jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jafar Abdi | Tahereh Pirhoushyaran | Fahimeh Hadavimoghaddam | Seyed Ali Madani | Abdolhossein Hemmati‐Sarapardeh | Seyyed Hamid Esmaeili-Faraj
Source: Journal of Energy Storage (article, 2023-04-21)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Modeling of capacitance for carbon-based supercapacitors using Super Learner algorithm Capacitance ; Supercapacitor ; Materials science ; Carbon fibers ; Microporous material ; Electrode ; Capacitor ; Analytical Chemistry (journal) ; Voltage ; Chemistry ; Composite material ; Electrical engineering ; Engineering ; Chromatography Supercapacitor Materials and Fabrication ; Conducting polymers and applications ; Advanced battery technologies research
Authors: Gemma Davison | Tabitha Jones | Jia Liu | Juhwan Kim | Yidan Yin | Doeun Kim | Weng‐I Katherine Chio | Ivan P. Parkin | Hyeon‐Ho Jeong | Tung‐Chun Lee
Source: Advanced Materials Technologies (article, 2023-01-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Computer‐Aided Design and Analysis of Spectrally Aligned Hybrid Plasmonic Nanojunctions for SERS Detection of Nucleobases Abstract Hybrid plasmonic nanojunctions with optimal surface‐enhanced Raman scattering (SERS) activity are designed via a computer‐aided approach, and fabricated via time‐controlled aqueous self‐assembly of core@shell gold@silver nanoparticles (Au@Ag NPs) with cucurbit[7]uril (CB7) upon simple mixing. The authors showed that SERS signals can be significantly boosted by the incorporation of a strong plasmonic metal and the spectral alignment between the maximal localized surface plasmon resonance (LSPR) and a laser wavelength used for SERS excitation. In a proof‐of‐concept application, SERS detection of nucleobases with a 633‐nm laser has been demonstrated by positioning them within the nanojunctions via formation of host–guest complexes with CB7, achieving rapid response with a detection limit down to sub‐nanomolar concentration and an enhancement factor (EF) up to ≈10 9 –10 10 , i.e., the minimum required EF for single‐molecule detection. Furthermore, machine‐learning‐driven multiplexing of nucleobases is demonstrated, which shows promise in point‐of‐care diagnosis of diseases related to oxidative damage of DNA and wastewater‐based epidemiology. Plasmon ; Nucleobase ; Raman scattering ; Materials science ; Plasmonic nanoparticles ; Laser ; Nanoparticle ; Biosensor ; Surface plasmon resonance ; Nanotechnology ; Optoelectronics ; Raman spectroscopy ; Chemistry ; DNA ; Optics ; Physics Gold and Silver Nanoparticles Synthesis and Applications ; Advanced biosensing and bioanalysis techniques ; Plasmonic and Surface Plasmon Research
Authors: Pema Lhamo | Biswanath Mahanty | Shishir Kumar Behera
Source: Biomass Conversion and Biorefinery (article, 2023-03-13)
Field: Materials Science — Biomaterials

Optimization of biomass and polyhydroxyalkanoate production by Cupriavidus necator using response surface methodology and genetic algorithm optimized artificial neural network Cupriavidus necator ; Response surface methodology ; Polyhydroxyalkanoates ; Biomass (ecology) ; Yield (engineering) ; Sucrose ; Nitrogen ; Carbon fibers ; Chemistry ; Food science ; Mathematics ; Materials science ; Biology ; Chromatography ; Algorithm ; Agronomy ; Organic chemistry ; Composite number ; Bacteria biodegradable polymer synthesis and properties ; Electrospun Nanofibers in Biomedical Applications ; Enzyme Catalysis and Immobilization
Authors: Sebastián A. Cuesta | Martín Moreno | Romina A. López | José R. Mora | J. L. Paz | Edgar Márquez
Source: Journal of Chemical Information and Modeling (article, 2023-01-03)
Field: Chemistry — Electrochemistry

ElectroPredictor: An Application to Predict Mayr’s Electrophilicity <i>E</i> through Implementation of an Ensemble Model Based on Machine Learning Algorithms Electrophilicity (<i>E</i>) is one of the most important parameters to understand the reactivity of an organic molecule. Although the theoretical electrophilicity index (ω) has been associated with <i>E</i> in a small homologous series, the use of <i>w</i> to predict <i>E</i> in a structurally heterogeneous set of compounds is not a trivial task. In this study, a robust ensemble model is created using Mayr's database of reactivity parameters. A combination of topological and quantum mechanical descriptors and different machine learning algorithms are employed for the model's development. The predictability of the model is assessed using different statistical parameters, and its validation is examined, including a training/test partition, an applicability domain, and a <i>y</i>-scrambling test. The global ensemble model presents a <i>Q</i><sub>5-fold</sub><sup>2</sup> of 0.909 and a <i>Q</i><sub>ext</sub><sup>2</sup> of 0.912, demonstrating an excellent predictability performance of <i>E</i> values and showing that <i>w</i> is not a good descriptor for the prediction of <i>E</i>, especially for the case of neutral compounds. <i>ElectroPredictor</i>, a noncommercial Python application (https://github.com/mmoreno1/ElectroPredictor), is developed to predict <i>E</i>. QM9, a well-known large dataset containing 133885 neutral molecules, is used to perform a virtual screening (94.0% coverage). Finally, the 10 most electrophilic molecules are analyzed as possible new Mayr's electrophiles, which have not yet been experimentally tested. This study confirms the necessity to build an ensemble model using nonlinear machine learning algorithms, topographic descriptors, and separating molecules into charged and neutral compounds to predict <i>E</i> with precision. Computer science ; Machine learning ; Artificial intelligence ; Ensemble learning ; Algorithm Electrochemical Analysis and Applications ; Computational Drug Discovery Methods ; Ionic liquids properties and applications
Authors: Etinosa Osaro | Krishnendu Mukherjee | Yamil J. Colón
Source: Industrial & Engineering Chemistry Research (article, 2023-08-08)
Field: Chemistry — Inorganic Chemistry

Active Learning for Adsorption Simulations: Evaluation, Criteria Analysis, and Recommendations for Metal–Organic Frameworks High-throughput molecular simulations and machine learning (ML) have been implemented to adequately screen a large number of metal–organic frameworks (MOFs) for applications involving adsorption. Grand canonical Monte Carlo (GCMC) simulations have proven effective in calculating the adsorption capacity at given pressures and temperatures, but they can require expensive computational resources. While they can be resource-efficient, ML models can require large datasets, creating a need for algorithms that can efficiently characterize adsorption; active learning (AL) can play a very important role in this regard. In this work, we make use of Gaussian process regression (GPR) to model pure component adsorption of nitrogen at 77 K from 10–5 to 1 bar, methane at 298 K from 10 –5 to 100 bar, carbon dioxide at 298 K from 10–5 to 100 bar, and hydrogen at 77 K from 10–5 to 100 bar on PCN-61, MgMOF-74, DUT-32, DUT-49, MOF-177, NU-800, UiO-66, ZIF-8, IRMOF-1, IRMOF-10, and IRMOF-16. The GPR model requires an initial training of the model with an initial dataset, the prior one, and, in this study of evaluating AL, we make use of three different prior selection schemes. Each prior scheme is updated with a sampling point resulting from the GP model uncertainties. This protocol continues until a maximum GPR relative error of 2% is attained. We make a recommendation on the best prior selection scheme for the total 44 adsorbate–adsorbent pairs primarily making use of the mean absolute error and the total amount of points required for convergence of the model. To further evaluate the AL framework, we apply the BET consistency criteria on the simulated and GP nitrogen isotherms and compare the resulting surface areas. Bar (unit) ; Adsorption ; Metal-organic framework ; Computer science ; Kriging ; Monte Carlo method ; Gaussian ; Materials science ; Chemistry ; Machine learning ; Physics ; Mathematics ; Computational chemistry ; Physical chemistry ; Statistics Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Carbon Dioxide Capture Technologies
Authors: Xiaoze Yuan | Yuwei Zhou | Qing Peng | Yong Yang | Yongwang Li | Xiaodong Wen
Source: npj Computational Materials (article, 2023-01-20)
Field: Materials Science — Materials Chemistry

Active learning to overcome exponential-wall problem for effective structure prediction of chemical-disordered materials Abstract Chemical-disordered materials have a wide range of applications whereas the determination of their structures or configurations is one of the most important and challenging problems. Traditional methods are extremely inefficient or intractable for large systems due to the notorious exponential-wall issue that the number of possible structures increase exponentially for N-body systems. Herein, we introduce an efficient approach to predict the thermodynamically stable structures of chemical-disordered materials via active-learning accompanied by first-principles calculations. Our method, named LAsou, can efficiently compress the sampling space and dramatically reduce the computational cost. Three distinct and typical finite-size systems are investigated, including the anion-disordered BaSc(O x F 1−x ) 3 (x = 0.667), the cation-disordered Ca 1−x Mn x CO 3 (x = 0.25) with larger size and the defect-disordered ε-FeC x (x = 0.5) with larger space. The commonly used enumeration method requires to explicitly calculate 2664, 1033, and 10496 configurations, respectively, while the LAsou method just needs to explicitly calculate about 15, 20, and 10 configurations, respectively. Besides the finite-size system, our LAsou method is ready for quasi-infinite size systems empowering materials design. Exponential function ; Space (punctuation) ; Exponential growth ; Range (aeronautics) ; Materials science ; Computer science ; Statistical physics ; Mathematics ; Physics ; Mathematical analysis Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Ferroelectric and Negative Capacitance Devices
Authors: Xiaona Ren | Muhammad Irfan Hussain | Yue Chang | Changchun Ge
Source: International Journal of Molecular Sciences (article, 2023-12-07)
Field: Materials Science — Materials Chemistry

State-of-the-Art Review on Amorphous Carbon Nanotubes: Synthesis, Structure, and Application Carbon nanotubes (CNTs) have rapidly received increasing attention and great interest as potential materials for energy storage and catalyst fields, which is due to their unique physicochemical and electrical properties. With continuous improvements in fabrication routes, CNTs have been modified with various types of materials, opening up new perspectives for research and state-of-the-art technologies. Amorphous CNTs (aCNTs) are carbon nanostructures that are distinctively different from their well-ordered counterparts, such as single-walled and multi-walled carbon nanotubes (SWCNTs and MWCNTs, respectively), while the atoms in aCNTs are grouped in a disordered, crystalline/non-crystalline manner. Owing to their unique structure and properties, aCNTs are attractive for energy storage, catalysis, and aerospace applications. In this review, we provide an overview of the synthetic routes of aCNTs, which include chemical vapor deposition, catalytic pyrolysis, and arc discharge. Detailed morphologies of aCNTs and the systematic elucidation of tunable properties are also summarized. Finally, we discuss the future perspectives as well as associated challenges of aCNTs. With this review, we aim to encourage further research for the widespread use of aCNTs in industry. Materials science ; Nanotechnology ; Carbon nanotube ; Chemical vapor deposition ; Amorphous solid ; Carbon fibers ; Catalysis ; Amorphous carbon ; Fabrication ; Pyrolysis ; Chemical engineering ; Composite number ; Organic chemistry ; Chemistry Carbon Nanotubes in Composites ; Supercapacitor Materials and Fabrication ; Graphene research and applications
Authors: Cailum M. K. Stienstra | Christian Ieritano | Alexander Haack | W. Scott Hopkins
Source: Analytical Chemistry (article, 2023-06-29)
Field: Chemistry — Spectroscopy

Bridging the Gap between Differential Mobility, Log <i>S</i>, and Log <i>P</i> Using Machine Learning and SHAP Analysis Aqueous solubility, log <i>S</i>, and the water-octanol partition coefficient, log <i>P</i>, are physicochemical properties that are used to screen the viability of drug candidates and to estimate mass transport in the environment. In this work, differential mobility spectrometry (DMS) experiments performed in microsolvating environments are used to train machine learning (ML) frameworks that predict the log <i>S</i> and log <i>P</i> of various molecule classes. In lieu of a consistent source of experimentally measured log <i>S</i> and log <i>P</i> values, the OPERA package was used to evaluate the aqueous solubility and hydrophobicity of 333 analytes. With ion mobility/DMS data (<i>e.g.</i>, CCS, dispersion curves) as input, we used ML regressors and ensemble stacking to derive relationships with a high degree of explainability, as assessed via SHapley Additive exPlanations (SHAP) analysis. The DMS-based regression models returned scores of <i>R</i><sup>2</sup> = 0.67 and RMSE = 1.03 ± 0.10 for log <i>S</i> predictions and <i>R</i><sup>2</sup> = 0.67 and RMSE = 1.20 ± 0.10 for log <i>P</i> after 5-fold random cross-validation. SHAP analysis reveals that the regressors strongly weighted gas-phase clustering in log <i>P</i> correlations. The addition of structural descriptors (<i>e.g.,</i> # of aromatic carbons) improved log <i>S</i> predictions to yield RMSE = 0.84 ± 0.07 and <i>R</i><sup>2</sup> = 0.78. Similarly, log <i>P</i> predictions using the same data resulted in an RMSE of 0.83 ± 0.04 and <i>R</i><sup>2</sup> = 0.84. The SHAP analysis of log <i>P</i> models highlights the need for additional experimental parameters describing hydrophobic interactions. These results were achieved with a smaller dataset (333 instances) and minimal structural correlation compared to purely structure-based models, underscoring the value of employing DMS data in predictive models. Chemistry ; Solubility ; Partition coefficient ; Analytical Chemistry (journal) ; Mean squared error ; Aqueous solution ; Statistics ; Chromatography ; Mathematics ; Physical chemistry Analytical Chemistry and Chromatography ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Omar Dagdag | Hansang Kim
Source: Polymers (article, 2023-12-29)
Field: Materials Science — Polymers and Plastics

Progress in the Field of Cyclophosphazenes: Preparation, Properties, and Applications This review article provides a comprehensive overview of recent advancements in the realm of cyclophosphazenes, encompassing their preparation methodologies, distinctive properties, and diverse applications. The synthesis approaches are explored, highlighting advancements in the preparation of these cyclic compounds. The discussion extends to the distinctive properties exhibited by cyclophosphazenes, including thermal stability characteristics, and other relevant features. Furthermore, we examine the broad spectrum of applications for cyclophosphazenes in various fields, such as coatings, adhesives, composites, extractants, metal complexes, organometallic chemistry, medicine, and inorganic chemistry. This review aims to offer insights into the evolving landscape of cyclophosphazenes and their ever-expanding roles in contemporary scientific and technological arenas. Future possibilities are emphasized, and significant research data shortages are identified. Economic shortage ; Broad spectrum ; Realm ; Nanotechnology ; Management science ; Chemistry ; Engineering ; Biochemical engineering ; Computer science ; Materials science ; Combinatorial chemistry ; Philosophy ; Geography ; Archaeology Flame retardant materials and properties ; Synthesis and properties of polymers ; N-Heterocyclic Carbenes in Organic and Inorganic Chemistry
Authors: Kimia Zarean Mousaabadi | Ali A. Ensafi | Erfan Naghsh | Jin‐Song Hu | Behzad Rezaei
Source: Scientific Reports (article, 2023-08-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Dual Ni/Co-hemin metal–organic framework-PrGO for high-performance asymmetric hybrid supercapacitor In this study, we conducted direct synthesis of a dual metal-organic framework (Ni/Co-Hemin MOF) on phosphorous-doped reduced graphene oxide (PrGO) to serve as an active material in high-performance asymmetrical supercapacitors. The nanocomposite was utilized as an active material in supercapacitors, exhibiting a noteworthy specific capacitance of 963 C g<sup>-1</sup> at 1.0 A g<sup>-1</sup>, along with a high rate capability of 68.3% upon increasing the current density by 20 times, and superior cycling stability. Our comprehensive characterization and control experiments indicated that the improved performance can be attributed to the combined effect of the dual MOF and the presence of phosphorous, influencing the battery-type supercapacitor behavior of GO. Additionally, we fabricated an asymmetric hybrid supercapacitor (AHSC) using Ni/Co-Hemin/PrGO/Nickel foam (NF) and activated carbon (AC)/NF. This AHSC demonstrated a specific capacitance of 281 C g<sup>-1</sup> at 1.0 A g<sup>-1</sup>, an operating voltage of 1.80 V, an impressive energy density of 70.3 Wh kg<sup>-1</sup> at a high power density of 0.9 kW kg<sup>-1</sup>. Notably, three AHSC devices connected in series successfully powered a clock for approximately 42 min. These findings highlight the potential application of Hemin-based MOFs in advanced supercapacitor systems. Supercapacitor ; Hemin ; Materials science ; Capacitance ; Nanocomposite ; Graphene ; Energy storage ; Power density ; Nanotechnology ; Chemistry ; Power (physics) ; Electrode Supercapacitor Materials and Fabrication ; Advanced battery technologies research ; Advancements in Battery Materials
Authors: Zhi Wei Wang | Shi Chang-min | Pengfei Li | Wenzhu Wang | Wenzhen Xiao | Ting Sun | Jing Zhang
Source: Molecules (article, 2023-07-28)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Optical and Photocatalytic Properties of Cobalt-Doped LuFeO3 Powders Prepared by Oxalic Acid Assistance B-site cobalt (Co)-doped rare-earth orthoferrites ReFeO<sub>3</sub> have shown considerable enhancement in physical properties compared to their parent counterparts, and Co-doped LuFeO<sub>3</sub> has rarely been reported. In this work, LuFe<sub>1-x</sub>Co<sub>x</sub>O<sub>3</sub> (x = 0, 0.05, 0.1, 0.15) powders have been successfully prepared by a mechanochemical activation-assisted solid-state reaction (MAS) method at 1100 °C for 2 h. X-ray diffraction (XRD) and Fourier transform infrared (FTIR) spectroscopy studies demonstrated that a shrinkage in lattice parameters emerges when B-site Fe ions are substituted by Co ions. The morphology and elemental distribution were investigated by scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS). The UV-visible absorbance spectra show that LuFe<sub>0.85</sub>Co<sub>0.15</sub>O<sub>3</sub> powders have a narrower bandgap (1.75 eV) and higher absorbance than those of LuFeO<sub>3</sub> (2.06 eV), obviously improving the light utilization efficiency. Additionally, LuFe<sub>0.85</sub>Co<sub>0.15</sub>O<sub>3</sub> powders represent a higher photocatalytic capacity than LuFeO<sub>3</sub> powders and can almost completely degrade MO in 5.5 h with the assistance of oxalic acid under visible irradiation. We believe that the present study will promote the application of orthorhombic LuFeO<sub>3</sub> in photocatalysis. Photocatalysis ; Materials science ; Oxalic acid ; Orthorhombic crystal system ; Fourier transform infrared spectroscopy ; Absorbance ; Scanning electron microscope ; Band gap ; Nuclear chemistry ; Spectroscopy ; Cobalt ; Doping ; Infrared spectroscopy ; Analytical Chemistry (journal) ; Inorganic chemistry ; Chemical engineering ; Crystal structure ; Chemistry ; Crystallography ; Catalysis ; Organic chemistry ; Metallurgy ; Optoelectronics Multiferroics and related materials ; Advanced Condensed Matter Physics ; Ga2O3 and related materials
Authors: Mingxin Zhang | Lele Yang | Xiaoxuan Wu | Junjia Wang
Source: Research (review, 2023-01-01)
Field: Materials Science — Materials Chemistry

Black Phosphorus for Photonic Integrated Circuits Black phosphorus gives several advantages and complementarities over other two-dimensional materials. It has drawn extensive interest owing to its relatively high carrier mobility, wide tunable bandgap, and in-plane anisotropy in recent years. This manuscript briefly reviews the structure and physical properties of black phosphorus and targets on black phosphorus for photonic integrated circuits. Some of the applications are discussed including photodetection, optical modulation, light emission, and polarization conversion. Corresponding recent progresses, associated challenges, and future potentials are covered. Black phosphorus ; Photodetection ; Optoelectronics ; Photonics ; Polarization (electrochemistry) ; Photonic integrated circuit ; Materials science ; Electronic circuit ; Anisotropy ; Integrated circuit ; Engineering physics ; Nanotechnology ; Physics ; Optics ; Photodetector ; Engineering ; Chemistry ; Electrical engineering 2D Materials and Applications ; Perovskite Materials and Applications ; Advanced Fiber Laser Technologies
Authors: Qiushi Deng | Steffen Jeschke | Ratan K. Mishra | Sebastian Spicher | Samir Darouich | Eduard Schreiner | Philipp Eiden | Peter Deglmann | Jan N. Gorges | Xiaobo Chen | Patrick Keil | Ivan Cole
Source: Corrosion Science (article, 2023-11-30)
Field: Materials Science — Materials Chemistry

Design of alkyl-substituted aminothiazoles to optimise corrosion inhibition for galvanised steel: A combined experimental and molecular modelling approach This work proposes a combined experimental and molecular modelling approach to clarify the complex substitution effect of aminothiazole as corrosion inhibitor for galvanised steel in saline aqueous environments. Electrochemical results have shown that introducing alkyl residues ameliorates inhibition efficiency to varied degrees. Umbrella sampling simulations supplemented by a newly developed GFN-force-field-based molecular approach reveal that the particular adsorption state renders a hydrophobic environment at the outer surface, where increased alkyl size leads to greater hydrophobicity for corrosion inhibition. Such an effect becomes less prominent with increasing concentration, attributed to the enhanced intermolecular interaction destabilising the adsorption process. Alkyl ; Corrosion ; Galvanization ; Adsorption ; Electrochemistry ; Materials science ; Corrosion inhibitor ; Molecular dynamics ; Aqueous solution ; Intermolecular force ; Chemistry ; Metallurgy ; Chemical engineering ; Inorganic chemistry ; Computational chemistry ; Molecule ; Organic chemistry ; Nanotechnology ; Layer (electronics) ; Physical chemistry ; Electrode Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Hydrogen embrittlement and corrosion behaviors in metals