jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: F.G. Quintero-Álvarez | C.K. Rojas-Mayorga | Didilia Ileana Mendoza‐Castillo | Ismael Alejandro Aguayo‐Villarreal | Adrián Bonilla‐Petriciolet
Source: Adsorption Science & Technology (article, 2022-01-01)
Field: Chemistry — Inorganic Chemistry

Physicochemical Modeling of the Adsorption of Pharmaceuticals on MIL-100-Fe and MIL-101-Fe MOFs The adsorption of naproxen (NAP), diclofenac (DFC), and acetaminophen (APAP) molecules from aqueous solutions using MIL-100-Fe and MIL-101-Fe metal organic frameworks (MOFs) has been analyzed and modeled. Adsorption isotherms of these pharmaceuticals were experimentally quantified at 30 and 40°C and pH 7. Textural parameters and surface chemistry of these MOFs were analyzed, and results were utilized to explain the pharmaceutical adsorption mechanism. Density Functional Theory (DFT) calculations were performed to understand the reactivity of pharmaceutical molecules, and a statistical physics model was employed to calculate the main physicochemical parameters related to the adsorption mechanism. Results showed that the adsorption of these pharmaceuticals on MOFs was multimolecular and exothermic. Both MOFs displayed the highest adsorption capacities, up to 2.19 and 1.71 mmol/g, for NAP and DFC molecules, respectively. MIL-101-Fe showed better pharmaceutical adsorption properties than MIL-100-Fe due to its highest content of Fe-O clusters and mesopore volume. Adsorption mechanism of these organic molecules could involve hydrogen bond, van der Waals forces, and electrostatic interactions with MOF surfaces. In particular, MIL-101-Fe MOF is a promising material to prepare composites with competitive adsorption capacities for facing the water pollution caused by pharmaceutical compounds. Adsorption ; Chemistry ; van der Waals force ; Molecule ; Hydrogen bond ; Metal-organic framework ; Mesoporous material ; Aqueous solution ; Naproxen ; Density functional theory ; Chemical engineering ; Inorganic chemistry ; Physical chemistry ; Organic chemistry ; Computational chemistry ; Catalysis Metal-Organic Frameworks: Synthesis and Applications ; X-ray Diffraction in Crystallography ; Adsorption and biosorption for pollutant removal
Authors: Angela F. Harper | Steffen Emge | Pieter C. M. M. Magusin | Clare P. Grey | Andrew J. Morris
Source: Chemical Science (article, 2022-12-21)
Field: Chemistry — Spectroscopy

Modelling amorphous materials <i>via</i> a joint solid-state NMR and X-ray absorption spectroscopy and DFT approach: application to alumina Understanding a material's electronic structure is crucial to the development of many functional devices from semiconductors to solar cells and Li-ion batteries. A material's properties, including electronic structure, are dependent on the arrangement of its atoms. However, structure determination (the process of uncovering the atomic arrangement), is impeded, both experimentally and computationally, by disorder. The lack of a verifiable atomic model presents a huge challenge when designing functional amorphous materials. Such materials may be characterised through their local atomic environments using, for example, solid-state NMR and XAS. By using these two spectroscopy methods to inform the sampling of configurations from <i>ab initio</i> molecular dynamics we devise and validate an amorphous model, choosing amorphous alumina to illustrate the approach due to its wide range of technological uses. Our model predicts two distinct geometric environments of AlO<sub>5</sub> coordination polyhedra and determines the origin of the pre-edge features in the Al K-edge XAS. From our model we construct an average electronic density of states for amorphous alumina, and identify localized states at the conduction band minimum (CBM). We show that the presence of a pre-edge peak in the XAS is a result of transitions from the Al 1s to Al 3s states at the CBM. Deconvoluting this XAS by coordination geometry reveals contributions from both AlO<sub>4</sub> and AlO<sub>5</sub> geometries at the CBM give rise to the pre-edge, which provides insight into the role of AlO<sub>5</sub> in the electronic structure of alumina. This work represents an important advance within the field of solid-state amorphous modelling, providing a method for developing amorphous models through the comparison of experimental and computationally derived spectra, which may then be used to determine the electronic structure of amorphous materials. X-ray absorption spectroscopy ; Amorphous solid ; Electronic structure ; XANES ; Materials science ; Spectroscopy ; Absorption spectroscopy ; Chemical physics ; Density functional theory ; Ab initio ; Coordination number ; Nanotechnology ; Computational chemistry ; Chemistry ; Crystallography ; Ion ; Physics ; Optics Advanced NMR Techniques and Applications ; X-ray Diffraction in Crystallography ; High-pressure geophysics and materials
Authors: Yudong Cheng | Qun Yang | Jiangjing Wang | Theodoros Dimitriadis | Mathias Schumacher | Huiru Zhang | Maximilian J. Müller | Narges Amini | Fan Yang | Alexander Schoekel | Julian Pries | Riccardo Mazzarello | Matthias Wuttig | Hai‐Bin Yu | Shuai Wei
Source: Nature Communications (article, 2022-11-29)
Field: Materials Science — Materials Chemistry

Highly tunable β-relaxation enables the tailoring of crystallization in phase-change materials In glasses, secondary (β-) relaxations are the predominant source of atomic dynamics. Recently, they have been discovered in covalently bonded glasses, i.e., amorphous phase-change materials (PCMs). However, it is unclear what the mechanism of β-relaxations is in covalent systems and how they are related to crystallization behaviors of PCMs that are crucial properties for non-volatile memories and neuromorphic applications. Here we show direct evidence that crystallization is strongly linked to β-relaxations. We find that the β-relaxation in Ge<sub>15</sub>Sb<sub>85</sub> possesses a high tunability, which enables a manipulation of crystallization kinetics by an order of magnitude. In-situ synchrotron X-ray scattering, dielectric functions, and ab-initio calculations indicate that the weakened β-relaxation intensity stems from a local reinforcement of Peierls-like distortions, which increases the rigidity of the bonding network and decreases the dynamic heterogeneity. Our findings offer a conceptually new approach to tuning the crystallization of PCMs based on manipulating the β-relaxations. Crystallization ; Phase change ; Materials science ; Relaxation (psychology) ; Phase (matter) ; Chemical physics ; Nanotechnology ; Chemical engineering ; Chemistry ; Engineering physics ; Physics ; Medicine Phase-change materials and chalcogenides ; Liquid Crystal Research Advancements ; Material Dynamics and Properties
Authors: Cody Aplin | Shawn K. Milano | Kara A. Zielinski | Lois Pollack | Richard A. Cerione
Source: The Journal of Physical Chemistry B (review, 2022-08-27)
Field: Materials Science — Materials Chemistry

Evolving Experimental Techniques for Structure-Based Drug Design Structure-based drug design (SBDD) is a prominent method in rational drug development and has traditionally benefitted from the atomic models of protein targets obtained using X-ray crystallography at cryogenic temperatures. In this perspective, we highlight recent advances in the development of structural techniques that are capable of probing dynamic information about protein targets. First, we discuss advances in the field of X-ray crystallography including serial room-temperature crystallography as a method for obtaining high-resolution conformational dynamics of protein-inhibitor complexes. Next, we look at cryogenic electron microscopy (cryoEM), another high-resolution technique that has recently been used to study proteins and protein complexes that are too difficult to crystallize. Finally, we present small-angle X-ray scattering (SAXS) as a potential high-throughput screening tool to identify inhibitors that target protein complexes and protein oligomerization. Small-angle X-ray scattering ; Drug discovery ; Drug design ; High resolution ; Structural biology ; Computational biology ; Protein structure ; Resolution (logic) ; Nanotechnology ; Chemistry ; Crystallography ; Computer science ; Scattering ; Materials science ; Physics ; Biochemistry ; Biology ; Artificial intelligence Enzyme Structure and Function ; Advanced Electron Microscopy Techniques and Applications ; Protein Structure and Dynamics
Authors: Heba Ali | M. Ismail
Source: Journal of Polymers and the Environment (article, 2022-04-06)
Field: Chemistry — Organic Chemistry

Recyclable and Biodegradable Ag@Chitosan Nanocomposite Beads Synthesized in One-step for Catalytic Hydrogenation of 4-Nitrophenol Abstract The study presents the in-situ synthesis of recyclable bio-nanocomposite beads based on chitosan (Cs) and silver (Ag) nanoparticles for utilization in catalytic processes. For the first time, alkaline ethanol solution was used as a physical crosslinking and reducing agent to synthesize cross-linked Ag@Cs biocomposite hydro-gel beads in one-step. The catalytic activity of the prepared beads was evaluated before and after the integration of various Ag contents through hydrogenation of toxic nitrophenol to amino-phenol, as a safer configuration. XRD, SEM, EDX, BET, UV-Vis, HRTEM, FT-IR, and TGA analyses were used to characterize the resulted samples. XRD, EDX, and optical measurements confirmed the successful immobilization of Ag into Cs beads. The grown Ag atoms are well dispersed in the Cs, with an average diameter of around 6.7 nm, according to HRTEM. Because of the synergistic effect of Ag sensitization and large surface area, the optumium Ag@Cs composite beads have high catalytic activity with a kinetic rate constant of 0.143 min − 1 , compared to 0.0001 min − 1 for pure Cs beads. Furthermore, regeneration studies were performed to ensure its catalytic stability in the removal of nitrophenol. Finally, the mechanism of Ag@Cs biocomposite hydrogel beads fabrication in a single pot was thoroughly discussed. Materials science ; Biocomposite ; Nanocomposite ; Chitosan ; Catalysis ; High-resolution transmission electron microscopy ; Nuclear chemistry ; Chemical engineering ; 4-Nitrophenol ; Nanoparticle ; Nanotechnology ; Composite number ; Organic chemistry ; Composite material ; Transmission electron microscopy ; Chemistry Nanomaterials for catalytic reactions ; Advanced Photocatalysis Techniques ; Copper-based nanomaterials and applications
Authors: Tony Knightley | Alex Yakovlev | Víctor Pacheco‐Peña
Source: Advanced Optical Materials (article, 2022-12-22)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Neural Network Design of Multilayer Metamaterial for Temporal Differentiation Abstract Controlling wave−matter interactions with metamaterials (MTMs) for the calculation of mathematical operations has become an important paradigm for analogue computing given their ability to dramatically increase computational processing speeds. Here, motivated by the importance of performing mathematical operations on temporal signals, multilayer MTMs with the ability to calculate the derivative of temporally modulated signals are proposed, designed, and studied. To do this, a neural network (NN) based algorithm is used to design the multilayer structures (alternating layers of indium tin oxide (ITO) and titanium dioxide (TiO 2 )) that can calculate the first temporal derivative of the envelope of an impinging electromagnetic signal modulated at telecom wavelengths (1550 nm). Different designs are presented using multiple incident temporal signals including a modulated Gaussian as well as modulated arbitrary functions, demonstrating an excellent agreement between the predicted results (NN results) and the theoretical (ideal) values. It is shown how the proposed NN‐based algorithm can complete its search of the design space for the layer thicknesses of the multilayer MTM after just a few seconds, with a low mean square error in the order of (or below) 10 −4 when comparing the predicted results with the theoretical spectrum of the ideal temporal derivative. Metamaterial ; Materials science ; Artificial neural network ; Envelope (radar) ; Gaussian ; Ideal (ethics) ; Derivative (finance) ; Computer science ; Indium tin oxide ; Wavelength ; Topology (electrical circuits) ; Algorithm ; Optics ; Optoelectronics ; Layer (electronics) ; Physics ; Mathematics ; Nanotechnology ; Telecommunications ; Artificial intelligence ; Radar Metamaterials and Metasurfaces Applications ; Photonic Crystals and Applications ; Animal Vocal Communication and Behavior
Authors: Yi Sun | Wenjin Zheng | Shiyu Ji | Anwei Sun | Wei Shuai | Nan Zheng | Yu Han | Gang Xiao | Meng Ni | Haoran Xu
Source: Sustainable Energy Technologies and Assessments (article, 2022-06-02)
Field: Materials Science — Materials Chemistry

Dynamic behavior of high-temperature CO2/H2O co-electrolysis coupled with real fluctuating renewable power Mole fraction ; Electrolysis ; Renewable energy ; Fraction (chemistry) ; Chemistry ; Thermodynamics ; Materials science ; Environmental science ; Mechanics ; Physics ; Electrode ; Electrical engineering ; Engineering ; Physical chemistry ; Chromatography Advancements in Solid Oxide Fuel Cells ; Advanced battery technologies research ; Chemical Looping and Thermochemical Processes
Authors: Emily Xi Tan | Yichao Chen | Yih Hong Lee | Yong Xiang Leong | Shi Xuan Leong | Chelsea Violita Stanley | Chi Seng Pun | Xing Yi Ling
Source: Nanoscale Horizons (article, 2022-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Incorporating plasmonic featurization with machine learning to achieve accurate and bidirectional prediction of nanoparticle size and size distribution Determination of nanoparticle size and size distribution is important because these key parameters dictate nanomaterials' properties and applications. Yet, it is only accomplishable using low-throughput electron microscopy. Herein, we incorporate plasmonic-domain-driven feature engineering with machine learning (ML) for accurate and bidirectional prediction of both parameters for complete characterization of nanoparticle ensembles. Using gold nanospheres as our model system, our ML approach achieves the lowest prediction errors of 2.3% and ±1.0 nm for ensemble size and size distribution respectively, which is 3-6 times lower than previously reported ML or Mie approaches. Knowledge elicitation from the plasmonic domain and concomitant translation into featurization allow us to mitigate noise and boost data interpretability. This enables us to overcome challenges arising from size anisotropy and small sample size limitations to achieve highly generalizable ML models. We further showcase inverse prediction capabilities, using size and size distribution as inputs to generate spectra with LSPRs that closely match experimental data. This work illustrates a ML-empowered total nanocharacterization strategy that is rapid (<30 s), versatile, and applicable over a wide size range of 200 nm. Nanoparticle ; Plasmon ; Distribution (mathematics) ; Computer science ; Nanotechnology ; Artificial intelligence ; Materials science ; Mathematics ; Optoelectronics Gold and Silver Nanoparticles Synthesis and Applications ; Machine Learning in Materials Science ; Nanoparticle-Based Drug Delivery
Authors: Mohammed A. Ibrahim | Mohammed Ali A. Shaban | Yaseen Rashid Hasan | Mohanad J. M‐Ridha | Haitham A. Hussein | Khalid M. Abed | Sabah J. Mohammed | Mohd Hafizuddin Muhamad | Hassimi Abu Hasan
Source: Journal of Ecological Engineering (article, 2022-04-21)
Field: Chemistry — Organic Chemistry

Simultaneous Adsorption of Ternary Antibiotics (Levoﬂoxacin, Meropenem, and Tetracycline) by SunFlower Husk Coated with Copper Oxide Nanoparticles In this study, a new adsorbent derived from sunflower husk powder and coated in CuO nanoparticles (CSFH) was investigated to evaluate the simultaneous adsorption of Levofloxacin (LEV), Meropenem (MER), and Tetracycline (TEC) from an aqueous solution. Significant improvements in the adsorption capacity of the sunflower husk were identified after the powder particles had been coated in CuO nanoparticles. Kinetic data were correlated using a pseudo-second-order model, and was successful for the three antibiotics. Moreover, high compatibility was identified between the LEV, MER, and TEC, isotherm data, and the Langmuir model, which produced a better fit to suit the isotherm curves. In addition, the spontaneous and exothermic nature of the adsorption process was crucial for transforming the three antibiotics into CSFH. The greatest CSFH adsorption capacity was in MER (131.83 mg/g), followed by TEC (96.95 mg/g), and LEV (62.24 mg/g). These findings thus indicate that CSFH is one of the most effective and efficient adsorbents to use for eliminating wastewater contaminated with antibiotic residue. Meropenem ; Husk ; Tetracycline ; Adsorption ; Chemistry ; Nuclear chemistry ; Sunflower ; Copper ; Ternary operation ; Antibiotics ; Microbiology ; Organic chemistry ; Biochemistry ; Biology ; Antibiotic resistance ; Botany Nanomaterials for catalytic reactions
Authors: Luis Barroso-Luque | Julia H. Yang | Fengyu Xie | Tina Chen | Ronald L. Kam | Zinab Jadidi | Peichen Zhong | Gerbrand Ceder
Source: The Journal of Open Source Software (article, 2022-09-29)
Field: Materials Science — Materials Chemistry

smol: A Python package for cluster expansions andbeyond The growing research focus on multi-principal element materials-spanning a variety of applications, such as electrochemical The cluster expansion (CE) method is a formal and effective way to construct functions of atomic configuration by coarse-graining materials properties, such as formation energies, in terms of species occupancy lattice models The cluster expansion method coupled with Monte Carlo sampling (CE-MC) is an established and effective way to resolve atomic details underlying important thermodynamic properties Python (programming language) ; Computer science ; R package ; Programming language ; Cluster (spacecraft) ; Computer graphics (images) Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices
Authors: Gabriel Greene‐Diniz | David Zsolt Manrique | Wassil Sennane | Yann Magnin | Elvira Shishenina | Philippe Cordier | Philip L. Llewellyn | Michał Krompiec | Marko J. Rančić | David Muñoz Ramo
Source: EPJ Quantum Technology (article, 2022-12-01)
Field: Chemistry — Inorganic Chemistry

Modelling carbon capture on metal-organic frameworks with quantum computing Abstract Despite the recent progress in quantum computational algorithms for chemistry, there is a dearth of quantum computational simulations focused on material science applications, especially for the energy sector, where next generation sorbing materials are urgently needed to battle climate change. To drive their development, quantum computing is applied to the problem of CO 2 adsorption in Al-fumarate Metal-Organic Frameworks. Fragmentation strategies based on Density Matrix Embedding Theory are applied, using a variational quantum algorithm as a fragment solver, along with active space selection to minimise qubit number. By investigating different fragmentation strategies and solvers, we propose a methodology to apply quantum computing to Al-fumarate interacting with a CO 2 molecule, demonstrating the feasibility of treating a complex porous system as a concrete application of quantum computing. We also present emulated hardware calculations and report the impact of device noise on calculations of chemical dissociation, and how the choice of error mitigation scheme can impact this type of calculation in different ways. Our work paves the way for the use of quantum computing techniques in the quest of sorbents optimisation for more efficient carbon capture and conversion applications. Quantum computer ; Computer science ; Qubit ; Computational science ; Quantum ; Supercomputer ; Nanotechnology ; Materials science ; Physics ; Parallel computing ; Quantum mechanics Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Machine Learning in Materials Science
Authors: Thomas C. Booth | Evita Wiegers | Esther A. H. Warnert | Kathleen M. Schmainda | Frank Riemer | Ruben Emanuel Nechifor | Vera C. Keil | Gilbert Hangel | Patrícia Figueiredo | María del Mar Álvarez‐Torres | Otto Mølby Henriksen
Source: Frontiers in Oncology (review, 2022-02-28)
Field: Materials Science — Materials Chemistry

High-Grade Glioma Treatment Response Monitoring Biomarkers: A Position Statement on the Evidence Supporting the Use of Advanced MRI Techniques in the Clinic, and the Latest Bench-to-Bedside Developments. Part 2: Spectroscopy, Chemical Exchange Saturation, Multiparametric Imaging, and Radiomics Advanced MRI techniques show huge promise in treatment response assessment. The clinical readiness analysis highlights that most monitoring biomarkers require standardized international consensus guidelines, with more facilitation regarding technique implementation and reporting in the clinic. Medicine ; Modalities ; Medical physics ; Bench to bedside ; Biomarker ; Magnetic resonance imaging ; Radiology ; Chemistry Lanthanide and Transition Metal Complexes ; Advanced MRI Techniques and Applications ; MRI in cancer diagnosis
Authors: Sylvain Merel
Source: Chemosphere (review, 2022-12-01)
Field: Chemistry — Spectroscopy

Critical assessment of the Kendrick mass defect analysis as an innovative approach to process high resolution mass spectrometry data for environmental applications Mass spectrometry ; Mass ; Resolution (logic) ; Chemistry ; High resolution ; Process (computing) ; Chemical nomenclature ; Data processing ; Analytical Chemistry (journal) ; Environmental chemistry ; Process engineering ; Mass spectrum ; Chromatography ; Computer science ; Organic chemistry ; Engineering ; Database ; Artificial intelligence ; Remote sensing ; Geology Mass Spectrometry Techniques and Applications ; Analytical chemistry methods development ; Advanced Chemical Sensor Technologies
Authors: Haile Yan | Xiaoming Huang | Claude Esling
Source: Frontiers in Materials (article, 2022-02-24)
Field: Materials Science — Materials Chemistry

Recent Progress in Crystallographic Characterization, Magnetoresponsive and Elastocaloric Effects of Ni-Mn-In-Based Heusler Alloys—A Review Ni-Mn-In-based magnetic shape memory alloys have promising applications in numerous state-of-the-art technologies, such as solid-state refrigeration and smart sensing, resulting from the magnetic field-induced inverse martensitic transformation. This paper aims at presenting a comprehensive review of the recent research progress of Ni-Mn-In-based alloys. First, the crystallographic characterization of these compounds that strongly affects functional behaviors, including the crystal structure of modulated martensite, the self-organization of martensite variants and the strain path during martensitic transformation, are reviewed. Second, the current research progress in functional behaviors, including magnetic shape memory, magnetocaloric and elastocaloric effects, are summarized. Finally, the main bottlenecks hindering the technical development and some possible solutions to overcome these difficulties are discussed. This review is expected to provide some useful insights for the design of novel advanced magnetic shape memory alloys. Magnetic refrigeration ; Shape-memory alloy ; Diffusionless transformation ; Materials science ; Characterization (materials science) ; Martensite ; Magnetic shape-memory alloy ; Pseudoelasticity ; Nanotechnology ; Magnetic field ; Magnetization ; Microstructure ; Metallurgy ; Magnetic anisotropy ; Physics Shape Memory Alloy Transformations ; Creativity in Education and Neuroscience ; Magnetic and transport properties of perovskites and related materials