Frontiers in Chemistry, Materials and Catalysis
Frontiers in Chemistry, Materials and Catalysis is a peer-reviewed journal advancing chemistry, materials science, and catalysis. It covers nanomaterials, green chemistry, sustainable materials, and catalytic processes for energy and industry. Emphasizing innovation, it bridges fundamental and applied research to drive progress in sustainable chemistry and materials development.
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Statistical Modeling of Active Site Distributions on ZrO2-Decorated Copper
Inverse catalysts exhibit significant site heterogeneity that challenges traditional single-site descriptions. In this study, a statistical model was developed to describe the distribution of active sites at ZrO2–Cu interfaces using DFT-derived energetics. Five dominant site classes were identified and weighted according to their thermodynamic stability. Ensemble-averaged microkinetic simulations show that interfacial Zr–O–Cu motifs contribute over 65% of the methanol formation rate, despite occupying less than 30% of the surface area. The ensemble model improves agreement with experimentally reported turnover frequencies compared to single-site approximations. This work highlights the necessity of statistical treatments for realistic modeling of inverse catalysts.
Copyright (c) 2026 Kenji Yamamoto, Hiroshi Tanaka, Yuki Sato (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
Formate-Dominated Reaction Networks on ZrO2-Modified Cu Surfaces
Reaction pathway selection critically determines product distribution in CO2 hydrogenation. In this study, a comprehensive reaction network consisting of 41 elementary steps was constructed using density functional theory to compare formate- and CO-mediated pathways on ZrO2-decorated Cu surfaces. Energetic span analysis shows that ZrO2 modification reduces the energetic span of the formate route from 1.05 eV on Cu to 0.69 eV at the interface, while simultaneously increasing the barrier for CO formation. Microkinetic simulations predict methanol selectivities exceeding 75% across a temperature range of 500–540 K, in contrast to CO-dominated behavior on Cu. The analysis identifies hydrogenation of bidentate formate as the primary rate-determining step under industrially relevant pressures. These results clarify how inverse catalysts fundamentally reshape the reaction network to favor methanol formation.
Copyright (c) 2026 Jean-Pierre Dubois, Camille Laurent, François Martin (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
Dual Activity of Ag-Doped TiO2 Gels and Osteoporotic Implant Interface Responses
The clinical longevity of orthopedic implants in osteoporotic patients is frequently compromised by two primary failure modalities: insufficient osseointegration due to low bone density and the development of implant-associated infections. This paper investigates the dual functional capability of silver-doped titanium dioxide (Ag-doped TiO2) sol-gel coatings to address these concurrent challenges. We synthesized nanostructured TiO2 gels with varying concentrations of silver ions to evaluate their physicochemical properties, antibacterial efficacy against Staphylococcus aureus, and osteogenic potential using osteoporotic animal models. The study focuses on the interfacial responses, specifically analyzing how the release kinetics of silver ions interact with the compromised biological environment typical of osteoporosis. Our results indicate that a controlled doping level achieves a critical balance, significantly reducing bacterial colonization through the release of reactive oxygen species while simultaneously preserving osteoblast viability and promoting mineralization. The surface topography modifications induced by the sol-gel process further enhance the mechanical interlocking at the bone-implant interface. These findings suggest that Ag-doped TiO2 gels represent a promising surface modification strategy for next-generation implants designed for patients with compromised bone quality.
Copyright (c) 2026 Liam J. O'Connor, Anastasia Smirnova (Author) https://creativecommons.org/licenses/by-sa/4.0
PF-MD Coupled Model and Suppression Strategies for Lithium Dendrite Growth in Solid-State Batteries
The transition from liquid organic electrolytes to solid-state electrolytes offers a promising pathway toward high-energy-density lithium-metal batteries with enhanced safety profiles. However, the practical implementation of solid-state batteries is severely hindered by the pervasive issue of lithium dendrite growth, which leads to short circuits and mechanical degradation of the electrolyte. Traditional modeling approaches, relying solely on continuum mechanics or atomistic simulations, fail to capture the multiscale nature of dendrite propagation, which involves atomic-level interface kinetics and mesoscale morphological evolution. This paper presents a comprehensive multiscale framework coupling Molecular Dynamics simulations with Phase-Field modeling to investigate the mechanisms of lithium dendrite growth and evaluate effective suppression strategies. The Molecular Dynamics component determines the temperature-dependent transport properties and interfacial energies, which are subsequently integrated into the Phase-Field model to simulate microstructural evolution under varying electrochemical and mechanical conditions. Our results elucidate the critical role of grain boundary inhomogeneity and interfacial stress accumulation in promoting dendritic structures. Furthermore, we propose and validate suppression strategies, including the optimization of external stack pressure and the engineering of high-modulus artificial interlayers. The coupled model demonstrates that tailoring the mechanical properties of the solid electrolyte interphase can significantly retard dendrite velocity, providing theoretical guidance for the rational design of next-generation solid-state batteries.
Copyright (c) 2026 Do-Hyun Cho, Arthur C. Clarke, Elizabeth J. Swan, Michael D. Ross (Author) https://creativecommons.org/licenses/by-sa/4.0
Research on the mechanism by which a high-fiber diet regulates the gut microbiota-bile acid metabolism axis and improves glucose metabolism
This study examined how a high-fiber diet affects the gut–bile-acid pathway and blood sugar control in mice with type 2 diabetes. Thirty-six male C57BL/6J mice were assigned to three groups: normal control, diabetic control, and high-fiber diet. After 12 weeks, bile acids, gene markers, and gut bacterial changes were tested. LC–MS/MS showed that total secondary bile acids increased by 62%, intestinal FXR expression dropped by 35%, and plasma FGF19 rose about two times compared with diabetic controls. Gut RNA data showed higher activity of bile-acid–related genes and more Faecalibacterium, which was strongly linked with dehydroxylation levels (r = 0.78, P < 0.01). These results show that a high-fiber diet changes bile-acid balance through gut bacteria and adjusts the FXR–FGF19 signal to support better glucose control. The findings suggest that adding dietary fiber may be a simple and safe way to improve metabolism in diabetes.
Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
Correlating Electron Affinity with Redox Activity in Layered Birnessite
Birnessite is a layered manganese oxide used as an oxidant in water treatment, but the electronic factor that controls its reactivity is not well defined. We studied eleven natural and synthetic birnessites with different interlayer cations and Mn(III)/Mn(IV) ratios to test whether electron affinity (EA) governs oxidation. EA from ultraviolet photoelectron spectroscopy with optical-gap support was 5.56–6.06 eV. At pH 7.0 and 25 °C, Fe(II) and As(III) oxidation followed a pseudo-second-order model. The rate constant k2 rose from 0.0048 to 0.022 g mg⁻¹ min⁻¹ for Fe(II) and from 0.0026 to 0.015 g mg⁻¹ min⁻¹ for As(III) as EA increased, with an exponential fit R2=0.95. The apparent activation energy fell from 46±2 to 29±2 kJ mol⁻¹ across the same EA range. After five cycles, high-EA samples kept 81–87% of the initial rate, while low-EA samples kept 54–62%, in line with less MnOOH seen by XPS. These results show that electron affinity is a quantitative descriptor of oxidizing activity in birnessite and suggest that tuning interlayer chemistry, hydration, and defects can yield durable manganese-oxide catalysts for pollutant removal.
Neural Surrogate Modeling for Optical Coherence Tomography Reconstruction with Real-Time Constraints
Optical Coherence Tomography (OCT) has established itself as a paramount imaging modality in ophthalmology, cardiology, and dermatology, offering non-invasive, high-resolution cross-sectional visualization of biological tissues. However, the computational burden associated with high-fidelity image reconstruction—particularly when employing iterative Compressed Sensing (CS) algorithms or complex dispersion compensation techniques—often precludes real-time application in time-sensitive clinical environments such as intraoperative surgical guidance. This paper introduces a novel Neural Surrogate Modeling framework designed to approximate the complex inverse scattering physics of OCT reconstruction while strictly adhering to real-time latency constraints. By leveraging a hardware-aware deep learning architecture, specifically a lightweight Fourier-domain convolutional neural network optimized via neural architecture search, we successfully map raw interferometric data directly to structural images, bypassing the latency of traditional iterative solvers. We introduce a multi-objective loss function that balances structural fidelity, perceptual quality, and sparsity constraints. Furthermore, we provide a comprehensive analysis of model quantization and tensor acceleration techniques necessary to deploy these models on edge-computing devices. Our results demonstrate that the proposed neural surrogate achieves reconstruction quality competitive with state-of-the-art iterative methods (PSNR > 32 dB) while operating at inference speeds exceeding 150 frames per second on standard GPU hardware, effectively bridging the gap between high-fidelity imaging and real-time feedback loops.
Generative Inverse Design of Intelligent Packaging Materials with Integrated Sensing Capabilities
The development of intelligent packaging materials with integrated sensing capabilities represents a paradigm shift from traditional passive packaging systems to dynamic, responsive materials that can monitor and communicate food quality, safety, and environmental conditions in real-time. This paper presents a comprehensive investigation of generative inverse design approaches for creating next-generation packaging materials that seamlessly integrate sensing functionalities with structural performance requirements. Through the application of machine learning algorithms, particularly Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), we demonstrate how computational frameworks can accelerate the discovery of novel material compositions and microstructures that exhibit both superior mechanical properties and enhanced sensing capabilities. Our methodology combines multi-objective optimization techniques with physics-informed neural networks to navigate the complex design space of bio-based polymers, conductive fillers, and responsive elements. The results indicate that generative inverse design can successfully identify material candidates that achieve target conductivity values, mechanical strength parameters, and environmental responsiveness while maintaining biodegradability and food-contact safety requirements. This research establishes a foundation for automated materials discovery in intelligent packaging applications and provides insights into the fundamental relationships between material composition, processing conditions, and functional performance in smart packaging systems.
Reinforcement Learning Strategies for Multi-Scale Geometric Representation in Molecular Property Prediction
Molecular property prediction represents a cornerstone of computational drug discovery, where accurate prediction of molecular properties from structural representations enables accelerated pharmaceutical development and reduced experimental costs. Traditional approaches rely on hand-crafted molecular descriptors or simple graph representations that fail to capture the rich multi-scale geometric information inherent in molecular structures. This paper presents a novel framework that integrates Reinforcement Learning (RL) strategies with multi-scale geometric representations for enhanced molecular property prediction. The proposed Multi-Scale Geometric Reinforcement Learning (MSGRL) framework combines graph neural networks operating at different geometric scales with adaptive reinforcement learning agents that learn optimal feature extraction strategies. Our approach employs a hierarchical representation scheme that captures molecular information from irregular geometric manifolds to structured numerical encodings, while reinforcement learning agents dynamically adjust the importance weights of different representation modalities based on prediction performance feedback. The framework addresses key challenges including geometric data irregularity, multi-modal representation integration, and adaptive learning across diverse molecular property types. Experimental evaluation across diverse molecular property prediction tasks demonstrates significant improvements over state-of-the-art approaches, with performance gains comparable to the best D-MPNN Features across benchmark datasets including QM9, ESOL, FreeSolv, Tox21, and BBBP. The adaptive nature of the reinforcement learning component enables the framework to automatically discover optimal geometric representation strategies for different molecular property types, eliminating the need for manual feature engineering while providing interpretable insights into structure-property relationships.
Copyright (c) 2025 Kevin Johnson , Aisha Khan , Thomas Wu (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Machine Learning for Predicting Performance and Degradability of Bio-Based Food Packaging
As global demand for sustainable food packaging intensifies, bio-based materials have emerged as promising alternatives to petroleum-derived plastics. However, their adoption at industrial scale is hindered by inconsistencies in mechanical performance, barrier properties, and biodegradability under varying environmental conditions. This study proposes a machine learning (ML)-based framework to predict the performance and environmental behavior of bio-based food packaging materials based on their composition, processing parameters, and environmental variables. Multiple ML algorithms, including Random Forest (RF), Support Vector Regression (SVR), and Artificial Neural Networks (ANN), were trained using experimental datasets derived from published studies and lab-generated data. Key target variables included tensile strength, water vapor transmission rate (WVTR), oxygen permeability, and biodegradation time. Results demonstrate that ensemble models such as RF provided high prediction accuracy and interpretability, enabling efficient material screening and optimization. The study highlights the potential of data-driven tools to accelerate the development of eco-friendly packaging solutions with tailored properties, reducing trial-and-error experimentation and promoting circular economy objectives.
Photocatalytic Degradation of Environmental Pollutants: Advances in Material Design
Photocatalytic degradation of environmental pollutants has emerged as a promising technology for addressing the growing concerns of pollution and environmental sustainability. This review focuses on recent advancements in material design for photocatalytic processes, highlighting new materials, innovative design strategies, and their effectiveness in degrading a range of pollutants. Advances in semiconductor photocatalysts, including titanium dioxide (TiO₂), zinc oxide (ZnO), and novel composite materials, are discussed in terms of their photocatalytic activity, stability, and practical applications. The integration of nanotechnology and surface modifications has significantly enhanced the efficiency of photocatalytic systems. This paper provides a comprehensive overview of the current state of research, challenges, and future directions for improving photocatalytic materials to achieve effective and sustainable environmental remediation.
Copyright (c) 2024 Frontiers in Chemistry, Materials and Catalysis
Single-Atom Catalysts: Precision in Catalysis for Chemical Transformations
Single-atom catalysts (SACs) represent a transformative approach in catalytic science, offering unparalleled precision and efficiency in chemical transformations. By isolating catalytic metal atoms within a support matrix, SACs achieve high selectivity and activity that traditional catalysts often lack. This paper explores the fundamental principles underlying SACs, their synthesis methods, and their applications across various chemical reactions. We highlight recent advances in SAC technology and discuss the impact of SACs on sustainable chemistry. The review provides a critical analysis of the challenges and opportunities associated with SACs, aiming to offer insights into their future developments.
Understanding Catalyst Deactivation: Mechanisms and Materials Solutions
Catalyst deactivation poses significant challenges in industrial catalytic processes, impacting efficiency and economic viability. This paper provides a comprehensive review of catalyst deactivation mechanisms, including poisoning, fouling, sintering, and thermal degradation. We explore material solutions aimed at mitigating these issues, emphasizing advancements in catalyst design, support materials, and regeneration techniques. By examining recent research and developments, this paper highlights the strategies to enhance catalyst longevity and performance. The insights presented are intended to guide the development of more robust catalysts and improve the sustainability of catalytic processes.
Carbon-Based Catalysts: Graphene and Beyond in Modern Catalytic Applications
Carbon-based catalysts, particularly graphene and its derivatives, have emerged as revolutionary materials in the field of catalysis due to their unique structural, electronic, and chemical properties. This paper provides a comprehensive review of recent advancements in the use of graphene and other carbon-based materials in catalytic applications. We explore the synthesis, modification, and functionalization of these catalysts, their performance in various reactions, and their potential for future innovations. By examining case studies and recent developments, we highlight the role of carbon-based catalysts in enhancing reaction efficiency, selectivity, and sustainability across multiple domains, including energy conversion, environmental remediation, and chemical synthesis.
Metal-Free Catalysts: Expanding the Horizons of Organic and Sustainable Chemistry
Metal-free catalysts have emerged as a pivotal advancement in the field of organic chemistry, offering an environmentally friendly alternative to traditional metal-based catalysts. This article explores the development, applications, and advantages of metal-free catalysts in various chemical reactions. By focusing on their roles in sustainable chemistry, we highlight their potential in reducing toxic waste, improving reaction efficiencies, and expanding the scope of organic synthesis. Key areas of application, including cross-coupling reactions, oxidation, and reduction processes, are examined. The review also addresses challenges and future directions for metal-free catalysis, emphasizing the need for continued innovation in this rapidly evolving field.
High-Throughput Screening Methods for Catalyst Development: Bridging Experimentation and Theory
High-throughput screening (HTS) methods have revolutionized catalyst development by enabling rapid and efficient evaluation of numerous candidates. This paper reviews the integration of HTS techniques with theoretical approaches to enhance catalyst discovery and optimization. It explores the advancements in experimental HTS technologies, the role of computational models in predicting catalytic performance, and the synergy between experimental and theoretical methods. By bridging these methodologies, researchers can accelerate the development of catalysts with improved activity, selectivity, and stability. The paper highlights case studies, discusses challenges, and provides future perspectives on the role of HTS in advancing catalyst research.
Designing Electrocatalysts for Water Splitting: Innovations in Sustainable Energy Production
Electrocatalysis for water splitting has emerged as a pivotal technology for sustainable hydrogen production, crucial for advancing renewable energy systems. This article reviews recent innovations in designing electrocatalysts to enhance the efficiency and economic viability of water splitting processes. We discuss the latest materials and methods used in the development of electrocatalysts, including novel metal-based and non-metal-based catalysts, composite materials, and advanced fabrication techniques. The review also explores the challenges in scaling up these technologies and their integration into practical energy systems. By synthesizing current research, this article provides insights into future directions and potential breakthroughs in the field.
Catalytic Conversion of Biomass to Value-Added Chemicals: A Materials Perspective
The catalytic conversion of biomass into value-added chemicals represents a promising approach to sustainable chemical production. This paper provides a comprehensive review of recent advancements in catalytic processes for biomass conversion, focusing on the role of various materials in enhancing efficiency and selectivity. We discuss the different types of catalysts, including heterogeneous, homogeneous, and biocatalysts, and their applications in converting biomass into useful chemicals such as biofuels, biopolymers, and specialty chemicals. The review highlights the challenges and opportunities in material selection, catalytic activity, stability, and economic feasibility. By integrating insights from material science and chemical engineering, this paper aims to provide a holistic perspective on the future directions for optimizing biomass conversion technologies.
Recent Developments in Polymer-Based Catalytic Materials: Structure and Functionality
Recent advancements in polymer-based catalytic materials have shown significant promise in enhancing catalytic processes across various industrial applications. These developments focus on optimizing the structure and functionality of polymers to improve catalytic efficiency, selectivity, and stability. This paper reviews recent innovations in polymer-based catalysts, highlighting new polymerization techniques, structural modifications, and their implications for catalytic performance. Key areas include the design of novel polymeric supports, incorporation of active catalytic sites, and integration with emerging technologies. The review also explores the challenges and future directions in the development of these materials.
Perovskite Materials in Photocatalysis: Harnessing Solar Energy for Chemical Reactions
Perovskite materials have garnered significant attention in the field of photocatalysis due to their exceptional optical and electronic properties. These materials, characterized by their unique crystal structures, offer a promising platform for harnessing solar energy to drive chemical reactions. This review explores the advancements in perovskite photocatalysts, focusing on their synthesis, properties, and applications. We discuss various strategies to enhance their photocatalytic efficiency, including modifications to their composition and structure. Additionally, the challenges associated with stability and scalability are addressed. This paper aims to provide a comprehensive overview of the current state of perovskite photocatalysts and their potential in advancing solar-driven chemical processes.
Biocatalysis and Enzyme Engineering: Pioneering Green Chemical Transformations
Biocatalysis and enzyme engineering represent transformative approaches in green chemistry, offering sustainable alternatives to traditional chemical processes. This paper explores the advancements in biocatalysis and enzyme engineering, focusing on their applications in green chemical transformations. We review recent developments in enzyme design, optimization, and industrial applications, highlighting how these innovations contribute to reducing environmental impact and improving process efficiency. The paper also examines case studies where biocatalysis has led to significant breakthroughs in areas such as pharmaceuticals, agriculture, and environmental remediation. By leveraging the unique properties of enzymes and employing advanced engineering techniques, this research underscores the potential of biocatalysis to drive forward sustainable chemical practices.
Heterogeneous Catalysis: Innovations in Material Synthesis and Applications
Heterogeneous catalysis remains a cornerstone of industrial and environmental chemistry, providing critical advancements in the synthesis and application of catalytic materials. This article explores recent innovations in the field, focusing on novel material synthesis techniques and their diverse applications. We review recent developments in catalyst design, including the use of nanomaterials, metal-organic frameworks, and hybrid composites. Advances in characterization techniques and their implications for understanding catalytic mechanisms are also discussed. The impact of these innovations on various industrial processes, from petrochemical refining to environmental remediation, is highlighted, offering insights into future directions for research and development.
Metal-Organic Frameworks (MOFs) in Catalysis: Bridging Chemistry and Material Science
Metal-Organic Frameworks (MOFs) represent a class of porous materials formed by the coordination of metal ions or clusters with organic ligands, creating a network structure with high surface areas and tunable properties. Their unique structural features and versatile chemical functionalities make them promising candidates for various catalytic applications. This paper explores the role of MOFs in catalysis, focusing on their synthesis, structural diversity, and catalytic performance. We discuss the mechanisms by which MOFs enhance catalytic reactions, including their ability to act as heterogeneous catalysts and their applications in organic transformations, environmental remediation, and energy conversion. The integration of MOFs with emerging technologies and the future prospects of MOF-based catalytic systems are also addressed.
Green Chemistry Approaches in Catalytic Material Design: Sustainable Solutions
Green chemistry offers innovative approaches to material design that promote environmental sustainability while enhancing catalytic performance. This paper explores the integration of green chemistry principles into the design and development of catalytic materials, focusing on methods that reduce waste, utilize safer chemicals, and improve efficiency. We review recent advancements in sustainable catalytic materials, including the use of renewable resources, environmentally benign synthesis methods, and the development of efficient, reusable catalysts. The paper highlights key examples of green chemistry in action, discusses the impact on various industries, and outlines future directions for research in sustainable catalytic material design.
Advances in Nanocatalysis: Enhancing Efficiency in Chemical Reactions
Nanocatalysis has emerged as a transformative field, significantly enhancing the efficiency and selectivity of chemical reactions. This article reviews recent advancements in nanocatalysis, focusing on the development of novel nanocatalysts, their synthesis, and their applications in various chemical processes. We discuss the impact of nanocatalysis on reaction kinetics, product yield, and environmental sustainability. Innovations in nanocatalyst design, including the use of core-shell structures, hybrid materials, and support materials, are explored. The review also addresses challenges and future directions in the field, emphasizing the need for more efficient and cost-effective nanocatalysts.