Structure-property Relationships Research Articles

An Exceptional Valorization of CuO Nanoparticles in Ionic Liquids as an Efficient Medium for the Electrophilic Substitution of Indole Towards the Formation of Bis(indolyl)methanes

Aim: Ionic liquids are promising green solvents with simple but unique structure-related physical properties such as negligible vapour pressure, exceptional thermal conductivity, remarkable thermal stability and their suitability and inertness towards a broad range of catalytic applications. CuO NPs have been addressed as a cost-effective and a reagent of a choice that necessitates only mild reaction conditions to offer a high yield of the desired products with exceptional selectivity in a short duration of time. Therefore, in the present work, attempts have been made to explore the catalytic potentials of CuO NPs in an ionic liquid medium to synthesize biologically important bis(indolyl)methanes. Background: Catalytic explorations of metal oxide nanoparticles in ionic liquids offers a cooperative effect that has a significant impact on the kinetics as well as on the outcome of the reaction. Therefore, such catalytic systems in the present times have seized the scientific community's interest from the perspectives of sustainable development in synthetic organic chemistry. The combination of metal oxide nanoparticles with highly tunable ionic liquids is not only used to synthesize simple organic molecules but also explored in the synthesis of complex organic molecules of high commercial and biological relevance. Objectives: The current work offers a rapid and robust protocol for synthesizing bis(indolyl)methanes via electrophilic substitution reaction between indole and various aldehydes in the presence of a CuO nanoparticles-ionic liquid system. The discussion focuses on the high tolerance of different functionalities by the catalytic system leading to the synthesis of bis(indolyl)methanes. Methods: CuO NPs have been synthesized via the co-precipitation method using ionic liquid. The applicability of metal oxide nanoparticles-IL matrix was further investigated in synthesizing bis(indolyl)methanes. Results: The FT-IR absorption below 600 cm-1 and the XRD pattern showing all the peaks in the diffraction diagram revealed the formation of CuO NPs. FESEM images show the flake-shaped morphology of CuO NPs and are found to be separated from the agglomerated clusters Conclusion: Ionic liquid-CuO NPs matrix reveals good to exceptional catalytic properties, and their advancements as a catalytic system at room temperature open new avenues for synthetic organic chemists.

A python based algorithmic approach to optimize sulfonamide drugs via mathematical modeling

This article explores the structural properties of eleven distinct chemical graphs that represent sulfonamide drugs using topological indices by developing python algorithm. To find significant relationships between the topological characteristics of these networks and the characteristics of the associated sulfonamide drugs. We use quantitative structure-property relationship (QSPR) approaches. In order to model and forecast these correlations and provide insights into the structure-activity relationships that are essential for drug design and optimization, linear regression is a vital tool. A thorough framework for comprehending the molecular characteristics and behavior of sulfonamide drugs is provided by the combination of topological indices, graph theory and statistical models which advances the field of pharmaceutical research and development.

Phase Transition Control in Molecular Solids via Complementarity of Hydrogen-Bond Strength.

Phase transitions in molecular solids involve synergistic changes in chemical and electronic structures, leading to diversification in physical and chemical properties. Despite the pivotal role of hydrogen bonds (H-bonds) in many phase-transition materials, it is rare and challenging to chemically regulate the dynamics and to elucidate the structure-property relationship. Here, four high-spin CoIIcom-pounds were isolated and systematically investigated by modifying the ligand terminal groups (X = S, Se) and substituents (Y = Cl, Br).S-ClandSe-Brundergo a reversible structural phase transition near room temperature, triggering the rotation of 15-crown-5 guests and the swing between syn- and anti-conformation of NCX-ligands, accompanied by switchable magnetism. Conversely,S-BrandSe-Clretain stability in ordered and disordered phases, respectively. H-bonds geometric analysis andab initiocalculations reveal that the electronegativity of X and Y affects the strength of NY-ap-H···X interactions. Entropy-driven structural phase transitions occur when the H-bond strength is appropriate; otherwise, the phase stays unchanged if it is too strong or weak. This work highlights a phase transition driven by H-bond strength complementarity - pairing strong acceptor with weak donor andvice versa, which offers a straightforward and effective approach for designing phase-transition molecular solids from a chemical perspective.

Constructing Porous Energetic Spherulites via Solvation-Growth Coupling for Enhanced Combustion.

The fabrication of materials with hierarchical structures has garnered great interest, owing to the potential for significantly enhancing their functions. Herein, a strategy of coupling molecular solvation and crystal growth is presented to fabricate porous spherulites of 2,4,6,8,10,12-hexanitro-2,4,6,8,10,12-hexaazaisowurtzitane (CL-20), an important energetic material. With the addition of polyvinylpyrrolidone in the antisolvent crystallization, the metastable solvate of CL-20 is formed and grows spherulitically, and spontaneously desolvates to obtain the porous spherulite when filtration, in which the characteristic peak of the nitro group of CL-20 shifts detected by the in situ micro-confocal Raman spectroscopy. The effect of polyvinylpyrrolidone is thought to induce the solvation of CL-20, confirmed by density functional theory calculations, meanwhile acting on the (020) face of CL-20 to trigger spherulitic growth, demonstrated through infrared spectroscopy and Rietveld refinement of powder X-ray diffraction. Moreover, compared to common CL-20 crystals, porous spherulites exhibit enhanced combustion with increases of 6.24% in peak pressure, 40.21% in pressurization rate, and 9.63% in pressure duration effect, indicating the capability of hierarchical structures to boost the energy release of energetic crystals. This work demonstrates a new route via solvation-growth coupling to construct hierarchical structures for organic crystals and provides insight into the structure-property relations for material design.

Precise Modulation of CO2 Sorption in Ti8Ce2-Oxo Clusters: Elucidating Lewis Acidity of the Ce Metal Sites and Structural Flexibility.

Investigating the structure-property correlation in porous materials is a fundamental and consistent focus in various scientific domains, especially within sorption research. Metal oxide clusters with capping ligands, characterized by intrinsic cavities formed through specific solid-state packing, demonstrate significant potential as versatile platforms for sorption investigations due to their precisely tunable atomic structures and inherent long-range order. This study presents a series of Ti8Ce2-oxo clusters with subtle variations in coordinated linkers and explores their sorption behavior. Notably, Ti8Ce2-BA (BA denotes benzoic acid) manifests a distinctive two-step profile during the CO2 adsorption, accompanied by a hysteresis loop. This observation marks a new instance within the metal oxide cluster field. Of intrigue, the presence of unsaturated Ce(IV) sites was found to be correlated with the stepped sorption property. Moreover, the introduction of an electrophilic fluorine atom, positioned ortho or para to the benzoic acid, facilitated precise control over gate pressure and stepped sorption quantities. Advanced in situ techniques systematically unraveled the underlying mechanism behind this unique sorption behavior. The findings elucidate that robust Lewis base-acid interactions are established between the CO2 molecules and Ce ions, consequently altering the conformation of coordinated linkers. Conversely, the F atoms primarily contribute to gate pressure variation by influencing the Lewis acidity of the Ce sites. This research advances the understanding in fabricating metal-oxo clusters with structural flexibility and provides profound insights into their host-guest interaction motifs. These insights hold substantial promise across diverse fields and offer valuable guidance for future adsorbent designs grounded in fundamental theories of structure-property relationships.

Correlating semiconductor nanoparticle architecture and applicability for the controlled encoding of luminescent polymer microparticles

Luminophore stained micro- and nanobeads made from organic polymers like polystyrene (PS) are broadly used in the life and material sciences as luminescent reporters, for bead-based assays, sensor arrays, printable barcodes, security inks, and the calibration of fluorescence microscopes and flow cytometers. Initially mostly prepared with organic dyes, meanwhile luminescent core/shell nanoparticles (NPs) like spherical semiconductor quantum dots (QDs) are increasingly employed for bead encoding. This is related to their narrower emission spectra, tuneability of emission color, broad wavelength excitability, and better photostability. However, correlations between particle architecture, morphology, and photoluminescence (PL) of the luminescent nanocrystals used for encoding and the optical properties of the NP-stained beads have been rarely explored. This encouraged us to perform a screening study on the incorporation of different types of luminescent core/shell semiconductor nanocrystals into polymer microparticles (PMPs) by a radical-induced polymerization reaction. Nanocrystals explored include CdSe/CdS QDs of varying CdS shell thickness, a CdSe/ZnS core/shell QD, CdSe/CdS quantum rods (QRs), and CdSe/CdS nanoplatelets (NPLs). Thereby, we focused on the applicability of these NPs for the polymerization synthesis approach used and quantified the preservation of the initial NP luminescence. The spectroscopic characterization of the resulting PMPs revealed the successful staining of the PMPs with luminescent CdSe/CdS QDs and CdSe/CdS NPLs. In contrast, usage of CdSe/CdS QRs and CdSe QDs with a ZnS shell did not yield luminescent PMPs. The results of this study provide new insights into structure–property relationships between NP stained PMPs and the initial luminescent NPs applied for staining and underline the importance of such studies for the performance optimization of NP-stained beads.

On the Comparative Analysis among Topological Indices for Rhombus Silicate and Oxide Structures

A topological index (TI) is a numeric digit that signalizes the whole chemical structure of a molecular network. TIs are helpful in predicting the bioactivity of molecular substances in investigations of quantitative structure-activity relationship (QSAR) and quantitative structure-property relationship (QSPR). TIs correlate various chemical and physical attributes of chemical substances such as melting and freezing point, strain energy, stability, temperature, volume, density, and pressure. There are several distance-based descriptors available in the literature, but connection-based TIs are considered more effective than degree-based TIs in measuring the chemical characteristics of molecular compounds. The present study focuses on computing the connection-based TIs for the most significant type of chemical structures, namely, rhombus silicate and rhombus oxide networks. At the end, we compare these structures on the basis of their computed result.

Low temperature activation of methane to hydrogen depending on tailored electron transfer over Ni–Cr composite oxide

Current industrial use of methane depends mainly on high-temperature conversion, resulting in high energy consumption and catalyst sintering inactivation. In this paper, metal oxide catalysts with different Ni/Cr ratios were obtained by the sol-gel method for the methane activation reaction. The structure-property relationships of the catalysts were investigated by XRD, XPS, H2-TPR, TG, N2 adsorption-desorption, and CV, focusing on the interaction between Ni–Cr in the composite oxide catalysts. The results showed that Ni0–NiCrO3/Cr2O3 structure was formed after the Ni–Cr oxides underwent the reduction and calcination process, which provided a structural support for electron transfer between the two metals. The least prone to sintering nickel metal particles and the highest content of high-valent chromium ions were present in Ni1Cr2. XPS confirmed that this counterpart acts as the donor and acceptor for electron transfer in the catalysts, accelerating oxidation-reduction reaction over the catalyst. The catalyst exhibited the most excellent conversion and product selectivity in the methane activation reaction.

Comparative Analysis of Chemical Descriptors by Machine Learning Reveals Atomistic Insights into Solute-Lipid Interactions.

This study explores the research area of drug solubility in lipid excipients, an area persistently complex despite recent advancements in understanding and predicting solubility based on molecular structure. To this end, this research investigated novel descriptor sets, employing machine learning techniques to understand the determinants governing interactions between solutes and medium-chain triglycerides (MCTs). Quantitative structure-property relationships (QSPR) were constructed on an extended solubility data set comprising 182 experimental values of structurally diverse drug molecules, including both development and marketed drugs to extract meaningful property relationships. Four classes of molecular descriptors, ranging from traditional representations to complex geometrical descriptions, were assessed and compared in terms of their predictive accuracy and interpretability. These include two-dimensional (2D) and three-dimensional (3D) descriptors, Abraham solvation parameters, extended connectivity fingerprints (ECFPs), and the smooth overlap of atomic position (SOAP) descriptor. Through testing three distinct regularized regression algorithms alongside various preprocessing schemes, the SOAP descriptor enabled the construction of a superior performing model in terms of interpretability and accuracy. Its atom-centered characteristics allowed contributions to be estimated at the atomic level, thereby enabling the ranking of prevalent molecular motifs and their influence on drug solubility in MCTs. The performance on a separate test set demonstrated high predictive accuracy (RMSE = 0.50) for 2D and 3D, SOAP, and Abraham Solvation descriptors. The model trained on ECFP4 descriptors resulted in inferior predictive accuracy. Lastly, uncertainty estimations for each model were introduced to assess their applicability domains and provide information on where the models may extrapolate in chemical space and, thus, where more data may be necessary to refine a data-driven approach to predict solubility in MCTs. Overall, the presented approaches further enable computationally informed formulation development by introducing a novel in silico approach for rational drug development and prediction of dose loading in lipids.

Well-Tailored Norbornene-Based Fluorinated Copolymers toward Modulating Icephobicity and Mechanical Robustness.

Well-tailored construction of icephobic surfaces with mechanical robustness and investigation of the structure-property relationships at the molecular level are highly desirable. Herein, a series of norbornene-based fluorinated polyolefin copolymers (FPOR-x) with varying norbornenyl dodecafluoroheptyl ester (NDFHE) molar fractions (0-100 mol %) were well-designed and fabricated via living ring-opening metathesis polymerization (ROMP) employing NDFHE and norbornenyl pentafluorophenyl ester (NPFPE) as the soft and hard segments, respectively. The mechanical and icephobic properties of the fluorinated copolymers can be regulated by adjusting the soft NDFHE contents. As a result, the well-designed norbornene-based copolymers exhibited a wide range of tunable mechanical properties, including tensile strength ranging from 0.2 to 26.4 MPa, elastic modulus ranging from 0.6 to 593.7 MPa, and breaking elongations ranging from 5718.7% to 3.7%, correlating with the proportion of soft NDFHE content. Furthermore, the synergistic interplay between soft and hard segments, particularly the hardness in the majority and softness in the minority or vice versa, could achieve a significant difference in the local modulus and enhance the propagations of cracks within the three-phase regions (soft regions/hard regions/ice), ultimately leading to a significant reduction in ice shear strength. Notably, FPOR-25% with a tensile strength of 12.0 MPa and an elastic modulus of 227.5 MPa exhibited a remarkably low ice shear strength of 57.7 kPa. This study not only highlights the relationship between the polymer molecular structure and surface icephobic properties but also breaks the limitations of icephobic surfaces with a low modulus.

Poly(silyl ether-sulfone-urethane)s: toward preparation of polyurethanes with high glass transition temperature

ABSTRACT This study was based on preparation of a new category of polyurethanes with improved glass transition temperature. Firstly, diphenylsilanediol was reacted with methylene diphenyl diisocyanate and also toluene diisocyanate in 2:1 stoichiometric ratio to prepare two different urethane diols. Then, polycondensation reactions of the obtained diols with 4,4′-dichlorodiphenyl sulfone resulted in preparation of novel poly(silyl ether-sulfone-urethane)s. Spectroscopic techniques and elemental analysis were used to wholly characterize the chemical structure of the monomers and the resulting polymers. Viscosity, solubility, thermal behavior and stability, and crystallinity of the polymers were studied and through that, structure-property relationship of the two polymers was investigated. The prepared poly(silyl ether-sulfone-urethane)s had improved glass transition temperature in comparison to conventional polyurethanes with Tg of 143–165°C. The resulted polymer from methylene diphenyl diisocyanate showed higher glass transition temperature but lower solubility (average solubility in polar solvents was 3.7 vs. 4.0 g/dL) than polymer derived from toluene diisocyanate.

Prediction of the effect of adsorption on the retention of organic compounds by NF/RO using QSPR-ANN

<p>Understanding the retention of organic compounds (OCs) is critical for membrane applications in water recycling. The objective of this study was to create an optimized model using Artificial Neural Networks for Quantitative Structure-Property Relationship (QSPR-ANN) to predict the effect of adsorption on the retention of organic compounds (OCs) by nanofiltration (NF) and reverse osmosis (RO). An optimal model (QSPR-ANNoptimal) characterized by a similar structure (13 neurons in the inputs layer, 11 neurons in the hidden layer, and 1 neuron in the output layer) is constructed to predict the effect of adsorption on the retention of organic compounds by membranes. A set of 273 data points was used to test the neural network. the data set was used 70% for training, 15% for validation, and 15% for testing. For the most promising neural network model, the calculated retention values were compared to the experimental retention values, and good correlations were found (the determination coefficient "R2 = 0.9872" and the root mean squared error "RMSE = 2.2743%" for the test phase). This indicates the good robustness of the established QSPR-ANN model and the possibility of predicting the various parameters that characterize the retention of OCs by RO/NF. Sensitivity analysis revealed that the effect of adsorption retention of organic compounds by reverses osmosis and nanofiltration membranes depends more precisely on two important interactions (hydrophobic/adsorption and steric hindrance).</p>

A review of helicoidal composites: From natural to bio-inspired damage tolerant materials

Helicoidal composites have been found in shrimp club, lobster claw, beetle cuticle, crab shell, scorpion pincer, and fish scale as a natural material. The helicoidal composite possesses excellent impact resistance and extraordinary damage tolerance due to its hierarchical structure and the unique helicoidal arrangement of its reinforcement fibres. Its structure and performance have been studied through various characterisation and mechanical testing methods. Based on the structure-property relationship of the natural helicoidal composite, researchers have been able to mimic the unique fibre arrangement and develop bio-inspired helicoidal composites with enhanced impact performance. Various helicoidal composites comprising of synthetic fibrous materials such as carbon fibre (CF), glass fibre (GF), and aramid fibre, and matrix materials such as thermoset and thermoplastic polymers have been developed through biomimicry. The failure mechanisms of the bio-inspired helicoidal composites have been studied and the advantages of arranging the fibre reinforcement into helicoidal architectures have been elucidated over conventional composite constructions such as quasi-isotropic (QI) and cross-ply layups. This review systematically elaborates the recent progress of the research work on both natural and bio-inspired helicoidal composites. It sheds light on the distinctive construction of the natural helicoidal composites found in different animals such as shrimps, lobsters, crabs, beetles, scorpions, and fish, and their energy absorption mechanisms. Different manufacturing methods for developing bio-inspired helicoidal composites are discussed and various reinforcements and matrix materials used in the composites are described. The processing-structure-property interrelationship of the bio-inspired helicoidal composites is summarised. This review will contribute to the advancement of the knowledge of the natural helicoidal composite and potentially help researchers to develop highly efficient bio-inspired damage tolerant helicoidal composites.

A Multiphysics Thermoelastoviscoplastic Damage Internal State Variable Constitutive Model including Magnetism.

We present a macroscale constitutive model that couples magnetism with thermal, elastic, plastic, and damage effects in an Internal State Variable (ISV) theory. Previous constitutive models did not include an interdependence between the internal magnetic (magnetostriction and magnetic flux) and mechanical fields. Although constitutive models explaining the mechanisms behind mechanical deformations caused by magnetization changes have been presented in the literature, they mainly focus on nanoscale structure-property relations. A fully coupled multiphysics macroscale ISV model presented herein admits lower length scale information from the nanoscale and microscale descriptions of the multiphysics behavior, thus capturing the effects of magnetic field forces with isotropic and anisotropic magnetization terms and moments under thermomechanical deformations. For the first time, this ISV modeling framework internally coheres to the kinematic, thermodynamic, and kinetic relationships of deformation using the evolving ISV histories. For the kinematics, a multiplicative decomposition of deformation gradient is employed including a magnetization term; hence, the Jacobian represents the conservation of mass and conservation of momentum including magnetism. The first and second laws of thermodynamics are used to constrain the appropriate constitutive relations through the Clausius-Duhem inequality. The kinetic framework employs a stress-strain relationship with a flow rule that couples the thermal, mechanical, and magnetic terms. Experimental data from the literature for three different materials (iron, nickel, and cobalt) are used to compare with the model's results showing good correlations.

Transmission Electron Microscopic and X-ray Diffraction Based Study of Crystallographic Bibliography Demonstrated on Silver, Copper and Titanium Nanocrystals: State of the Art Statical Review

This statistical review compares the crystallographic structures of functional nanocrystals composed of silver (Ag), copper (Cu) and titanium (Ti) using transmission electron microscopy (TEM) and X-ray diffraction (XRD) analyses. TEM provides high-resolution imaging to directly visualize individual nanoparticles' size, internal shape and crystallinity. Statistical analysis quantifies variations in lattice parameters, crystal structure, size distributions, phase compositions, lattice strains, preferred orientation and lattice volume of these three crystalline nanomaterials. The review highlights the complementary roles of TEM and XRD in comprehensive Ag, Cu and Ti nanocrystalline materials characterization. The crystallographic functional parameters of Ag were 2θ= 38.1° (111), 44.3° (200) and 64.4° (220); for Cu crystal 43.3° (111), 50.4° (200), 74.1° (220), 89.9° (311) and 95.1° (222) and 35.1° (100), 38.4° (002), 40.2° (101), 53.0° (102), 63.0° (103), 70.7° (110), 76.2° (112), 82.3° (201) demonstrated for Ti nanocrystals. The crystallographic predominant plane or Miller indices were also revealed by selected area electron diffraction (SAED) on TEM. The FCC structure of Ag and Cu is shown in larger lattice volumes compared to the HCP structure of Ti and prefer oriented. The degree of crystallinity of Ti, Cu and Ag nanocrystalline materials was observed at 90.0%, 98.0% and 100.0% respectively. This quantitative comparison provides valuable insights into the structural property relationships in these nanocrystals, enabling rational design strategies for optimizing their performance in various functional applications.

Heterocycle- and Amine-Free Electrochromic and Electrofluorochromic Molecules for Energy-Saving See-Through Smart Windows and Displays.

Electrochromic (EC) smart windows are an elegant alternative to dusty curtains, blinds, and traditional dimming devices. The EC energy storage smart windows and displays received remarkable attention in the optoelectronic industry as they hold promise for high energy efficiency, low power consumption, reversibility, and swift response to stimuli. However, achieving these properties remains challenging. Moreover, most EC molecules do not exhibit electrofluorochromism, which is highly essential for smart displays because its EC property can modulate the solar heat entering the building, and its electrofluorochromic (EFC) aspects can create lighting during the night. In this work, a structure-property relationship is utilized to develop new electrochromes that can store the injected charge, and these molecules indeed exhibit electrofluorochromism. The compounds are synthesized from tetrabenzofluorene with two aromatic acceptor units, and avoids the use of widely studied heterocycles and amine derivatives. The electrochromes switches from yellow to dark hue in solution, solid, and gel state. The compounds display exceptional electrochemical stability and reversibility in 1000 cycles and capacity retention of 93-100% in 300 charging-discharging cycles. The proof-of-concept device fabrication of the self-dimming EC smart window presented here demonstrates that it can furnish visual comfort, modulate transmitted light and glare, and reduce energy usage.

Interfacial compatible PolyMOF membranes as ratiometric fluorescence temperature sensors

Cryogenic temperature sensors play significant roles in manufacturing, metallurgy, and aerospace. Metal-organic frameworks (MOFs) as emerging crystal materials exhibited inherent advantages as fluorescence temperature sensors and researchers have shifted their focus toward incorporating MOFs materials into mixed matrix membranes (MMMs) for broader practical applications. However, the poor interfacial compatibility between MOFs particles and the polymer matrix would cause aggregation-induced fluorescence quenching and reduced energy transfer efficiency, thereby compromising temperature sensing performances. Herein, we adopt a post-synthetic copolymerization strategy to realize an interfacial-compatible membranous ratiometric luminescent thermometer based on the emissions of ligands and lanthanide ions. Benefiting from the controllable covalent cross-linking structures, the designed polyMOF membrane exhibited excellent interfacial compatibility and avoided MOFs irregular morphology agglomeration and phase separation, which contributed to outstanding fluorescence temperature sensing performance with excellent sensitivity ranging from 0.300 to 0.745 %·K−1 within the temperature range of 80–300 K, superior to recent reported MOFs-based thermometers. This research presents a promising approach for the design and fabrication of interfacial compatible MOFs-based membranes with excellent fluorescence temperature sensing properties, advancing the understanding of structure-property relationships and accelerating the practical applications.

Microbubble-encapsulated Cobalt Nitrato Complexes for Ultrasound-triggerable Nitric Oxide Delivery.

Cobalt complexes exhibit versatile reactivity with nitric oxide (NO), enabling their utilization in applications ranging from homogeneous catalysis to NO-based modulation of biological processes. However, the coordination geometry around the cobalt center is complex, the therapeutic window of NO is narrow, and controlled NO delivery is difficult. To better understand the complexation of cobalt with NO, we prepared four cobalt nitrato complexes and present a structure-property relationship for ultrasound-triggerable NO release. We hypothesized that modulation of the coordination geometry by ligand-modification would improve responsiveness to mechanical stimuli, like ultrasound. To enable eventual therapeutic testing, we here first demonstrate the in vitro tolerability of [Co(ethylenediamine)2(NO)(NO3)](NO3) in A431 epidermoid carcinoma cells and J774A.1 murine macrophages,and we subsequently show successful encapsulation of the complex in poly(butyl cyanoacrylate) microbubbles. These hybrid Co-NO-containing microbubbles may in the future aid in ultrasound imaging-guided treatment of NO-responsive vascular pathologies.

From Chemical Drawing to Electronic Properties of Semiconducting Polymers in Bulk: A Tool for Chemical Discovery.

A quantum chemistry (QC)/molecular dynamics (MD) scheme is developed to calculate electronic properties of semiconducting polymers in three steps: (i) constructing the polymer force field through a unified workflow, (ii) equilibrating polymer models, and (iii) calculating electronic structure properties (e.g., density of states and localization length) from the equilibrated models by QC approaches. Notably, as the second step of this scheme is generally the most time-consuming one, we introduce an alternative method to compute thermally averaged electronic properties in bulk, based on the simulation of a polymer chain in the solution of its repeat units, which is shown to reproduce the microstructure of polymer chains and their electrostatic effect (successfully tested for five benchmark polymers) 10 times faster than state-of-the-art methods. In fact, this scheme offers a consistent and speedy way of estimating electronic properties of polymers from their chemical drawings, thus ensuring the availability of a homogeneous set of simulations to derive structure-property relationships and material design principles. As an example, we show how the electrostatic effect of the polymer chain environment can disturb the localized electronic states at the band tails and how this effect is more significant in the case of diketopyrrolopyrrole polymers as compared to indacenodithiophene and dithiopheneindenofluorene ones.

Advanced microstructural characterization in high-strength steels via machine learning-enhanced high-speed nanoindentation and EBSD mapping

This research investigates the nanoscopic features of Advanced High-Strength Steels (AHSS) through a bottom-up approach employing high-speed nanoindentation mapping (HSNM) to elucidate structure-property relationships. The influence of grain boundaries on nanomechanical properties was documented, highlighting the challenge of SEM-EBSD analysis in differentiating phases with identical crystal structures (BCC, FCC, etc.). Integrating SEM-EBSD with HSNM in the same region of interest is essential for detailed insights into phase/microstructure distribution and accurate grain boundary identification. A modular four-step analysis protocol, designed and validated on ferritic-bainitic TRIP steels (TBF), leverages machine learning-enhanced HSNM for significant advancements in AHSS design. The initial phase involves the application of the expectation-maximization algorithm for probability distribution fitting of HSNM data, deriving primary mechanical phase statistics. This exclusively facilitates the correlation of elastic modulus and hardness for each phase/microstructure using nanoindentation data. Further refinement of phase/microstructure to mechanical property correlations was achieved through a supervised machine learning approach, ensuring precise association between EBSD and nanoindentation data. This includes detailed image analysis and clustering of nanoindentation data, enhancing the precision in phase recognition. This methodology addresses the critical challenges in developing 3rd Generation AHSS, aiming to fill the gap in accurately identifying and quantifying phases such as martensite, austenite, bainite, and ferrite, thereby reducing classification and measurement uncertainties. The approach contributes to the fundamental understanding of AHSS microstructures and provides a scalable framework for the comprehensive characterization of structural materials.