Sürdürülebilir Kalkınma Amaçları
Yayınlar
- Kitap BölümüYalnız künye2026Artificial Intelligence Nanocomposite Synergy for Next-Generation Fuel Desulfurization
Kostas, Rabia Yasa; Bozbeyoglu, Pinar; Altuner, Elif Esra; Elmas, Muzaffer
Fuel desulfurization is crucial due to the harmful effects of acid rain, air pollution, and health issues associated with sulfur compounds from fossil fuel combustion. Stricter international regulations require reducing sulfur content in fuels to ultra- low levels (<15 ppm), which current hydrodesulfurization (HDS) technology struggles to achieve due to high energy costs and inefficiencies with refractory aromatic sulfur compounds, such as dibenzothiophene (DBT). This gap in technology is driving the development of new solutions. This study explores the integration of materials science, chemical engineering, and artificial intelligence (AI) to meet ultra- low sulfur targets. It highlights alternative strategies, such as adsorptive (ADS) and oxidative (ODS) desulfurization, using advanced nanocomposite materials, including MetalOrganic Frameworks (MOFs), graphene hybrids, and zeolites. These innovative materials focus on “precision targeting” and “molecular recognition,” offering greater efficiency under milder conditions compared to traditional HDS methods AI and computational science are crucial for identifying the most suitable materials among a vast array of candidates. Combining Density Functional Theory (DFT) with Machine Learning (ML) accelerates material discovery by transitioning from a trial- and- error approach to an “inverse design” method. Successful AI tools, including Artificial Neural Networks (ANNs) and Genetic Algorithms (GAs), have been proven effective in optimising desulfurization processes through real- world examples. In summary, achieving cleaner fuels requires innovative materials guided by AI. The study also addresses the challenges of scaling this interdisciplinary approach and envisions the potential for transforming desulfurization from a cost centre into a profit centre through precise molecular separation.
- Kitap BölümüYalnız künye2026Artificial Intelligence-Assisted Design and Optimization of BioNanocomposites for Biomedical and Environmental Applications
Bozbeyoglu, Pinar; Kostas, Rabia Yasa; Altuner, Elif Esra; Elmas, Muzaffer
Bio-nanocomposites, produced from renewable resources, stand out as one of the promising next-generation material classes for both biomedical and environmental applications due to their biocompatible and biodegradable structures.However, the vast and complex design space of these materials, which depends on numerous variables including the type of biopolymer matrix and the type and concentration of the nanostructured filler, makes their optimization quite challenging with traditional “trial-and-error” based research methods.This study provides a general overview of an artificial intelligence (AI)-based methodology that holds the potential to overcome this complexity.In this context, the limitations of the traditional “forward screening” approach are discussed, and the “inverse design” paradigm, based on the idea of “designing a material with desired properties,” is proposed as a solution.The study outlines the potential roles of various AI tools in this field, from supervised learning models to deep learning approaches and optimisation algorithms.This theoretical framework is substantiated by practical case studies that range from drug delivery to sustainable packaging.The case studies demonstrate how AI not only enables the prediction of properties but also facilitates strategically developing materials for a particular function, thereby accelerating the discovery process.Finally, current challenges, such as data scarcity and the interpretability of models, are addressed, and future visions to overcome these obstacles, including physics-informed AI, autonomous discovery platforms, and human-AI partnerships, are discussed.It is concluded that this synergy has the capacity to stimulate a new era of innovation in sustainable materials science.
- Kitap BölümüYalnız künye2026Smart Nanocarriers and AI-Driven Release Systems for Targeted Antibiotic Delivery
Kostas, Rabia Yasa; Bozbeyoglu, Pinar; Altuner, Elif Esra; Elmas, Muzaffer
The antimicrobial resistance (AMR) crisis is not merely a consequence of the emergence ofresistant pathogens. Furthermore, the remaining shortcomings oftraditional antibiotic production and development processes have become a profound health threat, exacerbated by their pharmaceutical and pharmacokinetic limitations. This book chapter redefines the AMR crisis as a problem of drug and treatment discovery as well as drug delivery to target tissues, and examines the technological angle emerging at the intersection of nanotechnology and artificial intelligence (AI) in response to this problem. This review begins by examining the problems inherent in traditional antibiotic architecture, such as systemic toxicity, low bioavailability, poor stability, and, most importantly, the inability to penetrate protected sites such as bacterial biofilms or intracellular bunkers. Copyright © 2026 by IGI Global Scientific Publishing. All rights reserved. No part of this publication may be reproduced, stored or distributed in any form or by any means, electronic or mechanical, including photocopying, without written permission from the publisher. Use of this publication to train generative artificial intelligence (AI) technologies is expressly prohibited. The publisher reserves all rights to license its use for generative AI training and machine learning model development.