Structure-property Relationships Research Articles

Octahedral vs Tiara-like Pd6(SR)12 Clusters.

Atomically precise Pd-thiolate clusters are well-known for their well-defined structures and diverse applications involving catalysis, sensors, and biomedicine. While many of these clusters have been studied, their molecular structures typically feature a tiara-like arrangement. In this study, we present the first example of a non-tiara-like Pd-thiolate cluster: the octahedral Pd6(SC6H11)12 (denoted as Pd6-Oct). The composition and geometric structure of the cluster were characterized using electrospray ionization mass spectrometry (ESI-MS) together with single-crystal X-ray diffraction (SXRD). Despite having a similar chemical composition to tiara-like Pd6(SC2H4Ph)12 (denoted as Pd6-Tia), Pd6-Oct exhibits a distinctly different geometric structure. Additionally, UV-vis-NIR absorption spectroscopy combined with quantum chemical calculations provided valuable insights into the electronic structures of these clusters. The excited-state dynamics, host-guest chemistry, and the catalytic properties of Pd6-Oct and Pd6-Tia were examined to compare their structure-property relationships. This research represents significant advances in the synthesis and understanding of structure-property correlations in Pd-thiolate clusters.

Compatibility analysis of waste polymer recycling in asphalt binder using molecular descriptor and graph neural network

Recycling of waste polymers for valuable use is important for circular economy and environmental sustainability. This study introduces a novel approach to evaluating the compatibility between waste polymers and asphalt binders using advanced molecular representation models. The solubility parameters of waste polymers were predicted using traditional machine learning (ML) models and geometry-enhanced graph neural network (GeoGNN), respectively. The compatibility index was then calculated based on the absolute difference between the solubility parameters of polymers and asphalt. Results indicate that GeoGNN outperforms traditional ML and other GNN models due to its superior ability to capture complex spatial structures. The study also identifies key molecular descriptors that significantly influence solubility parameters of waste polymers. Given the variability in asphalt binder composition, the most compatible waste polymers differ across binders, making the data-driven approach especially valuable. The GeoGNN model greatly enhances the ability to assess compatibility in the polymer-asphalt system. This complements experimental techniques by integrating geometric information to analyze molecular features uncovering the structure-property relationship of material.

Metal-phenolic network composites: from fundamentals to applications.

Composites with tailored compositions and functions have attracted widespread scientific and industrial interest. Metal-phenolic networks (MPNs), which are composed of phenolic ligands and metal ions, are amorphous adhesive coordination polymers that have been combined with various functional components to create composites with potential in chemistry, biology, and materials science. This review aims to provide a comprehensive summary of both fundamental knowledge and advancements in the field of MPN composites. The advantages of amorphous MPNs, over crystalline metal-organic frameworks, for fabricating composites are highlighted, including their mild synthesis, diverse interactions, and numerous intrinsic functionalities. The formation mechanisms and state-of-the-art synthesis strategies of MPN composites are summarized to guide their rational design. Subsequently, a detailed overview of the chemical interactions and structure-property relationships of composites based on different functional components (e.g., small molecules, polymers, biomacromolecules) is provided. Finally, perspectives are offered on the current challenges and future directions of MPN composites. This tutorial review is expected to serve as a fundamental guide for researchers in the field of metal-organic materials and to provide insights and avenues to enhance the performance of existing functional materials in applications across diverse fields.

Emergent Research Trends on the Structural Relaxation Dynamics of Molecular Clusters: From Structure-Property Relationship to New Function Prediction.

ConspectusMolecular clusters (MCs) are monodispersed, precisely defined ensembles of atom collections featured with shape-persistent architectures that can deliver certain functions independently. Their molecular compositions and surface functionalities can be tailored feasibly in a predefined manner, and they can be applied as basic structural units to be engineered into materials with desirable hierarchical structures and enriched functions. The chemical systems also offer great opportunities for the design and fabrication of soft structural materials without the chain topologies of polymers. The bulks of MC assemblies demonstrate viscoelasticity that is used to be considered as the unique feature of polymers, while the MC systems are distinct from polymers since their elasticities are resilient even at temperatures 100 K above their glass transition temperatures. The understanding of their anomalous viscoelasticity and the extended studies of general structure-property relationships are desired for the development of new chemical systems for emergent functions and the possibilities to resolve the intrinsic trade-offs of traditional materials.Meanwhile, general macroscopic functions or properties of materials are related to the transportation of mass, momentum, and/or energy, and they are basically realized or directed by the motions of structural units at different length scales. Structural relaxation dynamics research is critical in quantifying motions ranging from fast bond deformation, bond break/formation, and diffusion of ions and particles to the cooperative motions of structure units. Due to the advancement of measurement technology for relaxation dynamics (e.g., quasi-elastic scattering and broadband dielectric spectroscopy), the structural relaxation dynamics of MC materials have been probed for the first time, and their multiple relaxation modes across several temporal scales were systematically studied to bridge the correlation between molecular structures and macroscopic functions. The fingerprint information from dynamics studies, e.g., the temperature dependence of relaxation time and certain property, e.g., ion conductivity, was proposed to quantify the structure-property relationship, and the microscopic mechanism on the mechanical properties, ion conduction, and gas absorption and separation of MC materials can be fully understood.In this Account, to elucidate the uniqueness of MC materials, especially in comparison with polymers, four topics are mainly summarized: structural features, relaxation dynamics characterization techniques, relaxation dynamics characteristics, and quantified understanding of the structure-property relationship. The capability for new function prediction from relaxation dynamics studies is also introduced, and the typical example in impact resistant materials is provided. The Account aims to prove the significance of relaxation dynamics characterization for material innovation, while it also confirms the potential of MCs for functional material fabrications.

Digital Mechanical Metamaterial with Programmable Functionality.

Digitization has brought a new era to the world, liberating information from physical media. The material structure-property relation is high-dimensional and nonlinear, and the digitization of structure-property relations may bring unprecedented functional programmability and diversity. Here, a new concept of digital mechanical metamaterial (DMM) is presented, where property design is realized by programming the digital states of the DMM to decouple the design of the structure and property. Transforming the binary stable states of a curved beam to the digital bit, one unit cell of DMM manifests three distinct deformation responses under compression, i.e., compression-twist coupling (CTC), compression-shear coupling (CSC), and pure compression (PC). These deformation modes show notable differences in motion and stiffness, which, by digitally programming a series of DMM, can yield a spectrum of functionalities, including information encryption, stress-strain relation customization, energy absorption in cushioning, effective vibration isolation, and tunable force transmission. This study pioneers a versatile material design paradigm that provides much greater freedom for the property design of intelligent mechanical metamaterials.

In Situ Reconstructing NiFe Oxalate Toward Overall Water Splitting.

Surface reconstruction plays an essential role in electrochemical catalysis. The structures, compositions, and functionalities of the real catalytic species and sites generated by reconstruction, however, are yet to be clearly understood, for the metastable or transit state of most reconstructed structures. Herein, a series of NiFe oxalates (NixFe1- xC2O4, x = 1, 0.9, 0.7, 0.6, 0.5, and 0) are synthesized for overall water splitting electrocatalysis. Whilst NixFe1-xC2O4 shows great hydrogen evolution reaction (HER) activity, the in situ reconstructed NixFe1-xOOH exhibits outstanding oxygen evolution reaction (OER) activity. As identified by the in situ Raman spectroscopy and quasi-in situ X-ray absorption spectroscopy (XAS) techniques, reconstructions from NixFe1-xC2O4 into defective NixFe1-xOOH and finally amorphous NixFe1-xOOH active species (R-NixFe1-xOOH) are confirmed upon cyclic voltammetry processes. Specifically, the fully reconstructed R-Ni0.6Fe0.4OOH demonstrates the best OER activity (179mV to reach 10mA cm-2), originating from its abundant real active sites and optimal d-band center. Benefiting from the reconstruction, an alkaline electrolyzer composed of a Ni0.6Fe0.4C2O4 cathode and an in situ reconstructed R-Ni0.6Fe0.4OOH anode achieves a superb overall water splitting performance (1.52 V@10mA cm-2). This work provides an in-depth structure-property relationship understanding on the reconstruction of catalysts and offers a new pathway to designing novel catalyst.

Machine learning analysis of a large set of homopolymers to predict glass transition temperatures

Glass transition temperature of polymers, Tg, is an important thermophysical property, which sometimes can be difficult to measure experimentally. In this regard, data-driven machine learning approaches are important alternatives to assess Tg values, in a high-throughput way. In this study, a large dataset of more than 900 polymers with reported glass transition temperature (Tg) was assembled from various public sources in order to develop a predictive model depicting the structure-property relationships. The collected dataset was curated, explored via cluster analysis, and then split into training and test sets for validation purposes and then polymer structures characterized by molecular descriptors. To find the models, several machine learning techniques, including multiple linear regression (MLR), k-nearest neighbor (k-NN), support vector machine (SVM), random forest (RF), gaussian processes for regression (GPR), and multi-layer perceptron (MLP) were explored. As result, a model with the subset of 15 descriptors accurately predicting the glass transition temperatures was developed. The electronic effect indices were determined to be important properties that positively contribute to the Tg values. The SVM-based model showed the best performance with determination coefficients (R2) of 0.813 and 0.770, for training and test sets, respectively. Also, the SVM model showed the lowest estimation error, RMSE = 0.062. In addition, the developed structure-property model was implemented as a web app to be used as an online computational tool to design and evaluate new homopolymers with desired glass transition profiles.

How to choose proper electron acceptor groups for highly efficient copper electrolyte-based dye-sensitized solar cells?

A better understanding of electron-accepting groups (EAGs) is important for the development of potentially efficient dyes. Density functional theory (DFT) and time-dependent DFT were employed in this study to investigate a variety of dyes derived from XY1b (11.8 %) in conjunction with prominent EAGs including 2-cyano-3-phenylacrylic acid (CPA), 2-cyano-acrylic acid (CA), and benzoic acid (BA). Various critical parameters pertaining to short-circuit current density (Jsc) and open-circuit photovoltage (Voc) were calculated, including light-harvesting ability, conduction band energy shift, dye-[Cu(tmby)2]2+ interaction, dye aggregation, and charge recombination, in order to illustrate the advantages and limitations of these EAGs. The findings indicate that dyes based on CA demonstrate a redshifted absorption spectrum, stronger electronic coupling with TiO2 conduction band, extended excited state lifetime, and enhanced dye regeneration driving force, resulting in higher Jsc compared to CPA and BA-based dyes. Regarding Voc, CA-based dyes show an increased conduction band energy shift due to their larger vertical dipole moment and reduced charge recombination with [Cu(tmby)2]2+. Additionally, it is suggested that future studies on structure–property relationships should prioritize identifying the optimal dye conformation, as it significantly influences adsorption configuration and vertical dipole moment. Overall, among the commonly utilized EAGs, CA stands out for its ability to enhance Jsc, Voc, and adsorption stability over CPA and BA-based dyes.

Optimized Drop-Casted Polyaniline Thin Films for High-Sensitivity Electrochemical and Optical pH Sensors

Conducting polymers used in chemical sensors are attractive because of their ability to confer reversible properties controlled by the doping/de-doping process. Polyaniline (PANI) is one of the most prominent materials used due to its ease of synthesis, tailored properties, and higher stability. Here, PANI thin films deposited by the drop-casting method on fluorine-doped tin oxide (FTO) substrates were used in electrochemical and optical sensors for pH measurement. The response of the devices was correlated with the deposition parameters; namely, the volume of deposition solution dropped on the substrate and the concentration of the solution, which was determined by the weight ratio of polymer to solvent. The characterisation of the samples aimed to determine the structure–property relationship of the films and showed that the chemical properties, oxidation states, and protonation level are similar for all samples, as concluded from the cyclic voltammetry and UV–VIS spectroscopic analysis. The sensing performance of the PANI film is correlated with its relative physical properties, thickness, and surface roughness. The highest electrochemical sensitivity obtained was 127.3 ± 6.2 mV/pH, twice the Nernst limit—the highest pH sensitivity reported to our knowledge—from the thicker and rougher sample. The highest optical sensitivity, 0.45 ± 0.05 1/pH, was obtained from a less rough sample, which is desirable as it reduces light scattering and sample oxidation. The results presented demonstrate the importance of understanding the structure–property relationship of materials for optimised sensors and their potential applications where high-sensitivity pH measurement is required.

Surface Ligands for Perovskite Quantum Dots.

The combination of the quantum confinement effect of quantum dots (QDs) and unique photoelectric properties of perovskite semiconductors make perovskite quantum dots (PQDs) a promising candidate for photoelectric devices. To truly unlock their potential, a deep understanding of structure-property relationship is paramount. Among the various factors influencing this relationship, the role of surface ligands cannot be overstated. The polarity, conductivity, stability, and interaction effects of these ligands with QD surfaces create complicated ligand-QDs relationships, which greatly influences the successful synthesis of QDs. In essence, the surface chemistry of ligands serves as a critical determinant in shaping the properties of both the resulting QDs and QD-based devices. To address this, our paper introduces an innovative approach to studying ligands, utilizing their inherent functional groups as a classification criterion. It is begun by discussing the types of surface defects of PQDs and the functional groups used for passivation, emphasizing the importance of analyzing ligands based on their functional groups. Then the passivation mechanisms of ligands with various functional groups and their impact on enhancing QD performance are delved into. Ultimately, this paper summarizes and offers several design principles and rules for PQDs surface ligands that can be applied in most scenarios.

Pentaerythritol-Based Compound as a Novel Leveler for Super-Conformal Copper Electroplating

Leveler is one of the most important organic additives in copper electroplating for microvia filling. To enhance the microvia filling efficiency and reduce the bath control difficulty, novel levelers with high filling performance and wide application concentration range has long been pursued. Herein, a novel leveler named L1 with four pyrrolidine rings linked by a pentaerythritol backbone is designed and synthesized. Compared with the previously reported leveler TPM-1, L1 shares the same nitrogen-containing group but has an additional pyrrolidine ring. The structure-property relationship of L1 is thoroughly characterized by electrochemical measurements, theoretical calculations, and electroplating experiments. Results show that with one more positively charged ammonium groups, L1 exhibits stronger interactions with Cl− and the accelerator SPS compared to TPM-1. However, due to the variation of the connecting group, its interaction with the suppressor PEG is much weaker. With L1 as the leveler, both good microvia filling performance and high-quality copper deposition was obtained within a wide concentration range. The findings indicate that L1 is a very promising leveler for microvia filling copper electroplating, and both nitrogen-containing groups and linking groups in a leveler significantly influence its properties and performances.

TRIBOINFORMATICS: MACHINE LEARNING METHODS FOR FRICTIONAL INSTABILITIES

The study of friction is traditionally a data-driven area with many experimental data and phenomenological models governing structure-property relationships. Triboinformatics is a new area combining Tribology with Machine Learning (ML) and Artificial Intelligence (AI) methods, which can help to establish correlations in data on friction and wear. This is particularly relevant to unstable motion, where deterministic models are difficult to build. There are several types of friction-induced instabilities including those caused by the velocity dependency of dry friction, coupling of friction with another process (wear, heat generation, etc.), the elastic Adams instabilities, and others. The onset of sliding is also an unstable process. ML/AI methods, such as Topological Data Analysis and various ML algorithms, which have been already used for various aspects of data analysis on friction, can be applied also to the frictional instabilities.

Low sintering temperature, interface characteristic and microwave/terahertz dielectric properties of ternary-phase Nd2O3-P2O5 based ceramics for patch antenna application

In this work, LiF was chosen as a sintering aid to lower the sintering temperature required for monoclinic NdPO4 (NPO) ceramic. A comprehensive investigation was conducted to analyze the impact of LiF on the sintering characteristics, microstructures, phase compositions, crystalline structures, interface characteristics, and microwave dielectric properties of NPO ceramic. Structure-property relationships were discussed based on the density, crystallite size, and lattice strain. The complex impedance spectroscopy revealed the mechanism underlying the interface characteristics of LiF on NPO ceramic. Additionally, far-infrared reflectance spectra and THz time-domain spectra revealed the intrinsic dielectric properties of NdPO4–1 wt.%LiF (NPO1). Notably, 1 wt.% LiF effectively reduced the sintering temperature from 1300 °C to 850 °C, significantly improving densification and dielectric properties. Typical properties such as Q × f = 55,840 GHz (at 9.58 GHz), εr = 10.4, and τf = −43.1 ppm °C−1 were obtained for NPO1 ceramic after sintering at 850 °C. Furthermore, its compatibility with Ag was explored as a prerequisite for LTCC applications. Finally, the NPO1 ceramic was designed as a microstrip antenna with high gain (6.83 dB) and low return loss (−18.85 dB) at a centre frequency of 2.56 GHz, demonstrating its potential for Wi-Fi and Bluetooth applications. This work provides a theoretical foundation and practical guidance for developing orthophosphate ceramics.

N-Doped and Bowl-Shaped Azabenzo[3,4]azulenofluorene: Synthesis and Characterization of Bridged Aza-Triarylamines.

Triarylamines are frequently employed as electron donor functional groups in a variety of organic electronic materials. We herein report the synthesis and in-depth characterisation of aza-analogues of fully bridged triarylamines, consisting of 5-, 6- and 7-membered rings. The impact of nitrogen doping and π-extension on the structural, electronic and optical-properties of these contorted heteroaromatic PAHs is thoroughly investigated and paves the way for an improved understanding of the structure-property relationships.

Monitoring the Structural Changes in Iridium Nanoparticles during Oxygen Evolution Electrocatalysis with Operando X-ray Total Scattering.

Understanding the structure of nanoparticles under (electro)catalytic operating conditions is crucial for uncovering structure-property relationships. By combining operando X-ray total scattering and pair distribution function analysis with operando small-angle X-ray scattering (SAXS), we obtained comprehensive structural information on ultrasmall (<3 nm) iridium nanoparticles and tracked their changes during oxygen evolution reaction (OER) in acid. When subjected to electrochemical conditions at reducing potentials, the metallic Ir nanoparticles are found to be decahedral. The iridium oxide formed in the electrochemical oxidation contains small rutile-like clusters composed of edge- and corner-connected [IrO6] octahedra of a very confined range. These rutile domains are smaller than 1 nm. Combined with complementary SAXS data analysis to extract the particle size, we find that the OER-active iridium oxide phase lacks crystalline order. Additionally, we observe an iridium oxide contraction under OER conditions, which is confirmed by operando X-ray absorption spectroscopy. Our results highlight the need for multitechnique operando studies for a complete understanding of the electrochemically formed Ir oxide active in OER.

Mapping structure-property relationships in fullerene systems: a computational study from C20 to C60

Fullerenes, as characteristic carbon nanomaterials, offer significant potential for diverse applications due to their structural diversity and tunable properties. Numerous isomers can exist for a specific fullerene size, yet a comprehensive understanding of their fundamental properties remains elusive. In this study, we construct an up-to-date computational database for C20–C60 fullerenes, consisting of 5770 structures, and calculate 12 fundamental properties using DFT, including stability (binding energy), electronic properties (HOMO-LUMO gap), and solubility (partition coefficient logP). Our findings reveal that the HOMO-LUMO gap weakly correlates with both binding energy and logP, indicating that electronic properties can be tailored for specific uses without affecting stability or solubility. In addition, we introduce a set of topological features and geometric measures to investigate structure-property relationships. We apply atom, bond, and hexagon features to effectively predict the stability of C20–C60 fullerenes, surpassing the conventional qualitative isolated pentagon rule, and demonstrating their robust transferability to larger-size fullerenes beyond C60. Our work offers guidance for optimizing fullerenes as electron acceptors in organic solar cells and lays a foundational understanding of their functionalization and applications in energy conversion and nanomaterial sciences.

Advancing Breathomics through Accurate Discrimination of Endogenous from Exogenous Volatiles in Breath.

Breathomics, a growing field in exposure monitoring and clinical diagnostics, has faced accuracy challenges due to unclear contributing factors. This study aims to enhance the potential of breathomics in various frontiers by categorizing exhaled volatile organic compounds (VOCs) as endogenous or exogenous. Analyzing ambient air and breath samples from 271 volunteers via TD-GC × GC-TOF MS/FID, we identify and quantify 50 common VOCs in exhaled breath. Advanced quantitative structure-property relationships and compartment models are employed to obtain VOCs kinetic parameters. This in-depth approach allows us to accurately determine the alveolar concentration of VOCs and further discern their origins, facilitating personalized application of breathomics in exposure assessment and disease diagnosis. Our findings demonstrate that prolonged external exposure turns humans into secondary pollutant sources. Analysis of endogenous VOCs reveals that internal exposure poses more significant health risks than external. Moreover, by correcting environmental backgrounds, we improve the accuracy of gastrointestinal disease diagnostic models by 15-25%. This advancement in identifying VOC origins via compartmental models promises to elevate the clinical relevance of breathomics, marking a leap forward in exposure assessment and precision medicine.

Platinum-Centered Oligoynes Capped by Boron Difluoride Formazanate Dyes and Their Thin-Film Properties.

Since the Nobel prize winning discovery that polyacetylene could act as a semiconductor, there has been tremendous efforts dedicated to understanding and harnessing the unusual properties of 𝜋-conjugated polymers. Much of this research has focused on the preparation of oligoynes and polyynes with well-defined numbers of repeating alkyne units as models for carbyne. These studies are usually hampered by a structure-property relationship where the stability of the resulting materials decrease with the incorporation of additional alkyne units. Here, we describe a series of oligoynes, with up to 12 alkyne units, where electron-rich [Pt(PBu3)2]2+ units are incorporated into the center of oligoyne backbones which are capped by electron-poor BF2 formazanate dyes. These compounds exhibit excellent stability and solubility, panchromatic absorption, and redox activity characteristic of their structural components. These traits facilitated thin-film studies of extended oligoyne materials, where it is shown that incorporating [Pt(PBu3)2]2+ units leads to smoother films, decreased conductivity on the microscale, and increased conductivity on the nanoscale when compared to metal-free analogs. Remarkably, our oligoynes have superior conductivity compared to the ubiquitous poly(3-hexylthiophene) semiconductor.

Metal-Carbon Bond Heterolysis Energy Scale for Model Palladium Catalysts.

Thermodynamic studies of transition-metal intermediates are crucial for understanding of metal-catalyzed transformations. Herein, a series of arylpalladium cyanomethanides were synthesized and characterized. Their palladium-carbon bond heterolysis energies (ΔGhet(Pd-C)) were determined in DMSO for the first time by equilibrium methods. ΔGhet(Pd-C) values of 7.9-19.1 kcal/mol, located between the ΔGhet(Pd-O) and ΔGhet(Pd-N) scales previously established, are much smaller than the corresponding ΔGhet(C-H)s of phenylacetonitrile (30.0 kcal/mol). Linear free energy relationship (LEFR) analysis reveals insights into the structure-property relationship and the factor dictating the thermodynamics of metalation. These ΔGhet(Pd-X)s in combination with ΔGhet(X-H)s are successfully used to diagnose the reaction feasibility and selectivity of X-H bond activation.

Prediction of Dielectric Constant in Series of Polymers by Quantitative Structure-Property Relationship (QSPR)

This work is devoted to the investigation of dielectric permittivity which is influenced by electronic, ionic, and dipolar polarization mechanisms, contributing to the material’s capacity to store electrical energy. In this study, an extended dataset of 86 polymers was analyzed, and two quantitative structure–property relationship (QSPR) models were developed to predict dielectric permittivity. From an initial set of 1273 descriptors, the most relevant ones were selected using a genetic algorithm, and machine learning models were built using the Gradient Boosting Regressor (GBR). In contrast to Multiple Linear Regression (MLR)- and Partial Least Squares (PLS)-based models, the gradient boosting models excel in handling nonlinear relationships and multicollinearity, iteratively optimizing decision trees to improve accuracy without overfitting. The developed GBR models showed high R2 coefficients of 0.938 and 0.822, for the training and test sets, respectively. An Accumulated Local Effect (ALE) technique was applied to assess the relationship between the selected descriptors—eight for the GB_A model and six for the GB_B model, and their impact on target property. ALE analysis revealed that descriptors such as TDB09m had a strong positive effect on permittivity, while MLOGP2 showed a negative effect. These results highlight the effectiveness of the GBR approach in predicting the dielectric properties of polymers, offering improved accuracy and interpretability.