Structure-property Relationships Research Articles

Structural-property relationship in Pt(N^{NO})Cl: The effect of hydrogenating the Schiff base ligand on spectral, biomolecule-binding and anticancer properties

Structural-property relationship in Pt(N^{NO})Cl: The effect of hydrogenating the Schiff base ligand on spectral, biomolecule-binding and anticancer properties

Accelerating the identification of stable configurations in mixed-anion perovskite materials

Mixed-anion perovskite materials exhibit tunable properties, such as band gaps, stability, and charge transport, by modulating the composition and arrangement of the anions. This tunability enables a wide range of applications in fields such as optoelectronics, catalysis, and energy storage. The structural diversity enriches the material properties and enhances performance; however, it also poses a significant challenge in determining stable structures. The stability of such alloy materials depends not only on the elemental composition but also on the arrangement of X/Y elements within the lattice. To improve the understanding of the structure–property relationship in these alloy systems, a thorough exploration of the vast compositional and configurational space is essential. Herein, we integrate the cluster expansion (CE) method with the atom classification model (ACM) to efficiently pre-screen candidate structures and identify stable configurations. By considering the arrangement of anions, which exhibit short-range ordering within octahedra and randomness between octahedra, we have designed correlation functions for the ACM to reasonably reflect these characteristics. This approach enabled us to identify configurations of BaTa(O1−xNx)3, RbPb(F1−xOx)3 and CsPb(Br1−xClx)3 with higher stabilities without significantly increasing computational costs.

Systematic evaluation of sampling rate influences and variability in POCIS using meta-analysis and quantitative structure property relationship (QSPR).

Despite the significant benefits of aquatic passive sampling (low detection limits and time-weighted average concentrations), the use of passive samplers is impeded by uncertainties, particularly concerning the accuracy of sampling rates. This study employed a systematic evaluation approach based on the combination of meta-analysis and quantitative structure-property relationships (QSPR) models to address these issues. A comprehensive meta-analysis based on extensive data from 298 studies on the Polar Organic Chemical Integrative Sampler (POCIS) identified essential configuration parameters, including the receiving phase (type, mass) and the diffusion-limiting membrane (type, thickness, pore size), as key factors influencing uptake kinetic parameters. The incomplete availability of these details across studies potentially impacts data reproducibility and comparability. The subsequent meta-regression and subgroup analysis were performed to reveal the most significant factors contributing to sampling rate variability and inter-study heterogeneity. The flow rate and octanol-water partitioning (Kow or pH-dependent Dow) were identified from all environmental factors and chemical properties. Furthermore, the impact of chemical properties on the sampling rates of POCIS was predicted by Quantitative Structure-Property Relationship (QSPR) models using 2D descriptors and random forest regression. The analysis highlighted that the electrotopological state and molecular mass are the most important chemical properties influencing the sampling rate. This study systematically unraveled the most important impact factors on reliable estimates of passive sampling rates, and these causes of uncertainty should be further considered in aquatic monitoring and assessment with passive samplers.

Zwitterionic Hydrogels: From Synthetic Design to Biomedical Applications.

Zwitterionic hydrogels have emerged as a highly promising class of biomaterials, attracting considerable attention due to their unique properties and diverse biomedical applications. Zwitterionic moieties, with their balanced positive and negative charges, endow hydrogels with exceptional hydration, resistance to nonspecific protein adsorption, and low immunogenicity due to their distinctive molecular structure. These properties facilitate various biomedical applications, such as medical device coatings, tissue engineering, drug delivery, and biosensing. This review explores the structure-property relationships in zwitterionic hydrogels, highlighting recent advances in their design principles, synthesis methods, structural characteristics, and biomedical applications. To meet the evolving and growing demand for the biomedical field, this review examines current challenges and explores future research directions for optimizing the multifunctional properties of zwitterionic hydrogels. As promising candidates for advanced biomaterials, zwitterionic hydrogels are poised to address critical challenges in biomedical applications, paving the way for improved therapeutic outcomes and broader applicability in healthcare.

A Bis-perylene Diimide Macrocycle Chiroptical Switch.

Helical assemblies of organic dyes are ubiquitous chiral organic materials, with valuable properties including chiroptical switching due to the dynamic nature of supramolecular chirality. Herein, we report a novel chiral bis-perylene diimide macrocycle, which acts as a discrete molecular model for a chiral supramolecular assembly. Point chirality is installed through amino acid-derived imide groups which, upon macrocyclization, is translated into helical chirality in the perylene diimide dimer. In solution, the macrocycle's chiroptical properties are switchable, with both the sign (+/-) and amplitude (on/off) of the signal tuned using solvent and molecular recognition stimuli respectively. The chiral structure-property relationships identified from this macrocycle are important for the design of high fidelity supramolecular chiroptical switches.

4-Aminoquinoline as a privileged scaffold for the design of leishmanicidal agents: structure–property relationships and key biological targets

Leishmaniasis is one of the most important neglected tropical diseases, with more than two million new cases annually. It is endemic in several regions worldwide, representing a public health problem for more than 88 countries, in particular in the tropical and subtropical regions of developing countries. At the moment, there are neither approved vaccines nor effective drugs for the treatment of human leishmaniasis for any of its three typical clinical manifestations, and, importantly, the drugs of clinical use have several side effects, require complex administration regimens, present high cost, and are ineffective in many populations due to pathogen resistance. Moreover, beyond the pharmacological exigencies, there are other challenges concerning its parasitic nature, such as its great genetic plasticity and adaptability, enabling it to activate a battery of genes to develop resistance quickly. All these aspects demand the identification and development of new, safe, and effective chemical systems, which must not only be focused on medicinal chemistry and pharmacological aspects but also consider key aspects relative to parasite survival.In this sense, the quinolines and, in particular, 4-aminoquinoline, represent a privileged scaffold for the design of potential leishmanicidal candidates due not only to their versatility to generate highly active and selective compounds but also to their correlation with well-defined biological targets. These facts make it possible to generate safe leishmanicidal agents targeted at key aspects of parasite survival.The current review summarizes the most current examples of leishmanicidal agents based on 4-aminoquinolines focusing the analysis on two essential aspects: (i) structure–property relationship to identify the key pharmacophores and (ii) mode of action focused on key targets in parasite survival (e.g., depolarization of potential mitochondrial, accumulation into macrophage lysosome, and immunostimulation of host cells). With that information, we seek to give useful guidelines for interested researchers to face the drug discovery and development process for selective and potent leishmanicidal agents based on 4-aminoquinolines.

Influence of temperature and layer height on the structural and mechanical properties of continuous biocomposites by in–nozzle impregnation additive manufacturing

Purpose This study aims to assess the relationships between limit parametric settings of in-nozzle impregnation additive manufacturing, namely, nozzle temperature and layer height, on the micromorphology and induced mechanical properties of continuous flax yarns-reinforced biocomposites. Design/methodology/approach The additively manufactured biocomposites with different printing parameters were characterized by X-ray microcomputed tomography and tensile testing to link the process–structure–properties relationships regarding the internal morphologies of yarns, matrix and porosities and tensile properties. Findings Several types of morphology were defined regarding fiber, void, raster and interfaces. The results showed a competition between porosity development, coating effect and variation in fiber volume fraction on the biocomposite quality and mechanical performance when simultaneously varying the layer height and the temperature due to rheology-related phenomena and process-induced defects. Originality/value To the best of the authors’ knowledge, no previous study has been carried out on the relation between the internal micromorphologies in three directions of continuous biocomposites manufactured by in-nozzle impregnation additive manufacturing and the limit printing parameters. The findings are thought to help manufacturers master this technology for high-end applications.

Comparative study of degree, neighborhood and reverse degree based indices for drugs used in lung cancer treatment through QSPR analysis

Quantitative structure-property relationship (QSPR) modeling has emerged as a pivotal tool in the field of medicinal chemistry and drug design, offering a predictive framework for understanding the correlation between chemical structure and physicochemical properties. Topological indices are mathematical descriptors derived from the molecular graphs that capture structural features and connectivity, playing a crucial role in QSPR analysis by quantitatively relating chemical structures to their physicochemical properties and biological activities. Lung cancer is characterized by its aggressive nature and late-stage diagnosis, often limiting treatment options and significantly impacting patient survival rates. This study focuses on the selection of drugs used to treat lung cancer, including dacomitinib, selpercatinib, tepotinib, trametinib, sotorasib, etoposide, alectinib, paclitaxel, dabrafenib, entrectinib, crizotinib, ceritinib, lorlatinib, afatinib, pralsetinib, brigatinib, erlotinib, adagrasib, gefitinib, vinorelbine, gemcitabine, docetaxel, and pemetrexed. Using molecular structural measures such as degree, neighborhood degree sum, and modified reverse degree, we have developed QSPR models to predict physicochemical properties through the topological indices derived from these structural measures. We then conducted a comparative analysis, incorporating correlation analysis, to identify the model with the highest predictive accuracy.

Morphological Control of Y6 Thin Films Reveals Charge Transfer Is Facilitated by Co-facial Interactions.

The organic semiconductor Y6 has been extensively used as an acceptor in organic photovoltaic devices, yielding high efficiencies. Its unique properties include a high refractive index, intrinsic exciton dissociation, and barrierless charge generation in bulk heterojunctions. However, the direct impact of the crystal packing morphology on the photophysics of Y6 has remained elusive, hindering further development of heterojunction and homojunction devices. Herein, we study the photogenerated species in multiple distinct Y6 crystal packing geometries via transient absorption spectroscopy and photovoltaic measurements of the corresponding single-component devices. Our results reveal that "co-facial" interactions drive the generation of charge-transfer states in neat films of Y6 and that exciton dissociation can be switched on and off by controlling these interactions. Additionally, we find that a combination of long-range order and more co-facial packing interactions accelerates the charge-transfer generation process and increases the exciton to charge-transfer conversion efficiency. These insights provide valuable structure-property relationships for optimizing device performance.

Time-resolved Brownian tomography of single nanocrystals in liquid during oxidative etching

Colloidal nanocrystals inherently undergo structural changes during chemical reactions. The robust structure-property relationships, originating from their nanoscale dimensions, underscore the significance of comprehending the dynamic structural behavior of nanocrystals in reactive chemical media. Moreover, the complexity and heterogeneity inherent in their atomic structures require tracking of structural transitions in individual nanocrystals at three-dimensional (3D) atomic resolution. In this study, we introduce the method of time-resolved Brownian tomography to investigate the temporal evolution of the 3D atomic structures of individual nanocrystals in solution. The methodology is applied to examine the atomic-level structural transformations of Pt nanocrystals during oxidative etching. The time-resolved 3D atomic maps reveal the structural evolution of dissolving Pt nanocrystals, transitioning from a crystalline to a disordered structure. Our study demonstrates the emergence of a phase at the nanometer length scale that has received less attention in bulk thermodynamics.

PFSA‐Ionomer Dispersions to Thin‐Films: Interplay Between Sidechain Chemistry and Dispersion Solvent

AbstractIn fuel‐cell catalyst layers (CLs), ionomers exists as nanoscale thin‐films binding catalyst particles, wherein ionic and gaseous species transport to catalyst sites. To improve film function, ionomers have been designed based on the prototypical perfluorosulfonic acid (PFSA). Since CLs are fabricated through solution‐based processing of inks, understanding how ink/dispersion solvent impacts ionomer interactions and function is important to guide future ionomer design. Herein, PFSA and its chemical derivatives are characterized from dispersion (in solvent) to thin film (cast on support) to investigate the impact of solvent water content on ionomer dispersion (structure, acidity) and resulting film properties. Dispersion characterization reveals that secondary aggregate structure depends on the sidechain chemistry, but all PFSA‐based ionomers evolve similarly as dispersion becomes water‐rich. The differences in aggregation alter ionomer adsorption and thin film morphology. Hydrophilic domain spacing and orientation are affected primarily by sidechain chemistry, and modulated by solvent composition. Thin‐film conductivity correlates with hydration and nanodomain alignment, signifying the role of morphology in properties. Overall, this work provides insights into how chemistry can be used to coarse‐tune structure–property relationships, while processing solvent is for fine‐tuning of dispersion‐film interplay for controlling CL‐ionomer function, which can be translated to other chemistries and ink formulations.

Unravelling structure–property relationships in polyfluorene derivatives for optoelectronic advancements: a review

Unravelling structure–property relationships in polyfluorene derivatives for optoelectronic advancements: a review

Elucidating Mesostructural Effects on Thermal Conductivity for Enhanced Insulation Applications.

Thermal management is a key link in improving energy utilization and preparing insulation materials with excellent performance is the core technological issue. Complex and irregular pore structures of insulation materials hinder the exploration of structure-property relationships and the further promotion of material performance. Ordered mesoporous silica (OMS) is a kind of porous material with ordered frameworks. This work elucidates the effects of ordered porous architecture on the thermal conductivity of mesoporous silica. Herein, two typical OMS, SBA-15 and SBA-16, characterized by well-defined porous structures with distinct spatial orientations are synthesized to study the relevance between structure and thermal conductivity. Compared to the 3D cubic mesoporous structure of SBA-16, the 2D hexagonal structure of SBA-15 exhibits anisotropic effects that restrict both solid and gaseous conduction, thereby providing better thermal insulating. Due to the influence of porosity, the thermal conductivity is found to decrease strongly with increasing pore size and decreasing wall thickness. Moreover, OMS composite aerogels with outstanding thermal insulation, mechanical performance, and hydrophobicity are fabricated through incorporating OMS into cellulose nanofibers (CNF). Consequently, this work contributes to a deeper understanding of heat transfer in OMS and provides an idea for designing OMS-based composite materials, thereby advancing their potential applications.

Comparative study of degree and neighborhood degree sum-based topological indices for predicting physicochemical properties of skin cancer drug structures

Quantitative structure property relationship (QSPR) is essential in rational drug design by facilitating the prediction, optimization, and prioritization of potential drug candidates based on their chemical structures and properties, optimizing the utilization of computational resources and minimizing costs. Topological indices play a fundamental role in QSPR studies by providing a quantitative representation of molecular structures and facilitating the prediction of various chemical properties and activities. Metastatic skin cancer can be aggressive and potentially fatal, and thus developing effective drugs can improve patient outcomes, including overall survival rates and progression-free survival. This research work focuses on the structural behaviors of medications used in the treatment of skin cancer, including binimetinib, fisetin, encorafenib, picato, fluorouracil, trametinib, vemurafenib, imiquimod, odomzo, vismodegib, dacarbazine, cobimetinib, dabrafenib, sesamol, curcumin, doxorubicin, temozolomide, paclitaxel, itraconazole, and hyaluronan. We have developed QSPR models involving degree and neighborhood degree sum-based indices and conducted a comparative analysis to highlight the efficacy of the models.

Advanced Rheological, Dynamic Mechanical and Thermal Characterization of Phase-Separation Behavior of PLA/PCL Blends

This research presents a comprehensive investigation of PLA/PCL polymer blends using advanced rheological characterization, scanning electron microscopy (SEM), differential scanning calorimetry (DSC), and dynamic mechanical, thermal analysis (DMTA) to evaluate phase-separation behavior and functional properties. Polymer composites with various PLA/PCL ratios were fabricated via melt extrusion, a sustainable and scalable approach. The rheological studies revealed significant insights into the blends’ viscoelastic behavior, while SEM analyses provided detailed observations of microstructural phase separation. Thermal transitions and crystallization behaviors were evaluated through DSC, and the dynamic mechanical properties were examined via DMTA. The results confirmed that the tailored PLA/PCL blends exhibit properties suitable for advanced additive manufacturing (AM) and shape memory applications, merging flexibility and environmental sustainability. This study emphasizes the novelty of integrating multidisciplinary characterization methods to unravel the structure–property relationships in PLA/PCL systems. By addressing modern demands for eco-friendly, high-performance materials, this work establishes a foundation for the development of innovative polymer composites with potential applications in smart and responsive technologies.

Synthesis and Characterization of Lipid-Polyzwitterion Diblock Copolymers for Optimizing Micelle Formation to Enhance Anticancer Drug Delivery in 2D and 3D Cell Cultures.

Amphiphilic polymers with distinct polarity differences, known as sharp polarity contrast polymers (SPCPs), have gained much attention for their ability to form micelles with low critical micelle concentrations (CMCs) and potential in anticancer drug delivery. This study addresses the limited research on structure-property relationships of SPCPs by developing various SPCPs and exploring their physicochemical properties and biological applications. Specifically, the superhydrophobic aliphatic palmitoyl (Pal) was coupled to the superhydrophilic zwitterionic poly(2-methacryloyloxyethyl phosphorylcholine) (pMPC) to form Pal-pMPC diblock copolymers. Adjusting the lengths of hydrophilic chains allowed the creation of structures with varying hydrophilic-hydrophobic ratios for micelle formation. Comprehensive evaluations were carried out, including particle size, CMC, chain exchange rates, cellular uptake efficiency, and anticancer effectiveness. Our findings indicate that micelles with optimal hydrophilic-hydrophobic ratios significantly enhanced cellular uptake and cytotoxicity in both two-dimensional (2D) and three-dimensional (3D) tumor models, offering valuable insights for designing SPCPs for anticancer drug delivery.

Rational Design Two- or Four-Electron Reaction Pathway Covalent Organic Frameworks for Efficient and Selective Electrocatalytic Hydrogen Peroxide Production.

Covalent organic frameworks (COFs) are often employed in oxygen reduction reactions (ORR) for hydrogen peroxide production due to their tunable structures and compositions. However, COF electrocatalysts require precise structural engineering, such as heteroatoms or metal site doping, to modulate the reaction pathway during the ORR process. In this work, we designed a tetraphenyl-p-phenylenediamine based COF electrocatalyst, namely TPDA-BDA, which exhibited excellent two-electron (2e) ORR performance with high H2O2 selectivity of 89.7% and faraday efficiency (FE) of 86.7%, higher than the reported COFs to date for H2O2 electrosynthesis. The theoretical and experimental results showed that the rate-determining step energy barrier for reduction of O2 to OOH* intermediates was significantly reduced by replacing of bipyridine with biphenyl blocks, changing from 4e to 2e ORR reaction pathway. Also, the donor-acceptor characteristic and narrower optical band gap of TPDA-BDA COF enhanced the electronic conductivity and reduction ability, thus elevating the catalytic activity. As a result, the H2O2 selectivity was maintained above 85% even after 50 h stability test. This work reveals the structure-property relationship of COF electrocatalysts and provides a new strategy for rational design of high performance 2e ORR COF electrocatalysts for efficient and selective hydrogen peroxide production.

Structural Isomerism of {Ag14}10+ Nanocluster Encapsulated by Bowl-like Polyoxometalates.

The structural isomerism of atomically precise nanoclusters provides a preeminent theoretical model to investigate the structure-property relationships. Herein, we synthesized three bowl-like polyoxometalate (POM)-encapsulated Ag nanoclusters (denoted as {Ag14(Sb3W30)2}-1, {Ag14(Sb3W30)2}-1a, and {Ag14(Sb3W30)2}-2) via a facile one-pot solvothermal approach. Among them, for the first time, an unprecedented isomeric {Ag14}10+ nanoclusters are obtained in polyoxoanions {Ag14(Sb3W30)2}-1 and {Ag14(Sb3W30)2}-2, which should be probably induced by the different distribution of coordinating O atoms in two isomeric bowl-like {Sb3W30} ligands. Clusters {Ag14(Sb3W30)2}-1 and {Ag14(Sb3W30)2}-2 exhibit distinct electronic structures and physicochemical properties due to the different geometric structures of the {Ag14}10+ nanoclusters, but both clusters can effectively catalyze the visible-light-driven hydrogen evolution with over 22,000 turnovers (TONs) after 6-hour photocatalysis.

Feature engineering descriptors, transforms, and machine learning for grain boundaries and variable-sized atom clusters

Obtaining microscopic structure-property relationships for grain boundaries is challenging due to their complex atomic structures. Recent efforts use machine learning to derive these relationships, but the way the atomic grain boundary structure is represented can have a significant impact on the predictions. Key steps for property prediction common to grain boundaries and other variable-sized atom clustered structures include: (1) describing the atomic structure as a feature matrix, (2) transforming the variable-sized feature matrix to a fixed length common to all structures, and (3) applying a machine learning algorithm to predict properties from the transformed matrices. We examine how these steps and different combinations of engineered features impact the accuracy of grain boundary energy predictions using a database of over 7000 grain boundaries. Additionally, we assess how different engineered features support interpretability, offering insights into the physics of the structure-property relationships.

Universal Phase Identification of Block Copolymers From Physics‐Informed Machine Learning

ABSTRACTBlock copolymers play a vital role in materials science due to their diverse self‐assembly behavior. Traditionally, exploring the block copolymer self‐assembly and associated structure–property relationships involve iterative synthesis, characterization, and theory, which is labor‐intensive both experimentally and computationally. Here, we introduce a versatile, high‐throughput workflow toward materials discovery that integrates controlled polymerization and automated chromatographic separation with a novel physics‐informed machine‐learning algorithm for the rapid analysis of small‐angle X‐ray scattering data. Leveraging the expansive and high‐quality experimental data sets generated by fractionating polymers using automated chromatography, this machine‐learning method effectively reduces data dimensionality by extracting chemical‐independent features from SAXS data. This new approach allows for the rapid and accurate prediction of morphologies without repetitive and time‐consuming manual analysis, achieving out‐of‐sample predictive accuracy of around 95% for both novel and existing materials in the training data set. By focusing on a subset of samples with large predictive uncertainty, only a small fraction of the samples needs to be inspected to further improve accuracy. Collectively, the synergistic combination of controlled synthesis, automated chromatography, and data‐driven analysis creates a powerful workflow that markedly expedites the discovery of structure–property relationships in advanced soft materials.