jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Haoyang Zhao | Jeffrey Chor Keung Lam
Source: Crystals (article, 2025-06-16)
Field: Materials Science — Materials Chemistry

Preparation, Properties, and Applications of 2D Janus Transition Metal Dichalcogenides Structural symmetry significantly influences the fundamental characteristics of two-dimensional (2D) materials. In conventional transition metal dichalcogenides (TMDs), the absence of in-plane symmetry introduces distinct optoelectronic behaviors. To further enrich the functionality of such materials, recent efforts have focused on disrupting out-of-plane symmetry—often through the application of external electric fields—which leads to the generation of an intrinsic electric field within the lattice. This internal field alters the electronic band configuration, broadening the material’s applicability in fields like optoelectronics and spintronics. Among various engineered 2D systems, Janus transition metal dichalcogenides (JTMDs) have shown as a compelling class. Their intrinsic structural asymmetry, resulting from the replacement of chalcogen atoms on one side, naturally breaks out-of-plane symmetry and surpasses certain limitations of traditional TMDs. This unique arrangement imparts exceptional physical properties, such as vertical piezoelectric responses, pronounced Rashba spin splitting, and notable changes in Raman modes. These distinctive traits position JTMDs as promising candidates for use in sensors, spintronic devices, valleytronic applications, advanced optoelectronics, and catalytic processes. In this Review, we discuss the synthesis methods, structural features, properties, and potential applications of 2D JTMDs. We also highlight key challenges and propose future research directions. Compared with previous reviews, this work focusing on the latest scientific research breakthroughs and discoveries in recent years, not only provides an in-depth discussion of the out-of-plane asymmetry in JTMDs but also emphasizes recent advances in their synthesis techniques and the prospects for scalable industrial production. In addition, it highlights the rapid development of JTMD-based applications in recent years and explores their potential integration with machine learning and artificial intelligence for the development of next-generation intelligent devices. Janus ; Transition metal ; Nanotechnology ; Materials science ; Chemistry ; Organic chemistry ; Catalysis 2D Materials and Applications ; ZnO doping and properties ; Perovskite Materials and Applications
Authors: Waleed Iqbal Waseer | Muhammad Abuzar Baqir | Muhammad Saqlain | Muhammad Junaid Mughal | Shujaat Khan
Source: Journal of the Optical Society of America B (article, 2025-01-27)
Field: Materials Science — Materials Chemistry

Predictive modeling of MXene-based solar absorbers using a deep neural network Nanophotonic structures, such as photonic crystals, plasmonic nanostructures, and metamaterials, present transformative potential in advancing optical devices through innovative design capabilities. Among these, metamaterials—specifically metal–insulator–metal (MIM)-based absorbers—stand out for their ability to achieve high electromagnetic wave absorption across designated frequencies, proving valuable in applications ranging from solar cells to sensors. This study focuses on designing a high-efficiency MXene–SiO 2 –silver MIM metasurface absorber, leveraging deep learning techniques to streamline and enhance the simulation process traditionally reliant on finite-difference time-domain (FDTD) or finite-element methods (FEMs). By utilizing a deformable convolutional neural network (CNN), the proposed model predicts absorption spectra from metasurface geometries with superior accuracy and reduced computational demand while achieving dynamic spatial transformation handling. The model’s accuracy is further elucidated through Gradient-weighted Class Activation Mapping (Grad-CAM), which provides insights into the absorber’s most influential regions, supporting targeted optimization for specific applications. This deep learning approach demonstrates significant advantages over conventional methods in efficiency and adaptability, advancing the development of high-performance nanophotonic devices. Artificial neural network ; Computer science ; Materials science ; Artificial intelligence MXene and MAX Phase Materials
Authors: Milad Mousazadeh | Zeinab Hajalifard | Milad Basirifard | Shahabeddin Afsharghoochani | Sayedali Mirkhalafi | Işık Kabdaşlı | Khalid Hashim | Ismini Nakouti
Source: ACS ES&T Water (review, 2025-05-28)
Field: Chemistry — Electrochemistry

The Critical Role of Artificial Intelligence in Optimizing Electrochemical Processes for Water and Wastewater Remediation: A State-of-the-Art Review Artificial intelligence (AI) is transforming electrochemical water and wastewater treatment by enhancing efficiency, predictive accuracy, and process control. However, a comprehensive evaluation of AI models in optimizing electrochemical processes for pollutant removal is still lacking. This review addresses this gap by systematically analyzing AI applications in electrocoagulation (EC), electrooxidation (EO), electro-Fenton (EF), and electrodialysis (ED). Focusing on key advances and parameter optimization, it highlights how AI-driven models improve removal efficiency by capturing complex nonlinear interactions among variables such as current density, pH, electrode material, electrolyte composition, and pollutant concentration. Recent studies have notably shown that artificial neural networks (ANNs) and adaptive neuro-fuzzy inference systems (ANFIS) have achieved R2 values above 0.99 in EC and EO processes, outperforming traditional models. Hybrid AI approaches like ANN-GA and ANFIS-ACO have further optimized catalyst dosage and ion migration in EF and ED. While AI has demonstrated remarkable potential, challenges such as limited data availability, model interpretability, and real-world implementation remain significant obstacles. Integrating AI with mechanistic modeling and real-time monitoring may overcome these barriers and enable autonomous, energy-efficient treatment systems. This Perspective offers critical insights into current progress and future opportunities, underscoring the role of intelligent optimization in advancing sustainable and scalable electrochemical water treatment technologies. Wastewater ; Environmental remediation ; Environmental science ; Process engineering ; Biochemical engineering ; Waste management ; Computer science ; Environmental engineering ; Engineering ; Ecology ; Contamination ; Biology Electrochemical Analysis and Applications ; Water Quality Monitoring Technologies ; Machine Learning and ELM
Authors: Jing Wang | Chenxu Han | Hongxiang Zong | Xiangdong Ding | Jun Sun
Source: Computational Materials Science (article, 2025-01-21)
Field: Materials Science — Materials Chemistry

Effect of Re segregation on irradiation damage behavior of Mo-Re alloys Irradiation ; Materials science ; Crystallography ; Metallurgy ; Chemistry ; Physics ; Nuclear physics Fusion materials and technologies ; Nuclear Materials and Properties ; Advanced materials and composites
Authors: Yuming Wen | Bella Bella | Guoqiang Song | Jie Chang | Sibudjing Kawi | Chi‐Hwa Wang
Source: Journal of Energy Chemistry (article, 2025-01-27)
Field: Materials Science — Materials Chemistry

Machine learning insights into the production and characteristics of carbon nanotubes from methane catalytic decomposition Methane ; Carbon nanotube ; Decomposition ; Catalysis ; Production (economics) ; Chemical engineering ; Carbon fibers ; Materials science ; Environmental science ; Nanotechnology ; Chemistry ; Engineering ; Economics ; Composite material ; Organic chemistry Carbon Nanotubes in Composites ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science
Authors: Sivasubramanian Palanisamy | Nadir Ayrılmış | Kumar Sureshkumar | Carlo Santulli | Tabrej Khan | Harri Junaedi | Tamer A. Sebaey
Source: BioResources (review, 2025-01-03)
Field: Materials Science — Polymers and Plastics

Machine learning approaches to natural fiber composites: A review of methodologies and applications In recent years, the process of optimizing the design of natural fiber reinforcement in natural fiber composites (NFCs) with distinct properties has been redefined through the application of machine learning (ML). This work elucidates the functions of the types and applications of the ML algorithms and evolutionary computing techniques, with a particular focus on their applicability within the domain of NFCs. Moreover, the solution methodologies and associated databases were employed throughout various stages of the product development journey, from the raw material selection through the final end-use application for the NFCs. The strengths and limitations of the ML in the NFCs industry, together with relevant challenges, such as interpretability of ML models, in materials science was detailed. Finally, future directions and emerging trends in the ML are discussed. Materials science ; Composite material ; Natural fiber ; Fiber ; Natural (archaeology) Natural Fiber Reinforced Composites ; Innovative concrete reinforcement materials ; Innovations in Concrete and Construction Materials
Authors: A. Konter | Jaume Rostoll‐Berenguer | Céline Besnard | Baptiste Leforestier | Clément Mazet
Source: ACS Catalysis (article, 2025-01-28)
Field: Chemistry — Organic Chemistry

Enantioselective Synthesis of Pillar[5]arenes by Dynamic Kinetic Resolution via Pd-Catalyzed Suzuki Cross-Coupling We report a dynamic kinetic resolution based on a Pd-catalyzed asymmetric Suzuki cross-coupling reaction to afford enantioenriched and configurationally stable pillar[5]arenes. When applied to A1/A2-ditriflate-pillar[5]arenes, the method displays a high level of catalytic efficiency and stereoselectivity across a broad substrate scope. The approach can be extended to the synthesis of nearly enantiopure rim-differentiated and tetra-arylated macrocycles. The enantioenriched macrostructures can be readily derivatized into mono- and dinuclear transition metal complexes. The intricate conformational dynamics of these systems can be captured using a neural network model approach, together with metadynamics simulations. Experimental and computational analyses are consistent, with the second cross-coupling being enantio-determining in the sequential double cross-coupling reaction. Additional computational studies indicate that stereodifferentiation involves a combination of repulsive steric factors and stabilizing secondary interactions. Enantioselective synthesis ; Kinetic resolution ; Catalysis ; Pillar ; Chemistry ; Coupling (piping) ; Kinetic energy ; Combinatorial chemistry ; Palladium ; Organic chemistry ; Materials science Supramolecular Chemistry and Complexes ; Molecular spectroscopy and chirality ; Advanced NMR Techniques and Applications
Authors: Yuncong Pang | Yang Li | Yuzhe Gu | Bin Xu | Zihan Zhu | Xiaotian Wang | Yuan Liao | Liya Huang | Qiang Zhao
Source: Matter (article, 2025-03-18)
Field: Materials Science — Polymers and Plastics

Stretchable organic electrochemical transistors for sustained high-fidelity electrophysiology and deep learning-assisted sleep monitoring Electrophysiology ; Nanotechnology ; Transistor ; Sleep (system call) ; Materials science ; Neuroscience ; Psychology ; Computer science ; Engineering ; Electrical engineering ; Voltage Conducting polymers and applications ; Advanced Sensor and Energy Harvesting Materials ; Advanced Memory and Neural Computing
Authors: Lalitha Durairaj | M. Murugesan
Source: Synthetic Metals (article, 2025-04-12)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Recent innovations in EMI shielding materials for stealth technology EMI ; Electromagnetic shielding ; Electromagnetic interference ; Materials science ; Engineering physics ; Engineering ; Composite material ; Nanotechnology ; Forensic engineering ; Electrical engineering Electromagnetic wave absorption materials ; Advancements in Battery Materials ; Aluminum Alloys Composites Properties
Authors: Fei Shuang | K. Liu | Yucheng Ji | Wei Gao | Luca Laurenti | Poulumi Dey
Source: npj Computational Materials (article, 2025-05-03)
Field: Materials Science — Materials Chemistry

Modeling extensive defects in metals through classical potential-guided sampling and automated configuration reconstruction Abstract Extended defects such as dislocation networks and general grain boundaries are ubiquitous in metals, and accurate modeling these extensive defects is crucial to elucidate their deformation mechanisms. However, existing machine learning interatomic potentials (MLIPs) often fall short in adequately describing these defects, as their large characteristic scales exceed the computational limits of first-principles calculations. To address this challenge, we present a computational framework combining a defect genome constructed via empirical interatomic potential-guided sampling, with an automated reconstruction technique that enables accurate first-principles modeling of general defects by converting atomic clusters into periodic configurations. The effectiveness of this approach was validated through simulations of nanoindentation, tensile deformation, and fracture in BCC tungsten. This framework enhances the modeling accuracy of extended defects in crystalline materials and provides a robust foundation for advancing MLIP development by leveraging defect genomes strategically. Sampling (signal processing) ; Computer science ; Environmental science ; Computer vision Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Chengjie Zhou | Qingliang Liu | Xiao Fu | Wencheng Niu | Jingli Wang | Xingqiang Liu | Xuming Zou | Jinshui Miao | Fukai Shan | Zhenyu Yang
Source: Nano Letters (article, 2025-02-24)
Field: Materials Science — Materials Chemistry

Self-Powered and Reconfigurable Double-Terminal MoS<sub>2</sub> Photodetector for Image Recognition Two-dimensional (2D) semiconductors hold significant potential for the fabrication of self-powered reconfigurable devices due to their atomically thin body thickness and excellent heterogeneous integration capabilities. Despite these advantages, regulating polarity in 2D semiconductors remains challenging, and their complex heterostructures hinder practical applications. Here, we demonstrate a metal-semiconductor-metal (MSM) two-terminal structure to achieve self-powered and multiple reconfigurable functions in MoS<sub>2</sub> devices. By applying different voltage pulses to modulate the sulfur vacancy concentration, the device obtains a reversible internal electric field, leading to the bidirectional self-powered photocurrent that switches from negative to positive. This device achieves a fastest response speed of 224 μs and a maximum responsivity of 280 mA/W. Besides, we proposed a resistance-dependent method to achieve stable and reversible responsivity modulation and successfully implement the image processing and object detection using a convolutional neural network (CNN). This work provides a straightforward approach for integrating next-generation optoelectronic devices. Responsivity ; Photodetector ; Optoelectronics ; Photocurrent ; Materials science ; Semiconductor ; Heterojunction ; Image sensor ; Computer science ; Terminal (telecommunication) ; Fabrication ; Artificial intelligence ; Telecommunications 2D Materials and Applications ; Advanced Memory and Neural Computing ; Perovskite Materials and Applications
Authors: Kangling He | Jianping Tian | Yuanyuan Xia | Yifei Zhou | Xinjun Hu | Liangliang Xie | Haili Yang | Yuexiang Huang | Dan Huang
Source: Journal of Food Composition and Analysis (article, 2025-03-06)
Field: Chemistry — Analytical Chemistry

Detection of the amylose and amylopectin contents of rice by hyperspectral imaging combined with a CNN-AdaBoost model Hyperspectral imaging ; Amylopectin ; Amylose ; Pattern recognition (psychology) ; Artificial intelligence ; Computer science ; Chemistry ; Food science Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Water Quality Monitoring and Analysis
Authors: Qing An | Dawei Li | Wenhe Liao | Tingting Liu | Weiping Li
Source: Advanced Materials Technologies (article, 2025-05-21)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Lightweight Electromagnetic Wave‐Absorbing Metastructures: Advances in 3D Printing‐Based Design and Manufacturing Abstract 3D‐printed electromagnetic metastructures have revolutionized the design of lightweight, broadband electromagnetic wave absorbers through advanced control of wave impedance, scattering mechanisms, and multifunctional integration. Despite their potential for stealth technology, electromagnetic shielding, and microwave absorption, challenges remain in design methodology, material optimization, and scalable fabrication. This review systematically categorizes metastructure design into three approaches: 3D design, gradient impedance design, and bio‐inspired topology design, emphasizing their impact on absorption performance. Additive manufacturing techniques, including polymer‐ and ceramic‐based fabrication with hierarchical structural control, are analyzed for enhancing absorption efficiency and mechanical robustness. Key loss mechanisms “dielectric, magnetic losses, and structural resonance” are discussed to explain performance fundamentals. Critical challenges include balancing broadband absorption with mechanical strength, extending tunable frequency ranges, and scaling production for industrial applications. Future research should prioritize AI‐driven inverse design, multi‐functional material integration, and rapid multi‐scale manufacturing. The integration of machine learning and advanced 3D printing is poised to enable next‐generation high‐performance electromagnetic wave control materials. This review provides a roadmap for bridging fundamental research and practical applications in metastructure development. 3D printing ; Metamaterial ; Materials science ; Engineering ; Mechanical engineering ; Optoelectronics Electromagnetic wave absorption materials ; Advanced Antenna and Metasurface Technologies ; Metamaterials and Metasurfaces Applications
Authors: Xue Jiang | Dezhen Xue | Yang Bai | William Yi Wang | Jianjun Liu | Mingli Yang | Yanjing Su
Source: Review of Materials Research (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

AI4Materials: Transforming the landscape of materials science and enigneering New materials, crucial for economic and technological progress, are prioritized globally with strategies to accelerate their advancement through big data and AI. AI for Materials (AI4Mater) serves as an overall framework for integrating AI into Materials Science and Engineering, which is structured around three main elements: materials data infrastructure, AI4Mater techniques, and applications. This article reviews the development procedure and recent innovations in materials data infrastructure, machine learning in materials, autonomous experiment, intelligent computation, and intelligent manufacture. These efforts aim to foster open access to AI resources and enhance the collective advancement of materials science, ultimately accelerating breakthroughs and elevating the engineering application of new materials in a sustainable manner. Environmental science ; Geography Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Advanced Materials Characterization Techniques
Authors: Jilong Gao | Shaojin Wang | Ferruh Erdoğdu | Francesco Marra | Fabrizio Sarghini | Long Chen
Source: Trends in Food Science & Technology (article, 2025-08-06)
Field: Chemistry — Analytical Chemistry

Machine learning-driven innovations in food processing: A systematic review of applications, challenges, and future developments Computer science ; Food processing ; Systematic review ; Data science ; Management science ; Engineering ethics ; Engineering ; Political science ; MEDLINE Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Identification and Quantification in Food
Authors: Qi Qi | Zhuang Wang
Source: Environmental Toxicology and Chemistry (article, 2025-01-07)
Field: Materials Science — Materials Chemistry

Integrating machine learning and nano-QSAR models to predict the oxidative stress potential caused by single and mixed carbon nanomaterials in algal cells In silico methods are increasingly important in predicting the ecotoxicity of engineered nanomaterials (ENMs), encompassing both individual and mixture toxicity predictions. It is widely recognized that ENMs trigger oxidative stress effects by generating intracellular reactive oxygen species (ROS), serving as a key mechanism in their cytotoxicity studies. However, existing in silico methods still face significant challenges in predicting the oxidative stress effects induced by ENMs. Herein, we utilized laboratory-derived toxicity data and machine learning methods to develop quantitative nanostructure-activity relationship (nano-QSAR) classification and regression models, aiming to predict the oxidative stress effects of five carbon nanomaterials (fullerene, graphene, graphene oxide, single-walled carbon nanotubes, and multi-walled carbon nanotubes) and their binary mixtures on Scenedesmus obliquus cells. We constructed five nano-QSAR classification models by combining zeta potential (ζP) with the C4.5 decision tree, support vector machine, artificial neural network, naive Bayes, and K-nearest neighbor algorithms. Moreover, we constructed three classification models by integrating the features including ζP, hydrodynamic diameter (DH), and specific surface area (SSA) with the logistic regression, random forest, and Adaboost algorithms. The Accuracy, Recall, Precision and harmonic mean of Precision and Recall (F1-score) values of these models were all higher than 0.600, indicating an excellent performance in distinguishing whether CNMs have the potential to generate ROS. In addition, using the ζP, DH, and SSA descriptors, we combined decision tree regression, random forest regression, gradient boosting, and the Adaboost algorithm, and successfully constructed four nano-QSAR regression models with applicable application domains (all training and testing data points lie within 95% confidence intervals), goodness-of-fit (Rtrain2 ≥ 0.850), and robustness (cross-validation R2 ≥ 0.650) as well as predictive power (Rtest2 ≥ 0.610). The method developed would establish a fundamental basis for more precise evaluations of ecological risks posed by these materials from a mechanistic standpoint. Quantitative structure–activity relationship ; Oxidative stress ; Nanomaterials ; Nano- ; Carbon fibers ; Nanotechnology ; Chemistry ; Environmental chemistry ; Computer science ; Materials science ; Machine learning ; Engineering ; Chemical engineering ; Composite number ; Biochemistry Nanoparticles: synthesis and applications ; Graphene and Nanomaterials Applications ; Computational Drug Discovery Methods