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252 articles for “heterogeneity”
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Modern Catalytic Systems: Mechanisms, Materials Engineering, and Sustainable Applications
Abstract: Catalysis is a fundamental pillar of modern chemical science and industrial manufacturing, enabling efficient production of fuels, chemicals, pharmaceuticals, polymers, and environmentally important products through the acceleration of chemical reactions and reduction of activation energy barriers. This review provides a comprehensive overview of contemporary catalytic systems, with emphasis on the relationship between catalyst structure, reaction mechanisms, catalytic performance, and long-term stability. The discussion begins with the classification of homogeneous, heterogeneous, …
Published in Journal of Catalyst & Catalysis · Vol. 13, Issue 2, 2026 · pp. 01–09 Read article
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Multiscale Catalytic Strategies for Sustainable Chemical Production: Integrating Computational Catalysis, Process Technology and Biocatalytic Transformations
Abstract: The transition toward sustainable chemical manufacturing requires catalytic technologies capable of maximizing resource efficiency, minimizing greenhouse gas emissions, and enabling the utilization of renewable feedstocks. Recent advances in computational catalysis, process technology, and biocatalytic transformations have created opportunities for the development of integrated catalytic platforms spanning molecular, reactor, and process scales. Density functional theory (DFT), machine learning-assisted catalyst discovery, and multiscale modeling have accelerated the rational design of heterogeneous, homogeneous, …
Published in Journal of Catalyst & Catalysis · Vol. 13, Issue 2, 2026 · pp. 27–35 Read article
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Integrated Computational and Bio-catalytic Transformations: DFT-Guided Mechanistic Insights, Machine Learning, and Nano-biocatalyst Engineering for Sustainable Catalysis
Abstract: Computational catalysis has emerged as a transformative scientific discipline that integrates quantum chemistry, molecular modeling, machine learning, and density functional theory (DFT) to understand catalytic mechanisms and design highly efficient catalytic systems for sustainable industrial applications. The increasing global demand for environmentally responsible chemical manufacturing has accelerated research on advanced catalytic materials including transition metal catalysts, metal–organic frameworks (MOFs), homogeneous catalysts, heterogeneous systems, and bimetallic catalysts involving nickel and iron. …
Published in Journal of Catalyst & Catalysis · Vol. 13, Issue 2, 2026 · pp. 36–44 Read article
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From Quantum Chemistry to Bioprocess Intensification: Advanced Computational Modeling and Enzyme-Based Catalytic Platforms for Green Chemical Transformations
Abstract: Green chemistry requires the development of sustainable catalytic systems that minimize waste generation, reduce energy consumption, and improve process efficiency. Computational chemistry and biocatalysis have emerged as complementary approaches for environmentally responsible chemical manufacturing. Computational techniques such as quantum chemistry, density functional theory (DFT), molecular dynamics, and machine learning provide mechanistic insights into catalytic reactions and support the rational design of efficient catalysts. These approaches enable the prediction of reaction …
Published in Journal of Catalyst & Catalysis · Vol. 13, Issue 2, 2026 · pp. 45–52 Read article
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Computational Intelligence and Neuro-Fuzzy Modelling of Polymer Composites: A Critical Review of Performance Prediction and Optimization
Abstract: The increased variety in polymer matrices, reinforcements, fillers, and processing parameters has led to the need to better understand the structure-property, process-property relationships in order to accurately predict and optimize the performance of polymer composites. This paper reviews the applications of computational intelligence methods in polymer composites, with special focus on artificial neural networks, adaptive neuro-fuzzy inference systems, machine learning techniques, and hybrid optimization. The literature is analyzed based on …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
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Unveiling Actionable Pathogenic Genes for Precision Oncology in Brain Cancers: Glioblastoma and Astrocytoma
Abstract: Glioblastoma (GBM) and astrocytoma are aggressive primary brain tumors characterized by significant molecular heterogeneity, complicating effective treatment. This study employed targeted next-generation sequencing of 529 cancer-associated genes on matched tumor-normal pairs from GBM and astrocytoma patients. Somatic variants were identified using a rigorous bioinformatic pipeline adhering to GATK Best Practices, with variant allele frequencies (VAF) calculated to assess clonal dominance. Functional annotation and driver mutation classification were performed using Ensemble …
Published in Research and Reviews : Journal of Computational Biology · Vol. 15, Issue 2, 2026 · pp. 43–59 Read article
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Systemic Lupus Erythematosus Revisited: A Milestones in Classification, Mechanistic Insights on Pathogenesis, Diagnosis, and Evolving Treatment Paradigms
Abstract: Systemic lupus erythematosus (SLE) is a prototypic, multi-systemic chronic inflammatory autoimmune disorder characterized by extreme clinical heterogeneity and the potential to involve virtually any organ or tissue. The condition is driven by a fundamental breakdown in immune tolerance, resulting in the overproduction of pathogenic autoantibodies and the formation of immune complexes that precipitate widespread inflammation and organ damage. This review aims to provide an exhaustive synthesis of recent developments in …
Published in Research and Reviews: A Journal of Pharmacology · Vol. 16, Issue 3, 2026 Read article
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Fundamentals and Recent Advances in Microreactor Catalysis and Flow Chemistry
Abstract: The processing technology of chemical reactions has changed quite a bit over the last few decades. For a long time, chemists relied on batch reactors having large containers where all the starting materials are mixed, and the reaction happens in one go. While this approach works, it has several drawbacks: poor heat control, uneven mixing, safety risks, and a lot of waste. Microreactors and flow chemistry offer a much better …
Published in Journal of Modern Chemistry & Chemical Technology · Vol. 17, Issue 2, 2026 · pp. 07–18 Read article
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Deep Learning for Earth Observation Using Satellite Imagery: A Comprehensive Review
Abstract: Earth observation (EO) satellites provide continuous, large-scale information about the Earth's land, oceans, atmosphere, vegetation, infrastructure, and environmental conditions. The rapid growth of multispectral, hyperspectral, synthetic aperture radar (SAR), thermal, and high- resolution satellite missions has generated large volumes of heterogeneous spatial and temporal data. Conventional image-processing and machine-learning techniques often require manually designed features and may have difficulty representing the complex spatial, spectral, temporal, and multimodal characteristics of satellite …
Published in Research & Reviews : Journal of Space Science & Technology · Vol. 15, Issue 2, 2026 Read article
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Federated Learning for Privacy-Preserving AI Model Training Across Distributed Healthcare Systems
Abstract: Building effective AI diagnostic tools in clinical environments presents a fundamental contradiction — the patient data most critical to model performance is precisely the data subject to the strictest legal and institutional restrictions. Regulations such as HIPAA and GDPR, while essential for protecting patient rights, render conventional centralized training pipelines largely impractical in real hospital settings where data cannot be transferred, pooled, or shared across institutional boundaries. This paper presents …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 13, Issue 3, 2025 Read article
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Machine Learning-Based Task Scheduling and Resource Optimization in Edge-IoT Systems
Abstract: The rapid expansion of Internet of Things (IoT) applications has introduced significant challenges in managing computational workloads across distributed edge environments. Edge- IoT systems are characterized by limited computational capacity, dynamic task arrivals, and strict latency constraints. Traditional heuristic-based scheduling techniques often fail to adapt to fluctuating workloads and heterogeneous resource availability. This study proposes a machine learning-based task scheduling and resource optimization framework for Edge-IoT systems. The proposed model …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 13, Issue 3, 2025 Read article
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Next-Generation Biorepositories: Accelerating Infectious Disease Research and Vaccine Innovation
Abstract: Next-generation biorepositories have emerged as critical infrastructure for advancing infectious disease research and accelerating vaccine development. By integrating cellular, genomic, and clinical data within harmonized frameworks, these repositories overcome traditional limitations of fragmented datasets and limited interoperability. The evolution from conventional biobanks to digitally enabled, multi-omics platforms has enabled comprehensive analysis of pathogen–host interactions, facilitating the identification of novel vaccine targets and correlates of protection. The COVID-19 pandemic underscored the …
Published in International Journal of Vaccines · Vol. 3, Issue 2, 2026 Read article