jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kassa Belay Ibrahim | Tofik Ahmed Shifa | Sandro Zorzi | Marshet Getaye Sendeku | Elisa Moretti | Alberto Vomiero
Source: Progress in Materials Science (article, 2024-03-20)
Field: Materials Science — Materials Chemistry

Emerging 2D materials beyond mxenes and TMDs: Transition metal carbo-chalcogenides The discovery of graphene sparked significant interest in 2D materials, which present an ultra-thin layered structure with high anisotropy and adjustable energy-band structure. Interestingly, it opens the door for the development of the 2D materials family, which includes different classes of 2D materials. Among them, transition metal dichalcogenides (TMDs) and transition metal carbide MXenes (TMCs) have emerged. TMDs have unique layered structures, low cost, and are composed of earth abundant elements, but their poor electronic conductivity, poor cyclic stability, their structural and morphological changes during electrochemical measurements hinder their practical use. Recently, TMC MXenes have garnered attention in the 2D material world, but the issue of restacking and aggregation limits their direct use in large-scale energy conversion and storage. To address these challenges, hetero structures based on conductive TMCs MXenes and electrochemically active TMDs have emerged as a promising solution. However, understanding the solid/solid interface in heterostructured materials remains a challenge. To tackle this, 2D single component crystals with high capacity, low diffusion barrier, and good electronic conductivity are highly sought. The emergence of transition metal carbo-chalcogenides (TMCCs) has provided a potential solution, as these 2D nanosheets consist of TM2X2C, where TM represents transition metal, X is either S or Se, and C atom. This new class of 2D materials serves as a remedy by avoiding the challenges related to solid/solid interfaces often encountered in heterostructures. This review focuses on the latest developments in TMCCs, including their synthetic strategies, surface/interface engineering, and potential application in batteries, water splitting, and other electro-catalytic processes. The challenges and future perspectives of the design of TMCCs for electrochemical energy conversion and storage are also discussed. MXenes ; Materials science ; Nanotechnology ; Graphene ; Transition metal ; Heterojunction ; Carbide ; Water splitting ; Optoelectronics ; Chemistry ; Composite material MXene and MAX Phase Materials ; 2D Materials and Applications ; Graphene research and applications
Authors: Ivan Malashin | В С Тынченко | Andrei Gantimurov | Vladimir Nelyub | А. С. Бородулин
Source: Polymers (review, 2024-09-14)
Field: Materials Science — Materials Chemistry

Applications of Long Short-Term Memory (LSTM) Networks in Polymeric Sciences: A Review This review explores the application of Long Short-Term Memory (LSTM) networks, a specialized type of recurrent neural network (RNN), in the field of polymeric sciences. LSTM networks have shown notable effectiveness in modeling sequential data and predicting time-series outcomes, which are essential for understanding complex molecular structures and dynamic processes in polymers. This review delves into the use of LSTM models for predicting polymer properties, monitoring polymerization processes, and evaluating the degradation and mechanical performance of polymers. Additionally, it addresses the challenges related to data availability and interpretability. Through various case studies and comparative analyses, the review demonstrates the effectiveness of LSTM networks in different polymer science applications. Future directions are also discussed, with an emphasis on real-time applications and the need for interdisciplinary collaboration. The goal of this review is to connect advanced machine learning (ML) techniques with polymer science, thereby promoting innovation and improving predictive capabilities in the field. Interpretability ; Computer science ; Recurrent neural network ; Field (mathematics) ; Artificial intelligence ; Long short term memory ; Machine learning ; Deep learning ; Artificial neural network ; Data science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Sensor Technologies
Authors: Xiaoyun Lin | Xiaowei Du | Shican Wu | Shiyu Zhen | Wei Liu | Chunlei Pei | Peng Zhang | Zhi‐Jian Zhao | Jinlong Gong
Source: Nature Communications (article, 2024-09-17)
Field: Materials Science — Materials Chemistry

Machine learning-assisted dual-atom sites design with interpretable descriptors unifying electrocatalytic reactions Low-cost, efficient catalyst high-throughput screening is crucial for future renewable energy technology. Interpretable machine learning is a powerful method for accelerating catalyst design by extracting physical meaning but faces huge challenges. This paper describes an interpretable descriptor model to unify activity and selectivity prediction for multiple electrocatalytic reactions (i.e., O<sub>2</sub>/CO<sub>2</sub>/N<sub>2</sub> reduction and O<sub>2</sub> evolution reactions), utilizing only easily accessible intrinsic properties. This descriptor, named ARSC, successfully decouples the atomic property (A), reactant (R), synergistic (S), and coordination effects (C) on the d-band shape of dual-atom sites, which is built upon our developed physically meaningful feature engineering and feature selection/sparsification (PFESS) method. Driven by this descriptor, we can rapidly locate optimal catalysts for various products instead of over 50,000 density functional theory calculations. The model's universality has been validated by abundant reported works and subsequent experiments, where Co-Co/Ir-Qv3 are identified as optimal bifunctional oxygen reduction and evolution electrocatalysts. This work opens the avenue for intelligent catalyst design in high-dimensional systems linked with physical insights. Dual (grammatical number) ; Computer science ; Atom (system on chip) ; Artificial intelligence ; Chemistry ; Computational biology ; Biology ; Embedded system Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Fuel Cells and Related Materials
Authors: Oliver T. Unke | Martin Stöhr | Stefan Ganscha | Thomas Unterthiner | Hartmut Maennel | Sergii Kashubin | Daniel Ahlin | Michael Gastegger | Leonardo Medrano Sandonas | Joshua T. Berryman | Alexandre Tkatchenko | Klaus‐Robert Müller
Source: Science Advances (article, 2024-04-05)
Field: Materials Science — Materials Chemistry

Biomolecular dynamics with machine-learned quantum-mechanical force fields trained on diverse chemical fragments The GEMS method enables molecular dynamics simulations of large heterogeneous systems at ab initio quality. Dynamics (music) ; Molecular dynamics ; Quantum ; Ab initio ; Quantum chemical ; Computer science ; Quality (philosophy) ; Force field (fiction) ; Statistical physics ; Chemical physics ; Biological system ; Nanotechnology ; Physics ; Artificial intelligence ; Materials science ; Molecule ; Quantum mechanics ; Biology Machine Learning in Materials Science ; Protein Structure and Dynamics ; Advanced Electron Microscopy Techniques and Applications
Authors: Yuyan Zhu | Yang Wang | Xingchen Pang | Yongbo Jiang | Xiaoxian Liu | Qing Li | Zhen Wang | Chunsen Liu | Weida Hu | Peng Zhou
Source: Nature Communications (article, 2024-07-17)
Field: Materials Science — Materials Chemistry

Non-volatile 2D MoS2/black phosphorus heterojunction photodiodes in the near- to mid-infrared region Cutting-edge mid-wavelength infrared (MWIR) sensing technologies leverage infrared photodetectors, memory units, and computing units to enhance machine vision. Real-time processing and decision-making challenges emerge with the increasing number of intelligent pixels. However, current operations are limited to in-sensor computing capabilities for near-infrared technology, and high-performance MWIR detectors for multi-state switching functions are lacking. Here, we demonstrate a non-volatile MoS<sub>2</sub>/black phosphorus (BP) heterojunction MWIR photovoltaic detector featuring a semi-floating gate structure design, integrating near- to mid-infrared photodetection, memory and computing (PMC) functionalities. The PMC device exhibits the property of being able to store a stable responsivity, which varies linearly with the stored conductance state. Significantly, device weights (stable responsivity) can be programmed with power consumption as low as 1.8 fJ, and the blackbody peak responsivity can reach 1.68 A/W for the MWIR band. In the simulation of Faster Region with convolution neural network (CNN) based on the FLIR dataset, the PMC hardware responsivity weights can reach 89% mean Average Precision index of the feature extraction network software weights. This MWIR photovoltaic detector, with its versatile functionalities, holds significant promise for applications in advanced infrared object detection and recognition systems. Black phosphorus ; Heterojunction ; Photodiode ; Infrared ; Optoelectronics ; Phosphorus ; Materials science ; Environmental science ; Chemistry ; Environmental chemistry ; Optics ; Physics 2D Materials and Applications ; Perovskite Materials and Applications ; Gas Sensing Nanomaterials and Sensors
Authors: Zhenjie Yu | Moxin Li | Zhenyu Xing | Hao Gao | Zeyang Liu | Shiliang Pu | Hui Mao | Hong‐Ling Cai | Qiang Ma | Wenqi Ren | Jiang Zhu | Cheng Zhang
Source: Opto-Electronic Science (article, 2024-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Genetic algorithm assisted meta-atom design for high-performance metasurface optics Metasurfaces, composed of planar arrays of intricately designed meta-atom structures, possess remarkable capabilities in controlling electromagnetic waves in various ways. A critical aspect of metasurface design involves selecting suitable meta-atoms to achieve target functionalities such as phase retardation, amplitude modulation, and polarization conversion. Conventional design processes often involve extensive parameter sweeping, a laborious and computationally intensive task heavily reliant on designer expertise and judgement. Here, we present an efficient genetic algorithm assisted meta-atom optimization method for high-performance metasurface optics, which is compatible to both single- and multi-objective device design tasks. We first employ the method for a single-objective design task and implement a high-efficiency Pancharatnam-Berry phase based metalens with an average focusing efficiency exceeding 80% in the visible spectrum. We then employ the method for a dual-objective metasurface design task and construct an efficient spin-multiplexed structural beam generator. The device is capable of generating zeroth-order and first-order Bessel beams respectively under right-handed and left-handed circular polarized illumination, with associated generation efficiencies surpassing 88%. Finally, we implement a wavelength and spin co-multiplexed four-channel metahologram capable of projecting two spin-multiplexed holographic images under each operational wavelength, with efficiencies over 50%. Our work offers a streamlined and easy-to-implement approach to meta-atom design and optimization, empowering designers to create diverse high-performance and multifunctional metasurface optics. Genetic algorithm ; Atom (system on chip) ; Optics ; Algorithm ; Computer science ; Physics ; Optoelectronics ; Materials science ; Parallel computing Metamaterials and Metasurfaces Applications ; Satellite Communication Systems ; Advanced Antenna and Metasurface Technologies
Authors: Yuzhong Wang | Yifei Wang | Axiang Yu | Mingshuang Hu | Qiming Wang | Cheng Pang | Huimin Xiong | Yayun Cheng | Jiaran Qi
Source: Advanced Materials (article, 2024-11-25)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Non‐Interleaved Shared‐Aperture Full‐Stokes Metalens via Prior‐Knowledge‐Driven Inverse Design Characterization of electromagnetic wave polarization states is critical in various applications of materials, biomedical, and imaging. The emergence of metasurfaces opens up the possibility of implementing highly integrated full-Stokes imagers. Despite rapid development, prevailing schemes on metasurface-based full-Stokes imagers require interleaved or cascaded designs, inevitably resulting in performance deterioration, bulky size, and complicating the imaging procedure due to misalignment. To overcome these challenges, a non-interleaved shared-aperture full-Stokes metalens enabled by the prior-knowledge-driven inverse design methodology is proposed. The metalens can be directly deployed into imagers, performing high-accuracy diffraction-limited full-Stokes imaging without other ancillary components, breaking intrinsic constraints of efficiency and resolution in interleaved design. To demonstrate this, the metalens is integrated into the W-band passive imaging system, as an alternative solution, to perform polarimetric imaging, which reduces the reliance on the high cost and high complexity of the ortho-mode transducer and broadband correlator in traditional polarimetric radiometers. Furthermore, with the assistance of a 3D reconstruction method, the feasibility of multi-polarization information for contactless surface slope measurements is explored. This work may open new paradigms for the full-Stokes imager designing and broaden the applications of metasurface polarimetric imaging. Materials science ; Inverse ; Aperture (computer memory) ; Nanotechnology ; Optics ; Optoelectronics ; Physics ; Acoustics Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Optimization
Authors: Arun Karnwal | Amar Yasser Jassim | Ameer Abbas Mohammed | Vikas Sharma | Abdel Rahman Al-Tawaha | Iyyakkannu Sivanesan
Source: Pharmaceuticals (review, 2024-12-18)
Field: Materials Science — Materials Chemistry

Nanotechnology for Healthcare: Plant-Derived Nanoparticles in Disease Treatment and Regenerative Medicine Nanotechnology has revolutionised biomedical research, offering innovative healthcare solutions. Plant-based nanotechnology is emerging as a sustainable alternative, minimising environmental impacts and enhancing therapeutic effectiveness. This paper explores the potential of plant-derived nanoparticles (PNPs) in medicine, highlighting their biocompatibility, multifunctionality, and eco-friendliness. PNPs, synthesised through green methods, have demonstrated promising applications in drug delivery, cancer therapy, antimicrobial treatments, and tissue regeneration. Their unique properties, such as a high surface area and bioactive components, enable improved drug delivery, targeting, and controlled release, reducing side effects and enhancing treatment efficacy. Additionally, plant-derived compounds' inherent antimicrobial and antioxidant properties, retained within platinum nanoparticles (PNPs), present innovative opportunities for combating antimicrobial resistance and promoting wound healing. Despite their potential, challenges remain in standardising PNP synthesis, ensuring consistency, and scaling up production for industrial applications. This review emphasises the need for further research on PNP toxicity, biocompatibility, and regulatory frameworks to fully harness their capabilities in clinical and commercial applications. Plant-based nanotechnology represents a promising, greener alternative for advancing healthcare solutions, aligning with global sustainability goals. Nanotechnology ; Biocompatibility ; Drug delivery ; Sustainability ; Materials science ; Biology Nanoparticles: synthesis and applications
Authors: Zelin Chen | Jianhao Zhang | Liangyong Ni | Daohu Sheng | Ruru Gao | Weijin Li | Aming Xie
Source: Carbon (article, 2024-02-09)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Improving electromagnetic wave absorption property of metal borides/carbon nanocomposites by magnetic-electric balance and ion substitution tuning strategy Materials science ; Nanocomposite ; Dielectric ; Dielectric loss ; Boride ; Composite material ; Reflection loss ; Absorption (acoustics) ; Microwave ; Optoelectronics ; Composite number Electromagnetic wave absorption materials ; Advanced Antenna and Metasurface Technologies ; Flame retardant materials and properties
Authors: Katja Klinar | Jia Yan Law | V. Franco | Xavier Moya | Andrej Kitanovski
Source: Advanced Energy Materials (article, 2024-08-30)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Perspectives and Energy Applications of Magnetocaloric, Pyromagnetic, Electrocaloric, and Pyroelectric Materials Abstract This perspective provides an overview of the state of research and innovation in the areas of magnetocaloric and pyromagnetic materials, and electrocaloric and pyroelectric materials, including the overlapping sub‐areas of multicaloric and multipyro materials that can operate simultaneously under the application of magnetic and electric fields. These materials are critically examined for their potential to revolutionize cooling, heating, and energy‐harvesting applications. This perspective first summarizes the state‐of‐the‐art advancements and highlights recent significant developments. Then, it is identified and discussed that the prevailing challenges hindering the widespread adoption of technologies based on these materials. In this context, after consulting with members of the caloric and pyro communities, a technology roadmap is outlined to guide research efforts in overcoming current barriers to applications, with the goal of achieving impactful results by 2040. This roadmap emphasizes the importance of focusing on under‐researched materials, novel devices, and application spaces, paving the way for interdisciplinary efforts that can lead to significant reductions in carbon dioxide emissions. Materials science ; Pyroelectricity ; Magnetic refrigeration ; Electrocaloric effect ; Engineering physics ; Energy (signal processing) ; Thermodynamics ; Optoelectronics ; Magnetic field Magnetic and transport properties of perovskites and related materials ; Ferroelectric and Piezoelectric Materials ; Multiferroics and related materials
Authors: Marie Hennebelle | Pierre Villeneuve | Erwann Durand | Jérôme Lecomte | John van Duynhoven | Anne Meynier | Betül Yeşiltaş | Charlotte Jacobsen | Claire Berton‐Carabin
Source: Progress in Lipid Research (article, 2024-01-26)
Field: Chemistry — Organic Chemistry

Lipid oxidation in emulsions: New insights from the past two decades Lipid oxidation constitutes the main source of degradation of lipid-rich foods, including food emulsions. The complexity of the reactions at play combined with the increased demand from consumers for less processed and more natural foods result in additional challenges in controlling this phenomenon. This review provides an overview of the insights acquired over the past two decades on the understanding of lipid oxidation in oil-in-water (O/W) emulsions. After introducing the general structure of O/W emulsions and the classical mechanisms of lipid oxidation, the contribution of less studied oxidation products and the spatiotemporal resolution of these reactions will be discussed. We then highlight the impact of emulsion formulation on the mechanisms, taking into consideration the new trends in terms of emulsifiers as well as their own sensitivity to oxidation. Finally, novel antioxidant strategies that have emerged to meet the recent consumer's demand will be detailed. In an era defined by the pursuit of healthier, more natural, and sustainable food choices, a comprehensive understanding of lipid oxidation in emulsions is not only an academic quest, but also a crucial step towards meeting the evolving expectations of consumers and ensuring the quality and stability of lipid-rich food products. Lipid oxidation ; Emulsion ; Chemistry ; Lipid droplet ; Biochemical engineering ; Oxidation reduction ; Antioxidant ; Food science ; Business ; Nanotechnology ; Computer science ; Biochemistry ; Materials science ; Engineering Edible Oils Quality and Analysis ; Antioxidant Activity and Oxidative Stress ; Dye analysis and toxicity
Authors: Xianhu Liu | Wenrui Zhang | Xin Zhang | Zhengui Zhou | Chunfeng Wang | Yamin Pan | Bin Hu | Chuntai Liu | Caofeng Pan | Changyu Shen
Source: Nature Communications (article, 2024-04-09)
Field: Materials Science — Materials Chemistry

Transparent ultrahigh-molecular-weight polyethylene/MXene films with efficient UV-absorption for thermal management The rational use and conversion of energy are the primary means for achieving the goal of carbon neutrality. MXenes can be used for photothermal conversion, but their opaque appearance limits wider applications. Herein, we successfully develop visible-light transparent and UV-absorbing polymer composite film by solution blending the MXene with polyethylene and then vacuum pressing. The resulting film could be quickly heated to 65 °C under 400 mW cm<sup>-2</sup> light irradiation and maintained over 85% visible-light transmittance as well as low haze (<12%). The findings of the indoor heat insulation test demonstrate that the temperature of the glass house model covered by this film was 6-7 °C lower than that of the uncovered model, revealing the potential of transparent film in energy-saving applications. In order to mimic the energy-saving condition of the building in various climates, a typical building model with this film as the outer layer of the window was created using the EnergyPlus building energy consumption software. According to predictions, they could reduce yearly refrigeration energy used by 31-61 MJ m<sup>-2</sup>, and 3%-12% of the total energy used for refrigeration in such structures. This work imply that the film has wide potential for use as transparent devices in energy-related applications. Opacity ; Materials science ; Photothermal therapy ; MXenes ; Absorption (acoustics) ; Polyethylene ; Polymer ; Carbon fibers ; Optoelectronics ; Thermal ; Nanotechnology ; Composite number ; Chemical engineering ; Optics ; Composite material MXene and MAX Phase Materials ; Advanced Sensor and Energy Harvesting Materials ; Advanced Photocatalysis Techniques
Authors: Xiao Tang | Qi Hao | Xiangyu Hou | Leilei Lan | Mingze Li | Lei Yao | Xing Zhao | Zhenhua Ni | Xingce Fan | Teng Qiu
Source: Advanced Materials (article, 2024-02-01)
Field: Materials Science — Materials Chemistry

Exploring and Engineering 2D Transition Metal Dichalcogenides toward Ultimate SERS Performance Surface-enhanced Raman spectroscopy (SERS) is an ultrasensitive surface analysis technique that is widely used in chemical sensing, bioanalysis, and environmental monitoring. The design of the SERS substrates is crucial for obtaining high-quality SERS signals. Recently, 2D transition metal dichalcogenides (2D TMDs) have emerged as high-performance SERS substrates due to their superior stability, ease of fabrication, biocompatibility, controllable doping, and tunable bandgaps and excitons. In this review, a systematic overview of the latest advancements in 2D TMDs SERS substrates is provided. This review comprehensively summarizes the candidate 2D TMDs SERS materials, elucidates their working principles for SERS, explores the strategies to optimize their SERS performance, and highlights their practical applications. Particularly delved into are the material engineering strategies, including defect engineering, alloy engineering, thickness engineering, and heterojunction engineering. Additionally, the challenges and future prospects associated with the development of 2D TMDs SERS substrates are discussed, outlining potential directions that may lead to significant breakthroughs in practical applications. Nanotechnology ; Materials science ; Surface engineering ; Raman spectroscopy ; Transition metal ; Surface-enhanced Raman spectroscopy ; Molecular engineering ; Bioanalysis ; Raman scattering ; Catalysis ; Chemistry 2D Materials and Applications ; Gold and Silver Nanoparticles Synthesis and Applications ; Advanced Photocatalysis Techniques