jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Muhammad Hadi | Khalid Mujasam Batoo | Ankush Chauhan | Omar M. Al-Dossary | Ritesh Verma | Yujie Yang
Source: Magnetochemistry (article, 2021-04-14)
Field: Materials Science — Materials Chemistry

Tuning of Structural, Dielectric, and Electronic Properties of Cu Doped Co–Zn Ferrite Nanoparticles for Multilayer Inductor Chip Applications Herein, we report the synthesis of nanoparticles and doping of Cu-doped Co–Zn ferrites using the auto-combustion sol–gel synthesis technique. X-ray diffraction studies confirmed the single-phase structure of the samples with space group Fd3m and crystallite size in the range of 20.57–32.69 nm. Transmission electron microscopy micrographs and selected area electron diffraction patterns confirmed the polycrystalline nature of the ferrite nanoparticles. Energy-dispersive X-ray spectroscopy revealed the elemental composition in the absence of any impurity phases. Fourier-transform infrared studies showed the presence of two prominent peaks at approximately 420 cm−1 and 580 cm−1, showing metal–oxygen stretching and the formation of ferrite composite. X-ray photoelectron spectroscopy was employed to determine the oxidation states of Fe, Co, Zn, and Cu and O vacancies based on which cationic distributions at tetrahedral and octahedral sites are proposed. Dielectric spectroscopy showed that the samples exhibit Maxwell–Wagner interfacial polarization, which decreases as the frequency of the applied field increases. The dielectric loss of the samples was less than 1, confirming that the samples can be used for the fabrication of multilayer inductor chips. The ac conductivity of the samples increased with increasing doping and with frequency, and this has been explained by the hopping model. The hysteresis loops revealed that coercivity decreases slightly with doping, while the highest saturation magnetization of 55.61 emu/g was obtained when x = 0.1. The magnetic anisotropic constant was found to be less than 0.5, which suggests that the samples exhibit uniaxial anisotropy rather than cubic anisotropy. The squareness ratio indicates that the samples are useful in high-frequency applications. Materials science ; Dielectric ; Analytical Chemistry (journal) ; Crystallite ; Coercivity ; Dielectric loss ; Doping ; Ferrite (magnet) ; Nanoparticle ; Magnetization ; Nanotechnology ; Chemistry ; Condensed matter physics ; Optoelectronics ; Metallurgy ; Magnetic field ; Composite material Magnetic Properties and Synthesis of Ferrites ; Electromagnetic wave absorption materials ; Pineapple and bromelain studies
Authors: Abbas J. Kadhem | Guillermina J. Gentile | María Fidalgo de Cortalezzi
Source: Molecules (review, 2021-10-15)
Field: Chemistry — Analytical Chemistry

Molecularly Imprinted Polymers (MIPs) in Sensors for Environmental and Biomedical Applications: A Review Molecular imprinted polymers are custom made materials with specific recognition sites for a target molecule. Their specificity and the variety of materials and physical shapes in which they can be fabricated make them ideal components for sensing platforms. Despite their excellent properties, MIP-based sensors have rarely left the academic laboratory environment. This work presents a comprehensive review of recent reports in the environmental and biomedical fields, with a focus on electrochemical and optical signaling mechanisms. The discussion aims to identify knowledge gaps that hinder the translation of MIP-based technology from research laboratories to commercialization. Molecularly imprinted polymer ; Nanotechnology ; Commercialization ; Molecular imprinting ; Materials science ; Polymer ; Computer science ; Biochemical engineering ; Engineering ; Chemistry ; Business Analytical chemistry methods development ; Electrochemical Analysis and Applications ; Analytical Chemistry and Sensors
Authors: Cheng‐Wei Ju | Hanzhi Bai | Bo Li | Rizhang Liu
Source: Journal of Chemical Information and Modeling (article, 2021-02-23)
Field: Materials Science — Materials Chemistry

Machine Learning Enables Highly Accurate Predictions of Photophysical Properties of Organic Fluorescent Materials: Emission Wavelengths and Quantum Yields The development of functional organic fluorescent materials calls for fast and accurate predictions of photophysical parameters for processes such as high-throughput virtual screening, while the task is challenged by the limitations of quantum mechanical calculations. We establish a database covering >4300 solvated organic fluorescent dyes with 3000 distinct compounds and develop a new machine learning approach aimed at efficient and accurate predictions of emission wavelength and photoluminescence quantum yield (PLQY). Our feature engineering has given rise to a functionalized structure descriptor (FSD) and a comprehensive general solvent descriptor (CGSD), whereby a highly black-box computational framework is realized with consistently good accuracy across different dye families, ability of describing substitution effects and solvent effects, efficiency for large-scale predictions, and workability with on-the-fly learning. Evaluations with unseen molecules suggest a remarkable mean absolute error of 0.13 for PLQY and 0.080 eV for emission energy, the latter comparable to time-dependent density functional theory (TD-DFT) calculations. An online prediction platform was constructed based on the ensemble model to make predictions in various solvents. Our statistical learning methodology will complement quantum mechanical calculations as an efficient alternative approach for the prediction of these parameters. Quantum yield ; Density functional theory ; Photoluminescence ; Quantum ; Fluorescence ; Computer science ; Biological system ; Wavelength ; Black box ; Materials science ; Quantum efficiency ; Chemistry ; Artificial intelligence ; Computational chemistry ; Physics ; Optoelectronics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Click Chemistry and Applications
Authors: Puneet Mishra | Jean‐Michel Roger | Delphine Jouan‐Rimbaud Bouveresse | Alessandra Biancolillo | Federico Marini | Alison Nordon | Douglas N. Rutledge
Source: TrAC Trends in Analytical Chemistry (article, 2021-01-29)
Field: Chemistry — Analytical Chemistry

Recent trends in multi-block data analysis in chemometrics for multi-source data integration In recent years, multi-modal measurements of process and product properties have become widely popular. Sometimes classical chemometric methods such as principal component analysis (PCA) and partial least squares regression (PLS) are not adequate to analyze this kind of data. In recent years, several multi-block methods have emerged for this purpose; however, their use is largely limited to chemometricians, and non-experts have little experience with such methods. In order to deal with this, the present review provides a brief overview of the multi-block data analysis concept, the various tasks that can be performed with it and the advantages and disadvantages of different techniques. Moreover, basic tasks ranging from multi-block data visualization to advanced innovative applications such as calibration transfer will be briefly highlighted. Finally, a summary of software resources available for multi-block data analysis is provided. Computer science ; Block (permutation group theory) ; Data mining ; Partial least squares regression ; Chemometrics ; Principal component analysis ; Process (computing) ; Visualization ; Data science ; Software ; Machine learning ; Artificial intelligence ; Mathematics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Fault Detection and Control Systems
Authors: Lakhdar Sidi Salah | Nassira Ouslimani | Dalila Bousba | Isabelle Huynen | Yann Danlée | H. Aksas
Source: Journal of Nanomaterials (article, 2021-09-20)
Field: Materials Science — Materials Chemistry

Carbon Nanotubes (CNTs) from Synthesis to Functionalized (CNTs) Using Conventional and New Chemical Approaches Carbon nanotubes (CNTs) have emerged worldwide because of their remarkable properties enlarging their field of applications. Functionalization of CNTs is a convenient strategy to tackle low dispersion and solubilization of CNTs in many solvents or polymers. It can be done by covalent or noncovalent surface functionalization that is briefly discussed regarding the current literature. Endohedral and exohedral are conventional methods based on covalent and van der Waals bonding forces that are created through CNT functionalization by various materials. In this paper, a review of new approaches and mechanisms of functionalization of CNTs is proposed, including amidation, fluorination, bromination, chlorination, hydrogenation, and electrophilic addition. Our analysis is supported by several characterization methods highlighting recent improvements hence extending the range of applicability of CNTs. Carbon nanotube ; Surface modification ; van der Waals force ; Materials science ; Nanotechnology ; Covalent bond ; Polymer ; Halogenation ; Non-covalent interactions ; Chemical engineering ; Organic chemistry ; Chemistry ; Molecule ; Hydrogen bond ; Composite material Carbon Nanotubes in Composites ; Graphene research and applications ; Fullerene Chemistry and Applications
Authors: Kristof M. Altus | Jennifer A. Love
Source: Communications Chemistry (review, 2021-12-10)
Field: Chemistry — Organic Chemistry

The continuum of carbon–hydrogen (C–H) activation mechanisms and terminology As a rapidly growing field across all areas of chemistry, C-H activation/functionalisation is being used to access a wide range of important molecular targets. Of particular interest is the development of a sustainable methodology for alkane functionalisation as a means for reducing hydrocarbon emissions. This Perspective aims to give an outline to the community with respect to commonly used terminology in C-H activation, as well as the mechanisms that are currently understood to operate for (cyclo)alkane activation/functionalisation. Terminology ; Alkane ; Chemistry ; Hydrogen molecule ; Hydrogen ; Hydrocarbon ; Nanotechnology ; Materials science ; Organic chemistry ; Philosophy Catalytic C–H Functionalization Methods ; Carbon dioxide utilization in catalysis ; Asymmetric Hydrogenation and Catalysis
Authors: Yuemin Bian | Xiang-Qun Xie
Source: Journal of Molecular Modeling (article, 2021-02-04)
Field: Materials Science — Materials Chemistry

Generative chemistry: drug discovery with deep learning generative models Generative grammar ; Drug discovery ; Deep learning ; Cheminformatics ; Generative model ; Generative adversarial network Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning in Bioinformatics
Authors: Lennard Böselt | Moritz Thürlemann | Sereina Riniker
Source: Journal of Chemical Theory and Computation (article, 2021-04-05)
Field: Materials Science — Materials Chemistry

Machine Learning in QM/MM Molecular Dynamics Simulations of Condensed-Phase Systems Quantum mechanics/molecular mechanics (QM/MM) molecular dynamics (MD) simulations have been developed to simulate molecular systems, where an explicit description of changes in the electronic structure is necessary. However, QM/MM MD simulations are computationally expensive compared to fully classical simulations as all valence electrons are treated explicitly and a self-consistent field (SCF) procedure is required. Recently, approaches have been proposed to replace the QM description with machine-learned (ML) models. However, condensed-phase systems pose a challenge for these approaches due to long-range interactions. Here, we establish a workflow, which incorporates the MM environment as an element type in a high-dimensional neural network potential (HDNNP). The fitted HDNNP describes the potential-energy surface of the QM particles with an electrostatic embedding scheme. Thus, the MM particles feel a force from the polarized QM particles. To achieve chemical accuracy, we find that even simple systems require models with a strong gradient regularization, a large number of data points, and a substantial number of parameters. To address this issue, we extend our approach to a Δ-learning scheme, where the ML model learns the difference between a reference method (density functional theory (DFT)) and a cheaper semiempirical method (density functional tight binding (DFTB)). We show that such a scheme reaches the accuracy of the DFT reference method while requiring significantly less parameters. Furthermore, the Δ-learning scheme is capable of correctly incorporating long-range interactions within a cutoff of 1.4 nm. It is validated by performing MD simulations of retinoic acid in water and the interaction between <i>S</i>-adenoslymethioniat and cytosine in water. The presented results indicate that Δ-learning is a promising approach for (QM)ML/MM MD simulations of condensed-phase systems. Molecular dynamics ; QM/MM ; Computer science ; Statistical physics ; Embedding ; Force field (fiction) ; Regularization (linguistics) ; Valence electron ; Physics ; Electron ; Artificial intelligence ; Quantum mechanics Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Advanced Chemical Physics Studies
Authors: Michael A. Skinnider | R. Greg Stacey | David S. Wishart | Leonard J. Foster
Source: Nature Machine Intelligence (article, 2021-07-19)
Field: Materials Science — Materials Chemistry

Chemical language models enable navigation in sparsely populated chemical space Chemical space ; Computer science ; Generative model ; Artificial intelligence ; Generative grammar ; Benchmark (surveying) ; Machine learning ; Field (mathematics) ; Deep learning ; Quality (philosophy) ; Space (punctuation) ; Artificial neural network ; Drug discovery ; Mathematics ; Bioinformatics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Microbial Natural Products and Biosynthesis
Authors: Yini Zhang | Qifu Luan | Jingmin Jiang | Yanjie Li
Source: Frontiers in Plant Science (article, 2021-10-18)
Field: Chemistry — Analytical Chemistry

Prediction and Utilization of Malondialdehyde in Exotic Pine Under Drought Stress Using Near-Infrared Spectroscopy Drought is a major abiotic stress that adversely affects the growth and productivity of plants. Malondialdehyde (MDA), a substance produced by membrane lipids in response to reactive oxygen species (ROS), can be used as a drought indicator to evaluate the degree of plasma membrane damage and the ability of plants to drought stress tolerance. Still measuring MDA is usually a labor- and time-consuming task. In this study, near-infrared (NIR) spectroscopy combined with partial least squares (PLS) was used to obtain rapid and high-throughput measurements of MDA, and the application of this technique to plant drought stress experiments was also investigated. Two exotic conifer tree species, namely, slash pine (<i>Pinus elliottii</i>) and loblolly pine (<i>Pinus taeda</i>), were used as plant material exposed to drought stress; different types of spectral preprocessing methods and important feature-selection algorithms were applied to the PLS model to calibrate it and obtain the best MDA-predicting model. The results show that the best PLS model is established <i>via</i> the combined treatment of detrended variable-significant multivariate correlation algorithm (DET-sMC), where latent variables (LVs) were 6. This model has a respectable predictive capability, with a correlation coefficient (<i>R</i> <sup>2</sup>) of 0.66, a root mean square error (RMSE) of 2.28%, and a residual prediction deviation (RPD) of 1.51, and it was successfully implemented in drought stress experiments as a reliable and non-destructive method to detect the MDA content in real time. Mean squared error ; Drought stress ; Malondialdehyde ; Pinus pinaster ; Coefficient of determination ; Partial least squares regression ; Environmental science ; Multivariate statistics ; Abiotic stress ; Biological system ; Mathematics ; Chemistry ; Botany ; Statistics ; Biology ; Oxidative stress Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Leaf Properties and Growth Measurement
Authors: Ceren Karaman | Onur Karaman | Necip Atar | Mehmet Lütfi Yola
Source: Physical Chemistry Chemical Physics (article, 2021-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Sustainable electrode material for high-energy supercapacitor: biomass-derived graphene-like porous carbon with three-dimensional hierarchically ordered ion highways Biomass-derived carbonaceous materials have been deemed to be one of the up-and-coming electrode materials for high-performance energy storage systems due to their cost-neutral abundant resources, sustainable nature, easy synthesis methods, and environmentally benign features. In this work, various graphene-like porous carbon networks (GPCs) with three-dimensional (3D) hierarchically ordered "ion highways" have been synthesized by the carbonization/activation of orange-peel wastes for use as an electrode material in high-energy supercapacitors. The porous structures and surface morphologies of the GPCs were rationally fine-tuned as a function of the activation agent ratio. The prepared GPCs offered superior specific surface area in addition to a 3D porous structure with a fine-tuned pore size distribution. The electrochemical behaviors of all the GPCs were evaluated in 6.0 M KOH aqueous electrolyte via a three-electrode electrochemical setup. Owing to their synergistic characteristics, including superior specific surface area (1150 m2 g-1), large pore volume, and fine-tuned 3D porous architecture, GPC-3.0 (synthesized with a KOH : GPC ratio of 3.0, by wt.) exhibited the best capacitive behavior amongst the studied GPCs. The 3D hierarchically ordered architecture acts like well-designed ion highways that boost electron transportation, thereby enhancing electrochemical energy storage. A coin-cell-type symmetrical supercapacitor based on GPC-3.0 was tested in both 1.0 M Na2SO4 (salt-in-water) and 12.0 m NaNO3 (water-in-salt) electrolytes. The supercapacitor cell based on the water-in-salt electrolyte offered a wide operating voltage of 2.3 V. The obtained energy density and power density values were comparable to those of commercial high-performance electrical double-layer capacitors. Such notable findings will shed light on next-generation high-rate electrochemical energy storage systems based on biomass-derived carbonaceous materials. Supercapacitor ; Materials science ; Graphene ; Porosity ; Carbon fibers ; Power density ; Nickel ; Electrode ; Energy storage ; Ion ; Chemical engineering ; Biomass (ecology) ; Metal ; Nanotechnology ; Composite number ; Composite material ; Electrochemistry ; Power (physics) ; Metallurgy ; Chemistry ; Organic chemistry Supercapacitor Materials and Fabrication ; Advancements in Battery Materials ; Advanced Battery Materials and Technologies
Authors: Apurba Nandi | Chen Qu | Paul L. Houston | Riccardo Conte | Joel M. Bowman
Source: The Journal of Chemical Physics (article, 2021-02-04)
Field: Materials Science — Materials Chemistry

Δ -machine learning for potential energy surfaces: A PIP approach to bring a DFT-based PES to CCSD(T) level of theory "Δ-machine learning" refers to a machine learning approach to bring a property such as a potential energy surface (PES) based on low-level (LL) density functional theory (DFT) energies and gradients close to a coupled cluster (CC) level of accuracy. Here, we present such an approach that uses the permutationally invariant polynomial (PIP) method to fit high-dimensional PESs. The approach is represented by a simple equation, in obvious notation V<sub>LL→CC</sub> = V<sub>LL</sub> + ΔV<sub>CC-LL</sub>, and demonstrated for CH<sub>4</sub>, H<sub>3</sub>O<sup>+</sup>, and trans and cis-N-methyl acetamide (NMA), CH<sub>3</sub>CONHCH<sub>3</sub>. For these molecules, the LL PES, V<sub>LL</sub>, is a PIP fit to DFT/B3LYP/6-31+G(d) energies and gradients and ΔV<sub>CC-LL</sub> is a precise PIP fit obtained using a low-order PIP basis set and based on a relatively small number of CCSD(T) energies. For CH<sub>4</sub>, these are new calculations adopting an aug-cc-pVDZ basis, for H<sub>3</sub>O<sup>+</sup>, previous CCSD(T)-F12/aug-cc-pVQZ energies are used, while for NMA, new CCSD(T)-F12/aug-cc-pVDZ calculations are performed. With as few as 200 CCSD(T) energies, the new PESs are in excellent agreement with benchmark CCSD(T) results for the small molecules, and for 12-atom NMA, training is done with 4696 CCSD(T) energies. Invariant (physics) ; Property (philosophy) ; Set (abstract data type) ; Energy (signal processing) ; Notation ; Simple (philosophy) ; Polynomial ; Potential energy Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Crystallography and molecular interactions
Authors: Hao Wei | Yangnan Hu | Junguo Wang | Xia Gao | Xiaoyun Qian | Mingliang Tang
Source: International Journal of Nanomedicine (review, 2021-08-01)
Field: Materials Science — Biomaterials

Superparamagnetic Iron Oxide Nanoparticles: Cytotoxicity, Metabolism, and Cellular Behavior in Biomedicine Applications Superparamagnetic iron oxide nanoparticles (SPIONs) have been widely investigated and applied in the field of biomedicine due to their excellent superparamagnetic properties and reliable traceability. However, with the optimization of core composition, shell types and transfection agents, the cytotoxicity and metabolism of different SPIONs have great differences, and the labeled cells also show different cellular behaviors. Therefore, a holistic review of the construction and application of SPIONs is desired. This review focuses the advances of SPIONs in the field of biomedicine in recent years. After summarizing the toxicity of different SPIONs, the uptake, distribution and metabolism of SPIONs in vitro were discussed. Then, the regulation of labeled-cells behavior is outlined. Furthermore, the major challenges in the optimization process of SPIONs and insights on its future developments are proposed. Biomedicine ; Nanotechnology ; Cytotoxicity ; Cellular metabolism ; Nanoparticle ; Materials science ; Biochemical engineering ; Chemistry ; In vitro ; Metabolism ; Bioinformatics ; Biology ; Engineering ; Biochemistry Nanoparticle-Based Drug Delivery ; Characterization and Applications of Magnetic Nanoparticles ; Iron oxide chemistry and applications
Authors: Eric M. Johnson | Stefan Ilić | Amanda J. Morris
Source: ACS Central Science (review, 2021-02-11)
Field: Chemistry — Inorganic Chemistry

Design Strategies for Enhanced Conductivity in Metal–Organic Frameworks Metal-organic frameworks (MOFs) are a class of materials which exhibit permanent porosity, high surface area, and crystallinity. As a highly tunable middle ground between heterogeneous and homogeneous species, MOFs have the potential to suit a wide variety of applications, many of which require conductive materials. The continued development of conductive MOFs has provided an ever-growing library of materials with both intrinsic and guest-promoted conductivity, and factors which limit or enhance conductivity in MOFs have become more apparent. In this Outlook, the factors which are believed to influence the future of MOF conductivity most heavily are highlighted along with proposed methods of further developing these fields. Fundamental studies derived from these methods may provide pathways to raise conductivity across a wide range of MOF structures. Conductivity ; Electrical conductor ; Metal-organic framework ; Homogeneous ; Crystallinity ; Nanotechnology ; Materials science ; Porosity ; Chemistry ; Composite material ; Physics ; Adsorption ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Corrosion Behavior and Inhibition