jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Christina Glaubitz | Amélie Bazzoni | Liliane Ackermann-Hirschi | Laura Baraldi | Moritz Haeffner | Roman M. Fortunatus | Barbara Rothen‐Rutishauser | Sandor Balog | Alke Petri‐Fink
Source: The Journal of Physical Chemistry C (article, 2023-12-28)
Field: Materials Science — Surfaces, Coatings and Films

Leveraging Machine Learning for Size and Shape Analysis of Nanoparticles: A Shortcut to Electron Microscopy Characterizing nanoparticles (NPs) is crucial in nanoscience due to the direct influence of their physiochemical properties on their behavior. Various experimental techniques exist to analyze the size and shape of NPs, each with advantages, limitations, proneness to uncertainty, and resource requirements. One of them is electron microscopy (EM), often considered the gold standard, which offers visualization of the primary particles. However, despite its advantages, EM can be expensive, less accessible, and difficult to apply during dynamic processes. Therefore, using EM for specific experimental conditions, such as observing dynamic processes or visualizing low-contrast particles, is challenging. This study showcases the potential of machine learning in deriving EM parameters by utilizing cost-effective and dynamic techniques such as dynamic light scattering (DLS) and UV-vis spectroscopy. Our developed model successfully predicts the size and shape parameters of gold NPs based on DLS and UV-vis results. Furthermore, we demonstrate the practicality of our model in situations in which conducting EM measurements presents a challenge: Tracking in situ the synthesis of 100 nm gold NPs. Dynamic light scattering ; Nanoparticle tracking analysis ; Colloidal gold ; Nanoparticle ; Computer science ; Nanotechnology ; Materials science ; Tracking (education) ; Biological system ; Chemistry Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Norah A. M. Alsaif | Shaik Kareem Ahmmad | Z. Y. Khattari | A. M. Abdelghany | Adel M. El-Refaey | Y. S. Rammah | M. S. Shams | R. A. Elsad
Source: Optical Materials (article, 2023-02-24)
Field: Materials Science — Ceramics and Composites

Synthesis, structure, radiation attenuation efficacy as well as prediction of density using artificial intelligence techniques of lead borate lithium zinc strontium glasses Strontium ; Attenuation ; Lithium borate ; Analytical Chemistry (journal) ; Half-value layer ; Zinc borate ; Materials science ; Attenuation coefficient ; Mineralogy ; Mass attenuation coefficient ; Borate glass ; Chemistry ; Doping ; Radiation ; Optics ; Physics ; Metallurgy Glass properties and applications ; Radiation Shielding Materials Analysis ; Luminescence Properties of Advanced Materials
Authors: Giuliana Giunta | Gerardo Campos-Villalobos | Marjolein Dijkstra
Source: ACS Nano (article, 2023-11-27)
Field: Materials Science — Materials Chemistry

Coarse-Grained Many-Body Potentials of Ligand-Stabilized Nanoparticles from Machine-Learned Mean Forces Colloidal nanoparticles self-assemble into a variety of superstructures with distinctive optical, structural, and electronic properties. These nanoparticles are usually stabilized by a capping layer of organic ligands to prevent aggregation in the solvent. When the ligands are sufficiently long compared to the dimensions of the nanocrystal cores, the effective coarse-grained forces between pairs of nanoparticles are largely affected by the presence of neighboring particles. In order to efficiently investigate the self-assembly behavior of these complex colloidal systems, we propose a machine-learning approach to construct effective coarse-grained many-body interaction potentials. The multiscale methodology presented in this work constitutes a general bottom-up coarse-graining strategy where the coarse-grained forces acting on coarse-grained sites are extracted from measuring the vectorial mean forces on these sites in reference fine-grained simulations. These effective coarse-grained forces, i.e., gradients of the potential of mean force or of the free-energy surface, are represented by a simple linear model in terms of gradients of structural descriptors, which are scalar functions that are rotationally invariant. In this way, we also directly obtain the free-energy surface of the coarse-grained model as a function of all coarse-grained coordinates. We expect that this simple yet accurate coarse-graining framework for the many-body potential of mean force will enable the characterization, understanding, and prediction of the structure and phase behavior of relevant soft-matter systems by direct simulations. The key advantage of this method is its generality, which allows it to be applicable to a broad range of systems. To demonstrate the generality of our method, we also apply it to a colloid-polymer model system, where coarse-grained many-body interactions are pronounced. Granularity ; Potential of mean force ; Nanoparticle ; Biological system ; Statistical physics ; Chemical physics ; Soft matter ; Materials science ; Computer science ; Colloid ; Nanotechnology ; Molecular dynamics ; Physics ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Material Dynamics and Properties
Authors: Tereza Kucova | Michal Prauzek | Jaromír Konecny | Darius Andriukaitis | Mindaugas Žilys | Radek Martínek
Source: CAAI Transactions on Intelligence Technology (review, 2023-07-12)
Field: Materials Science — Materials Chemistry

Thermoelectric energy harvesting for internet of things devices using machine learning: A review Abstract Initiatives to minimise battery use, address sustainability, and reduce regular maintenance have driven the challenge to use alternative power sources to supply energy to devices deployed in Internet of Things (IoT) networks. As a key pillar of fifth generation (5G) and beyond 5G networks,IoT is estimated to reach 42 billion devices by the year 2025. Thermoelectric generators (TEGs) are solid state energy harvesters which reliably and renewably convert thermal energy into electrical energy. These devices are able to recover lost thermal energy, produce energy in extreme environments, generate electric power in remote areas, and power micro‐sensors. Applying the state of the art, the authorspresent a comprehensive review of machine learning (ML) approaches applied in combination with TEG‐powered IoT devices to manage and predict available energy. The application areas of TEG‐driven IoT devices that exploit as a heat source the temperature differences found in the environment, biological structures, machines, and other technologies are summarised. Based on detailed research of the state of the art in TEG‐powered devices, the authors investigated the research challenges, applied algorithms and application areas of this technology. The aims of the research were to devise new energy prediction and energy management systems based on ML methods, create supervised algorithms which better estimate incoming energy, and develop unsupervised and semi‐supervised approaches which provide adaptive and dynamic operation. The review results indicate that TEGs are a suitable energy harvesting technology for low‐power applications through their scalability, usability in ubiquitous temperature difference scenarios, and long operating lifetime. However, TEGs also have low energy efficiency (around 10%) and require a relatively constant heat source. Energy harvesting ; Scalability ; Computer science ; Exploit ; Usability ; Energy (signal processing) ; Internet of Things ; Efficient energy use ; Embedded system ; Electrical engineering ; Engineering ; Computer security ; Human–computer interaction ; Operating system Advanced Thermoelectric Materials and Devices ; Energy Harvesting in Wireless Networks ; Innovative Energy Harvesting Technologies
Authors: Guoqiang Fang | Wuliji Hasi | Xuanyu Sha | Guangxu Cao | Siqingaowa Han | Jinlei Wu | Xiang Lin | Zhouzhou Bao
Source: The Journal of Physical Chemistry Letters (article, 2023-08-10)
Field: Chemistry — Electrochemistry

Interfacial Self-Assembly of Surfactant-Free Au Nanoparticles as a Clean Surface-Enhanced Raman Scattering Substrate for Quantitative Detection of As<sup>5+</sup> in Combination with Convolutional Neural Networks Surface-enhanced Raman scattering (SERS) is a highly sensitive tool in the field of environmental testing. However, the detection and accurate quantification of weakly adsorbed molecules (such as heavy metal ions) remain a challenge. Herein, we combine clean SERS substrates capable of capturing heavy metal ions with convolutional neural network (CNN) algorithm models for quantitative detection of heavy metal ions in solution. The SERS substrate consists of surfactant-free Au nanoparticles (NPs) and l-cysteine molecules. As plasmonic nanobuilt blocks, surfactant-free Au NPs without physical or chemical barriers are more accessible to target molecules. The amino and carboxyl groups in the l-cysteine molecule can chelate As<sup>5+</sup> ions. The CNN algorithm model is applied to quantify and predict the concentration of As<sup>5+</sup> ions in samples. The results demonstrated that this strategy allows for fast and accurate prediction of As<sup>5+</sup> ion concentrations, and the determination coefficient between the predicted and actual values is as high as 0.991. Raman scattering ; Substrate (aquarium) ; Molecule ; Metal ions in aqueous solution ; Ion ; Pulmonary surfactant ; Nanoparticle ; Materials science ; Plasmon ; Raman spectroscopy ; Convolutional neural network ; Adsorption ; Nanotechnology ; Chemistry ; Analytical Chemistry (journal) ; Optoelectronics ; Chromatography ; Physical chemistry ; Computer science ; Organic chemistry ; Optics ; Physics Electrochemical Analysis and Applications ; Gold and Silver Nanoparticles Synthesis and Applications ; Biosensors and Analytical Detection
Authors: Reda Rabie | Taoufiq Saffaj | Ilham Bouzida | Ouadi Saidi | Malika Belgrir | Brahim Lakssir | El Mestafa El Hadrami
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2023-07-27)
Field: Chemistry — Analytical Chemistry

Optimized variable selection and machine learning models for olive oil quality assessment using portable near infrared spectroscopy Partial least squares regression ; Olive oil ; Quality (philosophy) ; Statistics ; Chemometrics ; Mean squared error ; Mathematics ; Feature selection ; Environmental science ; Computer science ; Chemistry ; Artificial intelligence ; Machine learning ; Food science Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Siddappa A. Patil | Kostiantyn O. Marichev | Shivaputra A. Patil | Alejandro Bugarin
Source: Surfaces and Interfaces (article, 2023-04-09)
Field: Materials Science — Materials Chemistry

Advances in the synthesis and applications of 2D MXene-metal nanomaterials MXenes ; Materials science ; Nanomaterials ; Nanotechnology ; Characterization (materials science) ; Carbide ; Noble metal ; Metal ; Metallurgy MXene and MAX Phase Materials ; Graphene research and applications ; Graphene and Nanomaterials Applications
Authors: Hao Wang | Weiwei Zhao
Source: Next Materials (article, 2023-10-14)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Application of hydrogel for energy storage and conversion Hydrogels have increasingly become a focus of interest within academic and industrial research spheres, particularly for their potential application in energy storage and conversion systems. This is largely due to their exceptional mechanical properties, inherent multifunctionality, and noteworthy biocompatibility. The goal of this review is to provide an in-depth analysis of the recent advancements made in the field of multifunctional hydrogels as applied to energy storage and conversion. The work reviews key factors that are critical to the functionality of hydrogels in energy storage and conversion processes, including mechanical strength, resistance to swelling, and conductivity. We explore contemporary methodologies for enhancing these essential properties. It provides insights into the existing and prospective uses of hydrogels in the realm of energy storage and conversion, specifically highlighting their role in supercapacitors, batteries, and ion thermoelectric systems. The present work anticipates potential future research trajectories, with an emphasis on the further development and application of hydrogels within energy conversion and storage systems. Self-healing hydrogels ; Energy storage ; Supercapacitor ; Nanotechnology ; Energy transformation ; Materials science ; Work (physics) ; Process engineering ; Efficient energy use ; Computer science ; Biochemical engineering ; Mechanical engineering ; Engineering ; Electrical engineering ; Chemistry ; Physics ; Capacitance Supercapacitor Materials and Fabrication ; Advanced Sensor and Energy Harvesting Materials ; Conducting polymers and applications
Authors: Siow Mean Loh | David M. Grant | Gavin S. Walker | Sanliang Ling
Source: International Journal of Hydrogen Energy (article, 2023-01-06)
Field: Materials Science — Materials Chemistry

Substitutional effect of Ti-based AB2 hydrogen storage alloys: A density functional theory study Stability of AB2 alloy in Laves phases C14 and C15 were studied by first-principle density functional theory simulations. A range of different combinations of B and C elements in the Ti1−xCxB2 alloys were considered. The formation energies of these alloys generally increase with the unit cell volumes of alloys. The volume also affects the stability of the corresponding metal hydride. We find that the formation energies and the hydrogenation enthalpies of AB2 alloys are likely to be determined by at least three factors: electronegativity, atomic radius and covalent radius. The enthalpies of AB2 hydrides increase with increasing compositionally-averaged electronegativity and volume change upon hydrogenation. However, the enthalpies of AB2 hydrides decrease with increasing compositionally-averaged atomic and covalent radii. This study provides useful insights for future exploration of AB2-type alloys for hydrogen storage applications. Electronegativity ; Atomic radius ; Hydrogen storage ; Hydride ; Density functional theory ; Alloy ; Hydrogen ; RADIUS ; Materials science ; Covalent bond ; Thermodynamics ; Metal ; Physical chemistry ; Chemistry ; Computational chemistry ; Metallurgy ; Physics ; Organic chemistry Hydrogen Storage and Materials ; MXene and MAX Phase Materials ; Magnesium Alloys: Properties and Applications
Authors: Vyshnavi Vennelakanti | Michael G. Taylor | Aditya Nandy | Chenru Duan | Heather J. Kulik
Source: The Journal of Chemical Physics (article, 2023-07-11)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Assessing the performance of approximate density functional theory on 95 experimentally characterized Fe(II) spin crossover complexes Spin crossover (SCO) complexes, which exhibit changes in spin state in response to external stimuli, have applications in molecular electronics and are challenging materials for computational design. We curate a dataset of 95 Fe(II) SCO complexes (SCO-95) from the Cambridge Structural Database that have available low- and high-temperature crystal structures and, in most cases, confirmed experimental spin transition temperatures (T1/2). We study these complexes using density functional theory (DFT) with 30 functionals spanning across multiple rungs of "Jacob's ladder" to understand the effect of exchange-correlation functional on electronic and Gibbs free energies associated with spin crossover. We specifically assess the effect of varying the Hartree-Fock exchange fraction (aHF) in structures and properties within the B3LYP family of functionals. We identify three best-performing functionals, a modified version of B3LYP (aHF = 0.10), M06-L, and TPSSh, that accurately predict SCO behavior for the majority of the complexes. While M06-L performs well, MN15-L, a more recently developed Minnesota functional, fails to predict SCO behavior for all complexes, which could be the result of differences in datasets used for parametrization of M06-L and MN15-L and also the increased number of parameters for MN15-L. Contrary to observations from prior studies, double-hybrids with higher aHF values are found to strongly stabilize high-spin states and therefore exhibit poor performance in predicting SCO behavior. Computationally predicted T1/2 values are consistent among the three functionals but show limited correlation to experimentally reported T1/2 values. These failures are attributed to the lack of crystal packing effects and counter-anions in the DFT calculations that would be needed to account for phenomena such as hysteresis and two-step SCO behavior. The SCO-95 set thus presents opportunities for method development, both in terms of increasing model complexity and method fidelity. Density functional theory ; Spin crossover ; Parametrization (atmospheric modeling) ; Spin (aerodynamics) ; Hybrid functional ; Crossover ; Spin states ; Functional theory ; Materials science ; Computational chemistry ; Chemistry ; Condensed matter physics ; Thermodynamics ; Physics ; Computer science ; Quantum mechanics ; Machine learning Magnetism in coordination complexes ; Metal-Catalyzed Oxygenation Mechanisms ; Electron Spin Resonance Studies
Authors: Furqan Zahoor | Mehwish Hanif | Usman Isyaku Bature | B. Srinivasu | Anupam Chattopadhyay | Fawnizu Azmadi Hussin | Haider Abbas | Farhad Merchant | Faisal Bashir
Source: Physica Scripta (article, 2023-07-18)
Field: Materials Science — Materials Chemistry

Carbon nanotube field effect transistors: an overview of device structure, modeling, fabrication and applications Abstract The research interest in the field of carbon nanotube field effect transistors (CNTFETs) in the post Moore era has witnessed a rapid growth primarily due to the fact that the conventional silicon based complementary metal oxide semiconductor (CMOS) devices are approaching its fundamental scaling limits. This has led to significant interest among the researchers to examine novel device technologies utilizing different materials to sustain the scaling limits of the modern day integrated circuits. Among various material alternatives, carbon nanotubes (CNTs) have been extensively investigated owing to their desirable properties such as minimal short channel effects, high mobility, and high normalized drive currents. CNTs form the most important component of CNTFETs, which are being viewed as the most feasible alternatives for the replacement of silicon transistors. In this manuscript, detailed description of the recent advances of state of the art in the field of CNTFETs with emphasis on the most broadly impactful applications for which they are being employed is presented. The future prospects of CNTFETs while considering aggressively scaled transistor technologies are also briefly discussed. Carbon nanotube field-effect transistor ; Carbon nanotube ; Materials science ; Nanotechnology ; Transistor ; Field-effect transistor ; CMOS ; Electronic circuit ; Engineering physics ; Electrical engineering ; Optoelectronics ; Voltage ; Engineering Carbon Nanotubes in Composites ; Advanced Memory and Neural Computing ; Nanowire Synthesis and Applications
Authors: Wenjie Xiong | Shuyue Wen | Xiaomin Zhang | Xingbang Hu | Youting Wu
Source: AIChE Journal (article, 2023-12-03)
Field: Chemistry — Organic Chemistry

Construction of hybrid ionic liquid‐catalysts for the highly effective conversion of <scp>H<sub>2</sub>S</scp> by nitriles into thioamides Abstract Described herein presents a kind of multisite hybrid ionic liquid‐catalysts and establishes a novel strategy for H 2 S utilization, which has been characterized by multiaspects to reveal internal structural characterization. Through chemical modification, the D101‐based hybrid ILs were used for the direct synthesis of thioamides from the addition of H 2 S and nitriles. D101‐[PMDETA][DMG] can achieve an impressive activity of quantitative conversion for benzonitrile. Reaction kinetics, adsorption thermodynamics, substrate universality, and catalyst cycling experiments have been systematically studied. A first‐order kinetic model was constructed and studied experimentally for the reaction of nitriles with H 2 S to produce thioamides. The internal mechanism was proposed through experiments and confirmed by DFT‐based theoretical calculations. Moreover, D101‐based hybrid ILs can be easily recovered, thus completing the cyclic process of thioamide multiplication. This work not only offers an effective method for the construction of organic‐inorganic hybrid materials but also provides a novel insight into efficient H 2 S conversion. Ionic liquid ; Chemistry ; Catalysis ; Benzonitrile ; Combinatorial chemistry ; Kinetics ; Chemical engineering ; Computational chemistry ; Organic chemistry Synthesis and Reactivity of Sulfur-Containing Compounds ; Sulfur-Based Synthesis Techniques ; Synthesis of heterocyclic compounds
Authors: Kexin Chen | Guangyong Chen | Junyou Li | Yuansheng Huang | Ercheng Wang | Tingjun Hou | Pheng‐Ann Heng
Source: Journal of Cheminformatics (article, 2023-04-10)
Field: Materials Science — Materials Chemistry

MetaRF: attention-based random forest for reaction yield prediction with a few trails Artificial intelligence has deeply revolutionized the field of medicinal chemistry with many impressive applications, but the success of these applications requires a massive amount of training samples with high-quality annotations, which seriously limits the wide usage of data-driven methods. In this paper, we focus on the reaction yield prediction problem, which assists chemists in selecting high-yield reactions in a new chemical space only with a few experimental trials. To attack this challenge, we first put forth MetaRF, an attention-based random forest model specially designed for the few-shot yield prediction, where the attention weight of a random forest is automatically optimized by the meta-learning framework and can be quickly adapted to predict the performance of new reagents while given a few additional samples. To improve the few-shot learning performance, we further introduce a dimension-reduction based sampling method to determine valuable samples to be experimentally tested and then learned. Our methodology is evaluated on three different datasets and acquires satisfactory performance on few-shot prediction. In high-throughput experimentation (HTE) datasets, the average yield of our methodology's top 10 high-yield reactions is relatively close to the results of ideal yield selection. Computer science ; Random forest ; Yield (engineering) ; Machine learning ; Artificial intelligence ; Field (mathematics) ; Sampling (signal processing) ; Selection (genetic algorithm) ; Focus (optics) ; Dimension (graph theory) ; Data mining ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Leonardo Bertolucci Coelho | Daniel Torres | Miguel Bernal | Gian Marco Paldino | Gianluca Bontempi | Jon Ustarroz
Source: Corrosion Science (article, 2023-03-13)
Field: Materials Science — Metals and Alloys

Probing the randomness of the local current distributions of 316 L stainless steel corrosion in NaCl solution Randomness ; Corrosion ; Pitting corrosion ; Code (set theory) ; Computer science ; Chloride ; Current (fluid) ; Materials science ; Electrochemistry ; Chemistry ; Physics ; Metallurgy ; Mathematics ; Statistics ; Thermodynamics ; Electrode ; Physical chemistry Hydrogen embrittlement and corrosion behaviors in metals ; Corrosion Behavior and Inhibition ; Machine Learning in Materials Science