Non-covalent interactions abetted supramolecular arrangements of N-Substituted benzylidene acetohydrazide to direct its solid-state network

Heterocyclic Donor Moiety Effect on Optical Nonlinearity Behavior of Chrysene-Based Chromophores with Push-Pull Configuration via the Quantum Chemical Approach

Organic-based nonlinear optical (NLO) materials may be used in many optical-electronic systems and other next-generation defense technologies. With the importance of NLO materials, a series of push-pull architecture (D-π-A) derivatives (DTMD2-DTMD6) were devised from DTMR1 through structural alteration of different efficient donor heterocyclic groups. Density functional theory-based computations were executed at the MPW1PW91/6-31G(d,p) level to explore the NLO behavior of the derivatives. To investigate the optoelectronic behavior of the said compounds, various analyses like the frontier molecular orbital (FMO), global reactivity parameters, density of state (DOS), absorption spectra (UV-vis), natural bond orbital, and transition density matrix (TDM) were performed. The derivatives have a smaller band gap (2.156-1.492 eV) and a larger bathochromic shift (λmax = 692.838-969.605 nm) as compared to the reference chromophore (ΔE = 2.306 eV and λmax = 677.949 nm). FMO analysis revealed substantial charge conduction out of the donor toward the acceptor via a spacer that was also shown by TDM and DOS analyses. All derivatives showed promising NLO results, with the maximum amplitude of linear polarizability ⟨α⟩ and first (βtotal) and second (γtotal) hyperpolarizabilities over their reference chromophore. DTMD2 contained the highest βtotal (7.220 × 10-27 esu) and γtotal (1.720 × 10-31 esu) values corresponding with the reduced band gap (1.492 eV), representing potential futures for a large NLO amplitude. This structural modification through the use of various donors has played a significant part in achieving promising NLO behavior in the modified compounds.

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.

Potential anticancer and antioxidant lauric acid-based hydrazone synthesis and computational study toward the electronic properties

The modification of natural products is one of the key areas of synthetic organic chemistry for obtaining valuable chemical building blocks that have medicinal significance. In this study, lauric acid-based hydrazones, namely (E)-N'-(2-nitrobenzylidene)dodecanehydrazide (NBDH), (E)-N'-(naphthalen-1-ylmethylene)dodecanehydrazide (NMDH), and (E)-N'-(4-fluorobenzylidene)dodecanehydrazide (FBDH), were synthesized and characterized using spectroscopic techniques. The newly synthesized lauric acid-based hydrazones were screened for their anticancer and antioxidant potential. The antioxidants showed their activity by inhibiting the oxidative chain reactions that produce reactive oxygen species. The antioxidant activity showed that NBDH exhibited the maximum DPPH inhibitory activity when compared with that of NMDH and FBDH, whereas the anticancer activity showed that FBDH exhibited maximum percent viability when compared to that of NBDH and NMDH. The reactivity and biological needs of the synthesized compounds NBDH, NMDH, and FBDH were met by performing geometrical, FT-IR vibrational, UV-visible, global reactivity parameters (GRP), MEP, FMO, NBO, ELF, LOL, and nonlinear optical (NLO) analysis at the DFT/B3LYP/6-311+G(d,p) level. NBO analysis confirmed the existence of extended conjugation and intramolecular charge transfer among NBDH, NMDH, and FBDH, which have the lowest gap in π → π*, which are in line with the FMO results where successful charge transfer occurred from the highest occupied molecular orbital (HOMO) to the lowest unoccupied molecular orbital (LUMO). GRP analysis confirmed the potential of NBDH, NMDH, and FBDH for biological, electronic, and NLO applications. It is clear from the comparative analysis of the urea molecule that NBDH, NMDH, and FBDH all comprise fine NLO properties.

Synthesis of novel 3-hydroxy-2-naphthoic hydrazones as selective chemosensors for cyanide ions

The development of an effective and selective chemosensor for CN^- ions has become the need of the hour due to their hazardous impact on the environment and humans. Herein, we report the synthesis of two novel chemosensors, IF-1 and IF-2 based on 3-hydroxy-2-naphthohydrazide and aldehyde derivatives that have shown selective sensing of CN^- ions. IF-2 exhibited exclusive binding with CN^- ions that is further confirmed by the binding constant value of 4.77 × 10⁴ M^-1 with a low detection limit (8.2 μM). The chemosensory potential is attributed to deprotonation of the labile Schiff base center by CN^- ions that results in a color change from colorless to yellow as visible by the naked eye. Accompanying this, a DFT study was also performed in order to find the interaction between the sensor (IF-1) and its ions (F^-). A notable charge transfer from 3-hydroxy-2-naphthamide to 2,4-di-tert-butyl-6-methylphenol, was indicated by the FMO analysis. The QTAIM analysis revealed that in the complex compound, the strongest pure hydrogen-hydrogen bonding was observed between H53 and H58, indicated by a ρ value of +0.017807. Due to its selective response, IF-2 can be successfully used for making test strips for the detection of CN^- ions.

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.

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.

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.

Synthesis, Structural, and Intriguing Electronic Properties of Symmetrical Bis-Aryl-α,β-Unsaturated Ketone Derivatives

Three symmetrical bis-aryl-α,β-unsaturated ketone derivatives, 2,6-di((E)-benzylidene)-cyclohexan-1-one (DBC), 2,6-bis((E)-4-chlorobenzylidene)cyclohexan-1-one (BCC), and (1E,1'E,4E,4'E)-5,5'-(1,4-phenylene)bis(2-methyl-1-phenylpenta-1,4-dien-3-one) (PBMP), have been prepared using the aldol condensation approach toward ketones having two enolizable sites. The structures of DBC, BCC, and PBMP have been resolved via spectrometric methods. Moreover, the crystal structure of PBMP is determined by the single-crystal X-ray diffraction (SC-XRD) technique, which revealed that the PBMP molecular assembly is stabilized by the intermolecular C-H···O bonding and C-O···π and weak T-shaped offset π···π stacking interactions. The Hirshfeld surface analysis (HSA) of the PBMP crystal structure was performed as well, and the results were compared with the results of DBC and BCC. The density functional theory (DFT) study results revealed that the longer conjugated molecule of PBMP has smaller but still quite significant HOMO-LUMO gaps compared to the smaller molecules of BCC and DBC. The natural population analysis (NPA) and natural bonding orbital (NBO) analysis were performed. Accordingly, the hydrogen bonding and dipole-dipole interactions stabilize the crystal structures of these compounds. Additionally, the NBO analysis showed numerous high-energy stabilizing interactions for the PBMP compound due to the presence of numerous delocalized and relatively easily polarizable π-electrons, thus implying its significant thermodynamic stability. According to the global reactivity parameter (GRP) analysis, the compounds BCC and DBC are relatively stable in redox processes and have high thermodynamic stability and relatively lower reactivity in general. The molecular electrostatic potential (MEP) analysis results imply potential formation of the intermolecular hydrogen bonding and dispersion interactions, which stabilizes the crystal structures of these compounds.

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.

Synthesis, Characterization, and DFT-Based Electronic and Nonlinear Optical Properties of Methyl 1-(arylsulfonyl)-2-aryl-1H-benzo[d]imidazole-6-carboxylates

Herein, a series of N-1-sulfonyl substituted derivatives of 2-substituted benzimidazoles (2a-2e) were designed and synthesized via structural tailoring of the acceptor part of donor-π-acceptor schemes, and their nonlinear optic (NLO) characteristics were reported. The structures of 2a-2e were investigated and their characterization was accomplished by employing spectroscopic procedures, i.e., UV-vis, FT-IR, and ¹H and ¹³C NMR. Further, a density functional theory (DFT) approach was used to calculate UV-vis, vibrational, and ¹H and ¹³C NMR techniques; frontier molecular orbitals (FMOs); global reactivity parameters (GRPs); natural bond orbitals (NBOs); optical and vibrational analysis; and nonlinear optics (NLO). The most promising results were obtained for 6-nitro-2-(4-nitrophenyl)-1-(4-nitrophenylsulfonyl)-1H-benzo[d]imidazole among entitled compounds, as it exhibited the highest ⟨α⟩ and β_tot values, showing it is an eye-catching NLO material. This DFT study evokes the interest of researchers regarding the development of benzimidazole-based tempting NLO compounds that could be beneficial in modern hi-tech applications.

Influence of End-Capped Modifications in the Nonlinear Optical Amplitude of Nonfullerene-Based Chromophores with a D-π-A Architecture: A DFT/TDDFT Study

Nonlinear optical (NLO) materials have several uses in many fields such as solid physics, biology, medicine, nuclear physics, and material research. Therefore, a series of nonfullerene-based derivatives (CC10D1-CC10D8) with a D-π-A configuration was planned for the NLO investigation using CC10R as the reference molecule with structural alternations at acceptor moieties. Natural bonding orbital (NBO), UV-vis spectra, frontier molecular orbitals (FMOs), global reactivity parameters (GRPs), transition density matrix (TDM), and density of states (DOS) were analyzed using the M06/6-311G(d,p) functional in chloroform solvent to understand the NLO responses of CC10R and CC10D1-CC10D8. The highest occupied molecular orbital (HOMO)-lowest unoccupied molecular orbital (LUMO) band gaps of CC10D1-CC10D6 were illustrated to be lower than that of CC10R, with the larger bathochromic shift (726.408-782.674 nm) resulting in a significant NLO response. Along with the band gap, the FMO method also identified an efficient interfacial charge transfer from D to A moieties via a π-bridge, which was further supported by the DOS and TDM map. Moreover, NBO calculations demonstrated that extended hyperconjugation and strong internal molecular interactions were important in their stabilization. The dipole moment (μ), linear polarizability ⟨α⟩, hyperpolarizability (β_total), and second-order hyperpolarizability (γ_total.) were studied for CC10R and CC10D1-CC10D8. Among all of the derivatives, CC10D2 was proven to be the most appropriate candidate because of its suitable NLO behavior such as being well-supported by a reduced band gap (2.093 eV) and having a suitable maximum absorption wavelength (782.674 nm). Therefore, CC10D2 was reported to have a greater value of first hyperpolarizability (208 659.330 a.u.) compared with other derivatives and CC10R. For the second hyperpolarizability, a greater value was obtained for CC10R (5.855 × 10⁷ a.u.), and its derivatives showed results comparable to that of the parent chromophore for γ_total. This theoretical framework reveals that structural customization with different acceptor units plays a significant role in obtaining attractive NLO materials for optoelectronic applications.