Comparative Study of Bis-Schiff Case Containing Conjugated Oligomers Based on Phosphate and Silane Moieties: Investigation of Photophysical and Thermal Properties

Oligo(azomethine)s bearing phosphate and silane moieties were the subject of an investigation within this study. The initial stage involved the synthesis of two Schiff base monomers, denoted as SCH-1 and SCH-2 (SCHs), each possessing a pair of hydroxyl functional groups. This was achieved through a loss of water between the aldehyde and diamine precursors. Subsequently, the Schiff base entities were subjected to oligomerization through HCl-mediated elimination due to the interaction between the hydroxyl groups of the Schiff bases and the chlorine moieties of dichlorodiethylsilane (Si) or phenyl dichlorophosphate (P). This procedure yielded distinct P-oligo(azomethine) (P1-P, P2-P) and Si-oligo(azomethine) (P1-Si and P2-Si) structures corresponding to each precursor. The molecular structures of the synthesized Schiff base monomers and oligo(azomethine)s were elucidated employing Fourier transform infrared, 1H NMR, and 13C NMR techniques. Thermal properties of the resulting products were assessed by utilizing thermogravimetric analysis (TG-DTG/DTA and DSC) techniques. Scanning electron microscopy (SEM) was employed to acquire high-resolution images and detailed surface information on the samples. Additionally, X-ray diffraction was employed to analyze the phase properties of the solid samples. Furthermore, the optical band gap (E g) values of the resulting P-oligo(azomethine)s and Si-oligo(azomethine)s were determined utilizing UV-vis spectrophotometer. The relatively low band gap values exhibited by the synthesized oligo(azomethine)s were indicative of their potential suitability as semiconductive materials in the realm of electronic and optoelectronic device fabrication. Photoluminescence (PL) measurements disclosed a green emission profile upon excitation by blue light. The oligo(azomethine)s incorporating methoxy groups demonstrated a red shift in comparison to their counterparts with methyl groups. Remarkably, no discernible fluctuations in fluorescence were observed over a 3600 s interval under consistent conditions. This observation underscored the inherent stability of the PL emission across the spectral range of exciting light. Thermal analyses unveiled high thermal stability of the synthesized oligo(azomethine)s, sustaining their structural integrity up to 220 °C. The char % of P-oligo(azomethine)s and Si-oligo(azomethine)s were observed to fall within the range of 29.45-55.47% at 1000 °C. SEM images revealed the absence of pores on the surface of P2-Si, which exhibited the highest limiting oxygen index and thermal heat release index (T HRI) values.

Machine learning assisted designing of non-fullerene electron acceptors: A quest for lower exciton binding energy

The designing of acceptors materials for the organic solar cells is a hot topic. The normal experimental methods are tedious and expensive for large screening. Machine learning guided exploration is more suitable solution. Bagging regression, random forest regression, gradient boosting regression, and linear regression are trained to predict exciton binding energy. Breaking Retrosynthetically Interesting Chemical Substructures (BRICS) methodology has utilized for designing of new non-fullerene acceptors (NFAs). The predicted values were used to select the designed NFAs. On the selected NFAs, clustering and chemical similarity analyses are also performed. Chemical fingerprints are used for this purpose, and the synthetic accessibility score of the new NFAs is also investigated.30 NFAs have selected with low exciton binding energy values. This approach will allow for the rapid screening of NFAs for organic solar cells. Our proposed framework stands out as a valuable tool for strategically selecting the most effective NFAs for organic solar cells and offers a streamlined approach for material discovery.

Chemistry Aspects and Designing Strategies of Flexible Materials for High-Performance Flexible Lithium-Ion Batteries

In recent years, flexible and wearable electronics such as smart cards, smart fabrics, bio-sensors, soft robotics, and internet-linked electronics have impacted our lives. In order to meet the requirements of more flexible and adaptable paradigm shifts, wearable products may need to be seamlessly integrated. A great deal of effort has been made in the last two decades to develop flexible lithium-ion batteries (FLIBs). The selection of suitable flexible materials is important for the development of flexible electrolytes self-supported and supported electrodes. This review is focused on the critical discussion of the factors that evaluate the flexibility of the materials and their potential path toward achieving the FLIBs. Following this analysis, we present how to evaluate the flexibility of the battery materials and FLIBs. We describe the chemistry of carbon-based materials, covalent-organic frameworks (COFs), metal-organic frameworks (MOFs), and MXene-based materials and their flexible cell design that represented excellent electrochemical performances during bending. Furthermore, the application of state-of-the-art solid polymer and solid electrolytes to accelerate the development of FLIBs is introduced. Analyzing the contributions and developments of different countries has also been highlighted in the past decade. In addition, the prospects and potential of flexible materials and their engineering are also discussed, providing the roadmap for further developments in this fast-evolving field of FLIB research.

Data mining and library generation to search electron-rich and electron-deficient building blocks for the designing of polymers for photoacoustic imaging

Photoacoustic imaging is a good method for biological imaging, for this purpose, materials with strong near infrared (NIR) absorbance are required. In the present study, machine learning models are used to predict the light absorption behavior of polymers. Molecular descriptors are utilized to train a variety of machine learning models. Building blocks are searched from chemical databases, as well as new building blocks are designed using chemical library enumeration method. The Breaking Retrosynthetically Interesting Chemical Substructures (BRICS) method is employed for the creation of 10,000 novel polymers. These polymers are designed based on the input of searched and selected building blocks. To enhance the process, the optimal machine learning model is utilized to predict the UV/visible absorption maxima of the newly designed polymers. Concurrently, chemical similarity analysis is also performed on the selected polymers, and synthetic accessibility of selected polymers is calculated. In summary, the polymers are all easy to synthesize, increasing their potential for practical applications.

Performance prediction of polymer-fullerene organic solar cells and data mining-assisted designing of new polymers

CONTEXT

Selecting high performance polymer materials for organic solar cells (OSCs) remains a compelling goal to improve device morphology, stability, and efficiency. To achieve these goals, machine learning has been reported as a powerful set of algorithms/techniques to solve complex problems and help/guide exploratory researchers to screen, map, and develop high performance materials. In present work, we have applied machine learning tools to screen data from reported studies and designed new polymer acceptor materials, respectively. Quantitative structure-activity relationship (QSAR) models were generated using machine learning-assisted simulation techniques. For this purpose, 3000 molecular descriptors are generated. Consequently, molecular descriptors having key effect on power conversion efficiency (PCE) were identified. Moreover, numerous regression models (e.g., random forest and bagging regressor models) were developed to predict the PCE. In particular, new materials were designed based on the similarity analysis. The GDB17 chemical database consisting of 166 million organic molecules in an ordered form is used for performing similarity analysis. A similarity behavior between GDB17 materials and the materials reported in literature is studied using RDKit (a cheminformatics software). Noteworthily, 100 monomers proved to be unique and effective, and PCEs of these monomers are predicted. Among these monomers, four monomers exhibited PCE higher than 14%, which is better than various reported studies. Our methodology provides a unique, time- and cost-efficient approach to screening and designing new polymers for OSCs using similarity analysis without revisiting the reported studies.

METHODS

To perform machine learning analysis, data from reported studies and online databases was collected. Different molecular descriptors were generated for polymer materials utilizing Dragon software. 3D structures of studied molecules were applied as input (SDF; structure data file format). Importantly, about 3000 molecular descriptors were generated. Comma-separated value (.csv) file format was used to export these molecular descriptors. To shortlist best descriptors, univariate regression analysis was performed. These descriptors were further utilized for training machine learning models. Moreover, necessary packages of Python for data analysis and visualization were imported such as Matplotlib, Numpy, Pandas, Scikit-learn, Seaborn, and Scipy. Random forest and bagging regressor models were applied for performing machine learning analysis. A cheminformatics software, RDKit, was applied for similarity analysis.

DFT investigations and molecular docking as potent inhibitors of SARS-CoV-2 main protease of 4-phenylpyrimidine

In this work, quantum chemical descriptors and a vibrational analysis of 4-Phenylpyrimidine (4-PPy) were also investigated. Through conformational analysis, the most stable conformer can be determined. The geometry of the molecular structure was optimized by using the density functional theory (DFT) at the B3LYP/6-311++G(d,p) level. The theoretically obtained FT-IR and FT-Raman spectral data agree with the experimental results. UV-Vis was done in the gas phase along with different solvents by the TD-DFT method and the PCM solvent model. Molecular electrostatic potential, natural bond orbital analysis, nonlinear optical properties, and global chemical reactivity parameters were described through the DFT method. Besides, the chemical implications of a molecule were explained using an electron localization function and a local orbital locator. We attempted to detect the antiviral activity of the 4-PPy compound by predicting molecular docking into coronavirus 2 (SARS-n-CoV-2) protein structures (6LU7, 6M03, and 6W63), because COVID-19 is known to have serious adverse effects in all areas of human life worldwide, and possible drugs need to be investigated for this. The results of the docking simulation demonstrate good affinities for binding to the receptors.

Efficient Synthesis of Imine-Carboxylic Acid Functionalized Compounds: Single Crystal, Hirshfeld Surface and Quantum Chemical Exploration

Two aminobenzoic acid based crystalline imines (HMBA and DHBA) were synthesized through a condensation reaction of 4-aminobenzoic acid and substituted benzaldehydes. Single-crystal X-ray diffraction was employed for the determination of structures of prepared Schiff bases. The stability of super molecular structures of both molecules was achieved by intramolecular H-bonding accompanied by strong, as well as comparatively weak, intermolecular attractive forces. The comparative analysis of the non-covalent forces in HMBA and DHBA was performed by Hirshfeld surface analysis and an interaction energy study between the molecular pairs. Along with the synthesis, quantum chemical calculations were also accomplished at M06/6-311G (d, p) functional of density functional theory (DFT). The frontier molecular orbitals (FMOs), molecular electrostatic potential (MEP), natural bond orbitals (NBOs), global reactivity parameters (GRPs) and natural population (NPA) analyses were also carried out. The findings of FMOs found that Egap for HMBA was examined to be smaller (3.477 eV) than that of DHBA (3.7933 eV), which indicated a greater charge transference rate in HMBA. Further, the NBO analysis showed the efficient intramolecular charge transfer (ICT), as studied by Hirshfeld surface analysis.

DFT calculations, molecular docking, in vitro antimicrobial and antidiabetic studies of green synthesized Schiff bases: as Covid-19 inhibitor

In this investigation, we synthesized Schiff bases 2-(2-methoxyphenoxy)-N-(4-methylbenzylidene)ethanamine, N-(4-methoxybenzylidene)-2-(2-methoxyphenoxy)ethanamine and 2-(2-methoxyphenoxy)-N-(4-nitrobenzylidene)ethanamine from 2-(2-methoxyphenoxy)ethanamine and various aromatic aldehydes by the environmentally friendly sonication method. The B3LYP method with a 6-311++G (d, p) basis set was used in the DFT calculation to obtain the optimized structure of the Schiff base MPEA-NIT. The compounds were tested in vitro for inhibition of bacterial growth (disc well method) and inhibition of α-amylase (starch-iodine method). The compounds tested showed inhibitory activities. In addition, they were subjected to PASS analysis, drug likeness, and bioactivity score predictions using online software. To confirm the experimental findings, molecular docking analyses of synthesized compounds on α-amylase (PDB ID: 1SMD), tRNA threonylcarbamoyladenosine (PDB ID: 5MVR), glycosyl transferase (PDB ID: 6D9T), and peptididoglycan D,D-transpeptidase (PDB ID: 6HZQ) were performed. The emergence of a new coronavirus epidemic necessitates the development of antiviral medications (SARS-CoV-2). Docking active site interactions were investigated to predict compounds' activity against COVID-19 by binding with the SARS-CoV-2 (PDB ID: 6Y84).Communicated by Ramaswamy H. Sarma.

Energy Level Prediction of Organic Semiconductors for Photodetectors and Mining of a Photovoltaic Database to Search for New Building Units

Due to the large versatility in organic semiconductors, selecting a suitable (organic semiconductor) material for photodetectors is a challenging task. Integrating computer science and artificial intelligence with conventional methods in optimization and material synthesis can guide experimental researchers to develop, design, predict and discover high-performance materials for photodetectors. To find high-performance organic semiconductor materials for photodetectors, it is crucial to establish a relationship between photovoltaic properties and chemical structures before performing synthetic procedures in laboratories. Moreover, the fast prediction of energy levels is desirable for designing better organic semiconductor photodetectors. Herein, we first collected large sets of data containing photovoltaic properties of organic semiconductor photodetectors reported in the literature. In addition, molecular descriptors that make it easy and fast to predict the required properties were used to train machine learning models. Power conversion efficiency and energy levels were also predicted. Multiple models were trained using experimental data. The light gradient boosting machine (LGBM) regression model and Hist gradient booting regression model are the best models. The best models were further tuned to achieve better prediction ability. The reliability of our designed approach was further verified by mining the photovoltaic database to search for new building units. The results revealed that good consistency is obtained between experimental outcomes and model predictions, indicating that machine learning is a powerful approach to predict the properties of photodetectors, which can facilitate their rapid development in various fields.

Molecular docking and simulation studies against nucleoside diphosphate kinase (NDK) of Pseudomonas aeruginosa with secondary metabolite identified by genome mining from paenibacillusehimensis

Pseudomonas aeruginosa is one of the leading opportunistic pathogens that causes nosocomial pneumonia and mostly in people with cystic fibrosis. In the present study, an in-silicoapproach was adopted to identify the novel drug target against Pseudomonas aeruginosa by employing subtractive genomics and molecular docking studies. Each step in the subtractive genomics scrutinized the bacterial proteome and determined a potential drug target against Pseudomonas aeruginosa. 71 essential proteins were obtained from the subcellular localization method that resides in the extracellular region. Metabolic pathways were studied to elucidate the unique pathways where the involvement of proteins present in the pathogen was predicted and a total of 6 unique pathways were determined. By, Genome mining of the source organism Paenibacillusehimensis, 9 ligands were obtained. The molecular docking analysis between the binding site of target protein NDK and ligands was carried out by employing the AutoDock Vina tool. Based on the highest binding affinity, Paenibactin, AnabaenopeptinNZ857 and Nostamide A complex with NDK protein with a lower binding energy of -7.5 kcal/mol, -7.4and -7.2 kcal/molrespectively were considered for the simulation studies. Molecular dynamics simulation studies showed the ligand in complex with protein was highly stable and rigid for a duration of 150 ns. For Paenibactin, AnabaenopeptinNZ857 and Nostamide Acomplex with protein, RMSD plot showed a deviation of ∼0.2-0.3 nm till ∼30ns/50 ns-110ns and further stabilized. The radius of the gyration plot clearly showed that the values stayed at ∼1.45 nm- 1.55 nm showing compactness and stability. The SASA stayed at the range ∼80nm2 and at least one total number of hydrogen bonds was shown throughout the 150 ns simulation for all three possible ligand-protein complexes. In the RMSF plot, the maximum fluctuation was ranged from ∼0.4-0.42 nm at the range between ∼57ns-60ns.The Paenibactin, AnabaenopeptinNZ857 and Nostamide A complex with NDK protein showed a stable, rigid and compact interaction throughout the simulation of duration 150 ns.Communicated by Ramaswamy H. Sarma.

Theoretical and Experimental Investigation of the Antioxidation Mechanism of Loureirin C by Radical Scavenging for Treatment of Stroke

Recent pharmacological studies have shown that dragon's blood has an anti-cerebral ischemia effect. Loureirin C (LC), a kind of dihydrochalcone compound in dragon's blood, is believed to be play an important role in the treatment of ischemia stroke, but fewer studies for LC have been done. In this paper, we report the first experimental and theoretical studies on the antioxidation mechanism of LC by radical scavenging. The experimental studies show that LC has almost no effect on cell viability under 15 μM for the SH-SY5Y cells without any treatments. For the SH-SY5Y cells with oxygen and glucose deprivation-reperfusion (OGD/R) treatment, LC increased the viability of SH-SY5Y cells. The results of 2',7'-Dichlorodihydrofluorescein diacetate (DCFH-DA) and MitoSox Red experiments indicate that LC is very efficient in inhibiting the generation of the intracellular/mitochondrial reactive oxygen species (ROS) or removing these two kinds of generated ROS. The density functional theory (DFT) calculations allowed us to elucidate the antioxidation mechanisms of LC. Fukui function analysis reveals the radical scavenging of LC by hydrogen abstraction mechanism, the complex formation by e-transfer, and radical adduct formation (RAF) mechanism. Among the H-abstraction, the complex formation by e-transfer, and radical adduct formation (RAF) reactions on LC, the H-abstraction at O-H35 position by OH^• is favorable with the smallest energy difference between the product and two reactants of the attack of OH^• to LC of -0.0748 Ha. The bond dissociation enthalpies (BDE), proton affinities (PA), ionization potential (IP), proton dissociation enthalpy (PDE), and electron transfer enthalpy (ETE) were calculated to determine thermodynamically preferred reaction pathway for hydrogen abstraction mechanism. In water, IP and the lowest PDE value at O3-H35 position are lower than the lowest BDE value at O3-H35 position; 41.8986 and 34.221 kcal/mol, respectively, indicating that SEPT mechanism is a preferred one in water in comparison with the HAT mechanism. The PA value of O3-H35 of LC in water is -17.8594 kcal/mol, thus the first step of SPLET would occur spontaneously. The minimum value of ETE is higher than the minimum value of PDE at O3-H35 position and IP value, 14.7332 and 22.4108 kcal/mol, respectively, which suggests that the SEPT mechanism is a preferred one in water in comparison with the SPLET mechanism. Thus, we can draw a conclusion that the SEPT mechanism of is the most favorite hydrogen abstraction mechanism in water, and O-H35 hydroxyl group has the greatest ability to donate H-atoms.

Synthesis, Drug-Likeness Evaluation of Some Heterocyclic Moieties Fused Indole Derivatives as Potential Antioxidants

BACKGROUND

Indole and its derivatives have a wide range of pharmacological effects, including analgesic, antimicrobial, antidepressant, anti-diabetic, anti-convulsant, anti-helminthic, and anti-inflammatory properties. They are crucial structural components of many of today's powerful antioxidant medications.

OBJECTIVE

Using the Schotten-Baumann reaction, the indole ring was linked to other key heterocyclic moieties such as morpholine, imidazole, piperidine, and piperazine at the active 3rd position and then tested for antioxidant activity.

METHODS

Synthesis of derivatives was accomplished under appropriate conditions and characterized by IR, NMR (1H and 13C), and mass spectrum. Using the Swiss ADME online application, ADME properties were also determined. The in vitro antioxidant activity was measured using DPPH and Reducing power method.

RESULTS

In the DPPH assay, compounds 5a (IC50=1.01±0.22 μg/mL), 5k (IC50=1.21 ± 0.07 μg/mL), whereas compounds 5a (EC50=23 ± 1.00 μg/mL), 5h (EC50=26±2.42 μg/mL) in the reducing power assay were most potent as compared with standard Ascorbic acid. Compounds 5a, 5h, and 5k demonstrated maximal potency equivalent to standard. Lipinski's rule was followed in ADME outcomes.

CONCLUSION

The synthesis and evaluation of indole derivatives to investigate their antioxidant action has received a lot of attention. These discoveries could lead to more effective antioxidant candidates being designed and developed.

Machine Learning-Assisted Prediction of the Biological Activity of Aromatase Inhibitors and Data Mining to Explore Similar Compounds

Designing molecules for drugs has been a hot topic for many decades. However, it is hard and expensive to find a new molecule. Thus, the cost of the final drug is also increased. Machine learning can provide the fastest way to predict the biological activity of druglike molecules. In the present work, machine learning models are trained for the prediction of the biological activity of aromatase inhibitors. Data was collected from the literature. Molecular descriptors are calculated to be used as independent features for model training. The results showed that the R ² values for linear regression, random forest regression, gradient boosting regression, and bagging regression are 0.58, 0.84, 0.77, and 0.80, respectively. Using these models, it is possible to predict the activity of new molecules in a short period of time and at a reasonable cost. Furthermore, Tanimoto similarity is used for similarity analysis, as well as a chemical database is mined to search for similar molecules. Nonetheless, this study provides a framework for repurposing other effective drug molecules to prevent cancer.

Machine Learning Assisted Prediction of Power Conversion Efficiency of All-Small Molecule Organic Solar Cells: A Data Visualization and Statistical Analysis

Organic solar cells are famous for their cheap solution processing. Their industrialization needs fast designing of efficient materials. For this purpose, testing of large number of materials is necessary. Machine learning is a better option due to cheaper prediction of power conversion efficiencies. In the present work, machine learning was used to predict power conversion efficiencies. Experimental data were collected from the literature to feed the machine learning models. A detailed data visualization analysis was performed to study the trends of the dataset. The relationship between descriptors and power conversion efficiency was quantitatively determined by Pearson correlations. The importance of features was also determined using feature importance analysis. More than 10 machine learning models were tried to find better models. Only the two best models (random forest regressor and bagging regressor) were selected for further analysis. The prediction ability of these models was high. The coefficient of determination (R2) values for the random forest regressor and bagging regressor models were 0.892 and 0.887, respectively. The Shapley additive explanation (SHAP) method was used to identify the impact of descriptors on the output of models.

DFT, hirshfeld and molecular docking studies of a hybrid compound - 2,4-Diamino-6-methyl-1,3,5-triazin-1-ium hydrogen oxalate as a promising anti -breast cancer agent

The six-membered heterocyclic ring - 1,3,5-triazine and its derivatives have garnered a lot of attention because they're good bioactive herbicides, cancer agents, and other things. One such triazine derivative, 2,4-diamino-6-methyl-1,3,5-triazin-1-ium hydrogen oxalate (DMTHO), was produced in this work, and the structure was optimised using density functional theory's B3LYP functional and the basis set 6–31++G (d,p). Additionally, the chemical underwent in-depth research using molecular docking analysis, Hirshfeld, and density functional theory. The electron densities distribution in the atoms is provided by natural orbital analysis, which also characterises the chemical bonding and reaction behaviour of the compound. The calculated HOMO and LUMO energies indicate that charge transfer occurs inside the molecule. Chemical reactivity traits including HOMO-LUMO energy gaps, softness, total energy, chemical hardness, electronic chemical potential, and electrophilicity of bioactive substances have all been subjected to analytical investigation. Total dipole moment (μ) and first-order hyperpolarizability (β) measurements for the investigated chemical indicate that DMTHO may exhibit microscopic nonlinear optical (NLO) behaviour with nonzero values. A quantitative description about intermolecular interactions in the produced crystal is provided by the Hirshfeld surface analysis. Further docking studies of the compound have been performed and the results reveals that the compound inhibit the breast cancer related protein - casein kinase (CK2) – and the possibility of developing as a potential anti breast cancer lead.

Electrochemical Methodologies for Investigating the Antioxidant Potential of Plant and Fruit Extracts: A Review

In recent years, the growing research interests in the applications of plant and fruit extracts (synthetic/stabilization materials for the nanomaterials, medicinal applications, functional foods, and nutraceuticals) have led to the development of new analytical techniques to be utilized for identifying numerous properties of these extracts. One of the main properties essential for the applicability of these plant extracts is the antioxidant capacity (AOC) that is conventionally determined by spectrophotometric techniques. Nowadays, electrochemical methodologies are emerging as alternative tools for quantifying this particular property of the extract. These methodologies address numerous drawbacks of the conventional spectroscopic approach, such as the utilization of expensive and hazardous solvents, extensive sample pre-treatment requirements, long reaction times, low sensitivity, etc. The electrochemical methodologies discussed in this review include cyclic voltammetry (CV), square wave voltammetry (SWV), differential pulse voltammetry (DPV), and chronoamperometry (CAP). This review presents a critical comparison between both the conventional and electrochemical approaches for the quantification of the parameter of AOC and discusses the numerous applications of the obtained bioextracts based on the AOC parameter.

Keto, thione, selone, and tellone carotenoids — Changing antioxidants to antireductants

Heterocarotenoids can be considered as xenobiotic compounds as they are foreign to living organisms. Thione carotenoids are heterocarotenoids that are particularly interesting because the presence of sulfur shifted the absorption to longer wavelengths than the corresponding keto carotenoids. This may be important for further applications such as the development of new pigments. Keto carotenoids are well-known antiradical molecules, however, nothing is known about heterocarotenoids acting as free radical scavengers. Thus, the main goal of this investigation is to study the antiradical properties of some heterocarotenoids, such as thione, selone, and tellone carotenoids. For this purpose, the energy differences between singlets and triplets are used to analyze the singlet oxygen quenching mechanism, and the electron transfer mechanism is investigated, taking into account that these may constitute antiradical molecules either donating or accepting electrons (antioxidants or antireductants). To analyze these mechanisms, vertical ionization energy (I), vertical electron affinity (A), and electrodonating (χ−) and electroaccepting (χ+) electronegativities were evaluated by applying density functional theory calculations. The investigated heterocarotenoids are as effective as keto carotenoids in terms of being either electron donors or acceptors, and therefore, they have a similar capacity for scavenging free radicals. Changing the C=O group to C=S, C=Se, or C=Te converts an antioxidant to an antireductant.