AirNet: predictive machine learning model for air quality forecasting using web interface DOI Creative Commons
Md Mahbubur Rahman,

Md. Emran Hussain Nayeem,

Md. Shorup Ahmed

et al.

ENVIRONMENTAL SYSTEMS RESEARCH, Journal Year: 2024, Volume and Issue: 13(1)

Published: Oct. 9, 2024

Air is one of the most significant elements environment. The increasing global air pollution crisis poses an unavoidable threat to human health, environmental sustainability, ecosystems, and earth's climate. has been referred as a silent killer due its insidious nature. Its indirect impact on health further underscores dangerous effects. Early detection quality can potentially save millions lives globally. A unique transformative approach harness power machine learning combat pollution. This research presents manual web-based automatic prediction system that provides real-time alerts status help prevent premature deaths, chronic diseases, other problems. pollutants, including carbon monoxide (CO), ozone (O3), nitrogen dioxide (NO2), particulate matter (PM 2.5), are used in this study for feature analysis extraction. utilizes publicly available data from 23,463 different cities worldwide. Data preprocessing was performed before feeding into models correlation evaluation. proposed uses various predict quality, Random Forest (100%), Logistic Regression (79%), Decision Tree Support Vector Machine (93%), Linear SVC (98%), K-Nearest Neighbor (99%), Multinomial Naïve Bayes (52%). user-friendly Django-based web interface offers accessible platform users monitor real-time, based two best-performing models: techniques.

Language: Английский

Emerging high-entropy compounds for electrochemical energy storage and conversion DOI
Da Liu,

Peifang Guo,

Hongge Pan

et al.

Progress in Materials Science, Journal Year: 2024, Volume and Issue: 145, P. 101300 - 101300

Published: April 18, 2024

Language: Английский

Citations

25
Neural interfaces: Bridging the brain to the world beyond healthcare DOI Creative Commons
Shumao Xu,

Yang Liu,

Hyun‐Jin Lee

et al.

Exploration, Journal Year: 2024, Volume and Issue: 4(5)

Published: March 14, 2024

Abstract Neural interfaces, emerging at the intersection of neurotechnology and urban planning, promise to transform how we interact with our surroundings communicate. By recording decoding neural signals, these interfaces facilitate direct connections between brain external devices, enabling seamless information exchange shared experiences. Nevertheless, their development is challenged by complexities in materials science, electrochemistry, algorithmic design. Electrophysiological crosstalk mismatch electrode rigidity tissue flexibility further complicate signal fidelity biocompatibility. Recent closed‐loop brain‐computer while promising for mood regulation cognitive enhancement, are limited accuracy adaptability user interfaces. This perspective outlines challenges discusses progress contrasting non‐invasive invasive approaches, explores dynamics stimulation interfacing. Emphasis placed on applications beyond healthcare, highlighting need implantable high‐resolution capabilities.

Language: Английский

Citations

22
A review of chalcogenide-based perovskites as the next novel materials: Solar cell and optoelectronic applications, catalysis and future perspectives DOI
George G. Njema, Joshua K. Kibet

Next Nanotechnology, Journal Year: 2024, Volume and Issue: 7, P. 100102 - 100102

Published: Sept. 11, 2024

Language: Английский

Citations

21
The emerging high-entropy cathode materials for advanced Na-ion batteries: advances and perspectives DOI
Peiyu Hou, Maosheng Gong,

Mohan Dong

et al.

Energy storage materials, Journal Year: 2024, Volume and Issue: 72, P. 103750 - 103750

Published: Aug. 26, 2024

Language: Английский

Citations

10
A mini review on the applications of artificial intelligence (AI) in surface chemistry and catalysis DOI

Faisal Al-Akayleh,

Ahmed S.A. Ali Agha,

Rami A. Abdel Rahem

et al.

Tenside Surfactants Detergents, Journal Year: 2024, Volume and Issue: 61(4), P. 285 - 296

Published: April 29, 2024

Abstract This review critically analyzes the incorporation of artificial intelligence (AI) in surface chemistry and catalysis to emphasize revolutionary impact AI techniques this field. The current examines various studies that using techniques, including machine learning (ML), deep (DL), neural networks (NNs), catalysis. It reviews literature on application models predicting adsorption behaviours, analyzing spectroscopic data, improving catalyst screening processes. combines both theoretical empirical provide a comprehensive synthesis findings. demonstrates applications have made remarkable progress properties nanostructured catalysts, discovering new materials for energy conversion, developing efficient bimetallic catalysts CO 2 reduction. AI-based analyses, particularly advanced NNs, provided significant insights into mechanisms dynamics catalytic reactions. will be shown plays crucial role by significantly accelerating discovery enhancing process optimization, resulting enhanced efficiency selectivity. mini-review highlights challenges data quality, model interpretability, scalability, ethical, environmental concerns AI-driven research. importance continued methodological advancements responsible implementation

Language: Английский

Citations

8
High-Entropy Oxides as Energy Materials: From Complexity to Rational Design DOI Creative Commons

Zhong Yang,

Xianglin Xiang,

Jian Yang

et al.

Materials Futures, Journal Year: 2024, Volume and Issue: 3(4), P. 042103 - 042103

Published: Oct. 8, 2024

Abstract High-entropy oxides (HEOs), with their multi-principal-element compositional diversity, have emerged as promising candidates in the realm of energy materials. This review encapsulates progress harnessing HEOs for conversion and storage applications, encompassing solar cells, electrocatalysis, photocatalysis, lithium-ion batteries, solid oxide fuel cells. The critical role theoretical calculations simulations is underscored, highlighting contribution to elucidating material stability, deciphering structure-activity relationships, enabling performance optimization. These computational tools been instrumental multi-scale modeling, high-throughput screening, integrating artificial intelligence design. Despite promise, challenges such fabrication complexity, cost, hurdles impede broad application HEOs. To address these, this delineates future research perspectives. include innovation cost-effective synthesis strategies, employment situ characterization micro-chemical insights, exploration unique physical phenomena refine performance, enhancement models precise structure-performance predictions. calls interdisciplinary synergy, fostering a collaborative approach between materials science, chemistry, physics, related disciplines. Collectively, these efforts are poised propel towards commercial viability new technologies, heralding innovative solutions pressing environmental challenges.

Language: Английский

Citations

8
Synthesis Strategies for High Entropy Nanoparticles DOI
Linlin Yang, Ren He,

Jiali Chai

et al.

Advanced Materials, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 30, 2024

Abstract Nanoparticles (NPs) of high entropy materials (HEMs) have attracted significant attention due to their versatility and wide range applications. HEM NPs can be synthesized by fragmenting bulk HEMs or disintegrating recrystallizing them. Alternatively, directly producing in NP form from atomic/ionic/molecular precursors presents a challenge. A widely adopted strategy involves thermodynamically driving formation leveraging the entropic contribution but incorporating strategies limit growth at elevated temperatures used for maximizing entropy. second approach is kinetically drive promoting rapid reactions homogeneous reactant mixtures using highly diluted precursor dissolutions. Additionally, experimental evidence suggests that enthalpy plays role processes moderate temperatures, with energy cost generating additional surfaces interfaces nanoscale stabilizing phase. This review critically assesses various synthesis developed preparation, highlighting key illustrative examples offering insights into underlying mechanisms. Such are critical fine‐tuning conditions achieve specific outcomes, ultimately enabling effective optimized generations these advanced both current emerging applications across scientific technological fields.

Language: Английский

Citations

8
Recent Progress and Challenges on Emerging High-Entropy Materials for Better Zn-Air and Zn-Ion Batteries DOI

Zhengran Wang,

Zhiwei Ni, Jian Chen

et al.

Energy storage materials, Journal Year: 2025, Volume and Issue: 75, P. 104064 - 104064

Published: Jan. 31, 2025

Language: Английский

Citations

1
Colloidal Nanoparticles of High Entropy Materials: Capabilities, Challenges, and Opportunities in Synthesis and Characterization DOI Creative Commons
Gaurav R. Dey,

Samuel S. Soliman,

Connor R. McCormick

et al.

ACS Nanoscience Au, Journal Year: 2023, Volume and Issue: 4(1), P. 3 - 20

Published: Nov. 16, 2023

Materials referred to as "high entropy" contain a large number of elements randomly distributed on the lattice sites crystalline solid, such that high configurational entropy is presumed contribute significantly their formation and stability. High temperatures are typically required achieve stabilization, which can make it challenging synthesize colloidal nanoparticles materials. Nonetheless, strategies emerging for synthesis nanoparticles, interest synergistic properties unique catalytic functions arise from constituent interactions. In this Perspective, we highlight classes materials have been made well insights into synthetic methods pathways by they form. We then discuss concept within context synthesized at much lower than drive formation. Next, identify address challenges opportunities in field nanoparticle synthesis. emphasize aspects characterization especially important consider materials, including powder X-ray diffraction elemental mapping with scanning transmission electron microscopy, among most commonly used techniques laboratory settings. Finally, share perspectives future directions involving an emphasis synthesis, characterization, fundamental knowledge needed anticipated advances key application areas.

Language: Английский

Citations

16
A 30-Year Review on Nanocomposites: Comprehensive Bibliometric Insights into Microstructural, Electrical, and Mechanical Properties Assisted by Artificial Intelligence DOI Open Access
Fernando Gomes de Souza, Shekhar Bhansali, Kaushik Pal

et al.

Materials, Journal Year: 2024, Volume and Issue: 17(5), P. 1088 - 1088

Published: Feb. 27, 2024

From 1990 to 2024, this study presents a groundbreaking bibliometric and sentiment analysis of nanocomposite literature, distinguishing itself from existing reviews through its unique computational methodology. Developed by our research group, novel approach systematically investigates the evolution nanocomposites, focusing on microstructural characterization, electrical properties, mechanical behaviors. By deploying advanced Boolean search strategies within Scopus database, we achieve meticulous extraction in-depth exploration thematic content, methodological advancement in field. Our uniquely identifies critical trends insights concerning microstructure, attributes, performance. The paper goes beyond traditional textual analytics evaluation, offering new interpretations data highlighting significant collaborative efforts influential studies domain. findings uncover language, shifts, global contributions, providing distinct comprehensive view dynamic research. A component is “State-of-the-Art Gaps Extracted Results Discussions” section, which delves into latest advancements This section details various types their properties introduces applications, especially films. tracing historical progress identifying emerging trends, emphasizes significance collaboration molding Moreover, “Literature Review Guided Artificial Intelligence” showcases an innovative AI-guided research, first Focusing articles 2023, selected based citation frequency, method offers perspective interplay between nanocomposites properties. It highlights composition, structure, functionality systems, integrating recent for overview current knowledge. analysis, with average score 0.638771, reflects positive trend academic discourse increasing recognition potential nanocomposites. another novelty, maps intellectual domain, emphasizing pivotal themes influence crosslinking time attributes. While acknowledging limitations, exemplifies indispensable role tools synthesizing understanding extensive body literature. work not only elucidates prevailing but also contributes insights, enhancing

Language: Английский

Citations

6