Molecular Structure Optimization Research Articles

Innovative application of green surfactants as eco-friendly scale inhibitors in industrial water systems.

Scale deposition poses significant challenges in various industrial utilities, necessitating the development of eco-friendly scale inhibitors in line with environmental regulations. This study investigates the potential of two natural surfactants, Casein and Rhamnolipid, as innovative inhibitors for calcium carbonate (CaCO3) scale formation, offering an alternative to traditional water treatment chemicals. The anti-scaling characteristics of these two green surfactants were performed using conductivity and electrochemical impedance spectroscopy (EIS) techniques. Additionally, X-ray diffraction (XRD) and scanning electron microscopy (SEM) were utilized to analyze the morphology of CaCO3 crystals and understand the structural changes induced by surfactant interaction. The results revealed that Rhamnolipids significantly outperform Casein in suppressing scale formation, attributed to the adsorption of their multiple functional groups onto scale microcrystals. This adsorption modifies the crystal structure and inhibits further growth. Computational studies were employed to investigate the inhibition mechanism of these surfactants. The spatial and electrical configurations of optimal molecular structures are also analyzed using the Density Functional Theory (DFT) approach. Monte Carlo simulations on the CaCO3 (104) surface demonstrated that Rhamnolipids exhibit superior inhibition compared to Casein, as evidenced by their higher adsorption energy, indicating a more stable binding to the surface. Overall, this research highlights the potential of these natural surfactants as sustainable scale inhibitors, particularly in industries such as food and pharmaceuticals, thereby contributing to environmentally friendly water treatment solutions.

Electron transport and quantum phase transitions in cumulene-connected C80H20 fulleryne: DFT and tight-binding studies

Molecular bridges are opening up exciting new applications in diverse fields, improving the efficiency of conductive inks, enhancing the performance of devices such as organic light-emitting diodes and low-cost solar cells, and advancing the development of highly sensitive sensors, chemical reactions, drug delivery systems, and more. In this paper, we study the electron transport properties of a C80H20 fulleryne (dodecahedryne) connected to two cumulene electrodes. Using density functional theory (DFT), we determine the optimal molecular bridge structure. Based on the IR vibration spectra, different stable phases of the molecular bridge are obtained. The corresponding tight-binding (TB) parameters of the cage are obtained by assigning appropriate values of the length and type of bonds for the fulleryne cage through matching the HOMO-LUMO gap between the DFT calculations and the TB parameters. The electron transport for the desired structures is investigated using the obtained tight-binding parameters and the non-equilibrium Green's function (NEGF) method. Finally, it is concluded that among the five possible configurations for the cumulene-dodecahedryne -cumulene molecular bridge, only one specific configuration—where the electrodes are one edge apart—exhibits metallic behavior, while other positions act as insulators. In addition, the system exhibits quantum phase transitions from metal to semiconductor and from insulator to metal in the presence of critical electric fields. The ability to control quantum phase transitions in these molecular systems can be leveraged to develop qubits for quantum computing. The unique properties can be utilized to design advanced molecular electronic devices.

Application of the Nirmala Index in Nanotechnology: Optimizing Molecular Structures for Advanced Nanomaterials

Abstract Chemical graph theory has played a key role in advancing our understanding of molecular structure by producing degree-based topological indices that forecast crucial physical and chemical properties. In this paper we attempt to investigate this recently defined Nirmala index as an invariant from different topological angles and applications on multiple molecular graph structures such as triangular, double, and alternate quadrilateral snake structures. This index offers a more profound comprehension of the connections between molecular structures and attributes. The next generation of ground breaking developments in nanotechnology, where the creation of devices and functional nanomaterials hinges on molecular interaction at the nanoscale, may also be greatly influenced by this index. These indices have potential applications in drug delivery systems and in molecular structure optimization related to nanotechnological fields like nanophotonics and nanoelectronics. Consequently, the Nirmala index will pave the way for the development of more sophisticated tools for the production of high-performing nanoscale materials.

Three bioactive compounds from Huangqin decoction ameliorate Irinotecan-induced diarrhea via dual-targeting of Escherichia coli and bacterial β-glucuronidase

Irinotecan (CPT-11) is a commonly prescribed chemotherapeutic for the treatment of colon cancer. Unfortunately, acute and delayed diarrhea are prominent side effects of CPT-11 use, and this limits its therapeutic potential. The curative effect of Huangqin decoction (HQD) on chemotherapy-induced diarrhea has been proven. This study investigated the efficacy of the components of HQD (baicalein, baicalin, and paeoniflorin) on CPT-11-induced diarrhea and their underlying mechanisms. Baicalein was found to be the most effective component in improving CPT-11-induced enterotoxicity by intestinal permeability test, ELISA, fluorescence co-localization, and IHC. The combination of baicalin, baicalin and paeoniflorin can obtain similar therapeutic effect to that of HQD. Mendelian randomization analysis, 16 s rRNA sequencing, and fluorescence imaging revealed that baicalein and baicalin significantly inhibited β-glucuronidase (β-GUS) activity. Bacterial abundance analysis and scanning electron microscopy showed that baicalein inhibited the proliferation of Escherichia coli by destroying its cell wall. The molecular dynamics and site-directed mutagenesis results revealed the structural basis for the inhibition of β-GUS by baicalein and baicalin. The results above provide a new idea for the development of drug therapy for adjuvant chemotherapy and theoretical guidance for the optimization of molecular structure targeting β-GUS.Graphical

Quasi-Planar Core Based Spiro-Type Hole-Transporting Material for Dopant-Free Perovskite Solar Cells.

Hole-transporting material (HTMs) are crucial for obtaining the stability and high efficiency of perovskite solar cells (PSCs). However, the current state-of-the-art n-i-p PSCs relied on the use of 2,2',7,7'-tetrakis(N,N-di-p-methoxyphenylamine)-9,9'-spirobifluorene (spiro-OMeTAD) exhibit inferior intrinsic and ambient stability due to the p-dopant and hydrophilic Li-TFSI additive. In this study, a new spiro-type HTM with a critical quasi-planar core (Z-W-03) is developed to improve both the thermal and ambient stability of PSCs. The results suggest that the planar carbazole structure effectively passivates the trap states compared to the triphenylamine with a propeller-like conformation in spiro-OMeTAD. This passivation effect leads to the shallower trap states when the quasi-planar HTMs interact with the Pb-dimer. Consequently, the device using Z-W-03 achieves a higher Voc of 1.178 V compared to the spiro-OMeTAD's 1.155 V, resulting in an enhanced efficiency of 24.02 %. In addition, the double-column π-π stacking of Z-W-03 results in high hole mobility (~10-4 cm2 V-1 s-1) even without p-dopant. Moreover, when the surface interface is modified, the undoped Z-W-03 device can achieve an efficiency of nearly 23 %. Compared to the PSCs using spiro-OMeTAD, those with Z-W-03 exhibit enhanced stability under N2 and ambient conditions. This superior performance is attributed to the quasi-planar core structure and the presence of multiple CH/π and π-π intermolecular stacking in Z-W-03. The multiple CH/π and π-π intermolecular contacts of HTMs can improve the hole hopping transport. Therefore, it is imperative to focus on further molecular structure design and optimization of spiro-type HTMs incorporating quasi-planar cores and carbazole moieties for the commercialization of PSCs.

Quasi‐Planar Core Based Spiro‐Type Hole‐Transporting Material for Dopant‐Free Perovskite Solar Cells

AbstractHole‐transporting material (HTMs) are crucial for obtaining the stability and high efficiency of perovskite solar cells (PSCs). However, the current state‐of‐the‐art n‐i‐p PSCs relied on the use of 2,2′,7,7′‐tetrakis(N,N‐di‐p‐methoxyphenylamine)‐9,9′‐spirobifluorene (spiro‐OMeTAD) exhibit inferior intrinsic and ambient stability due to the p‐dopant and hydrophilic Li‐TFSI additive. In this study, a new spiro‐type HTM with a critical quasi‐planar core (Z‐W‐03) is developed to improve both the thermal and ambient stability of PSCs. The results suggest that the planar carbazole structure effectively passivates the trap states compared to the triphenylamine with a propeller‐like conformation in spiro‐OMeTAD. This passivation effect leads to the shallower trap states when the quasi‐planar HTMs interact with the Pb‐dimer. Consequently, the device using Z‐W‐03 achieves a higher Voc of 1.178 V compared to the spiro‐OMeTAD's 1.155 V, resulting in an enhanced efficiency of 24.02 %. In addition, the double‐column π–π stacking of Z‐W‐03 results in high hole mobility (~10−4 cm2 V−1 s−1) even without p‐dopant. Moreover, when the surface interface is modified, the undoped Z‐W‐03 device can achieve an efficiency of nearly 23 %. Compared to the PSCs using spiro‐OMeTAD, those with Z‐W‐03 exhibit enhanced stability under N2 and ambient conditions. This superior performance is attributed to the quasi‐planar core structure and the presence of multiple CH/π and π–π intermolecular stacking in Z‐W‐03. The multiple CH/π and π–π intermolecular contacts of HTMs can improve the hole hopping transport. Therefore, it is imperative to focus on further molecular structure design and optimization of spiro‐type HTMs incorporating quasi‐planar cores and carbazole moieties for the commercialization of PSCs.

Compressive behaviors and deformation mechanism of carbon fiber reinforced epoxy composites: A microscopic and molecular dynamics perspective

AbstractInvestigating the macro‐mechanical properties of polymer composites at the nanoscale is a challenging topic. Here we report the compressive performances of carbon fiber‐reinforced epoxy composites using molecular dynamics (MD) simulations. The evolution of microstructure and microscopic energy, including free volume, radius of gyration, dihedral angle, potential energy, and interaction energy, has been investigated to characterize compressive behaviors at the nanoscale. Molecular chains gradually bend, and the movement space of the molecular chains decreases during compression loading. The radius of gyration and dihedral angle are deformed into a state of irreversible plasticity to accommodate the microstructural transformation. The interaction energy increases and subsequently declines as the carbon fiber/epoxy interface gets closer, reflecting the transition of the internal structure. The inhomogeneity and interfacial concentration distribution of stress, and local molecular stiffness under various strains have been obtained to reveal the nanoscopic mechanism of epoxy failure and interface delamination during compression. This study reveals the compression behaviors and deformation mechanism of carbon fiber‐reinforced epoxy composites at the molecular level, providing directions and possibilities for molecular design and structural optimization of composites.Highlights The nanoscopic compression deformation of CFRP composites is studied. Microstructural evolution and conformation transformation are revealed. The evolution and transition of microscopic energy are analyzed. Micro‐mechanism of epoxy deformation and interface delamination is elucidated.

Photophysical, cyclic voltammetry, electron paramagnetic resonance, X-ray molecular structure, DFT calculations and molecular docking study of a new Mn(III) metalloporphyrin

Herein, we have presented a new manganese(III) metalloporphyrin, named, based on the X-ray molecular structure, the bis(4-dimethylaminopyridine)[meso‑tetra(para-chlorophenyl)porphyrinato]manganese(III) triflate 1.56 chloroform solvate 0.22 n-hexane solvate 0.22 hydrate with the following formula:[MnIII(TClPP)(DMAP)2](SO3CF3)·0.22(C6H14)·1.56(CHCl3)·0.22(H2O) (complex I).This coordination compound was characterized by FT-IR and cyclic voltammetry. The dichloromethane solution UV/Vis spectrum of I is typical of a Mn(III) high-spin (S = 2) porphyrin complex with a redshifted Soret band with λmax value of 487 nm. The single crystal X-diffraction technique was used to determine the molecular structure of our new Mn(III) bis(DMAP) porphyrin complex. Electron paramagnetic resonance (EPR) spectroscopy confirms that this new 3d4 Mn(III) metalloporphyrin (a non-Kramers system) in solid state is high-spin (S = 2). DFT/TD-DFT calculations on complex I were investigated, including (i) the molecular structure optimization using the DFT/B3LYP-D3/LanL2DZ level of theory, (ii) the frontier molecular orbital calculations and the deduction of the global indices of activities, (iii) the molecular electronic potential analysis (MEP), and (iv) the QTAIM and NCI-RDG analyses. Furthermore, complex I was tested against diverse amino acids of the selected Bcl-2 proteins using docking calculations.

Design of Co-Cured Multi-Component Thermosets with Enhanced Heat Resistance, Toughness, and Processability via a Machine Learning Approach.

Designing heat-resistant thermosets with excellent comprehensive performance has been a long-standing challenge. Co-curing of various high-performance thermosets is an effective strategy, however, the traditional trial-and-error experiments have long research cycles for discovering new materials. Herein, a two-step machine learning (ML) assisted approach is proposed to design heat-resistant co-cured resins composed of polyimide (PI) and silicon-containing arylacetylene (PSA), that is, poly(silicon-alkyne imide) (PSI). First, two ML prediction models are established to evaluate the processability of PIs and their compatibility with PSA. Then, another two ML models are developed to predict the thermal decomposition temperature and flexural strength of the co-cured PSI resins. The optimal molecular structures and compositions of PSI resins are high-throughput screened. The screened PSI resins are experimentally verified to exhibit enhanced heat resistance, toughness, and processability. The research framework established in this work can be generalized to the rational design of other advanced multi-component polymeric materials.

Benzothiazole-derived Compound with Antitumor Activıiy: Molecular Structure Determination Using Density Functional Theory (Dft) Method

The Gaussian computational chemistry software package was employed to investigate the molecular structure and energetics of benzothiazole, a compound known for its anti-tumor properties. Density functional theory (DFT) calculations were conducted using the Becke, 3-parameter, Lee-Yang-Parr (B3LYP) method, coupled with the LanL2DZ basis set. Molecular structure optimization was carried out to determine the most stable configurations of the benzothiazole compound. Furthermore, thorough analyses of molecular orbital energies, molecular properties, and molecular electrostatic potential surface maps were performed on the optimized molecular system. Our current research suggests that the compound 2-(4-aminophenyl) benzothiazole, containing benzothiazole, maybe a potential drug candidate for free radical species on cells due to its anti-cancer properties.

Promoting dissolution behaviors of low-carbon alcohol polyoxyethylene polyoxypropylene ether in CO2 by molecular structure optimization and adding cosolvent

The low-carbon alcohol polyoxyethylene (EO) polyoxypropylene (PO) ether surfactants have significant application potential in CO2 flooding for enhanced oil recovery (EOR) in water-bearing oil reservoirs because of their hydrophilic, lipophilic and CO2-philic properties. The dissolution of surfactants in CO2 is the important precondition for the application of the CO2 + surfactant systems injected in oil reservoir. This work combines experiments and molecular dynamics simulations to reveal the dissolution mechanism of low-carbon alcohol polyethers in CO2 at the molecular level in order to promote the dissolution behaviors of these surfactants. The results show that the appropriate adjustment of the molecular structure can promote the dissolution of surfactants in CO2 by the interactions of dispersion forces, Lewis acid–Lewis base (LA-LB) interactions, and potential hydrogen bonding. The increase of the numbers of EO groups, PO groups, and the length of the alkyl chain all lead to the increase of the cloud point pressure due to the weaken of the dispersion forces and LA-LB interactions between the surfactants and CO2 with the increase of the molecular weight of the surfactants. Under the premise of ensuring sufficient hydrophobic, lipophilic and CO2-philic properties of the surfactant molecules, decreasing the molecular weight as much as possible can increase the dissolution properties. Moreover, the distribution adjustment of EO and PO groups in surfactants has a significant impact on the dissolution behaviors. The molecules, in which EO and PO groups cross-dispersed and the terminal hydroxyl group connected to the EO group, have a better CO2-philicity than those with long EO chains and PO chains. Furthermore, ethanol can reduce the cloud point pressure of surfactants in CO2, because that the presence of ethanol can break the stronger van der Waals forces between CO2 molecules and enhance the LA-LB interactions and hydrogen bonding between the hydroxyl groups and CO2.

Design of SBS molecular structure and contribution mechanism from its soft and hard segments to modify asphalt

ABSTRACT The excellent performance of SBS-modified asphalt is attributed to the release of the SBS soft segment (PB segment) and hard segment (PS segment) in the asphalt. The release mechanism of the SBS soft and hard segments was studied using MD simulation and laboratory experiments, and the optimal molecular structure of SBS for asphalt modification was determined. MD simulation results revealed that SBS with a higher PS content exhibited better compatibility with asphalt. The larger and stronger molecular aggregates of the PS segment improved the high-temperature shear resistance of the asphalt but reduced the molecular mobility of the system. Additionally, radial distribution function analysis showed varying degrees of aggregation behaviour of asphaltenes with different segment ratios of polymer modifiers. Laboratory experiments were conducted using synthetically prepared SBS modifiers with specific segment ratios, as well as PB and PS monomer modifiers. Conventional tests and rheological experiments indicated that the PB segment improved the low-temperature performance of the asphalt, while the PS segment enhanced its high-temperature performance. The experimental results of the Cole–Cole plots are consistent with the solubility parameter results. Morphological studies revealed a two-phase continuous structure formed between SBS and asphalt when the SBS segment ratio was approximately 30/70.

Preparation and Properties of Polycarboxylate Superplasticizer Based on Chain Transfer Agents with Various Structures

AbstractPolycarboxylate superplasticizer (PCE) is a common admixture in high performance concrete. In the design of PCE molecules, it is necessary to ensure that the molecular weight of the polymer is moderate and the acid‐ether ratio is appropriate to improve the dispersion effect. Chain transfer agent is usually added in the synthesis of PCE to adjust the molecular weight and distribution of PCE. In this study, three sulfhydryl chain transfer agents with different electron‐withdrawing groups or electron‐donating groups were synthesized by nucleophilic addition reaction based on sodium hydrosulfide method. Then, three kinds of PCE containing sulfhydryl chain transfer agent were prepared and compared with commercial β‐mercaptopropionic acid superplasticizers. The yield stress and plastic viscosity of cement paste samples prepared by PCE−A and PCE−M were close to those prepared by PCE−P. PCE−M and PCE−A prepared by chain transfer agent with electron‐withdrawing group had higher dispersion and retention capacity than PCE−P, and had more obvious delay of cement hydration and lower hydration accumulated heat. Therefore, the optimization of molecular structure of chain transfer agent is helpful to improve the properties of concrete superplasticizer.

Tuning Reinforcement Learning Parameters for Cluster Selection to Enhance Evolutionary Algorithms.

The ability to find optimal molecular structures with desired properties is a popular challenge, with applications in areas such as drug discovery. Genetic algorithms are a common approach to global minima molecular searches due to their ability to search large regions of the energy landscape and decrease computational time via parallelization. In order to decrease the amount of unstable intermediate structures being produced and increase the overall efficiency of an evolutionary algorithm, clustering was introduced in multiple instances. However, there is little literature detailing the effects of differentiating the selection frequencies between clusters. In order to find a balance between exploration and exploitation in our genetic algorithm, we propose a system of clustering the starting population and choosing clusters for an evolutionary algorithm run via a dynamic probability that is dependent on the fitness of molecules generated by each cluster. We define four parameters, MFavOvrAll-A, MFavClus-B, NoNewFavClus-C, and Select-D, that correspond to a reward for producing the best structure overall, a reward for producing the best structure in its own cluster, a penalty for not producing the best structure, and a penalty based on the selection ratio of the cluster, respectively. A reward increases the probability of a cluster's future selection, while a penalty decreases it. In order to optimize these four parameters, we used a Gaussian distribution to approximate the evolutionary algorithm performance of each cluster and performed a grid search for different parameter combinations. Results show parameter MFavOvrAll-A (rewarding clusters for producing the best structure overall) and parameter Select-D (appearance penalty) have a significantly larger effect than parameters MFavClus-B and NoNewFavClus-C. In order to produce the most successful models, a balance between MFavOvrAll-A and Select-D must be made that reflects the exploitation vs exploration trade-off often seen in reinforcement learning algorithms. Results show that our reinforcement-learning-based method for selecting clusters outperforms an unclustered evolutionary algorithm for quinoline-like structure searches.

A molecular mechanics implementation of the cyclic cluster model

Abstract The implementation of the cyclic cluster model (CCM) for molecular mechanics is presented in the framework of the computational chemistry program deMon2k. Because the CCM is particularly well-suited for the description of periodic systems with defects, it can be used for periodic QM/MM approaches where the non-periodic QM part is treated as a defect in a periodic MM surrounding. To this end, we present here the explicit formulae for the evaluation of the Ewald sum and its first- and second-order derivatives as implemented in deMon2k. The outlined implementation was tested in molecular dynamics (MD) simulations and periodic structure optimization calculations. MD simulations of an argon system were carried out using the Nosé-Hoover chain (NHC) thermostat and the Martyna-Tobias-Klein (MTK) barostat to control the temperature and pressure of the system, respectively. For the validation of CCM structure optimization a set of molecular crystals were optimized using the Ewald method for the evaluation of the electrostatic interactions. Two optimization procedures for the determination of the atomic positions and CCM cell parameters were tested. Our results show that the simultaneous optimization of the atomic positions and cell parameters is most efficient.

Tensor improve equivariant graph neural network for molecular dynamics prediction

Molecular dynamics(MD) simulations are essential for molecular structure optimization, drug-drug interactions, and other fields of drug discovery by simulating the motion of microscopic particles to calculate their macroscopic properties (e.g., energy). The main problems of the existing work are as follows: (1) Failure to fully consider the chemical bonding constraints between atoms, (2) Group equivariance can help achieve robust and accurate predictions of MD under arbitrary reference transformations and should be incorporated into the model design, (3) Tensor information such as relative position, velocity, and torsion angle can be used to enhance the prediction of molecular dynamics. And the existing methods are mainly limited to the scalar domain.In this paper, we propose a new model—tensor improve equivariant graph neural network for molecular dynamics prediction (TEGNN): (1) The model materialization of chemical bond constraints between atoms into geometric constraints. The molecule’s forward kinematic information (position and velocity) is represented by generalized coordinates. In this way, the interatomic chemical bonding constraints are implicitly and naturally encoded in the forward kinematics, (2) The equivariant information transfer is allowed in TEGNN, which significantly improves the accuracy and computational efficiency of the final prediction, (3) TEGNN introduces equivariant locally complete frames into scalar-only equivariant graph neural networks, thus allowing the projection of tensor information of a given order onto the frame. On the N-body dataset of simulated molecular systems consisting of particles, sticks, and hinges, as well as on the real dataset MD17 for molecular dynamics prediction, multiple experiments support that TEGNN is ahead of the current state-of-the-art GNN in terms of prediction accuracy, constraint satisfaction, and data efficiency.We extend the current state-of-the-art equivariant neural network model. The proposed TEGNN accommodates more tensor information and considers the chemical bonding constraints between atoms during motion, ultimately improving the performance of predicting the kinematic states of molecules.

Identification and Mechanistic Analysis of Toxic Degradation Products in the Advanced Oxidation Pathways of Fluoroquinolone Antibiotics.

The degradation of fluoroquinolones (FQs) via advanced oxidation processes (AOPs) is a promising avenue, yet the complete mineralization of certain FQ molecules remains elusive, raising concerns about the formation of toxic by-products. This study delineates five primary AOP degradation pathways for 16 commercially available FQ molecules, inferred from existing literature. Density functional theory (DFT) was employed to calculate the bond dissociation energies within these pathways to elucidate the correlation between bond strength and molecular architecture. Subsequently, Comparative Molecular Similarity Index Analysis (CoMSIA) models were constructed for various degradation reactions, including piperazine ring cleavage, defluorination, hydroxylation, and piperazine ring hydroxylation. Three-dimensional contour maps generated from these models provide a deeper understanding of the interplay between FQ molecular structure and bond dissociation energy. Furthermore, toxicity predictions for 16 FQ molecules and their advanced oxidation intermediates, conducted using VEGA 1.2.3 software, indicate that degradation products from pathways P2 and P5 pose a heightened health risk relative to their parent compounds. Furthermore, the application of the Multwfn program to compute the Fukui function for FQ molecules discerns the disparity in degradation propensities, highlighting that N atoms with higher f0 values can augment the likelihood of piperazine ring cleavage. HOMO-LUMO distribution diagrams further confirm that methoxy substitution at the 1-position leads to a dilution of HOMOs on the piperazine ring and an increased energy gap for free radical reactions, diminishing the reactivity with hydroxyl radicals. This study elucidates the pivotal role of structural characteristics in FQ antibiotics for their degradation efficiency within AOPs and unveils the underlying mechanisms of bond dissociation energy disparities. The toxicity parameter predictions for FQ molecules and their intermediates offer unique perspectives and theoretical underpinnings for mitigating the use of high-risk FQs and for devising targeted degradation strategies to circumvent the generation of toxic intermediates in AOPs through molecular structure optimization.

A materials informatics driven fine-tuning of triazine-based electron-transport layer for organic light-emitting devices

Materials informatics in the development of organic light-emitting diode (OLED) related materials have been performed and exhibited the effectiveness for finding promising compounds with a desired property. However, the molecular structure optimization of the promising compounds through the conventional approach, namely the fine-tuning of molecules, still involves a significant amount of trial and error. This is because it is challenging to endow a single molecule with all the properties required for practical applications. The present work focused on fine-tuning triazine-based electron-transport materials using machine learning (ML) techniques. The prediction models based on localized datasets containing only triazine derivatives showed high prediction accuracy. The descriptors from density functional theory calculations enhanced the prediction of the glass transition temperature. The proposed multistep virtual screening approach extracted the promising triazine derivatives with the coexistence of higher electron mobility and glass transition temperature. Nine selected triazine compounds from 3,670,000 of the initial search space were synthesized and used as the electron transport layer for practical OLED devices. Their observed properties matched the predicted properties, and they enhanced the current efficiency and lifetime of the device. This paper provides a successful model for the ML assisted fine-tuning that effectively accelerates the development of practical materials.

Sensing, Imaging, and Therapeutic Strategies Endowing by Conjugate Polymers for Precision Medicine.

Conjugated polymers (CPs) have promising applications in biomedical fields, such as disease monitoring, real-time imaging diagnosis, and disease treatment. As a promising luminescent material with tunable emission, high brightness and excellent stability, CPs are widely used as fluorescent probes in biological detection and imaging. Rational molecular design and structural optimization have broadened absorption/emission range of CPs, which are more conductive for disease diagnosis and precision therapy. This review provides a comprehensive overview of recent advances in the application of CPs, aiming to elucidate their structural and functional relationships. The fluorescence properties of CPs and the mechanism of detection signal amplification are first discussed, followed by an elucidation of their emerging applications in biological detection. Subsequently, CPs-based imaging systems and therapeutic strategies are illustrated systematically. Finally, recent advancements in utilizing CPs as electroactive materials for bioelectronic devices are also investigated. Moreover, the challenges and outlooks of CPs for precision medicine are discussed. Through this systematic review, it is hoped to highlight the frontier progress of CPs and promote new breakthroughs in fundamental research and clinical transformation.

Ultraviolet response data of liquid crystals for bioprocess monitoring: A methodical exploration

From the homologues series of 2,3-n-dialkyloxy-4-(4-chlorobenzoyloxy)-4′-(4-methylbenzoyloxy)-azobenzene (nOClAB; n = 5, 6, 7, 8) have been chosen for bioprocess monitoring based on their ultraviolet (UV) response. The optimization of molecular structures of these compounds has been performed via functional B3LYP together with 6–31+G (d) basis set. Further, the UV response of these compounds have been analyzed by implementing the configuration interaction singles (CIS) method along with calculation of excited states using semiempirical ZINDO Hamiltonian. The INDO/S+CI approach has been used for comparative analysis. Geometries of four liquid crystals (LCs) in the biosensing, their usefulness, as well as their applications are described. The spectral associated parameters have been summarized. Using this data the UV response mechanism and its implication in analyzing bioprocess monitoring, bio sensing, and bio oscillator applications have been methodically explored. These liquid crystal structures are expected to fulfill the purpose of the processors that can transform the biomolecule signals into the optical signals.