthalpies of formation of small to medium-size chlorinated and brominated polycyclic aromatic hydrocarbon compounds. The enthalpy of formation is derived via the atomization method and compared against the recommended values. Statistical analysis indicates that G4 is the best method. For comparison, three commonly used density functional theory (DFT) methods (M06-2X, ωB97X-D and B2PLYP-D3) with various basis sets including 6-311++G(d, p), cc-pVTZ, and cc-pVQZ in the prediction of bond dissociation energies and enthalpies of formation have been tested using the optimized geometries at the same M06-2X/6-311++G(d, p) level of theory. It is found that ωB97X-D/6-311++G(d, p) shows the best performance in computing the bond dissociation energies, while ωB97X-D/cc-pVTZ exhibits the best prediction in enthalpy of formation of the studied reaction systems. The structural effect on the bond dissociation energies and enthalpy of formation of chlorinated and brominated polycyclic aromatic hydrocarbons are then systematically analyzed. Based on comparisons of the various methods, reliable DFT methods are recommended for future theoretical studies on large chlorinated and brominated polycyclic aromatic hydrocarbons considering both accuracy and computational cost. This work, to the authors' knowledge, is the first to systematically benchmark theoretical methods for the accurate prediction of thermodynamic properties for chlorinated and brominated polycyclic aromatic hydrocarbons.

Hindered rotor benchmarks for the transition states of free radical additions to unsaturated hydrocarbons.

The hindered internal rotors of 32 transition states (TSs) formed through four free radicals, namely methyl, vinyl, ethyl, methoxy (CH3, C2H3, C2H5, CH3) additions to acetylene, ethylene, allene, propyne, and propene (C2H2/C2H4/C3H4-a/C3H4-p/C3H6) are studied. To validate the uncertainties of rate constants that stem from the use of different electronic structure methods to treat hindered rotors, the rotations of the newly formed C-C and/or C-O rotors in the transition states are calculated using commonly used DFT methods (B3LYP, M06-2X, ωB97X-D and B2PLYP-D3 with two Pople basis sets (6-31+G(d,p), 6-311++G(d,p)) and cc-pVTZ). The hindrance potential energies V(χ) calculated using the M06-2X/6-311++G(d,p) method are benchmarked at the CCSD(T), CCSD(T)-F12, DLPNO-CCSD(T) levels of theory with cc-pVTZ-F12 and cc-pVXZ (X = T, Q) basis sets and are extrapolated to the complete basis set (CBS) limit. The DLPNO-CCSD(T)/CBS method is proven to reproduce the CCSD(T)/CBS energies within 0.5 kJ mol-1 and this method is selected as the benchmark for all of the rotors in this study. Rotational constants B(χ) are computed for each method based on the optimized geometries for the hindrance potential via the I(2,3) approximation. Thereafter, the V(χ) and B(χ) values are used to compute hindered internal rotation partition functions, QHR, as a function of temperature. The uncertainties in the V(χ), B(χ) and QHR calculations stem from the use of different DFT methods for the internal rotor treatment are discussed for these newly formed rotors. For rotors formed by + C2 alkenes/alkynes, the V(χ) and QHR values calculated using DFT methods are compared with the DLPNO-CCSD(T)/CBS results and analysed according to reaction types. Based on comparisons of the DFT methods with the benchmarking method, reliable DFT methods are recommended for the treatment of internal rotors for different reaction types considering both accuracy and computational cost. This work, to the authors' knowledge, is the first to systematically benchmark hindrance potentials which can be used to estimate uncertainties in theoretically derived rate constants arising from the choice of different electronic structure methods.