Structure-property Relationships Research Articles

Position Optimization of Bulky Tetraphenylsilane in Multiple Resonance Molecules for Highly Efficient Narrowband OLEDs.

Multiple resonance (MR)-type thermally activated delayed fluorescence (TADF) emitters have garnered significant interest due to their narrow full width at half maximum (FWHM) and high electroluminescence efficiency. However, the planar structures and large singlet-triplet energy gaps (ΔESTs) characteristic of MR-TADF molecules pose challenges to achieving high-performance devices. Herein, two isomeric compounds, p-TPS-BN and m-TPS-BN, are synthesized differing in the connection modes between a bulky tetraphenylsilane (TPS) group and an MR core. This strategy aims to suppress intermolecular interactions, reduce ΔEST values, and investigate how connection positions influence photoelectric properties. Both compounds exhibit remarkably small ΔEST values (0.08-0.09eV) and high internal quantum yields (95.0-97.8%). Notably, p-TPS-BN demonstrates a faster reverse intersystem crossing (RISC) with a rate constant of 2.54×10⁵s⁻¹, attributed to its optimal long-range charge transfer (LRCT) process. A narrowband device employing p-TPS-BN achieves a maximum external quantum efficiency of 35.8% with an FWHM of 36nm. This work offers an effective framework for studying structure-property relationships in MR molecules, paving the way for the development of high-efficiency electroluminescent devices.

Human interpretable structure-property relationships in chemistry using explainable machine learning and large language models

Explainable Artificial Intelligence (XAI) is an emerging field in AI that aims to address the opaque nature of machine learning models. Furthermore, it has been shown that XAI can be used to extract input-output relationships, making them a useful tool in chemistry to understand structure-property relationships. However, one of the main limitations of XAI methods is that they are developed for technically oriented users. We propose the XpertAI framework that integrates XAI methods with large language models (LLMs) accessing scientific literature to generate accessible natural language explanations of raw chemical data automatically. We conducted 5 case studies to evaluate the performance of XpertAI. Our results show that XpertAI combines the strengths of LLMs and XAI tools in generating specific, scientific, and interpretable explanations.

Crystal structure and photoluminescence properties of CuBrxI1−x(melamine) (0 ≤ x ≤ 1) complexes

AbstractOrganic–inorganic copper(I) halide complexes have attracted attention due to their optoelectronic properties and non‐toxic nature. In this study, we systematically synthesized copper(I) halide melamine complexes as single crystals, (CuBr(melamine) and CuI(melamine)) and polycrystalline powders, CuBrxI1‐x(melamine) (0 ≤ x ≤ 1). The crystal structures of CuBr(melamine) and CuI(melamine) are isotypic with a triclinic space group P , where copper(I) is connected with three adjacent halides along the a‐axis and a single melamine ligand along the c‐axis, forming an infinite stair‐chain structure. The photoluminescence emission peaks of CuBrxI1‐x(melamine) (0 ≤ x ≤ 1) show a red shift from 455 nm to 520 nm with the substitution of Br for I. This finding explains the structure–property relationship of organic–inorganic copper(I) halide complexes and provides guidelines for developing new organic–inorganic optoelectronic materials.

Structure–Property Relationship and Electronic Structure Calculation of Cubic YSZ Solid Electrolyte for Electrochemical Applications

Structure–Property Relationship and Electronic Structure Calculation of Cubic YSZ Solid Electrolyte for Electrochemical Applications

High performance energy-saving electrocatalysts for hydrogen evolution reaction: A minireview on the influence of structure and support

Abstract Generating hydrogen from water electrolysis is a promising clean energy strategy that may help alleviate energy crisis, which stimulates tremendous research interests in searching high performance catalysts with low energy consumption. In this article, we review recent progresses on the development of high performance electrocatalysts for hydrogen evolution reaction (HER) from the energy consumption perspective with the emphasis on the structure impact and support. In particular, the structural influence on the energy conversion efficiency and stability of HER electrocatalysts is discussed, and the role of support carriers in forming energy-saving catalytic systems with minimized non-HER energy loss, which are vital for practical processes, is highlighted. Moreover, a global design strategy incorporating both catalyst and support in the same integrated framework is advocated. This paper provides new insights into the structure-property relationship and the support effect in developing highly efficient and durable electrocatalysts for HER. 

Tailoring Water-in-DMSO Electrolyte for Ultra-stable Rechargeable Zinc Batteries.

Rechargeable zinc batteries (RZBs) are hindered by two primary challenges: instability of Zn anode and deterioration of the cathode structure in traditional aqueous electrolytes, largely attributable to the decomposition of active H2O. Here, we design and synthesize a non-flammable water-in-dimethyl sulfoxide electrolyte to address these issues. X-ray absorption spectroscopy, in situ techniques and computational simulations demonstrate that the activity of H2O in this electrolyte is extremely compressed, which not only suppresses the side reactions and increases the reversibility of Zn anode, but also diminishes the cathode dissolution and proton intercalation. The hybrid solid-electrolyte interface (SEI), formed in situ, helps Zn-Zn symmetric cell a prolonged lifespan exceeding 10000 hat 0.5 mA cm-2 and 600 h at a 60% discharge depth. The versatility of this electrolyte endows the Zn-VO2 full batteries ultra-stable cycling performance. This work provides insights into electrolyte structure-property relationships, and facilitates the design of high-performance RZBs.

Revealing the Dynamics of Sustainable Epoxy-Acrylate Networks from Recycled Plastics Blends and Oligomeric Lignin Precursors.

The growing pursuit of carbon circularity in material fabrication has led to the increased use of recycled and biobased resources, especially in epoxy resin systems. Fossil-based bisphenols are being replaced with recycled bisphenol A (r-BPA) and lignin derivatives, both derived from previous processes. In this study, r-BPA was chemically recycled from end-of-life televisions, then converted into r-DGEBA and r-DAGBA through glycidylation and acrylic acid ring-opening. These monomers were used to create six thermosets by reacting Jeffamine D230 with r-DGEBA/r-DAGBA in varying epoxide:acrylate ratios. Acrylates introduced thermo-reversible β-amino esters, enabling dynamic bonding in the epoxy formulation. To increase biobased content, glycidylated depolymerized lignin (GDL) was added to produce five additional polymers. The crosslinked networks were thoroughly characterized, examining their thermomechanical properties and establishing a structure-property relationship. The dissociative acrylate-amine interactions allowed for reversible crosslinks under specific thermal conditions, enabling shape programming and crosslink reversibility. The study demonstrates that incorporating recycled and biobased aromatic monomers facilitates the creation of dynamic, crosslinked structures with tunable properties. This represents progress toward versatile, reusable, and circular materials.

Industrial Automation using Artificial Intelligence

The pharmaceutical and consumer healthcare industries have been greatly impacted by artificial intelligence and machine learning. A subfield of computer science called artificial intelligence is able to analyze intricate medical data. The goal of artificial intelligence (AI) is to create intelligent modeling, which facilitates knowledge imagination, problem solving, and decision making. AI is becoming more and more significant in many areas of pharmacy, including drug discovery and formulation of drug delivery, process optimization, testing, and pharmacokinetics/pharmacodynamics (PK/PD) studies growth. This review focuses on the significant applications of AI in various pharmaceutical domains, including drug development and discovery, Many studies are being conducted to enhance the AI technology that is currently available in order to increase the efficiency of the pharmacy profession. Artificial intelligence and system mastery have seen a significant upsurge in recent years. It has lessened the effort required of humans to advance in their extraordinary lives. Numerous drug discovery implementations have been examined, demonstrating the technology's effectiveness in quantitative structure-property relationships (QSPR) and quantitative structure-activity relationships (QSAR). Additionally, they are employed in clinical trials to generate and interpret data gathered from patient information. The pharmaceutical industry is currently having trouble maintaining its drug development programs due to rising R&D expenses and declining productivity. In addition to helping with experimental design, machine learning algorithms can forecast the toxicity and pharmacokinetics of potential drugs. This capability lessens the need for extensive and expensive animal testing by enabling the prioritization and optimization of lead compounds. Artificial intelligence (AI) algorithms that examine actual patient data can support personalized medicine strategies, improving patient adherence and treatment outcomes

Development Validation of UV Spectrophoto Meter Method for Determination of Polyherbal Powder

The pharmaceutical and consumer healthcare industries have been greatly impacted by artificial intelligence and machine learning. A subfield of computer science called artificial intelligence is able to analyze intricate medical data. The goal of artificial intelligence (AI) is to create intelligent modeling, which facilitates knowledge imagination, problem solving, and decision making. AI is becoming more and more significant in many areas of pharmacy, including drug discovery and formulation of drug delivery, process optimization, testing, and pharmacokinetics/pharmacodynamics (PK/PD) studies growth. This review focuses on the significant applications of AI in various pharmaceutical domains, including drug development and discovery, Many studies are being conducted to enhance the AI technology that is currently available in order to increase the efficiency of the pharmacy profession. Artificial intelligence and system mastery have seen a significant upsurge in recent years. It has lessened the effort required of humans to advance in their extraordinary lives. Numerous drug discovery implementations have been examined, demonstrating the technology's effectiveness in quantitative structure-property relationships (QSPR) and quantitative structure-activity relationships (QSAR). Additionally, they are employed in clinical trials to generate and interpret data gathered from patient information. The pharmaceutical industry is currently having trouble maintaining its drug development programs due to rising R&D expenses and declining productivity. In addition to helping with experimental design, machine learning algorithms can forecast the toxicity and pharmacokinetics of potential drugs. This capability lessens the need for extensive and expensive animal testing by enabling the prioritization and optimization of lead compounds. Artificial intelligence (AI) algorithms that examine actual patient data can support personalized medicine strategies, improving patient adherence and treatment outcomes

Analysis of Giant-Shell CdSe/CdS Quantum Dots via Analytical Ultracentrifugation Combined with Spectrally Resolved Photoluminescence.

Knowledge of the structure-property relationships of functional nanomaterials, including, for example, their size- and composition-dependent photoluminescence (PL) and particle-to-particle variations, is crucial for their design and reproducibility. Herein, the Angstrom-resolution capability of an analytical ultracentrifuge combined with an in-line multiwavelength emission detection system (MWE-AUC) for measuring the sedimentation coefficient-resolved spectrally corrected PL spectra of dispersed nanoparticles is demonstrated. The capabilities of this technique are shown for giant-shell CdSe/CdS quantum dots (g-QDs) with a PL quantum yield (PL QY) close to unity capped with oleic acid and oleylamine ligands. The MWE-AUC PL measurements are calibrated and validated with certified fluorescence standards. The spectrally corrected and size-dependent PL spectra of the g-QDs derived from a single MWE-AUC experiment are then analyzed and compared with the results of single-particle spectroscopic studies, yielding the PL spectra, decay kinetics, and blinking behavior of individual g-QDs. This study underlines the vast potential of MWE-AUC with in-line optical detection for the characterization of advanced nanomaterials with a complex structure.

A python approach for prediction of physicochemical properties of anti-arrhythmia drugs using topological descriptors

In recent years, machine learning has gained substantial attention for its ability to predict complex chemical and biological properties, including those of pharmaceutical compounds. This study proposes a machine learning-based quantitative structure-property relationship (QSPR) model for predicting the physicochemical properties of anti-arrhythmia drugs using topological descriptors. Anti-arrhythmic drug development is challenging due to the complex relationship between chemical structure and drug efficacy. To address this, we applied machine learning techniques, specifically linear regression models combined with K-fold cross-validation, to predict critical properties such as Density, Boiling Point, Flash Point, Bioconcentration Factor (BCF), Organic Carbon Partition Coefficient (KOC), Polarizability, and Molar Volume. The models were developed using data from ten anti-arrhythmic drugs ( to ). We evaluated the models based on performance metrics such as R and and obtained significant results. Most accurate predictions are obtained for polarizability from models with H(G) and .

Data-Driven Approach for Designing Eco-Friendly Heterocyclic Compounds for the Soil Microbiome.

Soil microbiota plays crucial roles in maintaining the health, productivity, and nutrient cycling of terrestrial ecosystems. The persistence and prevalence of heterocyclic compounds in soil pose significant risks to soil health. However, understanding the links between heterocyclic compounds and microbial responses remains challenging due to the complexity of microbial communities and their various chemical structures. This study developed a machine-learning approach that integrates the properties of chemical structures with the diversity of soil bacteria and functions to predict the impact of heterocyclic compounds on the microbial community and improve the design of eco-friendly heterocyclic compounds. We screened the key chemical structures of heterocyclic compounds─particularly those with topological polar surface areas (<74.2 Å2 or 111.3-154.1 Å2), carboxyl groups, and dissociation constant, which maintained high soil bacterial diversity and functions, revealing threshold effects where specific structural parameters dictated microbial responses. These eco-friendly compounds stabilize communities and increase beneficial carbon and nitrogen cycle functions. By applying these design parameters, we quantitatively assessed the eco-friendliness scores of 811 heterocyclic compounds, providing a robust foundation for guiding future applications. Our study disentangles the critical chemical structure-related properties that influence the soil microbial community and establishes a computational framework for designing eco-friendly compounds with ecological benefits from an ecological perspective.

Multiscale Understanding of Anion Exchange Membrane Fuel Cells: Mechanisms, Electrocatalysts, Polymers, and Cell Management.

Anion exchange membrane fuel cells (AEMFCs) are among the most promising sustainable electrochemical technologies to help solve energy challenges. Compared to proton exchange membrane fuel cells (PEMFCs), AEMFCs offer a broader choice of catalyst materials and a less corrosive operating environment for the bipolar plates and the membrane. This can lead to potentially lower costs and longer operational life than PEMFCs. These significant advantages have made AEMFCs highly competitive in the future fuel cell market, particularly after advancements in developing non-platinum-group-metal anode electrocatalysts, anion exchange membranes and ionomers, and in understanding the relationships between cell operating conditions and mass transport in AEMFCs. This review aims to compile recent literature to provide a comprehensive understanding of AEMFCs in three key areas: i) the mechanisms of the hydrogen oxidation reaction (HOR) and the oxygen reduction reaction (ORR) in alkaline media; ii) recent advancements in the synthesis routes and structure-property relationships of cutting-edge HOR and ORR electrocatalysts, as well as anion exchange membranes and ionomers; and iii) fuel cell operating conditions, including water management and impact of CO2. Finally, based on these aspects, the future development and perspectives of AEMFCs are proposed.

Probing Functional Thin Films with Grazing Incidence X-Ray Scattering: The Power of Indexing

Grazing incidence small- and wide-angle X-ray scattering (GISAXS, GIWAXS) has been widely applied for the study of functional thin films, be it for the characterization of nanostructured morphologies in block copolymers, nanocomposites, and nanoparticle assemblies, or for the packing and orientation of aromatic molecules or conjugated polymers. Solution-processed thin films are typically uniaxial powders, with a specific crystallographic plane oriented parallel to the substrate surface while ordered domains assume random orientations laterally. The convenient GISAXS/GIWAXS scattering geometry facilitates obtaining complete information about thin film structure as well as the ability to study samples in well-defined sample environments, as controlled by temperature, exposure to solvent vapor and drying, or coating processes. Moreover, with suitable X-ray sources and detectors, information about the ordering kinetics and phase transitions can be obtained down to the millisecond scale. The scattering geometry and an interactive graphical tool to index such scattering patterns will be discussed here. Furthermore, it will be demonstrated that proper indexing of the X-ray scattering patterns can provide deep insight into thin film structure–property relationships and the kinetics of structure formation. Recent examples of nanostructures and molecular organization in thin films will be discussed, as well as self-assembly processes leading to such structures.

Eugenol-Based Epoxy Materials with High Biobased Carbon Content and Their Structure–Property Relationships

Eugenol-Based Epoxy Materials with High Biobased Carbon Content and Their Structure–Property Relationships

A Spectrochemical Series for Electron Spin Relaxation.

Controlling the rate of electron spin relaxation in paramagnetic molecules is essential for contemporary applications in molecular magnetism and quantum information science. However, the physical mechanisms of spin relaxation remain incompletely understood, and new spectroscopic observables play an important role in evaluating spin dynamics mechanisms and structure-property relationships. Here, we use cryogenic magnetic circular dichroism (MCD) spectroscopy and pulse electron paramagnetic resonance (EPR) in tandem to examine the impact of ligand field (d-d) excited states on spin relaxation rates. We employ a broad scope of square-planar Cu(II) compounds with varying ligand field strength, including CuS4, CuN4, CuN2O2, and CuO4 first coordination spheres. An unexpectedly strong correlation exists between spin relaxation rates and the average d-d excitation energy (R2 = 0.97). The relaxation rate trends as the inverse 11th power of the excited-state energies, whereas simplified theoretical models predict only an inverse second power dependence. These experimental results directly implicate ligand field excited states as playing a critical role in the ground-state spin relaxation mechanism. Furthermore, ligand field strength is revealed to be a particularly powerful design principle for spin dynamics, enabling formation of a spectrochemical series for spin relaxation.

Regioisomeric π-Extended Nanographene with Long-lived Phosphorescence Afterglow.

The cutouts of graphene sheets, particularly those with a nonplanar topology, present vast opportunities for advancement. Even a slight deviation from the planar structure can lead to intriguing (chiro)optical features for helically twisted nanographenes. In this context, we introduce two regioisomeric π-extended nanographenes that exhibit distinct excited-state characteristics. The helicene structure and the photophysical features can be easily tuned by changing the connecting position of the nanographene to the carbazole core (2,7- and 3,6-). Single-crystal X-ray diffraction analysis confirmed the formation of nanographenes with bent and helical conformations. Both derivatives exhibited thermally activated delayed fluorescence at room temperature and phosphorescence at low temperatures. Notably, the nanographene with the bent structure displayed an impressive red afterglow lasting over 30 seconds, in contrast to the very weak afterglow observed in the helical structure. DFT calculations revealed the existence of an isoenergetic higher triplet state (T8) and comparatively weak spin-orbit coupling (T1-S0), thereby enabling the bent nanographene to exhibit a long-lived component and strong afterglow. Our findings highlight the significance of regioisomeric nanographenes with exceptional optical properties and offer a deeper understanding of the structure-property relationship in nonplanar nanographenes.

Colloidal High Entropy Alloy Nanoparticles: Synthetic Strategies and Electrocatalytic Properties.

High entropy alloy (HEA) nanoparticles (NPs) have attracted much attention recently due to their unprecedented chemical properties. As such, HEA NPs have been used as materials with superior activity toward electrocatalytic applications. Specifically, solid solutions that form randomly mixed single-phased structures have received the most focus in the early stages of HEA NP development for their entropic-driven design and multifunctionality. Advances to non-colloidal and colloidal synthetic methods have allowed for the fabrication of solid solution HEA NPs with varying compositions and complexity to be applied to many practical applications such as fuel cells, energy storage and agriculture. In this review, the current colloidal methods and catalytic mechanisms for solid solution HEA NP synthesis are investigated from the physical chemistry perspective. A comprehensive discussion on the theory, techniques, and electrocatalytic applications of colloidal syntheses for successful solid solution HEA NP formation is presented. Finally, promising perspectives for the continued development of physical insights into structure-property relationships towards improved HEA NP synthesis and application are discussed.

Chitosan Approaches in Colon Cancer Therapy: Understanding its Macromolecular Interactions and Structure-Property Relationships

Colon cancer is still a significant cause of cancer-related deaths and, therefore, new treatment approaches are required. As a naturally derived polysaccharide, Chitosan has become one of the most promising materials in polymer-based drug delivery systems because of its outstanding biocompatibility, biodegradability, and pH-responsive drug release. This review explores the expanding roles of Chitosan and its chemical modifications in colon cancer therapy, emphasizing the relationship between macromolecular interactions and structure-property dynamics. Recent progress in Chitosan-based nanocarriers, including nanoparticles, nanogels and hybrid systems, highlights their significant potential to overcome multidrug resistance and minimize off-target toxicity. These advancements are closely aligned with developments in precision medicine, incorporating stimuli-responsive components and targeted ligands for enhanced efficacy. While preclinical and clinical studies yield promising results, critical challenges such as scalability, reproducibility and regulatory compliance remain obstacles to clinical translation. This review emphasizes the transformative potential of Chitosan in addressing therapeutic barriers in colon cancer treatment. Looking ahead, future advancements should focus on utilizing cutting-edge technologies, such as artificial intelligence and 3D bioprinting, to enhance drug formulations and develop personalized therapeutic solutions that are more effective and tailored to individual needs.

Quantitative read-across structure-property relationship (q-RASPR): a novel approach to estimate the bioaccumulative potential for diverse classes of industrial chemicals in aquatic organisms.

The Bioconcentration Factor (BCF) is used to evaluate the bioaccumulation potential of chemical substances in reference organisms, and it directly correlates with ecotoxicity. Traditional in vivo BCF estimation methods are costly, time-consuming, and involve animal sacrifice. Many in silico technologies are used to avoid the problems associated with in vivo testing. This study aims to develop a quantitative read across structure-property relationship (q-RASPR) model using a structurally diverse dataset consisting of 1303 compounds by combining quantitative structure-property relationship (QSPR) and read-across (RA) algorithms. The model incorporates simple, interpretable, and reproducible 2D molecular descriptors along with RASAR descriptors. The PLS-based q-RASPR model demonstrated robust performance with internal validation metrics (R2 = 0.727 and Q2(LOO) = 0.723) and external validation metrics (Q2F1 = 0.739, Q2F2 = 0.739, and CCC = 0.858). These results indicate that the q-RASPR model is statistically superior to the corresponding QSPR model. Furthermore, screening of 1694 compounds from the Pesticide Properties Database (PPDB) was performed using the PLS-based q-RASPR model for assessing the eco-toxicological bioaccumulative potential of various compounds, ensuring the external predictability of the developed model and confirming the real-world application of the developed model. This model offers a reliable tool for predicting the BCF of new or untested compounds, thereby helping to develop safe and environment-friendly chemicals.